=0;--i) ret[i] = [];\n }\n for(i=n;i>=0;--i) ret[i].push(k[i]);\n ret[n+1].push(Ai);\n }\n } else gather(Ai,ret,k);\n }\n }\n if(k.length>n) k.pop();\n return ret;\n}\n\n// 6. Coordinate matrices\nnumeric.cLU = function LU(A) {\n var I = A[0], J = A[1], V = A[2];\n var p = I.length, m=0, i,j,k,a,b,c;\n for(i=0;im) m=I[i];\n m++;\n var L = Array(m), U = Array(m), left = numeric.rep([m],Infinity), right = numeric.rep([m],-Infinity);\n var Ui, Uj,alpha;\n for(k=0;k
right[i]) right[i] = j;\n }\n for(i=0;i right[i+1]) right[i+1] = right[i]; }\n for(i=m-1;i>=1;i--) { if(left[i]=0;i--) {\n while(Uj[k] > i) {\n ret[i] -= Uv[k]*ret[Uj[k]];\n k--;\n }\n ret[i] /= Uv[k];\n k--;\n }\n return ret;\n};\n\nnumeric.cgrid = function grid(n,shape) {\n if(typeof n === \"number\") n = [n,n];\n var ret = numeric.rep(n,-1);\n var i,j,count;\n if(typeof shape !== \"function\") {\n switch(shape) {\n case 'L':\n shape = function(i,j) { return (i>=n[0]/2 || jN) N = Ai[k]; }\n N++;\n ret = numeric.rep([N],0);\n for(k=0;k1) {\n mid = floor((p+q)/2);\n if(x[mid] <= x0) p = mid;\n else q = mid;\n }\n return this._at(x0,p);\n }\n var n = x0.length, i, ret = Array(n);\n for(i=n-1;i!==-1;--i) ret[i] = this.at(x0[i]);\n return ret;\n}\nnumeric.Spline.prototype.diff = function diff() {\n var x = this.x;\n var yl = this.yl;\n var yr = this.yr;\n var kl = this.kl;\n var kr = this.kr;\n var n = yl.length;\n var i,dx,dy;\n var zl = kl, zr = kr, pl = Array(n), pr = Array(n);\n var add = numeric.add, mul = numeric.mul, div = numeric.div, sub = numeric.sub;\n for(i=n-1;i!==-1;--i) {\n dx = x[i+1]-x[i];\n dy = sub(yr[i+1],yl[i]);\n pl[i] = div(add(mul(dy, 6),mul(kl[i],-4*dx),mul(kr[i+1],-2*dx)),dx*dx);\n pr[i+1] = div(add(mul(dy,-6),mul(kl[i], 2*dx),mul(kr[i+1], 4*dx)),dx*dx);\n }\n return new numeric.Spline(x,zl,zr,pl,pr);\n}\nnumeric.Spline.prototype.roots = function roots() {\n function sqr(x) { return x*x; }\n function heval(y0,y1,k0,k1,x) {\n var A = k0*2-(y1-y0);\n var B = -k1*2+(y1-y0);\n var t = (x+1)*0.5;\n var s = t*(1-t);\n return (1-t)*y0+t*y1+A*s*(1-t)+B*s*t;\n }\n var ret = [];\n var x = this.x, yl = this.yl, yr = this.yr, kl = this.kl, kr = this.kr;\n if(typeof yl[0] === \"number\") {\n yl = [yl];\n yr = [yr];\n kl = [kl];\n kr = [kr];\n }\n var m = yl.length,n=x.length-1,i,j,k,y,s,t;\n var ai,bi,ci,di, ret = Array(m),ri,k0,k1,y0,y1,A,B,D,dx,cx,stops,z0,z1,zm,t0,t1,tm;\n var sqrt = Math.sqrt;\n for(i=0;i!==m;++i) {\n ai = yl[i];\n bi = yr[i];\n ci = kl[i];\n di = kr[i];\n ri = [];\n for(j=0;j!==n;j++) {\n if(j>0 && bi[j]*ai[j]<0) ri.push(x[j]);\n dx = (x[j+1]-x[j]);\n cx = x[j];\n y0 = ai[j];\n y1 = bi[j+1];\n k0 = ci[j]/dx;\n k1 = di[j+1]/dx;\n D = sqr(k0-k1+3*(y0-y1)) + 12*k1*y0;\n A = k1+3*y0+2*k0-3*y1;\n B = 3*(k1+k0+2*(y0-y1));\n if(D<=0) {\n z0 = A/B;\n if(z0>x[j] && z0x[j] && z0x[j] && z10) {\n t0 = t1;\n z0 = z1;\n continue;\n }\n var side = 0;\n while(1) {\n tm = (z0*t1-z1*t0)/(z0-z1);\n if(tm <= t0 || tm >= t1) { break; }\n zm = this._at(tm,j);\n if(zm*z1>0) {\n t1 = tm;\n z1 = zm;\n if(side === -1) z0*=0.5;\n side = -1;\n } else if(zm*z0>0) {\n t0 = tm;\n z0 = zm;\n if(side === 1) z1*=0.5;\n side = 1;\n } else break;\n }\n ri.push(tm);\n t0 = stops[k+1];\n z0 = this._at(t0, j);\n }\n if(z1 === 0) ri.push(t1);\n }\n ret[i] = ri;\n }\n if(typeof this.yl[0] === \"number\") return ret[0];\n return ret;\n}\nnumeric.spline = function spline(x,y,k1,kn) {\n var n = x.length, b = [], dx = [], dy = [];\n var i;\n var sub = numeric.sub,mul = numeric.mul,add = numeric.add;\n for(i=n-2;i>=0;i--) { dx[i] = x[i+1]-x[i]; dy[i] = sub(y[i+1],y[i]); }\n if(typeof k1 === \"string\" || typeof kn === \"string\") { \n k1 = kn = \"periodic\";\n }\n // Build sparse tridiagonal system\n var T = [[],[],[]];\n switch(typeof k1) {\n case \"undefined\":\n b[0] = mul(3/(dx[0]*dx[0]),dy[0]);\n T[0].push(0,0);\n T[1].push(0,1);\n T[2].push(2/dx[0],1/dx[0]);\n break;\n case \"string\":\n b[0] = add(mul(3/(dx[n-2]*dx[n-2]),dy[n-2]),mul(3/(dx[0]*dx[0]),dy[0]));\n T[0].push(0,0,0);\n T[1].push(n-2,0,1);\n T[2].push(1/dx[n-2],2/dx[n-2]+2/dx[0],1/dx[0]);\n break;\n default:\n b[0] = k1;\n T[0].push(0);\n T[1].push(0);\n T[2].push(1);\n break;\n }\n for(i=1;i20) { throw new Error(\"Numerical gradient fails\"); }\n x0[i] = x[i]+h;\n f1 = f(x0);\n x0[i] = x[i]-h;\n f2 = f(x0);\n x0[i] = x[i];\n if(isNaN(f1) || isNaN(f2)) { h/=16; continue; }\n J[i] = (f1-f2)/(2*h);\n t0 = x[i]-h;\n t1 = x[i];\n t2 = x[i]+h;\n d1 = (f1-f0)/h;\n d2 = (f0-f2)/h;\n N = max(abs(J[i]),abs(f0),abs(f1),abs(f2),abs(t0),abs(t1),abs(t2),1e-8);\n errest = min(max(abs(d1-J[i]),abs(d2-J[i]),abs(d1-d2))/N,h/N);\n if(errest>eps) { h/=16; }\n else break;\n }\n }\n return J;\n}\n\nnumeric.uncmin = function uncmin(f,x0,tol,gradient,maxit,callback,options) {\n var grad = numeric.gradient;\n if(typeof options === \"undefined\") { options = {}; }\n if(typeof tol === \"undefined\") { tol = 1e-8; }\n if(typeof gradient === \"undefined\") { gradient = function(x) { return grad(f,x); }; }\n if(typeof maxit === \"undefined\") maxit = 1000;\n x0 = numeric.clone(x0);\n var n = x0.length;\n var f0 = f(x0),f1,df0;\n if(isNaN(f0)) throw new Error('uncmin: f(x0) is a NaN!');\n var max = Math.max, norm2 = numeric.norm2;\n tol = max(tol,numeric.epsilon);\n var step,g0,g1,H1 = options.Hinv || numeric.identity(n);\n var dot = numeric.dot, inv = numeric.inv, sub = numeric.sub, add = numeric.add, ten = numeric.tensor, div = numeric.div, mul = numeric.mul;\n var all = numeric.all, isfinite = numeric.isFinite, neg = numeric.neg;\n var it=0,i,s,x1,y,Hy,Hs,ys,i0,t,nstep,t1,t2;\n var msg = \"\";\n g0 = gradient(x0);\n while(it= 0.1*t*df0 || isNaN(f1)) {\n t *= 0.5;\n ++it;\n continue;\n }\n break;\n }\n if(t*nstep < tol) { msg = \"Line search step size smaller than tol\"; break; }\n if(it === maxit) { msg = \"maxit reached during line search\"; break; }\n g1 = gradient(x1);\n y = sub(g1,g0);\n ys = dot(y,s);\n Hy = dot(H1,y);\n H1 = sub(add(H1,\n mul(\n (ys+dot(y,Hy))/(ys*ys),\n ten(s,s) )),\n div(add(ten(Hy,s),ten(s,Hy)),ys));\n x0 = x1;\n f0 = f1;\n g0 = g1;\n ++it;\n }\n return {solution: x0, f: f0, gradient: g0, invHessian: H1, iterations:it, message: msg};\n}\n\n// 10. Ode solver (Dormand-Prince)\nnumeric.Dopri = function Dopri(x,y,f,ymid,iterations,msg,events) {\n this.x = x;\n this.y = y;\n this.f = f;\n this.ymid = ymid;\n this.iterations = iterations;\n this.events = events;\n this.message = msg;\n}\nnumeric.Dopri.prototype._at = function _at(xi,j) {\n function sqr(x) { return x*x; }\n var sol = this;\n var xs = sol.x;\n var ys = sol.y;\n var k1 = sol.f;\n var ymid = sol.ymid;\n var n = xs.length;\n var x0,x1,xh,y0,y1,yh,xi;\n var floor = Math.floor,h;\n var c = 0.5;\n var add = numeric.add, mul = numeric.mul,sub = numeric.sub, p,q,w;\n x0 = xs[j];\n x1 = xs[j+1];\n y0 = ys[j];\n y1 = ys[j+1];\n h = x1-x0;\n xh = x0+c*h;\n yh = ymid[j];\n p = sub(k1[j ],mul(y0,1/(x0-xh)+2/(x0-x1)));\n q = sub(k1[j+1],mul(y1,1/(x1-xh)+2/(x1-x0)));\n w = [sqr(xi - x1) * (xi - xh) / sqr(x0 - x1) / (x0 - xh),\n sqr(xi - x0) * sqr(xi - x1) / sqr(x0 - xh) / sqr(x1 - xh),\n sqr(xi - x0) * (xi - xh) / sqr(x1 - x0) / (x1 - xh),\n (xi - x0) * sqr(xi - x1) * (xi - xh) / sqr(x0-x1) / (x0 - xh),\n (xi - x1) * sqr(xi - x0) * (xi - xh) / sqr(x0-x1) / (x1 - xh)];\n return add(add(add(add(mul(y0,w[0]),\n mul(yh,w[1])),\n mul(y1,w[2])),\n mul( p,w[3])),\n mul( q,w[4]));\n}\nnumeric.Dopri.prototype.at = function at(x) {\n var i,j,k,floor = Math.floor;\n if(typeof x !== \"number\") {\n var n = x.length, ret = Array(n);\n for(i=n-1;i!==-1;--i) {\n ret[i] = this.at(x[i]);\n }\n return ret;\n }\n var x0 = this.x;\n i = 0; j = x0.length-1;\n while(j-i>1) {\n k = floor(0.5*(i+j));\n if(x0[k] <= x) i = k;\n else j = k;\n }\n return this._at(x,i);\n}\n\nnumeric.dopri = function dopri(x0,x1,y0,f,tol,maxit,event) {\n if(typeof tol === \"undefined\") { tol = 1e-6; }\n if(typeof maxit === \"undefined\") { maxit = 1000; }\n var xs = [x0], ys = [y0], k1 = [f(x0,y0)], k2,k3,k4,k5,k6,k7, ymid = [];\n var A2 = 1/5;\n var A3 = [3/40,9/40];\n var A4 = [44/45,-56/15,32/9];\n var A5 = [19372/6561,-25360/2187,64448/6561,-212/729];\n var A6 = [9017/3168,-355/33,46732/5247,49/176,-5103/18656];\n var b = [35/384,0,500/1113,125/192,-2187/6784,11/84];\n var bm = [0.5*6025192743/30085553152,\n 0,\n 0.5*51252292925/65400821598,\n 0.5*-2691868925/45128329728,\n 0.5*187940372067/1594534317056,\n 0.5*-1776094331/19743644256,\n 0.5*11237099/235043384];\n var c = [1/5,3/10,4/5,8/9,1,1];\n var e = [-71/57600,0,71/16695,-71/1920,17253/339200,-22/525,1/40];\n var i = 0,er,j;\n var h = (x1-x0)/10;\n var it = 0;\n var add = numeric.add, mul = numeric.mul, y1,erinf;\n var max = Math.max, min = Math.min, abs = Math.abs, norminf = numeric.norminf,pow = Math.pow;\n var any = numeric.any, lt = numeric.lt, and = numeric.and, sub = numeric.sub;\n var e0, e1, ev;\n var ret = new numeric.Dopri(xs,ys,k1,ymid,-1,\"\");\n if(typeof event === \"function\") e0 = event(x0,y0);\n while(x0x1) h = x1-x0;\n k2 = f(x0+c[0]*h, add(y0,mul( A2*h,k1[i])));\n k3 = f(x0+c[1]*h, add(add(y0,mul(A3[0]*h,k1[i])),mul(A3[1]*h,k2)));\n k4 = f(x0+c[2]*h, add(add(add(y0,mul(A4[0]*h,k1[i])),mul(A4[1]*h,k2)),mul(A4[2]*h,k3)));\n k5 = f(x0+c[3]*h, add(add(add(add(y0,mul(A5[0]*h,k1[i])),mul(A5[1]*h,k2)),mul(A5[2]*h,k3)),mul(A5[3]*h,k4)));\n k6 = f(x0+c[4]*h,add(add(add(add(add(y0,mul(A6[0]*h,k1[i])),mul(A6[1]*h,k2)),mul(A6[2]*h,k3)),mul(A6[3]*h,k4)),mul(A6[4]*h,k5)));\n y1 = add(add(add(add(add(y0,mul(k1[i],h*b[0])),mul(k3,h*b[2])),mul(k4,h*b[3])),mul(k5,h*b[4])),mul(k6,h*b[5]));\n k7 = f(x0+h,y1);\n er = add(add(add(add(add(mul(k1[i],h*e[0]),mul(k3,h*e[2])),mul(k4,h*e[3])),mul(k5,h*e[4])),mul(k6,h*e[5])),mul(k7,h*e[6]));\n if(typeof er === \"number\") erinf = abs(er);\n else erinf = norminf(er);\n if(erinf > tol) { // reject\n h = 0.2*h*pow(tol/erinf,0.25);\n if(x0+h === x0) {\n ret.msg = \"Step size became too small\";\n break;\n }\n continue;\n }\n ymid[i] = add(add(add(add(add(add(y0,\n mul(k1[i],h*bm[0])),\n mul(k3 ,h*bm[2])),\n mul(k4 ,h*bm[3])),\n mul(k5 ,h*bm[4])),\n mul(k6 ,h*bm[5])),\n mul(k7 ,h*bm[6]));\n ++i;\n xs[i] = x0+h;\n ys[i] = y1;\n k1[i] = k7;\n if(typeof event === \"function\") {\n var yi,xl = x0,xr = x0+0.5*h,xi;\n e1 = event(xr,ymid[i-1]);\n ev = and(lt(e0,0),lt(0,e1));\n if(!any(ev)) { xl = xr; xr = x0+h; e0 = e1; e1 = event(xr,y1); ev = and(lt(e0,0),lt(0,e1)); }\n if(any(ev)) {\n var xc, yc, en,ei;\n var side=0, sl = 1.0, sr = 1.0;\n while(1) {\n if(typeof e0 === \"number\") xi = (sr*e1*xl-sl*e0*xr)/(sr*e1-sl*e0);\n else {\n xi = xr;\n for(j=e0.length-1;j!==-1;--j) {\n if(e0[j]<0 && e1[j]>0) xi = min(xi,(sr*e1[j]*xl-sl*e0[j]*xr)/(sr*e1[j]-sl*e0[j]));\n }\n }\n if(xi <= xl || xi >= xr) break;\n yi = ret._at(xi, i-1);\n ei = event(xi,yi);\n en = and(lt(e0,0),lt(0,ei));\n if(any(en)) {\n xr = xi;\n e1 = ei;\n ev = en;\n sr = 1.0;\n if(side === -1) sl *= 0.5;\n else sl = 1.0;\n side = -1;\n } else {\n xl = xi;\n e0 = ei;\n sl = 1.0;\n if(side === 1) sr *= 0.5;\n else sr = 1.0;\n side = 1;\n }\n }\n y1 = ret._at(0.5*(x0+xi),i-1);\n ret.f[i] = f(xi,yi);\n ret.x[i] = xi;\n ret.y[i] = yi;\n ret.ymid[i-1] = y1;\n ret.events = ev;\n ret.iterations = it;\n return ret;\n }\n }\n x0 += h;\n y0 = y1;\n e0 = e1;\n h = min(0.8*h*pow(tol/erinf,0.25),4*h);\n }\n ret.iterations = it;\n return ret;\n}\n\n// 11. Ax = b\nnumeric.LU = function(A, fast) {\n fast = fast || false;\n\n var abs = Math.abs;\n var i, j, k, absAjk, Akk, Ak, Pk, Ai;\n var max;\n var n = A.length, n1 = n-1;\n var P = new Array(n);\n if(!fast) A = numeric.clone(A);\n\n for (k = 0; k < n; ++k) {\n Pk = k;\n Ak = A[k];\n max = abs(Ak[k]);\n for (j = k + 1; j < n; ++j) {\n absAjk = abs(A[j][k]);\n if (max < absAjk) {\n max = absAjk;\n Pk = j;\n }\n }\n P[k] = Pk;\n\n if (Pk != k) {\n A[k] = A[Pk];\n A[Pk] = Ak;\n Ak = A[k];\n }\n\n Akk = Ak[k];\n\n for (i = k + 1; i < n; ++i) {\n A[i][k] /= Akk;\n }\n\n for (i = k + 1; i < n; ++i) {\n Ai = A[i];\n for (j = k + 1; j < n1; ++j) {\n Ai[j] -= Ai[k] * Ak[j];\n ++j;\n Ai[j] -= Ai[k] * Ak[j];\n }\n if(j===n1) Ai[j] -= Ai[k] * Ak[j];\n }\n }\n\n return {\n LU: A,\n P: P\n };\n}\n\nnumeric.LUsolve = function LUsolve(LUP, b) {\n var i, j;\n var LU = LUP.LU;\n var n = LU.length;\n var x = numeric.clone(b);\n var P = LUP.P;\n var Pi, LUi, LUii, tmp;\n\n for (i=n-1;i!==-1;--i) x[i] = b[i];\n for (i = 0; i < n; ++i) {\n Pi = P[i];\n if (P[i] !== i) {\n tmp = x[i];\n x[i] = x[Pi];\n x[Pi] = tmp;\n }\n\n LUi = LU[i];\n for (j = 0; j < i; ++j) {\n x[i] -= x[j] * LUi[j];\n }\n }\n\n for (i = n - 1; i >= 0; --i) {\n LUi = LU[i];\n for (j = i + 1; j < n; ++j) {\n x[i] -= x[j] * LUi[j];\n }\n\n x[i] /= LUi[i];\n }\n\n return x;\n}\n\nnumeric.solve = function solve(A,b,fast) { return numeric.LUsolve(numeric.LU(A,fast), b); }\n\n// 12. Linear programming\nnumeric.echelonize = function echelonize(A) {\n var s = numeric.dim(A), m = s[0], n = s[1];\n var I = numeric.identity(m);\n var P = Array(m);\n var i,j,k,l,Ai,Ii,Z,a;\n var abs = Math.abs;\n var diveq = numeric.diveq;\n A = numeric.clone(A);\n for(i=0;ia1) alpha = a1;\n g = add(c,mul(alpha,p));\n H = dot(A1,A0);\n for(i=m-1;i!==-1;--i) H[i][i] += 1;\n d = solve(H,div(g,alpha),true);\n var t0 = div(z,dot(A,d));\n var t = 1.0;\n for(i=n-1;i!==-1;--i) if(t0[i]<0) t = min(t,-0.999*t0[i]);\n y = sub(x,mul(d,t));\n z = sub(b,dot(A,y));\n if(!all(gt(z,0))) return { solution: x, message: \"\", iterations: count };\n x = y;\n if(alpha=0) unbounded = false;\n else unbounded = true;\n }\n if(unbounded) return { solution: y, message: \"Unbounded\", iterations: count };\n }\n return { solution: x, message: \"maximum iteration count exceeded\", iterations:count };\n}\n\nnumeric._solveLP = function _solveLP(c,A,b,tol,maxit) {\n var m = c.length, n = b.length,y;\n var sum = numeric.sum, log = numeric.log, mul = numeric.mul, sub = numeric.sub, dot = numeric.dot, div = numeric.div, add = numeric.add;\n var c0 = numeric.rep([m],0).concat([1]);\n var J = numeric.rep([n,1],-1);\n var A0 = numeric.blockMatrix([[A , J ]]);\n var b0 = b;\n var y = numeric.rep([m],0).concat(Math.max(0,numeric.sup(numeric.neg(b)))+1);\n var x0 = numeric.__solveLP(c0,A0,b0,tol,maxit,y,false);\n var x = numeric.clone(x0.solution);\n x.length = m;\n var foo = numeric.inf(sub(b,dot(A,x)));\n if(foo<0) { return { solution: NaN, message: \"Infeasible\", iterations: x0.iterations }; }\n var ret = numeric.__solveLP(c, A, b, tol, maxit-x0.iterations, x, true);\n ret.iterations += x0.iterations;\n return ret;\n};\n\nnumeric.solveLP = function solveLP(c,A,b,Aeq,beq,tol,maxit) {\n if(typeof maxit === \"undefined\") maxit = 1000;\n if(typeof tol === \"undefined\") tol = numeric.epsilon;\n if(typeof Aeq === \"undefined\") return numeric._solveLP(c,A,b,tol,maxit);\n var m = Aeq.length, n = Aeq[0].length, o = A.length;\n var B = numeric.echelonize(Aeq);\n var flags = numeric.rep([n],0);\n var P = B.P;\n var Q = [];\n var i;\n for(i=P.length-1;i!==-1;--i) flags[P[i]] = 1;\n for(i=n-1;i!==-1;--i) if(flags[i]===0) Q.push(i);\n var g = numeric.getRange;\n var I = numeric.linspace(0,m-1), J = numeric.linspace(0,o-1);\n var Aeq2 = g(Aeq,I,Q), A1 = g(A,J,P), A2 = g(A,J,Q), dot = numeric.dot, sub = numeric.sub;\n var A3 = dot(A1,B.I);\n var A4 = sub(A2,dot(A3,Aeq2)), b4 = sub(b,dot(A3,beq));\n var c1 = Array(P.length), c2 = Array(Q.length);\n for(i=P.length-1;i!==-1;--i) c1[i] = c[P[i]];\n for(i=Q.length-1;i!==-1;--i) c2[i] = c[Q[i]];\n var c4 = sub(c2,dot(c1,dot(B.I,Aeq2)));\n var S = numeric._solveLP(c4,A4,b4,tol,maxit);\n var x2 = S.solution;\n if(x2!==x2) return S;\n var x1 = dot(B.I,sub(beq,dot(Aeq2,x2)));\n var x = Array(c.length);\n for(i=P.length-1;i!==-1;--i) x[P[i]] = x1[i];\n for(i=Q.length-1;i!==-1;--i) x[Q[i]] = x2[i];\n return { solution: x, message:S.message, iterations: S.iterations };\n}\n\nnumeric.MPStoLP = function MPStoLP(MPS) {\n if(MPS instanceof String) { MPS.split('\\n'); }\n var state = 0;\n var states = ['Initial state','NAME','ROWS','COLUMNS','RHS','BOUNDS','ENDATA'];\n var n = MPS.length;\n var i,j,z,N=0,rows = {}, sign = [], rl = 0, vars = {}, nv = 0;\n var name;\n var c = [], A = [], b = [];\n function err(e) { throw new Error('MPStoLP: '+e+'\\nLine '+i+': '+MPS[i]+'\\nCurrent state: '+states[state]+'\\n'); }\n for(i=0;i\n//\n// Math.seedrandom('yipee'); Sets Math.random to a function that is\n// initialized using the given explicit seed.\n//\n// Math.seedrandom(); Sets Math.random to a function that is\n// seeded using the current time, dom state,\n// and other accumulated local entropy.\n// The generated seed string is returned.\n//\n// Math.seedrandom('yowza', true);\n// Seeds using the given explicit seed mixed\n// together with accumulated entropy.\n//\n// \n// Seeds using physical random bits downloaded\n// from random.org.\n//\n// Seeds using urandom bits from call.jsonlib.com,\n// which is faster than random.org.\n//\n// Examples:\n//\n// Math.seedrandom(\"hello\"); // Use \"hello\" as the seed.\n// document.write(Math.random()); // Always 0.5463663768140734\n// document.write(Math.random()); // Always 0.43973793770592234\n// var rng1 = Math.random; // Remember the current prng.\n//\n// var autoseed = Math.seedrandom(); // New prng with an automatic seed.\n// document.write(Math.random()); // Pretty much unpredictable.\n//\n// Math.random = rng1; // Continue \"hello\" prng sequence.\n// document.write(Math.random()); // Always 0.554769432473455\n//\n// Math.seedrandom(autoseed); // Restart at the previous seed.\n// document.write(Math.random()); // Repeat the 'unpredictable' value.\n//\n// Notes:\n//\n// Each time seedrandom('arg') is called, entropy from the passed seed\n// is accumulated in a pool to help generate future seeds for the\n// zero-argument form of Math.seedrandom, so entropy can be injected over\n// time by calling seedrandom with explicit data repeatedly.\n//\n// On speed - This javascript implementation of Math.random() is about\n// 3-10x slower than the built-in Math.random() because it is not native\n// code, but this is typically fast enough anyway. Seeding is more expensive,\n// especially if you use auto-seeding. Some details (timings on Chrome 4):\n//\n// Our Math.random() - avg less than 0.002 milliseconds per call\n// seedrandom('explicit') - avg less than 0.5 milliseconds per call\n// seedrandom('explicit', true) - avg less than 2 milliseconds per call\n// seedrandom() - avg about 38 milliseconds per call\n//\n// LICENSE (BSD):\n//\n// Copyright 2010 David Bau, all rights reserved.\n//\n// Redistribution and use in source and binary forms, with or without\n// modification, are permitted provided that the following conditions are met:\n// \n// 1. Redistributions of source code must retain the above copyright\n// notice, this list of conditions and the following disclaimer.\n//\n// 2. Redistributions in binary form must reproduce the above copyright\n// notice, this list of conditions and the following disclaimer in the\n// documentation and/or other materials provided with the distribution.\n// \n// 3. Neither the name of this module nor the names of its contributors may\n// be used to endorse or promote products derived from this software\n// without specific prior written permission.\n// \n// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS\n// \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT\n// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR\n// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT\n// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,\n// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT\n// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,\n// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY\n// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT\n// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\n// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n//\n/**\n * All code is in an anonymous closure to keep the global namespace clean.\n *\n * @param {number=} overflow \n * @param {number=} startdenom\n */\n\n// Patched by Seb so that seedrandom.js does not pollute the Math object.\n// My tests suggest that doing Math.trouble = 1 makes Math lookups about 5%\n// slower.\nnumeric.seedrandom = { pow:Math.pow, random:Math.random };\n\n(function (pool, math, width, chunks, significance, overflow, startdenom) {\n\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nmath['seedrandom'] = function seedrandom(seed, use_entropy) {\n var key = [];\n var arc4;\n\n // Flatten the seed string or build one from local entropy if needed.\n seed = mixkey(flatten(\n use_entropy ? [seed, pool] :\n arguments.length ? seed :\n [new Date().getTime(), pool, window], 3), key);\n\n // Use the seed to initialize an ARC4 generator.\n arc4 = new ARC4(key);\n\n // Mix the randomness into accumulated entropy.\n mixkey(arc4.S, pool);\n\n // Override Math.random\n\n // This function returns a random double in [0, 1) that contains\n // randomness in every bit of the mantissa of the IEEE 754 value.\n\n math['random'] = function random() { // Closure to return a random double:\n var n = arc4.g(chunks); // Start with a numerator n < 2 ^ 48\n var d = startdenom; // and denominator d = 2 ^ 48.\n var x = 0; // and no 'extra last byte'.\n while (n < significance) { // Fill up all significant digits by\n n = (n + x) * width; // shifting numerator and\n d *= width; // denominator and generating a\n x = arc4.g(1); // new least-significant-byte.\n }\n while (n >= overflow) { // To avoid rounding up, before adding\n n /= 2; // last byte, shift everything\n d /= 2; // right using integer math until\n x >>>= 1; // we have exactly the desired bits.\n }\n return (n + x) / d; // Form the number within [0, 1).\n };\n\n // Return the seed that was used\n return seed;\n};\n\n//\n// ARC4\n//\n// An ARC4 implementation. The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4. Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\n/** @constructor */\nfunction ARC4(key) {\n var t, u, me = this, keylen = key.length;\n var i = 0, j = me.i = me.j = me.m = 0;\n me.S = [];\n me.c = [];\n\n // The empty key [] is treated as [0].\n if (!keylen) { key = [keylen++]; }\n\n // Set up S using the standard key scheduling algorithm.\n while (i < width) { me.S[i] = i++; }\n for (i = 0; i < width; i++) {\n t = me.S[i];\n j = lowbits(j + t + key[i % keylen]);\n u = me.S[j];\n me.S[i] = u;\n me.S[j] = t;\n }\n\n // The \"g\" method returns the next (count) outputs as one number.\n me.g = function getnext(count) {\n var s = me.S;\n var i = lowbits(me.i + 1); var t = s[i];\n var j = lowbits(me.j + t); var u = s[j];\n s[i] = u;\n s[j] = t;\n var r = s[lowbits(t + u)];\n while (--count) {\n i = lowbits(i + 1); t = s[i];\n j = lowbits(j + t); u = s[j];\n s[i] = u;\n s[j] = t;\n r = r * width + s[lowbits(t + u)];\n }\n me.i = i;\n me.j = j;\n return r;\n };\n // For robust unpredictability discard an initial batch of values.\n // See http://www.rsa.com/rsalabs/node.asp?id=2009\n me.g(width);\n}\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\n/** @param {Object=} result \n * @param {string=} prop\n * @param {string=} typ */\nfunction flatten(obj, depth, result, prop, typ) {\n result = [];\n typ = typeof(obj);\n if (depth && typ == 'object') {\n for (prop in obj) {\n if (prop.indexOf('S') < 5) { // Avoid FF3 bug (local/sessionStorage)\n try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n }\n }\n }\n return (result.length ? result : obj + (typ != 'string' ? '\\0' : ''));\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\n/** @param {number=} smear \n * @param {number=} j */\nfunction mixkey(seed, key, smear, j) {\n seed += ''; // Ensure the seed is a string\n smear = 0;\n for (j = 0; j < seed.length; j++) {\n key[lowbits(j)] =\n lowbits((smear ^= key[lowbits(j)] * 19) + seed.charCodeAt(j));\n }\n seed = '';\n for (j in key) { seed += String.fromCharCode(key[j]); }\n return seed;\n}\n\n//\n// lowbits()\n// A quick \"n mod width\" for width a power of 2.\n//\nfunction lowbits(n) { return n & (width - 1); }\n\n//\n// The following constants are related to IEEE 754 limits.\n//\nstartdenom = math.pow(width, chunks);\nsignificance = math.pow(2, significance);\noverflow = significance * 2;\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool. Because we do\n// not want to intefere with determinstic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n// End anonymous scope, and pass initial values.\n}(\n [], // pool: entropy pool starts empty\n numeric.seedrandom, // math: package containing random, pow, and seedrandom\n 256, // width: each RC4 output is 0 <= x < 256\n 6, // chunks: at least six RC4 outputs for each double\n 52 // significance: there are 52 significant digits in a double\n ));\n/* This file is a slightly modified version of quadprog.js from Alberto Santini.\n * It has been slightly modified by Sébastien Loisel to make sure that it handles\n * 0-based Arrays instead of 1-based Arrays.\n * License is in resources/LICENSE.quadprog */\n(function(exports) {\n\nfunction base0to1(A) {\n if(typeof A !== \"object\") { return A; }\n var ret = [], i,n=A.length;\n for(i=0;i meq) {\n work[l] = sum;\n } else {\n work[l] = -Math.abs(sum);\n if (sum > 0) {\n for (j = 1; j <= n; j = j + 1) {\n amat[j][i] = -amat[j][i];\n }\n bvec[i] = -bvec[i];\n }\n }\n }\n\n for (i = 1; i <= nact; i = i + 1) {\n work[iwsv + iact[i]] = 0;\n }\n\n nvl = 0;\n temp = 0;\n for (i = 1; i <= q; i = i + 1) {\n if (work[iwsv + i] < temp * work[iwnbv + i]) {\n nvl = i;\n temp = work[iwsv + i] / work[iwnbv + i];\n }\n }\n if (nvl === 0) {\n return 999;\n }\n\n return 0;\n }\n\n function fn_goto_55() {\n for (i = 1; i <= n; i = i + 1) {\n sum = 0;\n for (j = 1; j <= n; j = j + 1) {\n sum = sum + dmat[j][i] * amat[j][nvl];\n }\n work[i] = sum;\n }\n\n l1 = iwzv;\n for (i = 1; i <= n; i = i + 1) {\n work[l1 + i] = 0;\n }\n for (j = nact + 1; j <= n; j = j + 1) {\n for (i = 1; i <= n; i = i + 1) {\n work[l1 + i] = work[l1 + i] + dmat[i][j] * work[j];\n }\n }\n\n t1inf = true;\n for (i = nact; i >= 1; i = i - 1) {\n sum = work[i];\n l = iwrm + (i * (i + 3)) / 2;\n l1 = l - i;\n for (j = i + 1; j <= nact; j = j + 1) {\n sum = sum - work[l] * work[iwrv + j];\n l = l + j;\n }\n sum = sum / work[l1];\n work[iwrv + i] = sum;\n if (iact[i] < meq) {\n // continue;\n break;\n }\n if (sum < 0) {\n // continue;\n break;\n }\n t1inf = false;\n it1 = i;\n }\n\n if (!t1inf) {\n t1 = work[iwuv + it1] / work[iwrv + it1];\n for (i = 1; i <= nact; i = i + 1) {\n if (iact[i] < meq) {\n // continue;\n break;\n }\n if (work[iwrv + i] < 0) {\n // continue;\n break;\n }\n temp = work[iwuv + i] / work[iwrv + i];\n if (temp < t1) {\n t1 = temp;\n it1 = i;\n }\n }\n }\n\n sum = 0;\n for (i = iwzv + 1; i <= iwzv + n; i = i + 1) {\n sum = sum + work[i] * work[i];\n }\n if (Math.abs(sum) <= vsmall) {\n if (t1inf) {\n ierr[1] = 1;\n // GOTO 999\n return 999;\n } else {\n for (i = 1; i <= nact; i = i + 1) {\n work[iwuv + i] = work[iwuv + i] - t1 * work[iwrv + i];\n }\n work[iwuv + nact + 1] = work[iwuv + nact + 1] + t1;\n // GOTO 700\n return 700;\n }\n } else {\n sum = 0;\n for (i = 1; i <= n; i = i + 1) {\n sum = sum + work[iwzv + i] * amat[i][nvl];\n }\n tt = -work[iwsv + nvl] / sum;\n t2min = true;\n if (!t1inf) {\n if (t1 < tt) {\n tt = t1;\n t2min = false;\n }\n }\n\n for (i = 1; i <= n; i = i + 1) {\n sol[i] = sol[i] + tt * work[iwzv + i];\n if (Math.abs(sol[i]) < vsmall) {\n sol[i] = 0;\n }\n }\n\n crval[1] = crval[1] + tt * sum * (tt / 2 + work[iwuv + nact + 1]);\n for (i = 1; i <= nact; i = i + 1) {\n work[iwuv + i] = work[iwuv + i] - tt * work[iwrv + i];\n }\n work[iwuv + nact + 1] = work[iwuv + nact + 1] + tt;\n\n if (t2min) {\n nact = nact + 1;\n iact[nact] = nvl;\n\n l = iwrm + ((nact - 1) * nact) / 2 + 1;\n for (i = 1; i <= nact - 1; i = i + 1) {\n work[l] = work[i];\n l = l + 1;\n }\n\n if (nact === n) {\n work[l] = work[n];\n } else {\n for (i = n; i >= nact + 1; i = i - 1) {\n if (work[i] === 0) {\n // continue;\n break;\n }\n gc = Math.max(Math.abs(work[i - 1]), Math.abs(work[i]));\n gs = Math.min(Math.abs(work[i - 1]), Math.abs(work[i]));\n if (work[i - 1] >= 0) {\n temp = Math.abs(gc * Math.sqrt(1 + gs * gs / (gc * gc)));\n } else {\n temp = -Math.abs(gc * Math.sqrt(1 + gs * gs / (gc * gc)));\n }\n gc = work[i - 1] / temp;\n gs = work[i] / temp;\n\n if (gc === 1) {\n // continue;\n break;\n }\n if (gc === 0) {\n work[i - 1] = gs * temp;\n for (j = 1; j <= n; j = j + 1) {\n temp = dmat[j][i - 1];\n dmat[j][i - 1] = dmat[j][i];\n dmat[j][i] = temp;\n }\n } else {\n work[i - 1] = temp;\n nu = gs / (1 + gc);\n for (j = 1; j <= n; j = j + 1) {\n temp = gc * dmat[j][i - 1] + gs * dmat[j][i];\n dmat[j][i] = nu * (dmat[j][i - 1] + temp) - dmat[j][i];\n dmat[j][i - 1] = temp;\n\n }\n }\n }\n work[l] = work[nact];\n }\n } else {\n sum = -bvec[nvl];\n for (j = 1; j <= n; j = j + 1) {\n sum = sum + sol[j] * amat[j][nvl];\n }\n if (nvl > meq) {\n work[iwsv + nvl] = sum;\n } else {\n work[iwsv + nvl] = -Math.abs(sum);\n if (sum > 0) {\n for (j = 1; j <= n; j = j + 1) {\n amat[j][nvl] = -amat[j][nvl];\n }\n bvec[nvl] = -bvec[nvl];\n }\n }\n // GOTO 700\n return 700;\n }\n }\n\n return 0;\n }\n\n function fn_goto_797() {\n l = iwrm + (it1 * (it1 + 1)) / 2 + 1;\n l1 = l + it1;\n if (work[l1] === 0) {\n // GOTO 798\n return 798;\n }\n gc = Math.max(Math.abs(work[l1 - 1]), Math.abs(work[l1]));\n gs = Math.min(Math.abs(work[l1 - 1]), Math.abs(work[l1]));\n if (work[l1 - 1] >= 0) {\n temp = Math.abs(gc * Math.sqrt(1 + gs * gs / (gc * gc)));\n } else {\n temp = -Math.abs(gc * Math.sqrt(1 + gs * gs / (gc * gc)));\n }\n gc = work[l1 - 1] / temp;\n gs = work[l1] / temp;\n\n if (gc === 1) {\n // GOTO 798\n return 798;\n }\n if (gc === 0) {\n for (i = it1 + 1; i <= nact; i = i + 1) {\n temp = work[l1 - 1];\n work[l1 - 1] = work[l1];\n work[l1] = temp;\n l1 = l1 + i;\n }\n for (i = 1; i <= n; i = i + 1) {\n temp = dmat[i][it1];\n dmat[i][it1] = dmat[i][it1 + 1];\n dmat[i][it1 + 1] = temp;\n }\n } else {\n nu = gs / (1 + gc);\n for (i = it1 + 1; i <= nact; i = i + 1) {\n temp = gc * work[l1 - 1] + gs * work[l1];\n work[l1] = nu * (work[l1 - 1] + temp) - work[l1];\n work[l1 - 1] = temp;\n l1 = l1 + i;\n }\n for (i = 1; i <= n; i = i + 1) {\n temp = gc * dmat[i][it1] + gs * dmat[i][it1 + 1];\n dmat[i][it1 + 1] = nu * (dmat[i][it1] + temp) - dmat[i][it1 + 1];\n dmat[i][it1] = temp;\n }\n }\n\n return 0;\n }\n\n function fn_goto_798() {\n l1 = l - it1;\n for (i = 1; i <= it1; i = i + 1) {\n work[l1] = work[l];\n l = l + 1;\n l1 = l1 + 1;\n }\n\n work[iwuv + it1] = work[iwuv + it1 + 1];\n iact[it1] = iact[it1 + 1];\n it1 = it1 + 1;\n if (it1 < nact) {\n // GOTO 797\n return 797;\n }\n\n return 0;\n }\n\n function fn_goto_799() {\n work[iwuv + nact] = work[iwuv + nact + 1];\n work[iwuv + nact + 1] = 0;\n iact[nact] = 0;\n nact = nact - 1;\n iter[2] = iter[2] + 1;\n\n return 0;\n }\n\n go = 0;\n while (true) {\n go = fn_goto_50();\n if (go === 999) {\n return;\n }\n while (true) {\n go = fn_goto_55();\n if (go === 0) {\n break;\n }\n if (go === 999) {\n return;\n }\n if (go === 700) {\n if (it1 === nact) {\n fn_goto_799();\n } else {\n while (true) {\n fn_goto_797();\n go = fn_goto_798();\n if (go !== 797) {\n break;\n }\n }\n fn_goto_799();\n }\n }\n }\n }\n\n}\n\nfunction solveQP(Dmat, dvec, Amat, bvec, meq, factorized) {\n Dmat = base0to1(Dmat);\n dvec = base0to1(dvec);\n Amat = base0to1(Amat);\n var i, n, q,\n nact, r,\n crval = [], iact = [], sol = [], work = [], iter = [],\n message;\n\n meq = meq || 0;\n factorized = factorized ? base0to1(factorized) : [undefined, 0];\n bvec = bvec ? base0to1(bvec) : [];\n\n // In Fortran the array index starts from 1\n n = Dmat.length - 1;\n q = Amat[1].length - 1;\n\n if (!bvec) {\n for (i = 1; i <= q; i = i + 1) {\n bvec[i] = 0;\n }\n }\n for (i = 1; i <= q; i = i + 1) {\n iact[i] = 0;\n }\n nact = 0;\n r = Math.min(n, q);\n for (i = 1; i <= n; i = i + 1) {\n sol[i] = 0;\n }\n crval[1] = 0;\n for (i = 1; i <= (2 * n + (r * (r + 5)) / 2 + 2 * q + 1); i = i + 1) {\n work[i] = 0;\n }\n for (i = 1; i <= 2; i = i + 1) {\n iter[i] = 0;\n }\n\n qpgen2(Dmat, dvec, n, n, sol, crval, Amat,\n bvec, n, q, meq, iact, nact, iter, work, factorized);\n\n message = \"\";\n if (factorized[1] === 1) {\n message = \"constraints are inconsistent, no solution!\";\n }\n if (factorized[1] === 2) {\n message = \"matrix D in quadratic function is not positive definite!\";\n }\n\n return {\n solution: base1to0(sol),\n value: base1to0(crval),\n unconstrained_solution: base1to0(dvec),\n iterations: base1to0(iter),\n iact: base1to0(iact),\n message: message\n };\n}\nexports.solveQP = solveQP;\n}(numeric));\n/*\r\nShanti Rao sent me this routine by private email. I had to modify it\r\nslightly to work on Arrays instead of using a Matrix object.\r\nIt is apparently translated from http://stitchpanorama.sourceforge.net/Python/svd.py\r\n*/\r\n\r\nnumeric.svd= function svd(A) {\r\n var temp;\r\n//Compute the thin SVD from G. H. Golub and C. Reinsch, Numer. Math. 14, 403-420 (1970)\r\n\tvar prec= numeric.epsilon; //Math.pow(2,-52) // assumes double prec\r\n\tvar tolerance= 1.e-64/prec;\r\n\tvar itmax= 50;\r\n\tvar c=0;\r\n\tvar i=0;\r\n\tvar j=0;\r\n\tvar k=0;\r\n\tvar l=0;\r\n\t\r\n\tvar u= numeric.clone(A);\r\n\tvar m= u.length;\r\n\t\r\n\tvar n= u[0].length;\r\n\t\r\n\tif (m < n) throw \"Need more rows than columns\"\r\n\t\r\n\tvar e = new Array(n);\r\n\tvar q = new Array(n);\r\n\tfor (i=0; i b)\r\n\t\t\treturn a*Math.sqrt(1.0+(b*b/a/a))\r\n\t\telse if (b == 0.0) \r\n\t\t\treturn a\r\n\t\treturn b*Math.sqrt(1.0+(a*a/b/b))\r\n\t}\r\n\r\n\t//Householder's reduction to bidiagonal form\r\n\r\n\tvar f= 0.0;\r\n\tvar g= 0.0;\r\n\tvar h= 0.0;\r\n\tvar x= 0.0;\r\n\tvar y= 0.0;\r\n\tvar z= 0.0;\r\n\tvar s= 0.0;\r\n\t\r\n\tfor (i=0; i < n; i++)\r\n\t{\t\r\n\t\te[i]= g;\r\n\t\ts= 0.0;\r\n\t\tl= i+1;\r\n\t\tfor (j=i; j < m; j++) \r\n\t\t\ts += (u[j][i]*u[j][i]);\r\n\t\tif (s <= tolerance)\r\n\t\t\tg= 0.0;\r\n\t\telse\r\n\t\t{\t\r\n\t\t\tf= u[i][i];\r\n\t\t\tg= Math.sqrt(s);\r\n\t\t\tif (f >= 0.0) g= -g;\r\n\t\t\th= f*g-s\r\n\t\t\tu[i][i]=f-g;\r\n\t\t\tfor (j=l; j < n; j++)\r\n\t\t\t{\r\n\t\t\t\ts= 0.0\r\n\t\t\t\tfor (k=i; k < m; k++) \r\n\t\t\t\t\ts += u[k][i]*u[k][j]\r\n\t\t\t\tf= s/h\r\n\t\t\t\tfor (k=i; k < m; k++) \r\n\t\t\t\t\tu[k][j]+=f*u[k][i]\r\n\t\t\t}\r\n\t\t}\r\n\t\tq[i]= g\r\n\t\ts= 0.0\r\n\t\tfor (j=l; j < n; j++) \r\n\t\t\ts= s + u[i][j]*u[i][j]\r\n\t\tif (s <= tolerance)\r\n\t\t\tg= 0.0\r\n\t\telse\r\n\t\t{\t\r\n\t\t\tf= u[i][i+1]\r\n\t\t\tg= Math.sqrt(s)\r\n\t\t\tif (f >= 0.0) g= -g\r\n\t\t\th= f*g - s\r\n\t\t\tu[i][i+1] = f-g;\r\n\t\t\tfor (j=l; j < n; j++) e[j]= u[i][j]/h\r\n\t\t\tfor (j=l; j < m; j++)\r\n\t\t\t{\t\r\n\t\t\t\ts=0.0\r\n\t\t\t\tfor (k=l; k < n; k++) \r\n\t\t\t\t\ts += (u[j][k]*u[i][k])\r\n\t\t\t\tfor (k=l; k < n; k++) \r\n\t\t\t\t\tu[j][k]+=s*e[k]\r\n\t\t\t}\t\r\n\t\t}\r\n\t\ty= Math.abs(q[i])+Math.abs(e[i])\r\n\t\tif (y>x) \r\n\t\t\tx=y\r\n\t}\r\n\t\r\n\t// accumulation of right hand gtransformations\r\n\tfor (i=n-1; i != -1; i+= -1)\r\n\t{\t\r\n\t\tif (g != 0.0)\r\n\t\t{\r\n\t\t \th= g*u[i][i+1]\r\n\t\t\tfor (j=l; j < n; j++) \r\n\t\t\t\tv[j][i]=u[i][j]/h\r\n\t\t\tfor (j=l; j < n; j++)\r\n\t\t\t{\t\r\n\t\t\t\ts=0.0\r\n\t\t\t\tfor (k=l; k < n; k++) \r\n\t\t\t\t\ts += u[i][k]*v[k][j]\r\n\t\t\t\tfor (k=l; k < n; k++) \r\n\t\t\t\t\tv[k][j]+=(s*v[k][i])\r\n\t\t\t}\t\r\n\t\t}\r\n\t\tfor (j=l; j < n; j++)\r\n\t\t{\r\n\t\t\tv[i][j] = 0;\r\n\t\t\tv[j][i] = 0;\r\n\t\t}\r\n\t\tv[i][i] = 1;\r\n\t\tg= e[i]\r\n\t\tl= i\r\n\t}\r\n\t\r\n\t// accumulation of left hand transformations\r\n\tfor (i=n-1; i != -1; i+= -1)\r\n\t{\t\r\n\t\tl= i+1\r\n\t\tg= q[i]\r\n\t\tfor (j=l; j < n; j++) \r\n\t\t\tu[i][j] = 0;\r\n\t\tif (g != 0.0)\r\n\t\t{\r\n\t\t\th= u[i][i]*g\r\n\t\t\tfor (j=l; j < n; j++)\r\n\t\t\t{\r\n\t\t\t\ts=0.0\r\n\t\t\t\tfor (k=l; k < m; k++) s += u[k][i]*u[k][j];\r\n\t\t\t\tf= s/h\r\n\t\t\t\tfor (k=i; k < m; k++) u[k][j]+=f*u[k][i];\r\n\t\t\t}\r\n\t\t\tfor (j=i; j < m; j++) u[j][i] = u[j][i]/g;\r\n\t\t}\r\n\t\telse\r\n\t\t\tfor (j=i; j < m; j++) u[j][i] = 0;\r\n\t\tu[i][i] += 1;\r\n\t}\r\n\t\r\n\t// diagonalization of the bidiagonal form\r\n\tprec= prec*x\r\n\tfor (k=n-1; k != -1; k+= -1)\r\n\t{\r\n\t\tfor (var iteration=0; iteration < itmax; iteration++)\r\n\t\t{\t// test f splitting\r\n\t\t\tvar test_convergence = false\r\n\t\t\tfor (l=k; l != -1; l+= -1)\r\n\t\t\t{\t\r\n\t\t\t\tif (Math.abs(e[l]) <= prec)\r\n\t\t\t\t{\ttest_convergence= true\r\n\t\t\t\t\tbreak \r\n\t\t\t\t}\r\n\t\t\t\tif (Math.abs(q[l-1]) <= prec)\r\n\t\t\t\t\tbreak \r\n\t\t\t}\r\n\t\t\tif (!test_convergence)\r\n\t\t\t{\t// cancellation of e[l] if l>0\r\n\t\t\t\tc= 0.0\r\n\t\t\t\ts= 1.0\r\n\t\t\t\tvar l1= l-1\r\n\t\t\t\tfor (i =l; i= itmax-1)\r\n\t\t\t\tthrow 'Error: no convergence.'\r\n\t\t\t// shift from bottom 2x2 minor\r\n\t\t\tx= q[l]\r\n\t\t\ty= q[k-1]\r\n\t\t\tg= e[k-1]\r\n\t\t\th= e[k]\r\n\t\t\tf= ((y-z)*(y+z)+(g-h)*(g+h))/(2.0*h*y)\r\n\t\t\tg= pythag(f,1.0)\r\n\t\t\tif (f < 0.0)\r\n\t\t\t\tf= ((x-z)*(x+z)+h*(y/(f-g)-h))/x\r\n\t\t\telse\r\n\t\t\t\tf= ((x-z)*(x+z)+h*(y/(f+g)-h))/x\r\n\t\t\t// next QR transformation\r\n\t\t\tc= 1.0\r\n\t\t\ts= 1.0\r\n\t\t\tfor (i=l+1; i< k+1; i++)\r\n\t\t\t{\t\r\n\t\t\t\tg= e[i]\r\n\t\t\t\ty= q[i]\r\n\t\t\t\th= s*g\r\n\t\t\t\tg= c*g\r\n\t\t\t\tz= pythag(f,h)\r\n\t\t\t\te[i-1]= z\r\n\t\t\t\tc= f/z\r\n\t\t\t\ts= h/z\r\n\t\t\t\tf= x*c+g*s\r\n\t\t\t\tg= -x*s+g*c\r\n\t\t\t\th= y*s\r\n\t\t\t\ty= y*c\r\n\t\t\t\tfor (j=0; j < n; j++)\r\n\t\t\t\t{\t\r\n\t\t\t\t\tx= v[j][i-1]\r\n\t\t\t\t\tz= v[j][i]\r\n\t\t\t\t\tv[j][i-1] = x*c+z*s\r\n\t\t\t\t\tv[j][i] = -x*s+z*c\r\n\t\t\t\t}\r\n\t\t\t\tz= pythag(f,h)\r\n\t\t\t\tq[i-1]= z\r\n\t\t\t\tc= f/z\r\n\t\t\t\ts= h/z\r\n\t\t\t\tf= c*g+s*y\r\n\t\t\t\tx= -s*g+c*y\r\n\t\t\t\tfor (j=0; j < m; j++)\r\n\t\t\t\t{\r\n\t\t\t\t\ty= u[j][i-1]\r\n\t\t\t\t\tz= u[j][i]\r\n\t\t\t\t\tu[j][i-1] = y*c+z*s\r\n\t\t\t\t\tu[j][i] = -y*s+z*c\r\n\t\t\t\t}\r\n\t\t\t}\r\n\t\t\te[l]= 0.0\r\n\t\t\te[k]= f\r\n\t\t\tq[k]= x\r\n\t\t} \r\n\t}\r\n\t\t\r\n\t//vt= transpose(v)\r\n\t//return (u,q,vt)\r\n\tfor (i=0;i= 0; j--)\r\n\t {\r\n\t if (q[j] < q[i])\r\n\t {\r\n\t// writeln(i,'-',j)\r\n\t c = q[j]\r\n\t q[j] = q[i]\r\n\t q[i] = c\r\n\t for(k=0;k b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.greater(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction greater_(a, b) {\n let $a = convertToTensor(a, 'a', 'greater', 'string_or_numeric');\n let $b = convertToTensor(b, 'b', 'greater', 'string_or_numeric');\n [$a, $b] = makeTypesMatch($a, $b);\n assertAndGetBroadcastShape($a.shape, $b.shape);\n const inputs = { a: $a, b: $b };\n return ENGINE.runKernel(Greater, inputs);\n}\nexport const greater = op({ greater_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-1 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor1d` as it makes the code more readable.\n *\n * ```js\n * tf.tensor1d([1, 2, 3]).print();\n * ```\n *\n * @param values The values of the tensor. Can be array of numbers,\n * or a `TypedArray`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor1d(values, dtype) {\n assertNonNull(values);\n const inferredShape = inferShape(values, dtype);\n if (inferredShape.length !== 1) {\n throw new Error('tensor1d() requires values to be a flat/TypedArray');\n }\n const shape = null;\n return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { assert, assertNonNegativeIntegerDimensions, flatten, inferDtype, isTypedArray, sizeFromShape, toTypedArray } from '../util';\n/** This is shared code across all tensor creation methods. */\nexport function makeTensor(values, shape, inferredShape, dtype) {\n if (dtype == null) {\n dtype = inferDtype(values);\n }\n if (dtype === 'complex64') {\n throw new Error(`Cannot construct a complex64 tensor directly. ` +\n `Please use tf.complex(real, imag).`);\n }\n if (!isTypedArray(values) && !Array.isArray(values) &&\n typeof values !== 'number' && typeof values !== 'boolean' &&\n typeof values !== 'string') {\n throw new Error('values passed to tensor(values) must be a number/boolean/string or ' +\n 'an array of numbers/booleans/strings, or a TypedArray');\n }\n if (shape != null) {\n assertNonNegativeIntegerDimensions(shape);\n const providedSize = sizeFromShape(shape);\n const inferredSize = sizeFromShape(inferredShape);\n assert(providedSize === inferredSize, () => `Based on the provided shape, [${shape}], the tensor should have ` +\n `${providedSize} values but has ${inferredSize}`);\n for (let i = 0; i < inferredShape.length; ++i) {\n const inferred = inferredShape[i];\n const flatDimsDontMatch = i === inferredShape.length - 1 ?\n inferred !== sizeFromShape(shape.slice(i)) :\n true;\n assert(inferredShape[i] === shape[i] || !flatDimsDontMatch, () => `Error creating a new Tensor. Inferred shape ` +\n `(${inferredShape}) does not match the provided ` +\n `shape (${shape}). `);\n }\n }\n if (!isTypedArray(values) && !Array.isArray(values)) {\n values = [values];\n }\n shape = shape || inferredShape;\n values = dtype !== 'string' ?\n toTypedArray(values, dtype) :\n flatten(values, [], true);\n return ENGINE.makeTensor(values, shape, dtype);\n}\n//# 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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\n/**\n * Template that creates implementation for unary op.\n */\nexport function createSimpleUnaryImpl(op) {\n return (values, dtype, attrs) => {\n const newValues = util.getTypedArrayFromDType(dtype, values.length);\n for (let i = 0; i < values.length; ++i) {\n newValues[i] = op(values[i], attrs);\n }\n return newValues;\n };\n}\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Reverse } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Reverses a `tf.Tensor` along a specified axis.\n *\n * Also available are stricter rank-specific methods that assert that `x` is\n * of the given rank:\n * - `tf.reverse1d`\n * - `tf.reverse2d`\n * - `tf.reverse3d`\n * - `tf.reverse4d`\n *\n * Except `tf.reverse1d` (which does not have axis param), all methods have\n * same signature as this method.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * x.reverse().print();\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.reverse(axis).print();\n * ```\n * @param x The input tensor to be reversed.\n * @param axis The set of dimensions to reverse. Must be in the\n * range [-rank(x), rank(x)). Defaults to all axes.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction reverse_(x, axis) {\n const $x = convertToTensor(x, 'x', 'reverse');\n const inputs = { x: $x };\n const attrs = { dims: axis };\n return ENGINE.runKernel(Reverse, inputs, attrs);\n}\nexport const reverse = op({ reverse_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, buffer, Slice, slice_util, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function sliceImpl(vals, begin, size, shape, dtype) {\n const isContinous = slice_util.isSliceContinous(shape, begin, size);\n const length = util.sizeFromShape(size);\n const xStrides = util.computeStrides(shape);\n if (isContinous) {\n const flatOffset = slice_util.computeFlatOffset(begin, xStrides);\n if (dtype === 'string') {\n return vals.slice(flatOffset, flatOffset + length);\n }\n return vals.subarray(flatOffset, flatOffset + length);\n }\n const decodedData = dtype === 'string' ?\n backend_util.fromUint8ToStringArray(vals) :\n vals;\n const inBuf = buffer(shape, dtype, decodedData);\n const outBuf = buffer(size, dtype);\n for (let i = 0; i < outBuf.size; ++i) {\n const outLoc = outBuf.indexToLoc(i);\n const inLoc = outLoc.map((idx, j) => idx + begin[j]);\n outBuf.set(inBuf.get(...inLoc), ...outLoc);\n }\n if (dtype === 'string') {\n return backend_util.fromStringArrayToUint8(outBuf.values);\n }\n return outBuf.values;\n}\nexport function slice(args) {\n const { inputs, backend, attrs } = args;\n const { x } = inputs;\n const { begin, size } = attrs;\n assertNotComplex(x, 'slice');\n const [$begin, $size] = slice_util.parseSliceParams(x, begin, size);\n slice_util.assertParamsValid(x, $begin, $size);\n const vals = backend.data.get(x.dataId).values;\n const outVals = sliceImpl(vals, $begin, $size, x.shape, x.dtype);\n return backend.makeTensorInfo($size, x.dtype, outVals);\n}\nexport const sliceConfig = {\n kernelName: Slice,\n backendName: 'cpu',\n kernelFunc: slice\n};\n//# 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* @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { makeOnesTypedArray, sizeFromShape } from '../util';\nimport { complex } from './complex';\nimport { zeros } from './zeros';\n/**\n * Creates a `tf.Tensor` with all elements set to 1.\n *\n * ```js\n * tf.ones([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The type of an element in the resulting tensor. Defaults to\n * 'float'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function ones(shape, dtype = 'float32') {\n if (dtype === 'complex64') {\n const real = ones(shape, 'float32');\n const imag = zeros(shape, 'float32');\n return complex(real, imag);\n }\n const values = makeOnesTypedArray(sizeFromShape(shape), dtype);\n return ENGINE.makeTensor(values, shape, dtype);\n}\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Transpose } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Transposes the `tf.Tensor`. Permutes the dimensions according to `perm`.\n *\n * The returned `tf.Tensor`'s dimension `i` will correspond to the input\n * dimension `perm[i]`. If `perm` is not given, it is set to `[n-1...0]`,\n * where `n` is the rank of the input `tf.Tensor`. Hence by default, this\n * operation performs a regular matrix transpose on 2-D input `tf.Tensor`s.\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);\n *\n * a.transpose().print(); // or tf.transpose(a)\n * ```\n *\n * @param x The tensor to transpose.\n * @param perm The permutation of the dimensions of a.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction transpose_(x, perm) {\n const $x = convertToTensor(x, 'x', 'transpose');\n if (perm == null) {\n perm = $x.shape.map((s, i) => i).reverse();\n }\n util.assert($x.rank === perm.length, () => `Error in transpose: rank of input ${$x.rank} ` +\n `must match length of perm ${perm}.`);\n perm.forEach(axis => {\n util.assert(axis >= 0 && axis < $x.rank, () => `All entries in 'perm' must be between 0 and ${$x.rank - 1}` +\n ` but got ${perm}`);\n });\n if ($x.rank <= 1) {\n return $x.clone();\n }\n const inputs = { x: $x };\n const attrs = { perm };\n return ENGINE.runKernel(Transpose, inputs, attrs);\n}\nexport const transpose = op({ transpose_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport * as tensorflow from '../data/compiled_api';\nimport { getRegisteredOp } from './custom_op/register';\nimport { getNodeNameAndIndex } from './executors/utils';\nimport * as arithmetic from './op_list/arithmetic';\nimport * as basicMath from './op_list/basic_math';\nimport * as control from './op_list/control';\nimport * as convolution from './op_list/convolution';\nimport * as creation from './op_list/creation';\nimport * as dynamic from './op_list/dynamic';\nimport * as evaluation from './op_list/evaluation';\nimport * as graph from './op_list/graph';\nimport * as hashTable from './op_list/hash_table';\nimport * as image from './op_list/image';\nimport * as logical from './op_list/logical';\nimport * as matrices from './op_list/matrices';\nimport * as normalization from './op_list/normalization';\nimport * as reduction from './op_list/reduction';\nimport * as sliceJoin from './op_list/slice_join';\nimport * as sparse from './op_list/sparse';\nimport * as spectral from './op_list/spectral';\nimport * as string from './op_list/string';\nimport * as transformation from './op_list/transformation';\nexport class OperationMapper {\n // Singleton instance for the mapper\n static get Instance() {\n return this._instance || (this._instance = new this());\n }\n // Loads the op mapping from the JSON file.\n constructor() {\n const ops = [\n arithmetic, basicMath, control, convolution, creation, dynamic,\n evaluation, graph, hashTable, image, logical, matrices, normalization,\n reduction, sliceJoin, sparse, spectral, string, transformation\n ];\n const mappersJson = [].concat(...ops.map(op => op.json));\n this.opMappers = mappersJson.reduce((map, mapper) => {\n map[mapper.tfOpName] = mapper;\n return map;\n }, {});\n }\n // Converts the model inference graph from Tensorflow GraphDef to local\n // representation for TensorFlow.js API\n transformGraph(graph, signature = {}) {\n const tfNodes = graph.node;\n const placeholders = [];\n const weights = [];\n const initNodes = [];\n const nodes = tfNodes.reduce((map, node) => {\n map[node.name] = this.mapNode(node);\n if (node.op.startsWith('Placeholder')) {\n placeholders.push(map[node.name]);\n }\n else if (node.op === 'Const') {\n weights.push(map[node.name]);\n }\n else if (node.input == null || node.input.length === 0) {\n initNodes.push(map[node.name]);\n }\n return map;\n }, {});\n let inputs = [];\n const outputs = [];\n let inputNodeNameToKey = {};\n let outputNodeNameToKey = {};\n if (signature != null) {\n inputNodeNameToKey = this.mapSignatureEntries(signature.inputs);\n outputNodeNameToKey = this.mapSignatureEntries(signature.outputs);\n }\n const allNodes = Object.keys(nodes);\n allNodes.forEach(key => {\n const node = nodes[key];\n node.inputNames.forEach((name, index) => {\n const [nodeName, , outputName] = getNodeNameAndIndex(name);\n const inputNode = nodes[nodeName];\n if (inputNode.outputs != null) {\n const outputIndex = inputNode.outputs.indexOf(outputName);\n if (outputIndex !== -1) {\n const inputName = `${nodeName}:${outputIndex}`;\n // update the input name to use the mapped output index directly.\n node.inputNames[index] = inputName;\n }\n }\n node.inputs.push(inputNode);\n inputNode.children.push(node);\n });\n });\n // if signature has not outputs set, add any node that does not have\n // outputs.\n if (Object.keys(outputNodeNameToKey).length === 0) {\n allNodes.forEach(key => {\n const node = nodes[key];\n if (node.children.length === 0) {\n outputs.push(node);\n }\n });\n }\n else {\n Object.keys(outputNodeNameToKey).forEach(name => {\n const [nodeName,] = getNodeNameAndIndex(name);\n const node = nodes[nodeName];\n if (node != null) {\n node.signatureKey = outputNodeNameToKey[name];\n outputs.push(node);\n }\n });\n }\n if (Object.keys(inputNodeNameToKey).length > 0) {\n Object.keys(inputNodeNameToKey).forEach(name => {\n const [nodeName,] = getNodeNameAndIndex(name);\n const node = nodes[nodeName];\n if (node) {\n node.signatureKey = inputNodeNameToKey[name];\n inputs.push(node);\n }\n });\n }\n else {\n inputs = placeholders;\n }\n let functions = {};\n if (graph.library != null && graph.library.function != null) {\n functions = graph.library.function.reduce((functions, func) => {\n functions[func.signature.name] = this.mapFunction(func);\n return functions;\n }, {});\n }\n const result = { nodes, inputs, outputs, weights, placeholders, signature, functions };\n if (initNodes.length > 0) {\n result.initNodes = initNodes;\n }\n return result;\n }\n mapSignatureEntries(entries) {\n return Object.keys(entries || {})\n .reduce((prev, curr) => {\n prev[entries[curr].name] = curr;\n return prev;\n }, {});\n }\n mapNode(node) {\n // Unsupported ops will cause an error at run-time (not parse time), since\n // they may not be used by the actual execution subgraph.\n const mapper = getRegisteredOp(node.op) || this.opMappers[node.op] || {};\n if (node.attr == null) {\n node.attr = {};\n }\n const newNode = {\n name: node.name,\n op: node.op,\n category: mapper.category,\n inputNames: (node.input ||\n []).map(input => input.startsWith('^') ? input.substr(1) : input),\n inputs: [],\n children: [],\n inputParams: {},\n attrParams: {},\n rawAttrs: node.attr,\n outputs: mapper.outputs\n };\n if (mapper.inputs != null) {\n newNode.inputParams =\n mapper.inputs.reduce((map, param) => {\n map[param.name] = {\n type: param.type,\n inputIndexStart: param.start,\n inputIndexEnd: param.end\n };\n return map;\n }, {});\n }\n if (mapper.attrs != null) {\n newNode.attrParams =\n mapper.attrs.reduce((map, param) => {\n const type = param.type;\n let value = undefined;\n switch (param.type) {\n case 'string':\n value = getStringParam(node.attr, param.tfName, param.defaultValue);\n if (value === undefined && !!param.tfDeprecatedName) {\n value = getStringParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n }\n break;\n case 'string[]':\n value = getStringArrayParam(node.attr, param.tfName, param.defaultValue);\n if (value === undefined && !!param.tfDeprecatedName) {\n value = getStringArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n }\n break;\n case 'number':\n value = getNumberParam(node.attr, param.tfName, (param.defaultValue || 0));\n if (value === undefined && !!param.tfDeprecatedName) {\n value = getNumberParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n }\n break;\n case 'number[]':\n value = getNumericArrayParam(node.attr, param.tfName, param.defaultValue);\n if (value === undefined && !!param.tfDeprecatedName) {\n value = getNumericArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n }\n break;\n case 'bool':\n value = getBoolParam(node.attr, param.tfName, param.defaultValue);\n if (value === undefined && !!param.tfDeprecatedName) {\n value = getBoolParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n }\n break;\n case 'bool[]':\n value = getBoolArrayParam(node.attr, param.tfName, param.defaultValue);\n if (value === undefined && !!param.tfDeprecatedName) {\n value = getBoolArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n }\n break;\n case 'shape':\n value = getTensorShapeParam(node.attr, param.tfName, param.defaultValue);\n if (value === undefined && !!param.tfDeprecatedName) {\n value = getTensorShapeParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n }\n break;\n case 'shape[]':\n value = getTensorShapeArrayParam(node.attr, param.tfName, param.defaultValue);\n if (value === undefined && !!param.tfDeprecatedName) {\n value = getTensorShapeArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n }\n break;\n case 'dtype':\n value = getDtypeParam(node.attr, param.tfName, param.defaultValue);\n if (value === undefined && !!param.tfDeprecatedName) {\n value = getDtypeParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n }\n break;\n case 'dtype[]':\n value = getDtypeArrayParam(node.attr, param.tfName, param.defaultValue);\n if (value === undefined && !!param.tfDeprecatedName) {\n value = getDtypeArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n }\n break;\n case 'func':\n value = getFuncParam(node.attr, param.tfName, param.defaultValue);\n if (value === undefined && !!param.tfDeprecatedName) {\n value = getFuncParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n }\n break;\n case 'tensor':\n case 'tensors':\n break;\n default:\n throw new Error(`Unsupported param type: ${param.type} for op: ${node.op}`);\n }\n map[param.name] = { value, type };\n return map;\n }, {});\n }\n return newNode;\n }\n // map the TFunctionDef to TFJS graph object\n mapFunction(functionDef) {\n const tfNodes = functionDef.nodeDef;\n const placeholders = [];\n const weights = [];\n let nodes = {};\n if (tfNodes != null) {\n nodes = tfNodes.reduce((map, node) => {\n map[node.name] = this.mapNode(node);\n if (node.op === 'Const') {\n weights.push(map[node.name]);\n }\n return map;\n }, {});\n }\n const inputs = [];\n const outputs = [];\n functionDef.signature.inputArg.forEach(arg => {\n const [nodeName,] = getNodeNameAndIndex(arg.name);\n const node = {\n name: nodeName,\n op: 'Placeholder',\n inputs: [],\n inputNames: [],\n category: 'graph',\n inputParams: {},\n attrParams: { dtype: { value: parseDtypeParam(arg.type), type: 'dtype' } },\n children: []\n };\n node.signatureKey = arg.name;\n inputs.push(node);\n nodes[nodeName] = node;\n });\n const allNodes = Object.keys(nodes);\n allNodes.forEach(key => {\n const node = nodes[key];\n node.inputNames.forEach((name, index) => {\n const [nodeName, , outputName] = getNodeNameAndIndex(name);\n const inputNode = nodes[nodeName];\n if (inputNode.outputs != null) {\n const outputIndex = inputNode.outputs.indexOf(outputName);\n if (outputIndex !== -1) {\n const inputName = `${nodeName}:${outputIndex}`;\n // update the input name to use the mapped output index directly.\n node.inputNames[index] = inputName;\n }\n }\n node.inputs.push(inputNode);\n inputNode.children.push(node);\n });\n });\n const returnNodeMap = functionDef.ret;\n functionDef.signature.outputArg.forEach(output => {\n const [nodeName, index] = getNodeNameAndIndex(returnNodeMap[output.name]);\n const node = nodes[nodeName];\n if (node != null) {\n node.defaultOutput = index;\n outputs.push(node);\n }\n });\n const signature = this.mapArgsToSignature(functionDef);\n return { nodes, inputs, outputs, weights, placeholders, signature };\n }\n mapArgsToSignature(functionDef) {\n return {\n methodName: functionDef.signature.name,\n inputs: functionDef.signature.inputArg.reduce((map, arg) => {\n map[arg.name] = this.mapArgToTensorInfo(arg);\n return map;\n }, {}),\n outputs: functionDef.signature.outputArg.reduce((map, arg) => {\n map[arg.name] = this.mapArgToTensorInfo(arg, functionDef.ret);\n return map;\n }, {}),\n };\n }\n mapArgToTensorInfo(arg, nameMap) {\n let name = arg.name;\n if (nameMap != null) {\n name = nameMap[name];\n }\n return { name, dtype: arg.type };\n }\n}\nexport function decodeBase64(text) {\n const global = env().global;\n if (typeof global.atob !== 'undefined') {\n return global.atob(text);\n }\n else if (typeof Buffer !== 'undefined') {\n return new Buffer(text, 'base64').toString();\n }\n else {\n throw new Error('Unable to decode base64 in this environment. ' +\n 'Missing built-in atob() or Buffer()');\n }\n}\nexport function parseStringParam(s, keepCase) {\n const value = Array.isArray(s) ? String.fromCharCode.apply(null, s) : decodeBase64(s);\n return keepCase ? value : value.toLowerCase();\n}\nexport function getStringParam(attrs, name, def, keepCase = false) {\n const param = attrs[name];\n if (param != null) {\n return parseStringParam(param.s, keepCase);\n }\n return def;\n}\nexport function getBoolParam(attrs, name, def) {\n const param = attrs[name];\n return param ? param.b : def;\n}\nexport function getNumberParam(attrs, name, def) {\n const param = attrs[name] || {};\n const value = param['i'] != null ? param['i'] : (param['f'] != null ? param['f'] : def);\n return (typeof value === 'number') ? value : parseInt(value, 10);\n}\nexport function parseDtypeParam(value) {\n if (typeof (value) === 'string') {\n // tslint:disable-next-line:no-any\n value = tensorflow.DataType[value];\n }\n switch (value) {\n case tensorflow.DataType.DT_FLOAT:\n case tensorflow.DataType.DT_HALF:\n return 'float32';\n case tensorflow.DataType.DT_INT32:\n case tensorflow.DataType.DT_INT64:\n case tensorflow.DataType.DT_INT8:\n case tensorflow.DataType.DT_UINT8:\n return 'int32';\n case tensorflow.DataType.DT_BOOL:\n return 'bool';\n case tensorflow.DataType.DT_DOUBLE:\n return 'float32';\n case tensorflow.DataType.DT_STRING:\n return 'string';\n default:\n // Unknown dtype error will happen at runtime (instead of parse time),\n // since these nodes might not be used by the actual subgraph execution.\n return null;\n }\n}\nexport function getFuncParam(attrs, name, def) {\n const param = attrs[name];\n if (param && param.func) {\n return param.func.name;\n }\n return def;\n}\nexport function getDtypeParam(attrs, name, def) {\n const param = attrs[name];\n if (param && param.type) {\n return parseDtypeParam(param.type);\n }\n return def;\n}\nexport function getDtypeArrayParam(attrs, name, def) {\n const param = attrs[name];\n if (param && param.list && param.list.type) {\n return param.list.type.map(v => parseDtypeParam(v));\n }\n return def;\n}\nexport function parseTensorShapeParam(shape) {\n if (shape.unknownRank) {\n return undefined;\n }\n if (shape.dim != null) {\n return shape.dim.map(dim => (typeof dim.size === 'number') ? dim.size : parseInt(dim.size, 10));\n }\n return [];\n}\nexport function getTensorShapeParam(attrs, name, def) {\n const param = attrs[name];\n if (param && param.shape) {\n return parseTensorShapeParam(param.shape);\n }\n return def;\n}\nexport function getNumericArrayParam(attrs, name, def) {\n const param = attrs[name];\n if (param) {\n return ((param.list.f && param.list.f.length ? param.list.f :\n param.list.i) ||\n [])\n .map(v => (typeof v === 'number') ? v : parseInt(v, 10));\n }\n return def;\n}\nexport function getStringArrayParam(attrs, name, def, keepCase = false) {\n const param = attrs[name];\n if (param && param.list && param.list.s) {\n return param.list.s.map((v) => {\n return parseStringParam(v, keepCase);\n });\n }\n return def;\n}\nexport function getTensorShapeArrayParam(attrs, name, def) {\n const param = attrs[name];\n if (param && param.list && param.list.shape) {\n return param.list.shape.map((v) => {\n return parseTensorShapeParam(v);\n });\n }\n return def;\n}\nexport function getBoolArrayParam(attrs, name, def) {\n const param = attrs[name];\n if (param && param.list && param.list.b) {\n return param.list.b;\n }\n return def;\n}\n//# 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* @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {DataType, env} from '@tensorflow/tfjs-core';\n\nimport * as tensorflow from '../data/compiled_api';\n\nimport {getRegisteredOp} from './custom_op/register';\nimport {getNodeNameAndIndex} from './executors/utils';\nimport * as arithmetic from './op_list/arithmetic';\nimport * as basicMath from './op_list/basic_math';\nimport * as control from './op_list/control';\nimport * as convolution from './op_list/convolution';\nimport * as creation from './op_list/creation';\nimport * as dynamic from './op_list/dynamic';\nimport * as evaluation from './op_list/evaluation';\nimport * as graph from './op_list/graph';\nimport * as hashTable from './op_list/hash_table';\nimport * as image from './op_list/image';\nimport * as logical from './op_list/logical';\nimport * as matrices from './op_list/matrices';\nimport * as normalization from './op_list/normalization';\nimport * as reduction from './op_list/reduction';\nimport * as sliceJoin from './op_list/slice_join';\nimport * as sparse from './op_list/sparse';\nimport * as spectral from './op_list/spectral';\nimport * as string from './op_list/string';\nimport * as transformation from './op_list/transformation';\nimport {Graph, InputParamValue, Node, OpMapper, ParamValue} from './types';\n\nexport class OperationMapper {\n  private static _instance: OperationMapper;\n\n  private opMappers: {[key: string]: OpMapper};\n\n  // Singleton instance for the mapper\n  public static get Instance() {\n    return this._instance || (this._instance = new this());\n  }\n\n  // Loads the op mapping from the JSON file.\n  private constructor() {\n    const ops = [\n      arithmetic, basicMath, control, convolution, creation, dynamic,\n      evaluation, graph, hashTable, image, logical, matrices, normalization,\n      reduction, sliceJoin, sparse, spectral, string, transformation\n    ];\n    const mappersJson: OpMapper[] = [].concat(...ops.map(op => op.json));\n\n    this.opMappers = mappersJson.reduce<{[key: string]: OpMapper}>(\n        (map, mapper: OpMapper) => {\n          map[mapper.tfOpName] = mapper;\n          return map;\n        },\n        {});\n  }\n\n  // Converts the model inference graph from Tensorflow GraphDef to local\n  // representation for TensorFlow.js API\n  transformGraph(\n      graph: tensorflow.IGraphDef,\n      signature: tensorflow.ISignatureDef = {}): Graph {\n    const tfNodes = graph.node;\n    const placeholders: Node[] = [];\n    const weights: Node[] = [];\n    const initNodes: Node[] = [];\n    const nodes = tfNodes.reduce<{[key: string]: Node}>((map, node) => {\n      map[node.name] = this.mapNode(node);\n      if (node.op.startsWith('Placeholder')) {\n        placeholders.push(map[node.name]);\n      } else if (node.op === 'Const') {\n        weights.push(map[node.name]);\n      } else if (node.input == null || node.input.length === 0) {\n        initNodes.push(map[node.name]);\n      }\n      return map;\n    }, {});\n\n    let inputs: Node[] = [];\n    const outputs: Node[] = [];\n    let inputNodeNameToKey: {[key: string]: string} = {};\n    let outputNodeNameToKey: {[key: string]: string} = {};\n    if (signature != null) {\n      inputNodeNameToKey = this.mapSignatureEntries(signature.inputs);\n      outputNodeNameToKey = this.mapSignatureEntries(signature.outputs);\n    }\n    const allNodes = Object.keys(nodes);\n    allNodes.forEach(key => {\n      const node = nodes[key];\n      node.inputNames.forEach((name, index) => {\n        const [nodeName, , outputName] = getNodeNameAndIndex(name);\n        const inputNode = nodes[nodeName];\n        if (inputNode.outputs != null) {\n          const outputIndex = inputNode.outputs.indexOf(outputName);\n          if (outputIndex !== -1) {\n            const inputName = `${nodeName}:${outputIndex}`;\n            // update the input name to use the mapped output index directly.\n            node.inputNames[index] = inputName;\n          }\n        }\n        node.inputs.push(inputNode);\n        inputNode.children.push(node);\n      });\n    });\n\n    // if signature has not outputs set, add any node that does not have\n    // outputs.\n    if (Object.keys(outputNodeNameToKey).length === 0) {\n      allNodes.forEach(key => {\n        const node = nodes[key];\n        if (node.children.length === 0) {\n          outputs.push(node);\n        }\n      });\n    } else {\n      Object.keys(outputNodeNameToKey).forEach(name => {\n        const [nodeName, ] = getNodeNameAndIndex(name);\n        const node = nodes[nodeName];\n        if (node != null) {\n          node.signatureKey = outputNodeNameToKey[name];\n          outputs.push(node);\n        }\n      });\n    }\n\n    if (Object.keys(inputNodeNameToKey).length > 0) {\n      Object.keys(inputNodeNameToKey).forEach(name => {\n        const [nodeName, ] = getNodeNameAndIndex(name);\n        const node = nodes[nodeName];\n        if (node) {\n          node.signatureKey = inputNodeNameToKey[name];\n          inputs.push(node);\n        }\n      });\n    } else {\n      inputs = placeholders;\n    }\n\n    let functions = {};\n    if (graph.library != null && graph.library.function != null) {\n      functions = graph.library.function.reduce((functions, func) => {\n        functions[func.signature.name] = this.mapFunction(func);\n        return functions;\n      }, {} as {[key: string]: Graph});\n    }\n\n    const result: Graph =\n        {nodes, inputs, outputs, weights, placeholders, signature, functions};\n\n    if (initNodes.length > 0) {\n      result.initNodes = initNodes;\n    }\n\n    return result;\n  }\n\n  private mapSignatureEntries(entries: {[k: string]: tensorflow.ITensorInfo}) {\n    return Object.keys(entries || {})\n        .reduce<{[key: string]: string}>((prev, curr) => {\n          prev[entries[curr].name] = curr;\n          return prev;\n        }, {});\n  }\n\n  private mapNode(node: tensorflow.INodeDef): Node {\n    // Unsupported ops will cause an error at run-time (not parse time), since\n    // they may not be used by the actual execution subgraph.\n    const mapper =\n        getRegisteredOp(node.op) || this.opMappers[node.op] || {} as OpMapper;\n    if (node.attr == null) {\n      node.attr = {};\n    }\n\n    const newNode: Node = {\n      name: node.name,\n      op: node.op,\n      category: mapper.category,\n      inputNames:\n          (node.input ||\n           []).map(input => input.startsWith('^') ? input.substr(1) : input),\n      inputs: [],\n      children: [],\n      inputParams: {},\n      attrParams: {},\n      rawAttrs: node.attr,\n      outputs: mapper.outputs\n    };\n\n    if (mapper.inputs != null) {\n      newNode.inputParams =\n          mapper.inputs.reduce<{[key: string]: InputParamValue}>(\n              (map, param) => {\n                map[param.name] = {\n                  type: param.type,\n                  inputIndexStart: param.start,\n                  inputIndexEnd: param.end\n                };\n                return map;\n              },\n              {});\n    }\n    if (mapper.attrs != null) {\n      newNode.attrParams =\n          mapper.attrs.reduce<{[key: string]: ParamValue}>((map, param) => {\n            const type = param.type;\n            let value = undefined;\n            switch (param.type) {\n              case 'string':\n                value = getStringParam(\n                    node.attr, param.tfName, param.defaultValue as string);\n\n                if (value === undefined && !!param.tfDeprecatedName) {\n                  value = getStringParam(\n                      node.attr, param.tfDeprecatedName,\n                      param.defaultValue as string);\n                }\n                break;\n              case 'string[]':\n                value = getStringArrayParam(\n                    node.attr, param.tfName, param.defaultValue as string[]);\n\n                if (value === undefined && !!param.tfDeprecatedName) {\n                  value = getStringArrayParam(\n                      node.attr, param.tfDeprecatedName,\n                      param.defaultValue as string[]);\n                }\n                break;\n              case 'number':\n                value = getNumberParam(\n                    node.attr, param.tfName,\n                    (param.defaultValue || 0) as number);\n                if (value === undefined && !!param.tfDeprecatedName) {\n                  value = getNumberParam(\n                      node.attr, param.tfDeprecatedName,\n                      param.defaultValue as number);\n                }\n                break;\n              case 'number[]':\n                value = getNumericArrayParam(\n                    node.attr, param.tfName, param.defaultValue as number[]);\n                if (value === undefined && !!param.tfDeprecatedName) {\n                  value = getNumericArrayParam(\n                      node.attr, param.tfDeprecatedName,\n                      param.defaultValue as number[]);\n                }\n                break;\n              case 'bool':\n                value = getBoolParam(\n                    node.attr, param.tfName, param.defaultValue as boolean);\n                if (value === undefined && !!param.tfDeprecatedName) {\n                  value = getBoolParam(\n                      node.attr, param.tfDeprecatedName,\n                      param.defaultValue as boolean);\n                }\n                break;\n              case 'bool[]':\n                value = getBoolArrayParam(\n                    node.attr, param.tfName, param.defaultValue as boolean[]);\n                if (value === undefined && !!param.tfDeprecatedName) {\n                  value = getBoolArrayParam(\n                      node.attr, param.tfDeprecatedName,\n                      param.defaultValue as boolean[]);\n                }\n                break;\n              case 'shape':\n                value = getTensorShapeParam(\n                    node.attr, param.tfName, param.defaultValue as number[]);\n                if (value === undefined && !!param.tfDeprecatedName) {\n                  value = getTensorShapeParam(\n                      node.attr, param.tfDeprecatedName,\n                      param.defaultValue as number[]);\n                }\n                break;\n              case 'shape[]':\n                value = getTensorShapeArrayParam(\n                    node.attr, param.tfName, param.defaultValue as number[][]);\n                if (value === undefined && !!param.tfDeprecatedName) {\n                  value = getTensorShapeArrayParam(\n                      node.attr, param.tfDeprecatedName,\n                      param.defaultValue as number[][]);\n                }\n                break;\n              case 'dtype':\n                value = getDtypeParam(\n                    node.attr, param.tfName, param.defaultValue as DataType);\n                if (value === undefined && !!param.tfDeprecatedName) {\n                  value = getDtypeParam(\n                      node.attr, param.tfDeprecatedName,\n                      param.defaultValue as DataType);\n                }\n                break;\n              case 'dtype[]':\n                value = getDtypeArrayParam(\n                    node.attr, param.tfName, param.defaultValue as DataType[]);\n                if (value === undefined && !!param.tfDeprecatedName) {\n                  value = getDtypeArrayParam(\n                      node.attr, param.tfDeprecatedName,\n                      param.defaultValue as DataType[]);\n                }\n                break;\n              case 'func':\n                value = getFuncParam(\n                    node.attr, param.tfName, param.defaultValue as string);\n                if (value === undefined && !!param.tfDeprecatedName) {\n                  value = getFuncParam(\n                      node.attr, param.tfDeprecatedName,\n                      param.defaultValue as string);\n                }\n                break;\n              case 'tensor':\n              case 'tensors':\n                break;\n              default:\n                throw new Error(\n                    `Unsupported param type: ${param.type} for op: ${node.op}`);\n            }\n            map[param.name] = {value, type};\n            return map;\n          }, {});\n    }\n    return newNode;\n  }\n\n  // map the TFunctionDef to TFJS graph object\n  private mapFunction(functionDef: tensorflow.IFunctionDef): Graph {\n    const tfNodes = functionDef.nodeDef;\n    const placeholders: Node[] = [];\n    const weights: Node[] = [];\n    let nodes: {[key: string]: Node} = {};\n    if (tfNodes != null) {\n      nodes = tfNodes.reduce<{[key: string]: Node}>((map, node) => {\n        map[node.name] = this.mapNode(node);\n        if (node.op === 'Const') {\n          weights.push(map[node.name]);\n        }\n        return map;\n      }, {});\n    }\n    const inputs: Node[] = [];\n    const outputs: Node[] = [];\n\n    functionDef.signature.inputArg.forEach(arg => {\n      const [nodeName, ] = getNodeNameAndIndex(arg.name);\n      const node: Node = {\n        name: nodeName,\n        op: 'Placeholder',\n        inputs: [],\n        inputNames: [],\n        category: 'graph',\n        inputParams: {},\n        attrParams: {dtype: {value: parseDtypeParam(arg.type), type: 'dtype'}},\n        children: []\n      };\n      node.signatureKey = arg.name;\n      inputs.push(node);\n      nodes[nodeName] = node;\n    });\n\n    const allNodes = Object.keys(nodes);\n    allNodes.forEach(key => {\n      const node = nodes[key];\n      node.inputNames.forEach((name, index) => {\n        const [nodeName, , outputName] = getNodeNameAndIndex(name);\n        const inputNode = nodes[nodeName];\n        if (inputNode.outputs != null) {\n          const outputIndex = inputNode.outputs.indexOf(outputName);\n          if (outputIndex !== -1) {\n            const inputName = `${nodeName}:${outputIndex}`;\n            // update the input name to use the mapped output index directly.\n            node.inputNames[index] = inputName;\n          }\n        }\n        node.inputs.push(inputNode);\n        inputNode.children.push(node);\n      });\n    });\n\n    const returnNodeMap = functionDef.ret;\n\n    functionDef.signature.outputArg.forEach(output => {\n      const [nodeName, index] = getNodeNameAndIndex(returnNodeMap[output.name]);\n      const node = nodes[nodeName];\n      if (node != null) {\n        node.defaultOutput = index;\n        outputs.push(node);\n      }\n    });\n\n    const signature = this.mapArgsToSignature(functionDef);\n    return {nodes, inputs, outputs, weights, placeholders, signature};\n  }\n\n  private mapArgsToSignature(functionDef: tensorflow.IFunctionDef):\n      tensorflow.ISignatureDef {\n    return {\n      methodName: functionDef.signature.name,\n      inputs: functionDef.signature.inputArg.reduce(\n          (map, arg) => {\n            map[arg.name] = this.mapArgToTensorInfo(arg);\n            return map;\n          },\n          {} as {[key: string]: tensorflow.ITensorInfo}),\n      outputs: functionDef.signature.outputArg.reduce(\n          (map, arg) => {\n            map[arg.name] = this.mapArgToTensorInfo(arg, functionDef.ret);\n            return map;\n          },\n          {} as {[key: string]: tensorflow.ITensorInfo}),\n    };\n  }\n\n  private mapArgToTensorInfo(\n      arg: tensorflow.OpDef.IArgDef,\n      nameMap?: {[key: string]: string}): tensorflow.ITensorInfo {\n    let name = arg.name;\n    if (nameMap != null) {\n      name = nameMap[name];\n    }\n    return {name, dtype: arg.type};\n  }\n}\n\nexport function decodeBase64(text: string): string {\n  const global = env().global;\n  if (typeof global.atob !== 'undefined') {\n    return global.atob(text);\n  } else if (typeof Buffer !== 'undefined') {\n    return new Buffer(text, 'base64').toString();\n  } else {\n    throw new Error(\n        'Unable to decode base64 in this environment. ' +\n        'Missing built-in atob() or Buffer()');\n  }\n}\n\nexport function parseStringParam(s: []|string, keepCase: boolean): string {\n  const value =\n      Array.isArray(s) ? String.fromCharCode.apply(null, s) : decodeBase64(s);\n  return keepCase ? value : value.toLowerCase();\n}\n\nexport function getStringParam(\n    attrs: {[key: string]: tensorflow.IAttrValue}, name: string, def: string,\n    keepCase = false): string {\n  const param = attrs[name];\n  if (param != null) {\n    return parseStringParam(param.s, keepCase);\n  }\n  return def;\n}\n\nexport function getBoolParam(\n    attrs: {[key: string]: tensorflow.IAttrValue}, name: string,\n    def: boolean): boolean {\n  const param = attrs[name];\n  return param ? param.b : def;\n}\n\nexport function getNumberParam(\n    attrs: {[key: string]: tensorflow.IAttrValue}, name: string,\n    def: number): number {\n  const param = attrs[name] || {};\n  const value =\n      param['i'] != null ? param['i'] : (param['f'] != null ? param['f'] : def);\n  return (typeof value === 'number') ? value : parseInt(value, 10);\n}\n\nexport function parseDtypeParam(value: string|tensorflow.DataType): DataType {\n  if (typeof (value) === 'string') {\n    // tslint:disable-next-line:no-any\n    value = tensorflow.DataType[value as any];\n  }\n  switch (value) {\n    case tensorflow.DataType.DT_FLOAT:\n    case tensorflow.DataType.DT_HALF:\n      return 'float32';\n    case tensorflow.DataType.DT_INT32:\n    case tensorflow.DataType.DT_INT64:\n    case tensorflow.DataType.DT_INT8:\n    case tensorflow.DataType.DT_UINT8:\n      return 'int32';\n    case tensorflow.DataType.DT_BOOL:\n      return 'bool';\n    case tensorflow.DataType.DT_DOUBLE:\n      return 'float32';\n    case tensorflow.DataType.DT_STRING:\n      return 'string';\n    default:\n      // Unknown dtype error will happen at runtime (instead of parse time),\n      // since these nodes might not be used by the actual subgraph execution.\n      return null;\n  }\n}\n\nexport function getFuncParam(\n    attrs: {[key: string]: tensorflow.IAttrValue}, name: string,\n    def: string): string {\n  const param = attrs[name];\n  if (param && param.func) {\n    return param.func.name;\n  }\n  return def;\n}\n\nexport function getDtypeParam(\n    attrs: {[key: string]: tensorflow.IAttrValue}, name: string,\n    def: DataType): DataType {\n  const param = attrs[name];\n  if (param && param.type) {\n    return parseDtypeParam(param.type);\n  }\n  return def;\n}\n\nexport function getDtypeArrayParam(\n    attrs: {[key: string]: tensorflow.IAttrValue}, name: string,\n    def: DataType[]): DataType[] {\n  const param = attrs[name];\n  if (param && param.list && param.list.type) {\n    return param.list.type.map(v => parseDtypeParam(v));\n  }\n  return def;\n}\n\nexport function parseTensorShapeParam(shape: tensorflow.ITensorShape): number[]|\n    undefined {\n  if (shape.unknownRank) {\n    return undefined;\n  }\n  if (shape.dim != null) {\n    return shape.dim.map(\n        dim =>\n            (typeof dim.size === 'number') ? dim.size : parseInt(dim.size, 10));\n  }\n  return [];\n}\n\nexport function getTensorShapeParam(\n    attrs: {[key: string]: tensorflow.IAttrValue}, name: string,\n    def?: number[]): number[]|undefined {\n  const param = attrs[name];\n  if (param && param.shape) {\n    return parseTensorShapeParam(param.shape);\n  }\n  return def;\n}\n\nexport function getNumericArrayParam(\n    attrs: {[key: string]: tensorflow.IAttrValue}, name: string,\n    def: number[]): number[] {\n  const param = attrs[name];\n  if (param) {\n    return ((param.list.f && param.list.f.length ? param.list.f :\n                                                   param.list.i) ||\n            [])\n        .map(v => (typeof v === 'number') ? v : parseInt(v, 10));\n  }\n  return def;\n}\n\nexport function getStringArrayParam(\n    attrs: {[key: string]: tensorflow.IAttrValue}, name: string, def: string[],\n    keepCase = false): string[] {\n  const param = attrs[name];\n  if (param && param.list && param.list.s) {\n    return param.list.s.map((v) => {\n      return parseStringParam(v, keepCase);\n    });\n  }\n  return def;\n}\n\nexport function getTensorShapeArrayParam(\n    attrs: {[key: string]: tensorflow.IAttrValue}, name: string,\n    def: number[][]): number[][] {\n  const param = attrs[name];\n  if (param && param.list && param.list.shape) {\n    return param.list.shape.map((v) => {\n      return parseTensorShapeParam(v);\n    });\n  }\n  return def;\n}\n\nexport function getBoolArrayParam(\n    attrs: {[key: string]: tensorflow.IAttrValue}, name: string,\n    def: boolean[]): boolean[] {\n  const param = attrs[name];\n  if (param && param.list && param.list.b) {\n    return param.list.b;\n  }\n  return def;\n}\n"]}","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Complex } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Converts two real numbers to a complex number.\n *\n * Given a tensor `real` representing the real part of a complex number, and a\n * tensor `imag` representing the imaginary part of a complex number, this\n * operation returns complex numbers elementwise of the form [r0, i0, r1, i1],\n * where r represents the real part and i represents the imag part.\n *\n * The input tensors real and imag must have the same shape.\n *\n * ```js\n * const real = tf.tensor1d([2.25, 3.25]);\n * const imag = tf.tensor1d([4.75, 5.75]);\n * const complex = tf.complex(real, imag);\n *\n * complex.print();\n * ```\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction complex_(real, imag) {\n const $real = convertToTensor(real, 'real', 'complex');\n const $imag = convertToTensor(imag, 'imag', 'complex');\n util.assertShapesMatch($real.shape, $imag.shape, `real and imag shapes, ${$real.shape} and ${$imag.shape}, ` +\n `must match in call to tf.complex().`);\n const inputs = { real: $real, imag: $imag };\n return ENGINE.runKernel(Complex, inputs);\n}\nexport const complex = op({ complex_ });\n//# 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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Pow } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the power of one `tf.Tensor` to another. Supports broadcasting.\n *\n * Given a `tf.Tensor` x and a `tf.Tensor` y, this operation computes x^y for\n * corresponding elements in x and y. The result's dtype will be the upcasted\n * type of the `base` and `exp` dtypes.\n *\n * ```js\n * const a = tf.tensor([[2, 3], [4, 5]])\n * const b = tf.tensor([[1, 2], [3, 0]]).toInt();\n *\n * a.pow(b).print(); // or tf.pow(a, b)\n * ```\n *\n * ```js\n * const a = tf.tensor([[1, 2], [3, 4]])\n * const b = tf.tensor(2).toInt();\n *\n * a.pow(b).print(); // or tf.pow(a, b)\n * ```\n * We also expose `powStrict` which has the same signature as this op and\n * asserts that `base` and `exp` are the same shape (does not broadcast).\n *\n * @param base The base `tf.Tensor` to pow element-wise.\n * @param exp The exponent `tf.Tensor` to pow element-wise.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction pow_(base, exp) {\n let $base = convertToTensor(base, 'base', 'pow');\n let $exp = convertToTensor(exp, 'exp', 'pow');\n [$base, $exp] = makeTypesMatch($base, $exp);\n const inputs = { a: $base, b: $exp };\n return ENGINE.runKernel(Pow, inputs);\n}\nexport const pow = op({ pow_ });\n//# 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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Real } from '@tensorflow/tfjs-core';\nexport function real(args) {\n const { inputs, backend } = args;\n const { input } = inputs;\n const real = backend.data.get(input.dataId).complexTensorInfos.real;\n const realVal = backend.data.get(real.dataId).values;\n // When complex tensor is disposed, its underlying parts will be disposed too.\n // Make new tensor out of the real value of the complex. This makes sure the\n // value is still accessible even if complex tensor is disposed.\n return backend.makeTensorInfo(real.shape, real.dtype, realVal);\n}\nexport const realConfig = {\n kernelName: Real,\n backendName: 'cpu',\n kernelFunc: real\n};\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from './environment';\nimport { getGlobal } from './global_util';\nimport * as log from './log';\nconst kernelRegistry = getGlobal('kernelRegistry', () => new Map());\nconst gradRegistry = getGlobal('gradRegistry', () => new Map());\n/**\n * Returns the kernel function (code) associated with the provided names.\n *\n * @param kernelName The official name of the kernel.\n * @param backendName The official name of the backend.\n */\nexport function getKernel(kernelName, backendName) {\n const key = makeKey(kernelName, backendName);\n return kernelRegistry.get(key);\n}\n/**\n * Returns the registered gradient info associated with the provided kernel.\n * @param kernelName The official TF kernel name.\n */\nexport function getGradient(kernelName) {\n return gradRegistry.get(kernelName);\n}\nexport function getKernelsForBackend(backendName) {\n const it = kernelRegistry.entries();\n const result = [];\n while (true) {\n const { done, value } = it.next();\n if (done) {\n break;\n }\n const [key, config] = value;\n const [backend,] = key.split('_');\n if (backend === backendName) {\n result.push(config);\n }\n }\n return result;\n}\n/**\n * Registers the function (forward pass) for the kernel in a global registry.\n *\n * @param config A config object with the following properties:\n * - `kernelName` The official name of the kernel.\n * - `backendName` The official name of the backend.\n * - `kernelFunc` The function to run during the forward pass of the kernel.\n * - `setupFunc` Optional. Gets called once, after the backend initializes.\n * - `disposeFunc` Optional. Gets called once, right before the backend is\n * disposed.\n */\nexport function registerKernel(config) {\n const { kernelName, backendName } = config;\n const key = makeKey(kernelName, backendName);\n if (kernelRegistry.has(key)) {\n log.warn(`The kernel '${kernelName}' for backend ` +\n `'${backendName}' is already registered`);\n }\n kernelRegistry.set(key, config);\n}\n/**\n * Registers a gradient function for a given kernel in the global registry,\n * to be used during the back-propagation of that kernel.\n *\n * @param config An object with the following properties:\n * - `kernelName` The name of the kernel that the gradient function is for.\n * - `gradFunc` The function to run during back-propagation.\n */\nexport function registerGradient(config) {\n const { kernelName } = config;\n if (gradRegistry.has(kernelName)) {\n // TODO (yassogba) after 3.0 assess whether we need to keep this gated\n // to debug mode.\n if (env().getBool('DEBUG')) {\n log.warn(`Overriding the gradient for '${kernelName}'`);\n }\n }\n gradRegistry.set(kernelName, config);\n}\n/**\n * Removes the kernel function from the registry.\n *\n * @param kernelName The official name of the kernel.\n * @param backendName The official name of the backend.\n *\n */\nexport function unregisterKernel(kernelName, backendName) {\n const key = makeKey(kernelName, backendName);\n if (!kernelRegistry.has(key)) {\n throw new Error(`The kernel '${kernelName}' for backend ` +\n `'${backendName}' is not registered`);\n }\n kernelRegistry.delete(key);\n}\n/** Removes the registered gradient from the global registry. */\nexport function unregisterGradient(kernelName) {\n if (!gradRegistry.has(kernelName)) {\n throw new Error(`The gradient '${kernelName}' for backend is not registered`);\n }\n gradRegistry.delete(kernelName);\n}\n/**\n * Finds kernels that have already been registered to a backend and re-registers\n * them for a new backend. Useful for registering custom backends.\n * @param registeredBackendName Already registered backend.\n * @param newBackendName New backend.\n */\nexport function copyRegisteredKernels(registeredBackendName, newBackendName) {\n const kernels = getKernelsForBackend(registeredBackendName);\n kernels.forEach(kernelConfig => {\n const newKernelConfig = Object.assign({}, kernelConfig, { backendName: newBackendName });\n registerKernel(newKernelConfig);\n });\n}\nfunction makeKey(kernelName, backendName) {\n return `${backendName}_${kernelName}`;\n}\n//# sourceMappingURL=data:application/json;base64,{"version":3,"file":"kernel_registry.js","sourceRoot":"","sources":["../../../../../tfjs-core/src/kernel_registry.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AACH,OAAO,EAAC,GAAG,EAAC,MAAM,eAAe,CAAC;AAClC,OAAO,EAAC,SAAS,EAAC,MAAM,eAAe,CAAC;AACxC,OAAO,KAAK,GAAG,MAAM,OAAO,CAAC;AAK7B,MAAM,cAAc,GAChB,SAAS,CAAC,gBAAgB,EAAE,GAAG,EAAE,CAAC,IAAI,GAAG,EAAwB,CAAC,CAAC;AACvE,MAAM,YAAY,GACd,SAAS,CAAC,cAAc,EAAE,GAAG,EAAE,CAAC,IAAI,GAAG,EAAsB,CAAC,CAAC;AA8DnE;;;;;GAKG;AACH,MAAM,UAAU,SAAS,CACrB,UAAkB,EAAE,WAAmB;IACzC,MAAM,GAAG,GAAG,OAAO,CAAC,UAAU,EAAE,WAAW,CAAC,CAAC;IAC7C,OAAO,cAAc,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC;AACjC,CAAC;AAED;;;GAGG;AACH,MAAM,UAAU,WAAW,CAAC,UAAkB;IAC5C,OAAO,YAAY,CAAC,GAAG,CAAC,UAAU,CAAC,CAAC;AACtC,CAAC;AAED,MAAM,UAAU,oBAAoB,CAAC,WAAmB;IACtD,MAAM,EAAE,GAAG,cAAc,CAAC,OAAO,EAAE,CAAC;IACpC,MAAM,MAAM,GAAmB,EAAE,CAAC;IAElC,OAAO,IAAI,EAAE;QACX,MAAM,EAAC,IAAI,EAAE,KAAK,EAAC,GAAG,EAAE,CAAC,IAAI,EAAE,CAAC;QAChC,IAAI,IAAI,EAAE;YACR,MAAM;SACP;QACD,MAAM,CAAC,GAAG,EAAE,MAAM,CAAC,GAAG,KAAK,CAAC;QAC5B,MAAM,CAAC,OAAO,EAAG,GAAG,GAAG,CAAC,KAAK,CAAC,GAAG,CAAC,CAAC;QACnC,IAAI,OAAO,KAAK,WAAW,EAAE;YAC3B,MAAM,CAAC,IAAI,CAAC,MAAM,CAAC,CAAC;SACrB;KACF;IACD,OAAO,MAAM,CAAC;AAChB,CAAC;AAED;;;;;;;;;;GAUG;AACH,MAAM,UAAU,cAAc,CAAC,MAAoB;IACjD,MAAM,EAAC,UAAU,EAAE,WAAW,EAAC,GAAG,MAAM,CAAC;IACzC,MAAM,GAAG,GAAG,OAAO,CAAC,UAAU,EAAE,WAAW,CAAC,CAAC;IAC7C,IAAI,cAAc,CAAC,GAAG,CAAC,GAAG,CAAC,EAAE;QAC3B,GAAG,CAAC,IAAI,CACJ,eAAe,UAAU,gBAAgB;YACzC,IAAI,WAAW,yBAAyB,CAAC,CAAC;KAC/C;IACD,cAAc,CAAC,GAAG,CAAC,GAAG,EAAE,MAAM,CAAC,CAAC;AAClC,CAAC;AAED;;;;;;;GAOG;AACH,MAAM,UAAU,gBAAgB,CAAC,MAAkB;IACjD,MAAM,EAAC,UAAU,EAAC,GAAG,MAAM,CAAC;IAE5B,IAAI,YAAY,CAAC,GAAG,CAAC,UAAU,CAAC,EAAE;QAChC,sEAAsE;QACtE,iBAAiB;QACjB,IAAI,GAAG,EAAE,CAAC,OAAO,CAAC,OAAO,CAAC,EAAE;YAC1B,GAAG,CAAC,IAAI,CAAC,gCAAgC,UAAU,GAAG,CAAC,CAAC;SACzD;KACF;IACD,YAAY,CAAC,GAAG,CAAC,UAAU,EAAE,MAAM,CAAC,CAAC;AACvC,CAAC;AAED;;;;;;GAMG;AACH,MAAM,UAAU,gBAAgB,CAC5B,UAAkB,EAAE,WAAmB;IACzC,MAAM,GAAG,GAAG,OAAO,CAAC,UAAU,EAAE,WAAW,CAAC,CAAC;IAC7C,IAAI,CAAC,cAAc,CAAC,GAAG,CAAC,GAAG,CAAC,EAAE;QAC5B,MAAM,IAAI,KAAK,CACX,eAAe,UAAU,gBAAgB;YACzC,IAAI,WAAW,qBAAqB,CAAC,CAAC;KAC3C;IACD,cAAc,CAAC,MAAM,CAAC,GAAG,CAAC,CAAC;AAC7B,CAAC;AAED,gEAAgE;AAChE,MAAM,UAAU,kBAAkB,CAAC,UAAkB;IACnD,IAAI,CAAC,YAAY,CAAC,GAAG,CAAC,UAAU,CAAC,EAAE;QACjC,MAAM,IAAI,KAAK,CACX,iBAAiB,UAAU,iCAAiC,CAAC,CAAC;KACnE;IACD,YAAY,CAAC,MAAM,CAAC,UAAU,CAAC,CAAC;AAClC,CAAC;AAED;;;;;GAKG;AACH,MAAM,UAAU,qBAAqB,CACjC,qBAA6B,EAAE,cAAsB;IACvD,MAAM,OAAO,GAAG,oBAAoB,CAAC,qBAAqB,CAAC,CAAC;IAC5D,OAAO,CAAC,OAAO,CAAC,YAAY,CAAC,EAAE;QAC7B,MAAM,eAAe,GACjB,MAAM,CAAC,MAAM,CAAC,EAAE,EAAE,YAAY,EAAE,EAAC,WAAW,EAAE,cAAc,EAAC,CAAC,CAAC;QACnE,cAAc,CAAC,eAAe,CAAC,CAAC;IAClC,CAAC,CAAC,CAAC;AACL,CAAC;AAED,SAAS,OAAO,CAAC,UAAkB,EAAE,WAAmB;IACtD,OAAO,GAAG,WAAW,IAAI,UAAU,EAAE,CAAC;AACxC,CAAC","sourcesContent":["/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {env} from './environment';\nimport {getGlobal} from './global_util';\nimport * as log from './log';\nimport {NamedGradientMap} from './tape';\nimport {Tensor} from './tensor';\nimport {DataType, RecursiveArray} from './types';\n\nconst kernelRegistry =\n    getGlobal('kernelRegistry', () => new Map<string, KernelConfig>());\nconst gradRegistry =\n    getGlobal('gradRegistry', () => new Map<string, GradConfig>());\n\nexport type DataId = object;\n\ntype AttributeValue =\n    number|number[]|boolean|boolean[]|string|string[]|NamedAttrMap;\n\n/** These are extra non-tensor/primitive params passed to kernel functions. */\nexport type Attribute = AttributeValue|RecursiveArray<AttributeValue>;\n\n/** Specifies the code to run when executing a kernel. */\nexport type KernelFunc = (params: {\n  inputs: NamedTensorInfoMap,\n  backend: {},\n  attrs?: NamedAttrMap,\n}) => TensorInfo|TensorInfo[];\n\n/** The function to run when computing a gradient during backprop. */\nexport type GradFunc =\n    (dy: Tensor|Tensor[], saved: Tensor[], attrs: NamedAttrMap) =>\n        NamedGradientMap;\n\n/** Function that gets called after the backend initializes. */\nexport type KernelSetupFunc = (backend: {}) => void;\n/** Function that gets called right before the backend is disposed. */\nexport type KernelDisposeFunc = KernelSetupFunc;\n\n/** Config object for registering a kernel in the global registry. */\nexport interface KernelConfig {\n  kernelName: string;\n  backendName: string;\n  kernelFunc: KernelFunc;\n  setupFunc?: KernelSetupFunc;\n  disposeFunc?: KernelDisposeFunc;\n}\n\n/** Config object for registering a gradient in the global registry. */\nexport interface GradConfig {\n  kernelName: string;\n  inputsToSave?: string[];\n  // When saveAllInputs is true, all inputs will be saved. Only use this flag\n  // if inputs is an array of Tensors.\n  saveAllInputs?: boolean;\n  outputsToSave?: boolean[];\n  gradFunc: GradFunc;\n}\n\n/** Holds metadata for a given tensor. */\nexport interface TensorInfo {\n  dataId: DataId;\n  shape: number[];\n  dtype: DataType;\n}\n\nexport interface NamedTensorInfoMap {\n  [name: string]: TensorInfo|undefined;\n}\n\nexport interface NamedAttrMap {\n  [name: string]: Attribute;\n}\n\n/**\n * Returns the kernel function (code) associated with the provided names.\n *\n * @param kernelName The official name of the kernel.\n * @param backendName The official name of the backend.\n */\nexport function getKernel(\n    kernelName: string, backendName: string): KernelConfig {\n  const key = makeKey(kernelName, backendName);\n  return kernelRegistry.get(key);\n}\n\n/**\n * Returns the registered gradient info associated with the provided kernel.\n * @param kernelName The official TF kernel name.\n */\nexport function getGradient(kernelName: string): GradConfig {\n  return gradRegistry.get(kernelName);\n}\n\nexport function getKernelsForBackend(backendName: string): KernelConfig[] {\n  const it = kernelRegistry.entries();\n  const result: KernelConfig[] = [];\n\n  while (true) {\n    const {done, value} = it.next();\n    if (done) {\n      break;\n    }\n    const [key, config] = value;\n    const [backend, ] = key.split('_');\n    if (backend === backendName) {\n      result.push(config);\n    }\n  }\n  return result;\n}\n\n/**\n * Registers the function (forward pass) for the kernel in a global registry.\n *\n * @param config A config object with the following properties:\n * - `kernelName` The official name of the kernel.\n * - `backendName` The official name of the backend.\n * - `kernelFunc` The function to run during the forward pass of the kernel.\n * - `setupFunc` Optional. Gets called once, after the backend initializes.\n * - `disposeFunc` Optional. Gets called once, right before the backend is\n * disposed.\n */\nexport function registerKernel(config: KernelConfig) {\n  const {kernelName, backendName} = config;\n  const key = makeKey(kernelName, backendName);\n  if (kernelRegistry.has(key)) {\n    log.warn(\n        `The kernel '${kernelName}' for backend ` +\n        `'${backendName}' is already registered`);\n  }\n  kernelRegistry.set(key, config);\n}\n\n/**\n * Registers a gradient function for a given kernel in the global registry,\n * to be used during the back-propagation of that kernel.\n *\n * @param config An object with the following properties:\n * - `kernelName` The name of the kernel that the gradient function is for.\n * - `gradFunc` The function to run during back-propagation.\n */\nexport function registerGradient(config: GradConfig) {\n  const {kernelName} = config;\n\n  if (gradRegistry.has(kernelName)) {\n    // TODO (yassogba) after 3.0 assess whether we need to keep this gated\n    // to debug mode.\n    if (env().getBool('DEBUG')) {\n      log.warn(`Overriding the gradient for '${kernelName}'`);\n    }\n  }\n  gradRegistry.set(kernelName, config);\n}\n\n/**\n * Removes the kernel function from the registry.\n *\n * @param kernelName The official name of the kernel.\n * @param backendName The official name of the backend.\n *\n */\nexport function unregisterKernel(\n    kernelName: string, backendName: string): void {\n  const key = makeKey(kernelName, backendName);\n  if (!kernelRegistry.has(key)) {\n    throw new Error(\n        `The kernel '${kernelName}' for backend ` +\n        `'${backendName}' is not registered`);\n  }\n  kernelRegistry.delete(key);\n}\n\n/** Removes the registered gradient from the global registry. */\nexport function unregisterGradient(kernelName: string): void {\n  if (!gradRegistry.has(kernelName)) {\n    throw new Error(\n        `The gradient '${kernelName}' for backend is not registered`);\n  }\n  gradRegistry.delete(kernelName);\n}\n\n/**\n * Finds kernels that have already been registered to a backend and re-registers\n * them for a new backend. Useful for registering custom backends.\n * @param registeredBackendName Already registered backend.\n * @param newBackendName New backend.\n */\nexport function copyRegisteredKernels(\n    registeredBackendName: string, newBackendName: string): void {\n  const kernels = getKernelsForBackend(registeredBackendName);\n  kernels.forEach(kernelConfig => {\n    const newKernelConfig =\n        Object.assign({}, kernelConfig, {backendName: newBackendName});\n    registerKernel(newKernelConfig);\n  });\n}\n\nfunction makeKey(kernelName: string, backendName: string) {\n  return `${backendName}_${kernelName}`;\n}\n"]}","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LessEqual } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a <= b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.lessEqual(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction lessEqual_(a, b) {\n let $a = convertToTensor(a, 'a', 'lessEqual', 'string_or_numeric');\n let $b = convertToTensor(b, 'b', 'lessEqual', 'string_or_numeric');\n [$a, $b] = makeTypesMatch($a, $b);\n assertAndGetBroadcastShape($a.shape, $b.shape);\n const inputs = { a: $a, b: $b };\n return ENGINE.runKernel(LessEqual, inputs);\n}\nexport const lessEqual = op({ lessEqual_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { PadV2 } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Pads a `tf.Tensor` with a given value and paddings.\n *\n * This operation implements `CONSTANT` mode. For `REFLECT` and `SYMMETRIC`,\n * refer to `tf.mirrorPad`\n *\n * Also available are stricter rank-specific methods with the same signature\n * as this method that assert that `paddings` is of given length.\n * - `tf.pad1d`\n * - `tf.pad2d`\n * - `tf.pad3d`\n * - `tf.pad4d`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * x.pad([[1, 2]]).print();\n * ```\n * @param x The tensor to pad.\n * @param paddings An array of length `R` (the rank of the tensor), where\n * each element is a length-2 tuple of ints `[padBefore, padAfter]`,\n * specifying how much to pad along each dimension of the tensor.\n * @param constantValue The pad value to use. Defaults to 0.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction pad_(x, paddings, constantValue = 0) {\n const $x = convertToTensor(x, 'x', 'pad');\n if ($x.rank === 0) {\n throw new Error('pad(scalar) is not defined. Pass non-scalar to pad');\n }\n const attrs = { paddings, constantValue };\n const inputs = { x: $x };\n return ENGINE.runKernel(PadV2, inputs, attrs);\n}\nexport const pad = op({ pad_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Add } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc, createComplexBinaryKernelImpl } from '../utils/binary_utils';\nexport const addImpl = createSimpleBinaryKernelImpl(((a, b) => a + b));\nexport const addComplexImpl = createComplexBinaryKernelImpl(((aReal, aImag, bReal, bImag) => {\n return { real: aReal + bReal, imag: aImag + bImag };\n}));\nexport const add = binaryKernelFunc(Add, addImpl, addComplexImpl);\nexport const addConfig = {\n kernelName: Add,\n backendName: 'cpu',\n kernelFunc: add\n};\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Multiply } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc, createComplexBinaryKernelImpl } from '../utils/binary_utils';\nexport const multiplyImpl = createSimpleBinaryKernelImpl(((aValue, bValue) => aValue * bValue));\nexport const multiplyComplexImpl = createComplexBinaryKernelImpl(((aReal, aImag, bReal, bImag) => {\n return {\n real: aReal * bReal - aImag * bImag,\n imag: aReal * bImag + aImag * bReal\n };\n}));\nexport const multiply = binaryKernelFunc(Multiply, multiplyImpl, multiplyComplexImpl);\nexport const multiplyConfig = {\n kernelName: Multiply,\n backendName: 'cpu',\n kernelFunc: multiply\n};\n//# sourceMappingURL=data:application/json;base64,eyJ2ZXJzaW9uIjozLCJmaWxlIjoiTXVsdGlwbHkuanMiLCJzb3VyY2VSb290IjoiIiwic291cmNlcyI6WyIuLi8uLi8uLi8uLi8uLi8uLi90ZmpzLWJhY2tlbmQtY3B1L3NyYy9rZXJuZWxzL011bHRpcGx5LnRzIl0sIm5hbWVzIjpbXSwibWFwcGluZ3MiOiJBQUFBOzs7Ozs7Ozs7Ozs7Ozs7R0FlRztBQUVILE9BQU8sRUFBZSxRQUFRLEVBQUMsTUFBTSx1QkFBdUIsQ0FBQztBQUM3RCxPQUFPLEVBQUMsNEJBQTRCLEVBQUMsTUFBTSxzQkFBc0IsQ0FBQztBQUNsRSxPQUFPLEVBQUMsZ0JBQWdCLEVBQUUsNkJBQTZCLEVBQUMsTUFBTSx1QkFBdUIsQ0FBQztBQUV0RixNQUFNLENBQUMsTUFBTSxZQUFZLEdBQUcsNEJBQTRCLENBQ3BELENBQUMsQ0FBQyxNQUFjLEVBQUUsTUFBYyxFQUFFLEVBQUUsQ0FBQyxNQUFNLEdBQUcsTUFBTSxDQUFDLENBQUMsQ0FBQztBQUMzRCxNQUFNLENBQUMsTUFBTSxtQkFBbUIsR0FDNUIsNkJBQTZCLENBQUMsQ0FBQyxDQUFDLEtBQUssRUFBRSxLQUFLLEVBQUUsS0FBSyxFQUFFLEtBQUssRUFBRSxFQUFFO0lBQzVELE9BQU87UUFDTCxJQUFJLEVBQUUsS0FBSyxHQUFHLEtBQUssR0FBRyxLQUFLLEdBQUcsS0FBSztRQUNuQyxJQUFJLEVBQUUsS0FBSyxHQUFHLEtBQUssR0FBRyxLQUFLLEdBQUcsS0FBSztLQUNwQyxDQUFDO0FBQ0osQ0FBQyxDQUFDLENBQUMsQ0FBQztBQUVSLE1BQU0sQ0FBQyxNQUFNLFFBQVEsR0FDakIsZ0JBQWdCLENBQUMsUUFBUSxFQUFFLFlBQVksRUFBRSxtQkFBbUIsQ0FBQyxDQUFDO0FBRWxFLE1BQU0sQ0FBQyxNQUFNLGNBQWMsR0FBaUI7SUFDMUMsVUFBVSxFQUFFLFFBQVE7SUFDcEIsV0FBVyxFQUFFLEtBQUs7SUFDbEIsVUFBVSxFQUFFLFFBQVE7Q0FDckIsQ0FBQyIsInNvdXJjZXNDb250ZW50IjpbIi8qKlxuICogQGxpY2Vuc2VcbiAqIENvcHlyaWdodCAyMDIwIEdvb2dsZSBMTEMuIEFsbCBSaWdodHMgUmVzZXJ2ZWQuXG4gKiBMaWNlbnNlZCB1bmRlciB0aGUgQXBhY2hlIExpY2Vuc2UsIFZlcnNpb24gMi4wICh0aGUgXCJMaWNlbnNlXCIpO1xuICogeW91IG1heSBub3QgdXNlIHRoaXMgZmlsZSBleGNlcHQgaW4gY29tcGxpYW5jZSB3aXRoIHRoZSBMaWNlbnNlLlxuICogWW91IG1heSBvYnRhaW4gYSBjb3B5IG9mIHRoZSBMaWNlbnNlIGF0XG4gKlxuICogaHR0cDovL3d3dy5hcGFjaGUub3JnL2xpY2Vuc2VzL0xJQ0VOU0UtMi4wXG4gKlxuICogVW5sZXNzIHJlcXVpcmVkIGJ5IGFwcGxpY2FibGUgbGF3IG9yIGFncmVlZCB0byBpbiB3cml0aW5nLCBzb2Z0d2FyZVxuICogZGlzdHJpYnV0ZWQgdW5kZXIgdGhlIExpY2Vuc2UgaXMgZGlzdHJpYnV0ZWQgb24gYW4gXCJBUyBJU1wiIEJBU0lTLFxuICogV0lUSE9VVCBXQVJSQU5USUVTIE9SIENPTkRJVElPTlMgT0YgQU5ZIEtJTkQsIGVpdGhlciBleHByZXNzIG9yIGltcGxpZWQuXG4gKiBTZWUgdGhlIExpY2Vuc2UgZm9yIHRoZSBzcGVjaWZpYyBsYW5ndWFnZSBnb3Zlcm5pbmcgcGVybWlzc2lvbnMgYW5kXG4gKiBsaW1pdGF0aW9ucyB1bmRlciB0aGUgTGljZW5zZS5cbiAqID09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09XG4gKi9cblxuaW1wb3J0IHtLZXJuZWxDb25maWcsIE11bHRpcGx5fSBmcm9tICdAdGVuc29yZmxvdy90ZmpzLWNvcmUnO1xuaW1wb3J0IHtjcmVhdGVTaW1wbGVCaW5hcnlLZXJuZWxJbXBsfSBmcm9tICcuLi91dGlscy9iaW5hcnlfaW1wbCc7XG5pbXBvcnQge2JpbmFyeUtlcm5lbEZ1bmMsIGNyZWF0ZUNvbXBsZXhCaW5hcnlLZXJuZWxJbXBsfSBmcm9tICcuLi91dGlscy9iaW5hcnlfdXRpbHMnO1xuXG5leHBvcnQgY29uc3QgbXVsdGlwbHlJbXBsID0gY3JlYXRlU2ltcGxlQmluYXJ5S2VybmVsSW1wbChcbiAgICAoKGFWYWx1ZTogbnVtYmVyLCBiVmFsdWU6IG51bWJlcikgPT4gYVZhbHVlICogYlZhbHVlKSk7XG5leHBvcnQgY29uc3QgbXVsdGlwbHlDb21wbGV4SW1wbCA9XG4gICAgY3JlYXRlQ29tcGxleEJpbmFyeUtlcm5lbEltcGwoKChhUmVhbCwgYUltYWcsIGJSZWFsLCBiSW1hZykgPT4ge1xuICAgICAgcmV0dXJuIHtcbiAgICAgICAgcmVhbDogYVJlYWwgKiBiUmVhbCAtIGFJbWFnICogYkltYWcsXG4gICAgICAgIGltYWc6IGFSZWFsICogYkltYWcgKyBhSW1hZyAqIGJSZWFsXG4gICAgICB9O1xuICAgIH0pKTtcblxuZXhwb3J0IGNvbnN0IG11bHRpcGx5ID1cbiAgICBiaW5hcnlLZXJuZWxGdW5jKE11bHRpcGx5LCBtdWx0aXBseUltcGwsIG11bHRpcGx5Q29tcGxleEltcGwpO1xuXG5leHBvcnQgY29uc3QgbXVsdGlwbHlDb25maWc6IEtlcm5lbENvbmZpZyA9IHtcbiAga2VybmVsTmFtZTogTXVsdGlwbHksXG4gIGJhY2tlbmROYW1lOiAnY3B1JyxcbiAga2VybmVsRnVuYzogbXVsdGlwbHlcbn07XG4iXX0=","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Identity } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Creates a new tensor with the same values and shape as the specified\n * tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n *\n * x.clone().print();\n * ```\n *\n * @param x The tensor to clone.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction clone_(x) {\n const $x = convertToTensor(x, 'x', 'clone', 'string_or_numeric');\n const inputs = { x: $x };\n // Note this op is called tf.identity in python. Hence the kernel name used\n // here.\n return ENGINE.runKernel(Identity, inputs);\n}\nexport const clone = op({ clone_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sigmoid } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes sigmoid element-wise, `1 / (1 + exp(-x))`\n *\n * ```js\n * const x = tf.tensor1d([0, -1, 2, -3]);\n *\n * x.sigmoid().print(); // or tf.sigmoid(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sigmoid_(x) {\n const $x = convertToTensor(x, 'x', 'sigmoid', 'float32');\n const inputs = { x: $x };\n return ENGINE.runKernel(Sigmoid, inputs);\n}\nexport const sigmoid = op({ sigmoid_ });\n//# sourceMappingURL=data:application/json;base64,eyJ2ZXJzaW9uIjozLCJmaWxlIjoic2lnbW9pZC5qcyIsInNvdXJjZVJvb3QiOiIiLCJzb3VyY2VzIjpbIi4uLy4uLy4uLy4uLy4uLy4uL3RmanMtY29yZS9zcmMvb3BzL3NpZ21vaWQudHMiXSwibmFtZXMiOltdLCJtYXBwaW5ncyI6IkFBQUE7Ozs7Ozs7Ozs7Ozs7OztHQWVHO0FBRUgsT0FBTyxFQUFDLE1BQU0sRUFBQyxNQUFNLFdBQVcsQ0FBQztBQUNqQyxPQUFPLEVBQUMsT0FBTyxFQUFnQixNQUFNLGlCQUFpQixDQUFDO0FBR3ZELE9BQU8sRUFBQyxlQUFlLEVBQUMsTUFBTSxvQkFBb0IsQ0FBQztBQUduRCxPQUFPLEVBQUMsRUFBRSxFQUFDLE1BQU0sYUFBYSxDQUFDO0FBRS9COzs7Ozs7Ozs7OztHQVdHO0FBQ0gsU0FBUyxRQUFRLENBQW1CLENBQWU7SUFDakQsTUFBTSxFQUFFLEdBQUcsZUFBZSxDQUFDLENBQUMsRUFBRSxHQUFHLEVBQUUsU0FBUyxFQUFFLFNBQVMsQ0FBQyxDQUFDO0lBRXpELE1BQU0sTUFBTSxHQUFrQixFQUFDLENBQUMsRUFBRSxFQUFFLEVBQUMsQ0FBQztJQUV0QyxPQUFPLE1BQU0sQ0FBQyxTQUFTLENBQUMsT0FBTyxFQUFFLE1BQThCLENBQUMsQ0FBQztBQUNuRSxDQUFDO0FBQ0QsTUFBTSxDQUFDLE1BQU0sT0FBTyxHQUFHLEVBQUUsQ0FBQyxFQUFDLFFBQVEsRUFBQyxDQUFDLENBQUMiLCJzb3VyY2VzQ29udGVudCI6WyIvKipcbiAqIEBsaWNlbnNlXG4gKiBDb3B5cmlnaHQgMjAxOCBHb29nbGUgTExDLiBBbGwgUmlnaHRzIFJlc2VydmVkLlxuICogTGljZW5zZWQgdW5kZXIgdGhlIEFwYWNoZSBMaWNlbnNlLCBWZXJzaW9uIDIuMCAodGhlIFwiTGljZW5zZVwiKTtcbiAqIHlvdSBtYXkgbm90IHVzZSB0aGlzIGZpbGUgZXhjZXB0IGluIGNvbXBsaWFuY2Ugd2l0aCB0aGUgTGljZW5zZS5cbiAqIFlvdSBtYXkgb2J0YWluIGEgY29weSBvZiB0aGUgTGljZW5zZSBhdFxuICpcbiAqIGh0dHA6Ly93d3cuYXBhY2hlLm9yZy9saWNlbnNlcy9MSUNFTlNFLTIuMFxuICpcbiAqIFVubGVzcyByZXF1aXJlZCBieSBhcHBsaWNhYmxlIGxhdyBvciBhZ3JlZWQgdG8gaW4gd3JpdGluZywgc29mdHdhcmVcbiAqIGRpc3RyaWJ1dGVkIHVuZGVyIHRoZSBMaWNlbnNlIGlzIGRpc3RyaWJ1dGVkIG9uIGFuIFwiQVMgSVNcIiBCQVNJUyxcbiAqIFdJVEhPVVQgV0FSUkFOVElFUyBPUiBDT05ESVRJT05TIE9GIEFOWSBLSU5ELCBlaXRoZXIgZXhwcmVzcyBvciBpbXBsaWVkLlxuICogU2VlIHRoZSBMaWNlbnNlIGZvciB0aGUgc3BlY2lmaWMgbGFuZ3VhZ2UgZ292ZXJuaW5nIHBlcm1pc3Npb25zIGFuZFxuICogbGltaXRhdGlvbnMgdW5kZXIgdGhlIExpY2Vuc2UuXG4gKiA9PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PVxuICovXG5cbmltcG9ydCB7RU5HSU5FfSBmcm9tICcuLi9lbmdpbmUnO1xuaW1wb3J0IHtTaWdtb2lkLCBTaWdtb2lkSW5wdXRzfSBmcm9tICcuLi9rZXJuZWxfbmFtZXMnO1xuaW1wb3J0IHtUZW5zb3J9IGZyb20gJy4uL3RlbnNvcic7XG5pbXBvcnQge05hbWVkVGVuc29yTWFwfSBmcm9tICcuLi90ZW5zb3JfdHlwZXMnO1xuaW1wb3J0IHtjb252ZXJ0VG9UZW5zb3J9IGZyb20gJy4uL3RlbnNvcl91dGlsX2Vudic7XG5pbXBvcnQge1RlbnNvckxpa2V9IGZyb20gJy4uL3R5cGVzJztcblxuaW1wb3J0IHtvcH0gZnJvbSAnLi9vcGVyYXRpb24nO1xuXG4vKipcbiAqIENvbXB1dGVzIHNpZ21vaWQgZWxlbWVudC13aXNlLCBgMSAvICgxICsgZXhwKC14KSlgXG4gKlxuICogYGBganNcbiAqIGNvbnN0IHggPSB0Zi50ZW5zb3IxZChbMCwgLTEsIDIsIC0zXSk7XG4gKlxuICogeC5zaWdtb2lkKCkucHJpbnQoKTsgIC8vIG9yIHRmLnNpZ21vaWQoeClcbiAqIGBgYFxuICogQHBhcmFtIHggVGhlIGlucHV0IHRlbnNvci5cbiAqXG4gKiBAZG9jIHtoZWFkaW5nOiAnT3BlcmF0aW9ucycsIHN1YmhlYWRpbmc6ICdCYXNpYyBtYXRoJ31cbiAqL1xuZnVuY3Rpb24gc2lnbW9pZF88VCBleHRlbmRzIFRlbnNvcj4oeDogVHxUZW5zb3JMaWtlKTogVCB7XG4gIGNvbnN0ICR4ID0gY29udmVydFRvVGVuc29yKHgsICd4JywgJ3NpZ21vaWQnLCAnZmxvYXQzMicpO1xuXG4gIGNvbnN0IGlucHV0czogU2lnbW9pZElucHV0cyA9IHt4OiAkeH07XG5cbiAgcmV0dXJuIEVOR0lORS5ydW5LZXJuZWwoU2lnbW9pZCwgaW5wdXRzIGFzIHt9IGFzIE5hbWVkVGVuc29yTWFwKTtcbn1cbmV4cG9ydCBjb25zdCBzaWdtb2lkID0gb3Aoe3NpZ21vaWRffSk7XG4iXX0=","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\n/**\n * Apply a mapping function to a nested structure in a recursive manner.\n *\n * The result of the mapping is an object with the same nested structure (i.e.,\n * of arrays and dicts) as the input, except that some subtrees are replaced,\n * according to the results of the mapping function.\n *\n * Mappings are memoized. Thus, if the nested structure contains the same\n * object in multiple positions, the output will contain the same mapped object\n * in those positions. Cycles are not supported, however.\n *\n * @param input: The object to which to apply the mapping function.\n * @param mapFn: A function that expects a single node of the object tree, and\n * returns a `DeepMapResult`. The `DeepMapResult` either provides a\n * replacement value for that node (i.e., replacing the subtree), or indicates\n * that the node should be processed recursively.\n */\nexport function deepMap(input, mapFn) {\n return deepMapInternal(input, mapFn);\n}\n/**\n * @param seen: A Map of known object mappings (i.e., memoized results of\n * `mapFn()`)\n * @param containedIn: An set containing objects on the reference path currently\n * being processed (used to detect cycles).\n */\nfunction deepMapInternal(input, mapFn, seen = new Map(), containedIn = new Set()) {\n if (input == null) {\n return null;\n }\n if (typeof Blob === 'function' && input instanceof Blob) {\n return input.slice();\n }\n if (containedIn.has(input)) {\n throw new Error('Circular references are not supported.');\n }\n if (seen.has(input)) {\n return seen.get(input);\n }\n const result = mapFn(input);\n if (result.recurse && result.value !== null) {\n throw new Error('A deep map function may not return both a value and recurse=true.');\n }\n if (!result.recurse) {\n seen.set(input, result.value);\n return result.value;\n }\n else if (isIterable(input)) {\n // tslint:disable-next-line:no-any\n const mappedIterable = Array.isArray(input) ? [] : {};\n containedIn.add(input);\n for (const k in input) {\n const child = input[k];\n const childResult = deepMapInternal(child, mapFn, seen, containedIn);\n mappedIterable[k] = childResult;\n }\n containedIn.delete(input);\n if (input.__proto__) {\n mappedIterable.__proto__ = input.__proto__;\n }\n return mappedIterable;\n }\n else {\n throw new Error(`Can't recurse into non-iterable type: ${input}`);\n }\n}\n// TODO(soergel, kangyizhang) Reconsider naming of deepZip() to avoid confusion\n// with zip()\n/**\n * Zip nested structures together in a recursive manner.\n *\n * This has the effect of transposing or pivoting data, e.g. converting it from\n * a row-major representation to a column-major representation.\n *\n * For example, `deepZip([{a: 1, b: 2}, {a: 3, b: 4}])` returns\n * `{a: [1, 3], b: [2, 4]}`.\n *\n * The inputs should all have the same nested structure (i.e., of arrays and\n * dicts). The result is a single object with the same nested structure, where\n * the leaves are arrays collecting the values of the inputs at that location\n * (or, optionally, the result of a custom function applied to those arrays).\n *\n * @param inputs: An array of the objects to zip together.\n * @param zipFn: (optional) A function that expects an array of elements at a\n * single node of the object tree, and returns a `DeepMapResult`. The\n * `DeepMapResult` either provides a result value for that node (i.e.,\n * representing the subtree), or indicates that the node should be processed\n * recursively. The default zipFn recurses as far as possible and places\n * arrays at the leaves.\n */\nexport function deepZip(inputs, zipFn = zipToList) {\n return deepZipInternal(inputs, zipFn);\n}\n/**\n * @param containedIn: An set containing objects on the reference path currently\n * being processed (used to detect cycles).\n */\nfunction deepZipInternal(inputs, zipFn, containedIn = new Set()) {\n // The recursion follows the structure of input 0; it's assumed that all the\n // other inputs have the same structure.\n const input = inputs[0];\n if (containedIn.has(input)) {\n throw new Error('Circular references are not supported.');\n }\n const result = zipFn(inputs);\n if (result.recurse && result.value !== null) {\n throw new Error('A deep zip function may not return both a value and recurse=true.');\n }\n if (!result.recurse) {\n return result.value;\n }\n else if (isIterable(input)) {\n // tslint:disable-next-line:no-any\n const mappedIterable = Array.isArray(input) ? [] : {};\n containedIn.add(input);\n for (const k in input) {\n const children = inputs.map(x => x[k]);\n const childResult = deepZipInternal(children, zipFn, containedIn);\n mappedIterable[k] = childResult;\n }\n containedIn.delete(input);\n return mappedIterable;\n }\n else {\n throw new Error(`Can't recurse into non-iterable type: ${input}`);\n }\n}\n// tslint:disable-next-line:no-any\nexport function zipToList(x) {\n if (x === null) {\n return null;\n }\n // TODO(soergel): validate array type?\n if (isIterable(x[0])) {\n return { value: null, recurse: true };\n }\n else {\n return { value: x, recurse: false };\n }\n}\n/**\n * Apply an async mapping function to a nested structure in a recursive manner.\n *\n * This first creates a nested structure of Promises, and then awaits all of\n * those, resulting in a single Promise for a resolved nested structure.\n *\n * The result of the mapping is an object with the same nested structure (i.e.,\n * of arrays and dicts) as the input, except that some subtrees are replaced,\n * according to the results of the mapping function.\n *\n * Mappings are memoized. Thus, if the nested structure contains the same\n * object in multiple positions, the output will contain the same mapped object\n * in those positions. Cycles are not supported, however.\n *\n * @param input: The object to which to apply the mapping function.\n * @param mapFn: A function that expects a single node of the object tree, and\n * returns a `DeepMapAsyncResult`. The `DeepMapAsyncResult` either provides\n * a `Promise` for a replacement value for that node (i.e., replacing the\n * subtree), or indicates that the node should be processed recursively. Note\n * that the decision whether or not to recurse must be made immediately; only\n * the mapped value may be promised.\n */\nexport async function deepMapAndAwaitAll(input, mapFn) {\n const seen = new Map();\n // First do a normal deepMap, collecting Promises in 'seen' as a side effect.\n deepMapInternal(input, mapFn, seen);\n // Replace the Promises in 'seen' in place.\n // Note TypeScript provides no async map iteration, and regular map iteration\n // is broken too, so sadly we have to do Array.from() to make it work.\n // (There's no advantage to Promise.all(), and that would be tricky anyway.)\n for (const key of Array.from(seen.keys())) {\n const value = seen.get(key);\n if (tf.util.isPromise(value)) {\n const mappedValue = await value;\n seen.set(key, mappedValue);\n }\n }\n // Normal deepMap again, this time filling in the resolved values.\n // It's unfortunate that we have to do two passes.\n // TODO(soergel): test performance and think harder about a fast solution.\n const result = deepMapInternal(input, mapFn, seen);\n return result;\n}\n/**\n * Determine whether the argument is iterable.\n *\n * @returns true if the argument is an array or any non-Tensor object.\n */\n// tslint:disable-next-line:no-any\nexport function isIterable(obj) {\n let isTextDecoder = false;\n if (tf.env().get('IS_BROWSER')) {\n isTextDecoder = obj instanceof TextDecoder;\n }\n else {\n // tslint:disable-next-line:no-require-imports\n const { StringDecoder } = require('string_decoder');\n isTextDecoder = obj instanceof StringDecoder;\n }\n return obj != null && (!ArrayBuffer.isView(obj)) &&\n (Array.isArray(obj) ||\n (typeof obj === 'object' && !(obj instanceof tf.Tensor) &&\n !(obj instanceof Promise) && !isTextDecoder));\n}\n/**\n * Determine whether the argument can be converted to Tensor.\n *\n * Tensors, primitives, arrays, and TypedArrays all qualify; anything else does\n * not.\n *\n * @returns true if the argument can be converted to Tensor.\n */\n// tslint:disable-next-line:no-any\nexport function canTensorify(obj) {\n return obj == null || isPrimitive(obj) || Array.isArray(obj) ||\n (typeof obj === 'object' && (obj instanceof tf.Tensor)) ||\n tf.util.isTypedArray(obj);\n}\n/**\n * Returns true if the given `value` is a primitive type. Otherwise returns\n * false. This is equivalant to node util.isPrimitive\n */\nfunction isPrimitive(value) {\n return (value === null ||\n (typeof value !== 'object' && typeof value !== 'function'));\n}\n//# 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* @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n\nimport * as tf from '@tensorflow/tfjs-core';\n\n// tslint:disable:no-any\n\n/**\n * A return value for a mapping function that can be applied via deepMap.\n *\n * If recurse is true, the value should be empty, and iteration will continue\n * into the object or array.\n */\nexport type DeepMapResult = {\n  value: any,\n  recurse: boolean\n};\n\n/**\n * Apply a mapping function to a nested structure in a recursive manner.\n *\n * The result of the mapping is an object with the same nested structure (i.e.,\n * of arrays and dicts) as the input, except that some subtrees are replaced,\n * according to the results of the mapping function.\n *\n * Mappings are memoized.  Thus, if the nested structure contains the same\n * object in multiple positions, the output will contain the same mapped object\n * in those positions.  Cycles are not supported, however.\n *\n * @param input: The object to which to apply the mapping function.\n * @param mapFn: A function that expects a single node of the object tree, and\n *   returns a `DeepMapResult`.  The `DeepMapResult` either provides a\n *   replacement value for that node (i.e., replacing the subtree), or indicates\n *   that the node should be processed recursively.\n */\nexport function deepMap(input: any, mapFn: (x: any) => DeepMapResult): any|\n    any[] {\n  return deepMapInternal(input, mapFn);\n}\n\n/**\n * @param seen: A Map of known object mappings (i.e., memoized results of\n *   `mapFn()`)\n * @param containedIn: An set containing objects on the reference path currently\n *   being processed (used to detect cycles).\n */\nfunction deepMapInternal(\n    input: any, mapFn: (x: any) => DeepMapResult,\n    seen: Map<any, any> = new Map(), containedIn: Set<{}> = new Set()): any|\n    any[] {\n  if (input == null) {\n    return null;\n  }\n  if (typeof Blob === 'function' && input instanceof Blob) {\n    return input.slice();\n  }\n\n  if (containedIn.has(input)) {\n    throw new Error('Circular references are not supported.');\n  }\n  if (seen.has(input)) {\n    return seen.get(input);\n  }\n  const result = mapFn(input);\n\n  if (result.recurse && result.value !== null) {\n    throw new Error(\n        'A deep map function may not return both a value and recurse=true.');\n  }\n\n  if (!result.recurse) {\n    seen.set(input, result.value);\n    return result.value;\n  } else if (isIterable(input)) {\n    // tslint:disable-next-line:no-any\n    const mappedIterable: any|any[] = Array.isArray(input) ? [] : {};\n    containedIn.add(input);\n    for (const k in input) {\n      const child = input[k];\n      const childResult = deepMapInternal(child, mapFn, seen, containedIn);\n      mappedIterable[k] = childResult;\n    }\n    containedIn.delete(input);\n    if (input.__proto__) {\n      mappedIterable.__proto__ = input.__proto__;\n    }\n    return mappedIterable;\n  } else {\n    throw new Error(`Can't recurse into non-iterable type: ${input}`);\n  }\n}\n\n// TODO(soergel, kangyizhang) Reconsider naming of deepZip() to avoid confusion\n// with zip()\n\n/**\n * Zip nested structures together in a recursive manner.\n *\n * This has the effect of transposing or pivoting data, e.g. converting it from\n * a row-major representation to a column-major representation.\n *\n * For example, `deepZip([{a: 1, b: 2}, {a: 3, b: 4}])` returns\n * `{a: [1, 3], b: [2, 4]}`.\n *\n * The inputs should all have the same nested structure (i.e., of arrays and\n * dicts).  The result is a single object with the same nested structure, where\n * the leaves are arrays collecting the values of the inputs at that location\n * (or, optionally, the result of a custom function applied to those arrays).\n *\n * @param inputs: An array of the objects to zip together.\n * @param zipFn: (optional) A function that expects an array of elements at a\n *   single node of the object tree, and returns a `DeepMapResult`.  The\n *   `DeepMapResult` either provides a result value for that node (i.e.,\n *   representing the subtree), or indicates that the node should be processed\n *   recursively.  The default zipFn recurses as far as possible and places\n *   arrays at the leaves.\n */\nexport function deepZip(\n    inputs: any[], zipFn: (xs: any[]) => DeepMapResult = zipToList): any|any[] {\n  return deepZipInternal(inputs, zipFn);\n}\n\n/**\n * @param containedIn: An set containing objects on the reference path currently\n *   being processed (used to detect cycles).\n */\nfunction deepZipInternal(\n    inputs: any[], zipFn: (xs: any[]) => DeepMapResult,\n    containedIn: Set<{}> = new Set()): any|any[] {\n  // The recursion follows the structure of input 0; it's assumed that all the\n  // other inputs have the same structure.\n  const input = inputs[0];\n  if (containedIn.has(input)) {\n    throw new Error('Circular references are not supported.');\n  }\n  const result = zipFn(inputs);\n\n  if (result.recurse && result.value !== null) {\n    throw new Error(\n        'A deep zip function may not return both a value and recurse=true.');\n  }\n\n  if (!result.recurse) {\n    return result.value;\n  } else if (isIterable(input)) {\n    // tslint:disable-next-line:no-any\n    const mappedIterable: any|any[] = Array.isArray(input) ? [] : {};\n    containedIn.add(input);\n    for (const k in input) {\n      const children = inputs.map(x => x[k]);\n      const childResult = deepZipInternal(children, zipFn, containedIn);\n      mappedIterable[k] = childResult;\n    }\n    containedIn.delete(input);\n    return mappedIterable;\n  } else {\n    throw new Error(`Can't recurse into non-iterable type: ${input}`);\n  }\n}\n\n// tslint:disable-next-line:no-any\nexport function zipToList(x: any[]): DeepMapResult {\n  if (x === null) {\n    return null;\n  }\n  // TODO(soergel): validate array type?\n\n  if (isIterable(x[0])) {\n    return {value: null, recurse: true};\n  } else {\n    return {value: x, recurse: false};\n  }\n}\n\n/**\n * A return value for an async map function for use with deepMapAndAwaitAll.\n *\n * If recurse is true, the value should be empty, and iteration will continue\n * into the object or array.\n */\nexport type DeepMapAsyncResult = {\n  value: Promise<any>,\n  recurse: boolean\n};\n\n/**\n * Apply an async mapping function to a nested structure in a recursive manner.\n *\n * This first creates a nested structure of Promises, and then awaits all of\n * those, resulting in a single Promise for a resolved nested structure.\n *\n * The result of the mapping is an object with the same nested structure (i.e.,\n * of arrays and dicts) as the input, except that some subtrees are replaced,\n * according to the results of the mapping function.\n *\n * Mappings are memoized.  Thus, if the nested structure contains the same\n * object in multiple positions, the output will contain the same mapped object\n * in those positions.  Cycles are not supported, however.\n *\n * @param input: The object to which to apply the mapping function.\n * @param mapFn: A function that expects a single node of the object tree, and\n *   returns a `DeepMapAsyncResult`.  The `DeepMapAsyncResult` either provides\n *   a `Promise` for a replacement value for that node (i.e., replacing the\n *   subtree), or indicates that the node should be processed recursively.  Note\n *   that the decision whether or not to recurse must be made immediately; only\n *   the mapped value may be promised.\n */\nexport async function deepMapAndAwaitAll(\n    input: any, mapFn: (x: any) => DeepMapAsyncResult): Promise<any|any[]> {\n  const seen: Map<any, any> = new Map();\n\n  // First do a normal deepMap, collecting Promises in 'seen' as a side effect.\n  deepMapInternal(input, mapFn, seen);\n\n  // Replace the Promises in 'seen' in place.\n  // Note TypeScript provides no async map iteration, and regular map iteration\n  // is broken too, so sadly we have to do Array.from() to make it work.\n  // (There's no advantage to Promise.all(), and that would be tricky anyway.)\n  for (const key of Array.from(seen.keys())) {\n    const value = seen.get(key);\n    if (tf.util.isPromise(value)) {\n      const mappedValue = await value;\n      seen.set(key, mappedValue);\n    }\n  }\n\n  // Normal deepMap again, this time filling in the resolved values.\n  // It's unfortunate that we have to do two passes.\n  // TODO(soergel): test performance and think harder about a fast solution.\n  const result = deepMapInternal(input, mapFn, seen);\n  return result;\n}\n\n/**\n * Determine whether the argument is iterable.\n *\n * @returns true if the argument is an array or any non-Tensor object.\n */\n// tslint:disable-next-line:no-any\nexport function isIterable(obj: any): boolean {\n  let isTextDecoder = false;\n  if (tf.env().get('IS_BROWSER')) {\n    isTextDecoder = obj instanceof TextDecoder;\n  } else {\n    // tslint:disable-next-line:no-require-imports\n    const {StringDecoder} = require('string_decoder');\n    isTextDecoder = obj instanceof StringDecoder;\n  }\n  return obj != null && (!ArrayBuffer.isView(obj)) &&\n      (Array.isArray(obj) ||\n       (typeof obj === 'object' && !(obj instanceof tf.Tensor) &&\n        !(obj instanceof Promise) && !isTextDecoder));\n}\n\n/**\n * Determine whether the argument can be converted to Tensor.\n *\n * Tensors, primitives, arrays, and TypedArrays all qualify; anything else does\n * not.\n *\n * @returns true if the argument can be converted to Tensor.\n */\n// tslint:disable-next-line:no-any\nexport function canTensorify(obj: any): boolean {\n  return obj == null || isPrimitive(obj) || Array.isArray(obj) ||\n      (typeof obj === 'object' && (obj instanceof tf.Tensor)) ||\n      tf.util.isTypedArray(obj);\n}\n\n/**\n * Returns true if the given `value` is a primitive type. Otherwise returns\n * false. This is equivalant to node util.isPrimitive\n */\nfunction isPrimitive(value: any): boolean {\n  return (\n      value === null ||\n      (typeof value !== 'object' && typeof value !== 'function'));\n}\n"]}","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Log } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes natural logarithm of the input `tf.Tensor` element-wise: `ln(x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, Math.E]);\n *\n * x.log().print(); // or tf.log(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction log_(x) {\n const $x = convertToTensor(x, 'x', 'log', 'float32');\n const inputs = { x: $x };\n return ENGINE.runKernel(Log, inputs);\n}\nexport const log = op({ log_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LogicalAnd } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of `a AND b` element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([false, false, true, true], 'bool');\n * const b = tf.tensor1d([false, true, false, true], 'bool');\n *\n * a.logicalAnd(b).print();\n * ```\n *\n * @param a The first input tensor. Must be of dtype bool.\n * @param b The second input tensor. Must be of dtype bool.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalAnd_(a, b) {\n const $a = convertToTensor(a, 'a', 'logicalAnd', 'bool');\n const $b = convertToTensor(b, 'b', 'logicalAnd', 'bool');\n assertAndGetBroadcastShape($a.shape, $b.shape);\n const inputs = { a: $a, b: $b };\n return ENGINE.runKernel(LogicalAnd, inputs);\n}\nexport const logicalAnd = op({ logicalAnd_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { SplitV } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Splits a `tf.Tensor` into sub tensors.\n *\n * If `numOrSizeSplits` is a number, splits `x` along dimension `axis`\n * into `numOrSizeSplits` smaller tensors.\n * Requires that `numOrSizeSplits` evenly divides `x.shape[axis]`.\n *\n * If `numOrSizeSplits` is a number array, splits `x` into\n * `numOrSizeSplits.length` pieces. The shape of the `i`-th piece has the\n * same size as `x` except along dimension `axis` where the size is\n * `numOrSizeSplits[i]`.\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);\n * const [a, b] = tf.split(x, 2, 1);\n * a.print();\n * b.print();\n *\n * const [c, d, e] = tf.split(x, [1, 2, 1], 1);\n * c.print();\n * d.print();\n * e.print();\n * ```\n *\n * @param x The input tensor to split.\n * @param numOrSizeSplits Either an integer indicating the number of\n * splits along the axis or an array of integers containing the sizes of\n * each output tensor along the axis. If a number then it must evenly divide\n * `x.shape[axis]`; otherwise the sum of sizes must match `x.shape[axis]`.\n * Can contain one -1 indicating that dimension is to be inferred.\n * @param axis The dimension along which to split. Defaults to 0 (the first\n * dim).\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction split_(x, numOrSizeSplits, axis = 0) {\n const $x = convertToTensor(x, 'x', 'split');\n const inputs = { x: $x };\n const attr = { numOrSizeSplits, axis };\n return ENGINE.runKernel(SplitV, inputs, attr);\n}\nexport const split = op({ split_ });\n//# 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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Pack } from '../kernel_names';\nimport { convertToTensorArray } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Stacks a list of rank-`R` `tf.Tensor`s into one rank-`(R+1)` `tf.Tensor`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * const c = tf.tensor1d([5, 6]);\n * tf.stack([a, b, c]).print();\n * ```\n *\n * @param tensors A list of tensor objects with the same shape and dtype.\n * @param axis The axis to stack along. Defaults to 0 (the first dim).\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction stack_(tensors, axis = 0) {\n const $tensors = convertToTensorArray(tensors, 'tensors', 'stack', 'string_or_numeric');\n util.assert($tensors.length >= 1, () => 'Pass at least one tensor to tf.stack');\n if ($tensors.length > 0) {\n util.assert(axis <= $tensors[0].rank, () => 'Axis must be <= rank of the tensor');\n }\n const inputs = $tensors;\n const attrs = { axis };\n return ENGINE.runKernel(Pack, inputs, attrs);\n}\nexport const stack = op({ stack_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cast, util } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { zeros } from '../utils/zeros_impl';\nimport { complex } from './Complex';\nimport { identity } from './Identity';\nimport { real } from './Real';\nexport function cast(args) {\n const { inputs, backend, attrs } = args;\n const { x } = inputs;\n const { dtype } = attrs;\n // Casting to complex64.\n if (dtype === 'complex64') {\n if (x.dtype === 'complex64') {\n return identity({ inputs: { x }, backend });\n }\n const zerosTensorInfo = zeros(backend, x.shape, x.dtype);\n const floatX = cast({ inputs: { x }, backend, attrs: { dtype: 'float32' } });\n const result = complex({ inputs: { real: floatX, imag: zerosTensorInfo }, backend });\n backend.disposeIntermediateTensorInfo(zerosTensorInfo);\n backend.disposeIntermediateTensorInfo(floatX);\n return result;\n }\n // Casting from complex64\n if (x.dtype === 'complex64') {\n const realPart = real({ inputs: { input: x }, backend });\n const result = cast({ inputs: { x: realPart }, backend, attrs: { dtype } });\n backend.disposeIntermediateTensorInfo(realPart);\n return result;\n }\n if (!util.hasEncodingLoss(x.dtype, dtype)) {\n // We don't change the underlying data, since we cast to higher\n // precision.\n const result = identity({ inputs: { x }, backend });\n return { dataId: result.dataId, shape: result.shape, dtype };\n }\n if (dtype === 'int32') {\n const values = backend.data.get(x.dataId).values;\n const resultValues = Int32Array.from(values);\n return backend.makeTensorInfo(x.shape, 'int32', resultValues);\n }\n if (dtype === 'bool') {\n // This is essentially the result of notEqual(x, 0). 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All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Cast, CastAttrs, CastInputs, KernelConfig, KernelFunc, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {createSimpleBinaryKernelImpl} from '../utils/binary_impl';\nimport {zeros} from '../utils/zeros_impl';\n\nimport {complex} from './Complex';\nimport {identity} from './Identity';\nimport {real} from './Real';\n\nexport function cast(\n    args: {inputs: CastInputs, backend: MathBackendCPU, attrs: CastAttrs}):\n    TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {x} = inputs;\n  const {dtype} = attrs;\n\n  // Casting to complex64.\n  if (dtype === 'complex64') {\n    if (x.dtype === 'complex64') {\n      return identity({inputs: {x}, backend});\n    }\n\n    const zerosTensorInfo = zeros(backend, x.shape, x.dtype);\n    const floatX = cast({inputs: {x}, backend, attrs: {dtype: 'float32'}});\n\n    const result =\n        complex({inputs: {real: floatX, imag: zerosTensorInfo}, backend});\n\n    backend.disposeIntermediateTensorInfo(zerosTensorInfo);\n    backend.disposeIntermediateTensorInfo(floatX);\n\n    return result;\n  }\n\n  // Casting from complex64\n  if (x.dtype === 'complex64') {\n    const realPart = real({inputs: {input: x}, backend});\n    const result = cast({inputs: {x: realPart}, backend, attrs: {dtype}});\n\n    backend.disposeIntermediateTensorInfo(realPart);\n\n    return result;\n  }\n\n  if (!util.hasEncodingLoss(x.dtype, dtype)) {\n    // We don't change the underlying data, since we cast to higher\n    // precision.\n    const result = identity({inputs: {x}, backend});\n    return {dataId: result.dataId, shape: result.shape, dtype};\n  }\n\n  if (dtype === 'int32') {\n    const values = backend.data.get(x.dataId).values as TypedArray;\n    const resultValues = Int32Array.from(values);\n    return backend.makeTensorInfo(x.shape, 'int32', resultValues);\n  }\n\n  if (dtype === 'bool') {\n    // This is essentially the result of notEqual(x, 0). We avoid using\n    // kernel notEqual to avoid circular dependency, i.e. binary_utils ->\n    // cast -> notEqual -> binary_utils.\n    const xVals = backend.data.get(x.dataId).values as TypedArray;\n    const zero = util.toTypedArray([0], x.dtype);\n\n    const [resultData, resultShape] = createSimpleBinaryKernelImpl(\n        (a, b) => (a !== b) ? 1 : 0)(x.shape, [], xVals, zero, 'bool');\n\n    return backend.makeTensorInfo(resultShape, 'bool', resultData);\n  }\n\n  throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`);\n}\n\nexport const castConfig: KernelConfig = {\n  kernelName: Cast,\n  backendName: 'cpu',\n  kernelFunc: cast as {} as KernelFunc\n};\n"]}","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from './environment';\nexport function warn(...msg) {\n if (!(env().getBool('IS_TEST') || env().getBool('PROD'))) {\n console.warn(...msg);\n }\n}\nexport function log(...msg) {\n if (!(env().getBool('IS_TEST') || env().getBool('PROD'))) {\n console.log(...msg);\n }\n}\n//# sourceMappingURL=data:application/json;base64,eyJ2ZXJzaW9uIjozLCJmaWxlIjoibG9nLmpzIiwic291cmNlUm9vdCI6IiIsInNvdXJjZXMiOlsiLi4vLi4vLi4vLi4vLi4vdGZqcy1jb3JlL3NyYy9sb2cudHMiXSwibmFtZXMiOltdLCJtYXBwaW5ncyI6IkFBQUE7Ozs7Ozs7Ozs7Ozs7OztHQWVHO0FBRUgsT0FBTyxFQUFDLEdBQUcsRUFBQyxNQUFNLGVBQWUsQ0FBQztBQUVsQyxNQUFNLFVBQVUsSUFBSSxDQUFDLEdBQUcsR0FBYztJQUNwQyxJQUFJLENBQUMsQ0FBQyxHQUFHLEVBQUUsQ0FBQyxPQUFPLENBQUMsU0FBUyxDQUFDLElBQUksR0FBRyxFQUFFLENBQUMsT0FBTyxDQUFDLE1BQU0sQ0FBQyxDQUFDLEVBQUU7UUFDeEQsT0FBTyxDQUFDLElBQUksQ0FBQyxHQUFHLEdBQUcsQ0FBQyxDQUFDO0tBQ3RCO0FBQ0gsQ0FBQztBQUVELE1BQU0sVUFBVSxHQUFHLENBQUMsR0FBRyxHQUFjO0lBQ25DLElBQUksQ0FBQyxDQUFDLEdBQUcsRUFBRSxDQUFDLE9BQU8sQ0FBQyxTQUFTLENBQUMsSUFBSSxHQUFHLEVBQUUsQ0FBQyxPQUFPLENBQUMsTUFBTSxDQUFDLENBQUMsRUFBRTtRQUN4RCxPQUFPLENBQUMsR0FBRyxDQUFDLEdBQUcsR0FBRyxDQUFDLENBQUM7S0FDckI7QUFDSCxDQUFDIiwic291cmNlc0NvbnRlbnQiOlsiLyoqXG4gKiBAbGljZW5zZVxuICogQ29weXJpZ2h0IDIwMTggR29vZ2xlIExMQy4gQWxsIFJpZ2h0cyBSZXNlcnZlZC5cbiAqIExpY2Vuc2VkIHVuZGVyIHRoZSBBcGFjaGUgTGljZW5zZSwgVmVyc2lvbiAyLjAgKHRoZSBcIkxpY2Vuc2VcIik7XG4gKiB5b3UgbWF5IG5vdCB1c2UgdGhpcyBmaWxlIGV4Y2VwdCBpbiBjb21wbGlhbmNlIHdpdGggdGhlIExpY2Vuc2UuXG4gKiBZb3UgbWF5IG9idGFpbiBhIGNvcHkgb2YgdGhlIExpY2Vuc2UgYXRcbiAqXG4gKiBodHRwOi8vd3d3LmFwYWNoZS5vcmcvbGljZW5zZXMvTElDRU5TRS0yLjBcbiAqXG4gKiBVbmxlc3MgcmVxdWlyZWQgYnkgYXBwbGljYWJsZSBsYXcgb3IgYWdyZWVkIHRvIGluIHdyaXRpbmcsIHNvZnR3YXJlXG4gKiBkaXN0cmlidXRlZCB1bmRlciB0aGUgTGljZW5zZSBpcyBkaXN0cmlidXRlZCBvbiBhbiBcIkFTIElTXCIgQkFTSVMsXG4gKiBXSVRIT1VUIFdBUlJBTlRJRVMgT1IgQ09ORElUSU9OUyBPRiBBTlkgS0lORCwgZWl0aGVyIGV4cHJlc3Mgb3IgaW1wbGllZC5cbiAqIFNlZSB0aGUgTGljZW5zZSBmb3IgdGhlIHNwZWNpZmljIGxhbmd1YWdlIGdvdmVybmluZyBwZXJtaXNzaW9ucyBhbmRcbiAqIGxpbWl0YXRpb25zIHVuZGVyIHRoZSBMaWNlbnNlLlxuICogPT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT1cbiAqL1xuXG5pbXBvcnQge2Vudn0gZnJvbSAnLi9lbnZpcm9ubWVudCc7XG5cbmV4cG9ydCBmdW5jdGlvbiB3YXJuKC4uLm1zZzogQXJyYXk8e30+KTogdm9pZCB7XG4gIGlmICghKGVudigpLmdldEJvb2woJ0lTX1RFU1QnKSB8fCBlbnYoKS5nZXRCb29sKCdQUk9EJykpKSB7XG4gICAgY29uc29sZS53YXJuKC4uLm1zZyk7XG4gIH1cbn1cblxuZXhwb3J0IGZ1bmN0aW9uIGxvZyguLi5tc2c6IEFycmF5PHt9Pik6IHZvaWQge1xuICBpZiAoIShlbnYoKS5nZXRCb29sKCdJU19URVNUJykgfHwgZW52KCkuZ2V0Qm9vbCgnUFJPRCcpKSkge1xuICAgIGNvbnNvbGUubG9nKC4uLm1zZyk7XG4gIH1cbn1cbiJdfQ==","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ExpandDims } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Returns a `tf.Tensor` that has expanded rank, by inserting a dimension\n * into the tensor's shape.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * const axis = 1;\n * x.expandDims(axis).print();\n * ```\n *\n * @param x The input tensor whose dimensions to be expanded.\n * @param axis The dimension index at which to insert shape of `1`. Defaults\n * to 0 (the first dimension).\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction expandDims_(x, axis = 0) {\n const $x = convertToTensor(x, 'x', 'expandDims', 'string_or_numeric');\n util.assert(axis <= $x.rank, () => 'Axis must be <= rank of the tensor');\n const inputs = { input: $x };\n const attrs = { dim: axis };\n return ENGINE.runKernel(ExpandDims, inputs, attrs);\n}\nexport const expandDims = op({ expandDims_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n/** DataType enum. */\nexport var DataType;\n(function (DataType) {\n // Not a legal value for DataType. Used to indicate a DataType field\n // has not been set.\n DataType[DataType[\"DT_INVALID\"] = 0] = \"DT_INVALID\";\n // Data types that all computation devices are expected to be\n // capable to support.\n DataType[DataType[\"DT_FLOAT\"] = 1] = \"DT_FLOAT\";\n DataType[DataType[\"DT_DOUBLE\"] = 2] = \"DT_DOUBLE\";\n DataType[DataType[\"DT_INT32\"] = 3] = \"DT_INT32\";\n DataType[DataType[\"DT_UINT8\"] = 4] = \"DT_UINT8\";\n DataType[DataType[\"DT_INT16\"] = 5] = \"DT_INT16\";\n DataType[DataType[\"DT_INT8\"] = 6] = \"DT_INT8\";\n DataType[DataType[\"DT_STRING\"] = 7] = \"DT_STRING\";\n DataType[DataType[\"DT_COMPLEX64\"] = 8] = \"DT_COMPLEX64\";\n DataType[DataType[\"DT_INT64\"] = 9] = \"DT_INT64\";\n DataType[DataType[\"DT_BOOL\"] = 10] = \"DT_BOOL\";\n DataType[DataType[\"DT_QINT8\"] = 11] = \"DT_QINT8\";\n DataType[DataType[\"DT_QUINT8\"] = 12] = \"DT_QUINT8\";\n DataType[DataType[\"DT_QINT32\"] = 13] = \"DT_QINT32\";\n DataType[DataType[\"DT_BFLOAT16\"] = 14] = \"DT_BFLOAT16\";\n DataType[DataType[\"DT_QINT16\"] = 15] = \"DT_QINT16\";\n DataType[DataType[\"DT_QUINT16\"] = 16] = \"DT_QUINT16\";\n DataType[DataType[\"DT_UINT16\"] = 17] = \"DT_UINT16\";\n DataType[DataType[\"DT_COMPLEX128\"] = 18] = \"DT_COMPLEX128\";\n DataType[DataType[\"DT_HALF\"] = 19] = \"DT_HALF\";\n DataType[DataType[\"DT_RESOURCE\"] = 20] = \"DT_RESOURCE\";\n DataType[DataType[\"DT_VARIANT\"] = 21] = \"DT_VARIANT\";\n DataType[DataType[\"DT_UINT32\"] = 22] = \"DT_UINT32\";\n DataType[DataType[\"DT_UINT64\"] = 23] = \"DT_UINT64\";\n // Do not use! These are only for parameters. Every enum above\n // should have a corresponding value below (verified by types_test).\n DataType[DataType[\"DT_FLOAT_REF\"] = 101] = \"DT_FLOAT_REF\";\n DataType[DataType[\"DT_DOUBLE_REF\"] = 102] = \"DT_DOUBLE_REF\";\n DataType[DataType[\"DT_INT32_REF\"] = 103] = \"DT_INT32_REF\";\n DataType[DataType[\"DT_UINT8_REF\"] = 104] = \"DT_UINT8_REF\";\n DataType[DataType[\"DT_INT16_REF\"] = 105] = \"DT_INT16_REF\";\n DataType[DataType[\"DT_INT8_REF\"] = 106] = \"DT_INT8_REF\";\n DataType[DataType[\"DT_STRING_REF\"] = 107] = \"DT_STRING_REF\";\n DataType[DataType[\"DT_COMPLEX64_REF\"] = 108] = \"DT_COMPLEX64_REF\";\n DataType[DataType[\"DT_INT64_REF\"] = 109] = \"DT_INT64_REF\";\n DataType[DataType[\"DT_BOOL_REF\"] = 110] = \"DT_BOOL_REF\";\n DataType[DataType[\"DT_QINT8_REF\"] = 111] = \"DT_QINT8_REF\";\n DataType[DataType[\"DT_QUINT8_REF\"] = 112] = \"DT_QUINT8_REF\";\n DataType[DataType[\"DT_QINT32_REF\"] = 113] = \"DT_QINT32_REF\";\n DataType[DataType[\"DT_BFLOAT16_REF\"] = 114] = \"DT_BFLOAT16_REF\";\n DataType[DataType[\"DT_QINT16_REF\"] = 115] = \"DT_QINT16_REF\";\n DataType[DataType[\"DT_QUINT16_REF\"] = 116] = \"DT_QUINT16_REF\";\n DataType[DataType[\"DT_UINT16_REF\"] = 117] = \"DT_UINT16_REF\";\n DataType[DataType[\"DT_COMPLEX128_REF\"] = 118] = \"DT_COMPLEX128_REF\";\n DataType[DataType[\"DT_HALF_REF\"] = 119] = \"DT_HALF_REF\";\n DataType[DataType[\"DT_RESOURCE_REF\"] = 120] = \"DT_RESOURCE_REF\";\n DataType[DataType[\"DT_VARIANT_REF\"] = 121] = \"DT_VARIANT_REF\";\n DataType[DataType[\"DT_UINT32_REF\"] = 122] = \"DT_UINT32_REF\";\n DataType[DataType[\"DT_UINT64_REF\"] = 123] = \"DT_UINT64_REF\";\n})(DataType || (DataType = {}));\nexport var SaverDef;\n(function (SaverDef) {\n /** CheckpointFormatVersion enum. */\n let CheckpointFormatVersion;\n (function (CheckpointFormatVersion) {\n CheckpointFormatVersion[CheckpointFormatVersion[\"LEGACY\"] = 0] = \"LEGACY\";\n CheckpointFormatVersion[CheckpointFormatVersion[\"V1\"] = 1] = \"V1\";\n CheckpointFormatVersion[CheckpointFormatVersion[\"V2\"] = 2] = \"V2\";\n })(CheckpointFormatVersion = SaverDef.CheckpointFormatVersion || (SaverDef.CheckpointFormatVersion = {}));\n})(SaverDef || (SaverDef = {}));\n//# sourceMappingURL=data:application/json;base64,{"version":3,"file":"compiled_api.js","sourceRoot":"","sources":["../../../../../../tfjs-converter/src/data/compiled_api.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;;GAgBG;AAaH,qBAAqB;AACrB,MAAM,CAAN,IAAY,QAwDX;AAxDD,WAAY,QAAQ;IAClB,qEAAqE;IACrE,oBAAoB;IACpB,mDAAc,CAAA;IAEd,6DAA6D;IAC7D,sBAAsB;IACtB,+CAAY,CAAA;IACZ,iDAAa,CAAA;IACb,+CAAY,CAAA;IACZ,+CAAY,CAAA;IACZ,+CAAY,CAAA;IACZ,6CAAW,CAAA;IACX,iDAAa,CAAA;IACb,uDAAgB,CAAA;IAChB,+CAAY,CAAA;IACZ,8CAAY,CAAA;IACZ,gDAAa,CAAA;IACb,kDAAc,CAAA;IACd,kDAAc,CAAA;IACd,sDAAgB,CAAA;IAChB,kDAAc,CAAA;IACd,oDAAe,CAAA;IACf,kDAAc,CAAA;IACd,0DAAkB,CAAA;IAClB,8CAAY,CAAA;IACZ,sDAAgB,CAAA;IAChB,oDAAe,CAAA;IACf,kDAAc,CAAA;IACd,kDAAc,CAAA;IAEd,gEAAgE;IAChE,oEAAoE;IACpE,yDAAkB,CAAA;IAClB,2DAAmB,CAAA;IACnB,yDAAkB,CAAA;IAClB,yDAAkB,CAAA;IAClB,yDAAkB,CAAA;IAClB,uDAAiB,CAAA;IACjB,2DAAmB,CAAA;IACnB,iEAAsB,CAAA;IACtB,yDAAkB,CAAA;IAClB,uDAAiB,CAAA;IACjB,yDAAkB,CAAA;IAClB,2DAAmB,CAAA;IACnB,2DAAmB,CAAA;IACnB,+DAAqB,CAAA;IACrB,2DAAmB,CAAA;IACnB,6DAAoB,CAAA;IACpB,2DAAmB,CAAA;IACnB,mEAAuB,CAAA;IACvB,uDAAiB,CAAA;IACjB,+DAAqB,CAAA;IACrB,6DAAoB,CAAA;IACpB,2DAAmB,CAAA;IACnB,2DAAmB,CAAA;AACrB,CAAC,EAxDW,QAAQ,KAAR,QAAQ,QAwDnB;AA2PD,MAAM,KAAW,QAAQ,CAGxB;AAHD,WAAiB,QAAQ;IACvB,oCAAoC;IACpC,IAAY,uBAA0D;IAAtE,WAAY,uBAAuB;QAAE,yEAAY,CAAA;QAAE,iEAAQ,CAAA;QAAE,iEAAQ,CAAA;IAAA,CAAC,EAA1D,uBAAuB,GAAvB,gCAAuB,KAAvB,gCAAuB,QAAmC;AACxE,CAAC,EAHgB,QAAQ,KAAR,QAAQ,QAGxB","sourcesContent":["/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n\n/* tslint:disable */\n\n/** Properties of an Any. */\nexport declare interface IAny {\n  /** Any typeUrl */\n  typeUrl?: (string|null);\n\n  /** Any value */\n  value?: (Uint8Array|null);\n}\n\n/** DataType enum. */\nexport enum DataType {\n  // Not a legal value for DataType.  Used to indicate a DataType field\n  // has not been set.\n  DT_INVALID = 0,\n\n  // Data types that all computation devices are expected to be\n  // capable to support.\n  DT_FLOAT = 1,\n  DT_DOUBLE = 2,\n  DT_INT32 = 3,\n  DT_UINT8 = 4,\n  DT_INT16 = 5,\n  DT_INT8 = 6,\n  DT_STRING = 7,\n  DT_COMPLEX64 = 8,  // Single-precision complex\n  DT_INT64 = 9,\n  DT_BOOL = 10,\n  DT_QINT8 = 11,     // Quantized int8\n  DT_QUINT8 = 12,    // Quantized uint8\n  DT_QINT32 = 13,    // Quantized int32\n  DT_BFLOAT16 = 14,  // Float32 truncated to 16 bits.  Only for cast ops.\n  DT_QINT16 = 15,    // Quantized int16\n  DT_QUINT16 = 16,   // Quantized uint16\n  DT_UINT16 = 17,\n  DT_COMPLEX128 = 18,  // Double-precision complex\n  DT_HALF = 19,\n  DT_RESOURCE = 20,\n  DT_VARIANT = 21,  // Arbitrary C++ data types\n  DT_UINT32 = 22,\n  DT_UINT64 = 23,\n\n  // Do not use!  These are only for parameters.  Every enum above\n  // should have a corresponding value below (verified by types_test).\n  DT_FLOAT_REF = 101,\n  DT_DOUBLE_REF = 102,\n  DT_INT32_REF = 103,\n  DT_UINT8_REF = 104,\n  DT_INT16_REF = 105,\n  DT_INT8_REF = 106,\n  DT_STRING_REF = 107,\n  DT_COMPLEX64_REF = 108,\n  DT_INT64_REF = 109,\n  DT_BOOL_REF = 110,\n  DT_QINT8_REF = 111,\n  DT_QUINT8_REF = 112,\n  DT_QINT32_REF = 113,\n  DT_BFLOAT16_REF = 114,\n  DT_QINT16_REF = 115,\n  DT_QUINT16_REF = 116,\n  DT_UINT16_REF = 117,\n  DT_COMPLEX128_REF = 118,\n  DT_HALF_REF = 119,\n  DT_RESOURCE_REF = 120,\n  DT_VARIANT_REF = 121,\n  DT_UINT32_REF = 122,\n  DT_UINT64_REF = 123,\n}\n\n/** Properties of a TensorShape. */\nexport declare interface ITensorShape {\n  /** TensorShape dim */\n  dim?: (TensorShape.IDim[]|null);\n\n  /** TensorShape unknownRank */\n  unknownRank?: (boolean|null);\n}\n\nexport namespace TensorShape {\n  /** Properties of a Dim. */\n  export declare interface IDim {\n    /** Dim size */\n    size?: (number|string|null);\n\n    /** Dim name */\n    name?: (string|null);\n  }\n}\n\n/** Properties of a Tensor. */\nexport declare interface ITensor {\n  /** Tensor dtype */\n  dtype?: (DataType|null);\n\n  /** Tensor tensorShape */\n  tensorShape?: (ITensorShape|null);\n\n  /** Tensor versionNumber */\n  versionNumber?: (number|null);\n\n  /** Tensor tensorContent */\n  tensorContent?: (Uint8Array|null);\n\n  /** Tensor floatVal */\n  floatVal?: (number[]|null);\n\n  /** Tensor doubleVal */\n  doubleVal?: (number[]|null);\n\n  /** Tensor intVal */\n  intVal?: (number[]|null);\n\n  /** Tensor stringVal */\n  stringVal?: (Uint8Array[]|null);\n\n  /** Tensor scomplexVal */\n  scomplexVal?: (number[]|null);\n\n  /** Tensor int64Val */\n  int64Val?: ((number | string)[]|null);\n\n  /** Tensor boolVal */\n  boolVal?: (boolean[]|null);\n\n  /** Tensor uint32Val */\n  uint32Val?: (number[]|null);\n\n  /** Tensor uint64Val */\n  uint64Val?: ((number | string)[]|null);\n}\n\n/** Properties of an AttrValue. */\nexport declare interface IAttrValue {\n  /** AttrValue list */\n  list?: (AttrValue.IListValue|null);\n\n  /** AttrValue s */\n  s?: (string|null);\n\n  /** AttrValue i */\n  i?: (number|string|null);\n\n  /** AttrValue f */\n  f?: (number|null);\n\n  /** AttrValue b */\n  b?: (boolean|null);\n\n  /** AttrValue type */\n  type?: (DataType|null);\n\n  /** AttrValue shape */\n  shape?: (ITensorShape|null);\n\n  /** AttrValue tensor */\n  tensor?: (ITensor|null);\n\n  /** AttrValue placeholder */\n  placeholder?: (string|null);\n\n  /** AttrValue func */\n  func?: (INameAttrList|null);\n}\n\nexport namespace AttrValue {\n  /** Properties of a ListValue. */\n  export declare interface IListValue {\n    /** ListValue s */\n    s?: (string[]|null);\n\n    /** ListValue i */\n    i?: ((number | string)[]|null);\n\n    /** ListValue f */\n    f?: (number[]|null);\n\n    /** ListValue b */\n    b?: (boolean[]|null);\n\n    /** ListValue type */\n    type?: (DataType[]|null);\n\n    /** ListValue shape */\n    shape?: (ITensorShape[]|null);\n\n    /** ListValue tensor */\n    tensor?: (ITensor[]|null);\n\n    /** ListValue func */\n    func?: (INameAttrList[]|null);\n  }\n}\n\n/** Properties of a NameAttrList. */\nexport declare interface INameAttrList {\n  /** NameAttrList name */\n  name?: (string|null);\n\n  /** NameAttrList attr */\n  attr?: ({[k: string]: IAttrValue}|null);\n}\n\n/** Properties of a NodeDef. */\nexport declare interface INodeDef {\n  /** NodeDef name */\n  name?: (string|null);\n\n  /** NodeDef op */\n  op?: (string|null);\n\n  /** NodeDef input */\n  input?: (string[]|null);\n\n  /** NodeDef device */\n  device?: (string|null);\n\n  /** NodeDef attr */\n  attr?: ({[k: string]: IAttrValue}|null);\n}\n\n/** Properties of a VersionDef. */\nexport declare interface IVersionDef {\n  /** VersionDef producer */\n  producer?: (number|null);\n\n  /** VersionDef minConsumer */\n  minConsumer?: (number|null);\n\n  /** VersionDef badConsumers */\n  badConsumers?: (number[]|null);\n}\n\n/** Properties of a GraphDef. */\nexport declare interface IGraphDef {\n  /** GraphDef node */\n  node?: (INodeDef[]|null);\n\n  /** GraphDef versions */\n  versions?: (IVersionDef|null);\n\n  /** GraphDef library */\n  library?: (IFunctionDefLibrary|null);\n}\n\n/** Properties of a CollectionDef. */\nexport declare interface ICollectionDef {\n  /** CollectionDef nodeList */\n  nodeList?: (CollectionDef.INodeList|null);\n\n  /** CollectionDef bytesList */\n  bytesList?: (CollectionDef.IBytesList|null);\n\n  /** CollectionDef int64List */\n  int64List?: (CollectionDef.IInt64List|null);\n\n  /** CollectionDef floatList */\n  floatList?: (CollectionDef.IFloatList|null);\n\n  /** CollectionDef anyList */\n  anyList?: (CollectionDef.IAnyList|null);\n}\n\nexport namespace CollectionDef {\n  /** Properties of a NodeList. */\n  export declare interface INodeList {\n    /** NodeList value */\n    value?: (string[]|null);\n  }\n\n  /** Properties of a BytesList. */\n  export declare interface IBytesList {\n    /** BytesList value */\n    value?: (Uint8Array[]|null);\n  }\n\n  /** Properties of an Int64List. */\n  export declare interface IInt64List {\n    /** Int64List value */\n    value?: ((number | string)[]|null);\n  }\n\n  /** Properties of a FloatList. */\n  export declare interface IFloatList {\n    /** FloatList value */\n    value?: (number[]|null);\n  }\n\n  /** Properties of an AnyList. */\n  export declare interface IAnyList {\n    /** AnyList value */\n    value?: (IAny[]|null);\n  }\n}\n\n/** Properties of a SaverDef. */\nexport declare interface ISaverDef {\n  /** SaverDef filenameTensorName */\n  filenameTensorName?: (string|null);\n\n  /** SaverDef saveTensorName */\n  saveTensorName?: (string|null);\n\n  /** SaverDef restoreOpName */\n  restoreOpName?: (string|null);\n\n  /** SaverDef maxToKeep */\n  maxToKeep?: (number|null);\n\n  /** SaverDef sharded */\n  sharded?: (boolean|null);\n\n  /** SaverDef keepCheckpointEveryNHours */\n  keepCheckpointEveryNHours?: (number|null);\n\n  /** SaverDef version */\n  version?: (SaverDef.CheckpointFormatVersion|null);\n}\n\nexport namespace SaverDef {\n  /** CheckpointFormatVersion enum. */\n  export enum CheckpointFormatVersion {'LEGACY' = 0, 'V1' = 1, 'V2' = 2}\n}\n\n/** Properties of a TensorInfo. */\nexport declare interface ITensorInfo {\n  /** TensorInfo name */\n  name?: (string|null);\n\n  /** TensorInfo cooSparse */\n  cooSparse?: (TensorInfo.ICooSparse|null);\n\n  /** TensorInfo dtype */\n  dtype?: (DataType|null);\n\n  /** TensorInfo tensorShape */\n  tensorShape?: (ITensorShape|null);\n}\n\nexport namespace TensorInfo {\n  /** Properties of a CooSparse. */\n  export declare interface ICooSparse {\n    /** CooSparse valuesTensorName */\n    valuesTensorName?: (string|null);\n\n    /** CooSparse indicesTensorName */\n    indicesTensorName?: (string|null);\n\n    /** CooSparse denseShapeTensorName */\n    denseShapeTensorName?: (string|null);\n  }\n}\n\n/** Properties of a SignatureDef. */\nexport declare interface ISignatureDef {\n  /** SignatureDef inputs */\n  inputs?: ({[k: string]: ITensorInfo}|null);\n\n  /** SignatureDef outputs */\n  outputs?: ({[k: string]: ITensorInfo}|null);\n\n  /** SignatureDef methodName */\n  methodName?: (string|null);\n}\n\n/** Properties of an AssetFileDef. */\nexport declare interface IAssetFileDef {\n  /** AssetFileDef tensorInfo */\n  tensorInfo?: (ITensorInfo|null);\n\n  /** AssetFileDef filename */\n  filename?: (string|null);\n}\n\n/** Properties of an OpDef. */\nexport declare interface IOpDef {\n  /** OpDef name */\n  name?: (string|null);\n\n  /** OpDef inputArg */\n  inputArg?: (OpDef.IArgDef[]|null);\n\n  /** OpDef outputArg */\n  outputArg?: (OpDef.IArgDef[]|null);\n\n  /** OpDef attr */\n  attr?: (OpDef.IAttrDef[]|null);\n\n  /** OpDef deprecation */\n  deprecation?: (OpDef.IOpDeprecation|null);\n\n  /** OpDef summary */\n  summary?: (string|null);\n\n  /** OpDef description */\n  description?: (string|null);\n\n  /** OpDef isCommutative */\n  isCommutative?: (boolean|null);\n\n  /** OpDef isAggregate */\n  isAggregate?: (boolean|null);\n\n  /** OpDef isStateful */\n  isStateful?: (boolean|null);\n\n  /** OpDef allowsUninitializedInput */\n  allowsUninitializedInput?: (boolean|null);\n}\n\nexport namespace OpDef {\n  /** Properties of an ArgDef. */\n  export declare interface IArgDef {\n    /** ArgDef name */\n    name?: (string|null);\n\n    /** ArgDef description */\n    description?: (string|null);\n\n    /** ArgDef type */\n    type?: (DataType|null);\n\n    /** ArgDef typeAttr */\n    typeAttr?: (string|null);\n\n    /** ArgDef numberAttr */\n    numberAttr?: (string|null);\n\n    /** ArgDef typeListAttr */\n    typeListAttr?: (string|null);\n\n    /** ArgDef isRef */\n    isRef?: (boolean|null);\n  }\n\n  /** Properties of an AttrDef. */\n  export declare interface IAttrDef {\n    /** AttrDef name */\n    name?: (string|null);\n\n    /** AttrDef type */\n    type?: (string|null);\n\n    /** AttrDef defaultValue */\n    defaultValue?: (IAttrValue|null);\n\n    /** AttrDef description */\n    description?: (string|null);\n\n    /** AttrDef hasMinimum */\n    hasMinimum?: (boolean|null);\n\n    /** AttrDef minimum */\n    minimum?: (number|string|null);\n\n    /** AttrDef allowedValues */\n    allowedValues?: (IAttrValue|null);\n  }\n\n  /** Properties of an OpDeprecation. */\n  export declare interface IOpDeprecation {\n    /** OpDeprecation version */\n    version?: (number|null);\n\n    /** OpDeprecation explanation */\n    explanation?: (string|null);\n  }\n}\n\n/** Properties of an OpList. */\nexport declare interface IOpList {\n  /** OpList op */\n  op?: (IOpDef[]|null);\n}\n\n/** Properties of a MetaGraphDef. */\nexport declare interface IMetaGraphDef {\n  /** MetaGraphDef metaInfoDef */\n  metaInfoDef?: (MetaGraphDef.IMetaInfoDef|null);\n\n  /** MetaGraphDef graphDef */\n  graphDef?: (IGraphDef|null);\n\n  /** MetaGraphDef saverDef */\n  saverDef?: (ISaverDef|null);\n\n  /** MetaGraphDef collectionDef */\n  collectionDef?: ({[k: string]: ICollectionDef}|null);\n\n  /** MetaGraphDef signatureDef */\n  signatureDef?: ({[k: string]: ISignatureDef}|null);\n\n  /** MetaGraphDef assetFileDef */\n  assetFileDef?: (IAssetFileDef[]|null);\n}\n\nexport namespace MetaGraphDef {\n  /** Properties of a MetaInfoDef. */\n  export declare interface IMetaInfoDef {\n    /** MetaInfoDef metaGraphVersion */\n    metaGraphVersion?: (string|null);\n\n    /** MetaInfoDef strippedOpList */\n    strippedOpList?: (IOpList|null);\n\n    /** MetaInfoDef anyInfo */\n    anyInfo?: (IAny|null);\n\n    /** MetaInfoDef tags */\n    tags?: (string[]|null);\n\n    /** MetaInfoDef tensorflowVersion */\n    tensorflowVersion?: (string|null);\n\n    /** MetaInfoDef tensorflowGitVersion */\n    tensorflowGitVersion?: (string|null);\n  }\n}\n\n/** Properties of a SavedModel. */\nexport declare interface ISavedModel {\n  /** SavedModel savedModelSchemaVersion */\n  savedModelSchemaVersion?: (number|string|null);\n\n  /** SavedModel metaGraphs */\n  metaGraphs?: (IMetaGraphDef[]|null);\n}\n\n/** Properties of a FunctionDefLibrary. */\nexport declare interface IFunctionDefLibrary {\n  /** FunctionDefLibrary function */\n  'function'?: (IFunctionDef[]|null);\n\n  /** FunctionDefLibrary gradient */\n  gradient?: (IGradientDef[]|null);\n}\n\n/** Properties of a FunctionDef. */\nexport declare interface IFunctionDef {\n  /** FunctionDef signature */\n  signature?: (IOpDef|null);\n\n  /** FunctionDef attr */\n  attr?: ({[k: string]: IAttrValue}|null);\n\n  /** FunctionDef nodeDef */\n  nodeDef?: (INodeDef[]|null);\n\n  /** FunctionDef ret */\n  ret?: ({[k: string]: string}|null);\n}\n\n/** Properties of a GradientDef. */\nexport declare interface IGradientDef {\n  /** GradientDef functionName */\n  functionName?: (string|null);\n\n  /** GradientDef gradientFunc */\n  gradientFunc?: (string|null);\n}\n"]}","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Inserts a value into a sorted array. This method allows duplicate, meaning it\n * allows inserting duplicate value, in which case, the element will be inserted\n * at the lowest index of the value.\n * @param arr The array to modify.\n * @param element The element to insert.\n * @param comparator Optional. If no comparator is specified, elements are\n * compared using array_util.defaultComparator, which is suitable for Strings\n * and Numbers in ascending arrays. If the array contains multiple instances of\n * the target value, the left-most instance will be returned. To provide a\n * comparator, it should take 2 arguments to compare and return a negative,\n * zero, or a positive number.\n */\nexport function binaryInsert(arr, element, comparator) {\n const index = binarySearch(arr, element, comparator);\n const insertionPoint = index < 0 ? -(index + 1) : index;\n arr.splice(insertionPoint, 0, element);\n}\n/**\n * Searches the array for the target using binary search, returns the index\n * of the found element, or position to insert if element not found. If no\n * comparator is specified, elements are compared using array_\n * util.defaultComparator, which is suitable for Strings and Numbers in\n * ascending arrays. If the array contains multiple instances of the target\n * value, the left-most instance will be returned.\n * @param arr The array to be searched in.\n * @param target The target to be searched for.\n * @param comparator Should take 2 arguments to compare and return a negative,\n * zero, or a positive number.\n * @return Lowest index of the target value if found, otherwise the insertion\n * point where the target should be inserted, in the form of\n * (-insertionPoint - 1).\n */\nexport function binarySearch(arr, target, comparator) {\n return binarySearch_(arr, target, comparator || defaultComparator);\n}\n/**\n * Compares its two arguments for order.\n * @param a The first element to be compared.\n * @param b The second element to be compared.\n * @return A negative number, zero, or a positive number as the first\n * argument is less than, equal to, or greater than the second.\n */\nfunction defaultComparator(a, b) {\n return a > b ? 1 : a < b ? -1 : 0;\n}\nfunction binarySearch_(arr, target, comparator) {\n let left = 0;\n let right = arr.length;\n let middle = 0;\n let found = false;\n while (left < right) {\n middle = left + ((right - left) >>> 1);\n const compareResult = comparator(target, arr[middle]);\n if (compareResult > 0) {\n left = middle + 1;\n }\n else {\n right = middle;\n // If compareResult is 0, the value is found. We record it is found,\n // and then keep looking because there may be duplicate.\n found = !compareResult;\n }\n }\n return found ? left : -left - 1;\n}\n//# 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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { binaryInsert } from './non_max_suppression_util';\nexport function nonMaxSuppressionV3Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {\n return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, 0 /* softNmsSigma */);\n}\nexport function nonMaxSuppressionV4Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize) {\n return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, 0 /* softNmsSigma */, false /* returnScoresTensor */, padToMaxOutputSize /* padToMaxOutputSize */, true\n /* returnValidOutputs */ );\n}\nexport function nonMaxSuppressionV5Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {\n return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, true /* returnScoresTensor */);\n}\nfunction nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, returnScoresTensor = false, padToMaxOutputSize = false, returnValidOutputs = false) {\n // The list is sorted in ascending order, so that we can always pop the\n // candidate with the largest score in O(1) time.\n const candidates = [];\n for (let i = 0; i < scores.length; i++) {\n if (scores[i] > scoreThreshold) {\n candidates.push({ score: scores[i], boxIndex: i, suppressBeginIndex: 0 });\n }\n }\n candidates.sort(ascendingComparator);\n // If softNmsSigma is 0, the outcome of this algorithm is exactly same as\n // before.\n const scale = softNmsSigma > 0 ? (-0.5 / softNmsSigma) : 0.0;\n const selectedIndices = [];\n const selectedScores = [];\n while (selectedIndices.length < maxOutputSize && candidates.length > 0) {\n const candidate = candidates.pop();\n const { score: originalScore, boxIndex, suppressBeginIndex } = candidate;\n if (originalScore < scoreThreshold) {\n break;\n }\n // Overlapping boxes are likely to have similar scores, therefore we\n // iterate through the previously selected boxes backwards in order to\n // see if candidate's score should be suppressed. We use\n // suppressBeginIndex to track and ensure a candidate can be suppressed\n // by a selected box no more than once. Also, if the overlap exceeds\n // iouThreshold, we simply ignore the candidate.\n let ignoreCandidate = false;\n for (let j = selectedIndices.length - 1; j >= suppressBeginIndex; --j) {\n const iou = intersectionOverUnion(boxes, boxIndex, selectedIndices[j]);\n if (iou >= iouThreshold) {\n ignoreCandidate = true;\n break;\n }\n candidate.score =\n candidate.score * suppressWeight(iouThreshold, scale, iou);\n if (candidate.score <= scoreThreshold) {\n break;\n }\n }\n // At this point, if `candidate.score` has not dropped below\n // `scoreThreshold`, then we know that we went through all of the\n // previous selections and can safely update `suppressBeginIndex` to the\n // end of the selected array. Then we can re-insert the candidate with\n // the updated score and suppressBeginIndex back in the candidate list.\n // If on the other hand, `candidate.score` has dropped below the score\n // threshold, we will not add it back to the candidates list.\n candidate.suppressBeginIndex = selectedIndices.length;\n if (!ignoreCandidate) {\n // Candidate has passed all the tests, and is not suppressed, so\n // select the candidate.\n if (candidate.score === originalScore) {\n selectedIndices.push(boxIndex);\n selectedScores.push(candidate.score);\n }\n else if (candidate.score > scoreThreshold) {\n // Candidate's score is suppressed but is still high enough to be\n // considered, so add back to the candidates list.\n binaryInsert(candidates, candidate, ascendingComparator);\n }\n }\n }\n // NonMaxSuppressionV4 feature: padding output to maxOutputSize.\n const validOutputs = selectedIndices.length;\n const elemsToPad = maxOutputSize - validOutputs;\n if (padToMaxOutputSize && elemsToPad > 0) {\n selectedIndices.push(...new Array(elemsToPad).fill(0));\n selectedScores.push(...new Array(elemsToPad).fill(0.0));\n }\n const result = { selectedIndices };\n if (returnScoresTensor) {\n result['selectedScores'] = selectedScores;\n }\n if (returnValidOutputs) {\n result['validOutputs'] = validOutputs;\n }\n return result;\n}\nfunction intersectionOverUnion(boxes, i, j) {\n const iCoord = boxes.subarray(i * 4, i * 4 + 4);\n const jCoord = boxes.subarray(j * 4, j * 4 + 4);\n const yminI = Math.min(iCoord[0], iCoord[2]);\n const xminI = Math.min(iCoord[1], iCoord[3]);\n const ymaxI = Math.max(iCoord[0], iCoord[2]);\n const xmaxI = Math.max(iCoord[1], iCoord[3]);\n const yminJ = Math.min(jCoord[0], jCoord[2]);\n const xminJ = Math.min(jCoord[1], jCoord[3]);\n const ymaxJ = Math.max(jCoord[0], jCoord[2]);\n const xmaxJ = Math.max(jCoord[1], jCoord[3]);\n const areaI = (ymaxI - yminI) * (xmaxI - xminI);\n const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);\n if (areaI <= 0 || areaJ <= 0) {\n return 0.0;\n }\n const intersectionYmin = Math.max(yminI, yminJ);\n const intersectionXmin = Math.max(xminI, xminJ);\n const intersectionYmax = Math.min(ymaxI, ymaxJ);\n const intersectionXmax = Math.min(xmaxI, xmaxJ);\n const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0) *\n Math.max(intersectionXmax - intersectionXmin, 0.0);\n return intersectionArea / (areaI + areaJ - intersectionArea);\n}\n// A Gaussian penalty function, this method always returns values in [0, 1].\n// The weight is a function of similarity, the more overlap two boxes are, the\n// smaller the weight is, meaning highly overlapping boxe will be significantly\n// penalized. On the other hand, a non-overlapping box will not be penalized.\nfunction suppressWeight(iouThreshold, scale, iou) {\n const weight = Math.exp(scale * iou * iou);\n return iou <= iouThreshold ? weight : 0.0;\n}\nfunction ascendingComparator(c1, c2) {\n // For objects with same scores, we make the object with the larger index go\n // first. In an array that pops from the end, this means that the object with\n // the smaller index will be popped first. This ensures the same output as\n // the TensorFlow python version.\n return (c1.score - c2.score) ||\n ((c1.score === c2.score) && (c2.boxIndex - c1.boxIndex));\n}\n//# 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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {TypedArray} from '../types';\nimport {binaryInsert} from './non_max_suppression_util';\n\n/**\n * Implementation of the NonMaxSuppression kernel shared between webgl and cpu.\n */\ninterface Candidate {\n  score: number;\n  boxIndex: number;\n  suppressBeginIndex: number;\n}\n\ninterface NonMaxSuppressionResult {\n  selectedIndices: number[];\n  selectedScores?: number[];\n  validOutputs?: number;\n}\n\nexport function nonMaxSuppressionV3Impl(\n    boxes: TypedArray, scores: TypedArray, maxOutputSize: number,\n    iouThreshold: number, scoreThreshold: number): NonMaxSuppressionResult {\n  return nonMaxSuppressionImpl_(\n      boxes, scores, maxOutputSize, iouThreshold, scoreThreshold,\n      0 /* softNmsSigma */);\n}\n\nexport function nonMaxSuppressionV4Impl(\n    boxes: TypedArray, scores: TypedArray, maxOutputSize: number,\n    iouThreshold: number, scoreThreshold: number,\n    padToMaxOutputSize: boolean): NonMaxSuppressionResult {\n  return nonMaxSuppressionImpl_(\n      boxes, scores, maxOutputSize, iouThreshold, scoreThreshold,\n      0 /* softNmsSigma */, false /* returnScoresTensor */,\n      padToMaxOutputSize /* padToMaxOutputSize */, true\n      /* returnValidOutputs */);\n}\n\nexport function nonMaxSuppressionV5Impl(\n    boxes: TypedArray, scores: TypedArray, maxOutputSize: number,\n    iouThreshold: number, scoreThreshold: number,\n    softNmsSigma: number): NonMaxSuppressionResult {\n  return nonMaxSuppressionImpl_(\n      boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma,\n      true /* returnScoresTensor */);\n}\n\nfunction nonMaxSuppressionImpl_(\n    boxes: TypedArray, scores: TypedArray, maxOutputSize: number,\n    iouThreshold: number, scoreThreshold: number, softNmsSigma: number,\n    returnScoresTensor = false, padToMaxOutputSize = false,\n    returnValidOutputs = false): NonMaxSuppressionResult {\n  // The list is sorted in ascending order, so that we can always pop the\n  // candidate with the largest score in O(1) time.\n  const candidates = [];\n\n  for (let i = 0; i < scores.length; i++) {\n    if (scores[i] > scoreThreshold) {\n      candidates.push({score: scores[i], boxIndex: i, suppressBeginIndex: 0});\n    }\n  }\n\n  candidates.sort(ascendingComparator);\n\n  // If softNmsSigma is 0, the outcome of this algorithm is exactly same as\n  // before.\n  const scale = softNmsSigma > 0 ? (-0.5 / softNmsSigma) : 0.0;\n\n  const selectedIndices: number[] = [];\n  const selectedScores: number[] = [];\n\n  while (selectedIndices.length < maxOutputSize && candidates.length > 0) {\n    const candidate = candidates.pop();\n    const {score: originalScore, boxIndex, suppressBeginIndex} = candidate;\n\n    if (originalScore < scoreThreshold) {\n      break;\n    }\n\n    // Overlapping boxes are likely to have similar scores, therefore we\n    // iterate through the previously selected boxes backwards in order to\n    // see if candidate's score should be suppressed. We use\n    // suppressBeginIndex to track and ensure a candidate can be suppressed\n    // by a selected box no more than once. Also, if the overlap exceeds\n    // iouThreshold, we simply ignore the candidate.\n    let ignoreCandidate = false;\n    for (let j = selectedIndices.length - 1; j >= suppressBeginIndex; --j) {\n      const iou = intersectionOverUnion(boxes, boxIndex, selectedIndices[j]);\n\n      if (iou >= iouThreshold) {\n        ignoreCandidate = true;\n        break;\n      }\n\n      candidate.score =\n          candidate.score * suppressWeight(iouThreshold, scale, iou);\n\n      if (candidate.score <= scoreThreshold) {\n        break;\n      }\n    }\n\n    // At this point, if `candidate.score` has not dropped below\n    // `scoreThreshold`, then we know that we went through all of the\n    // previous selections and can safely update `suppressBeginIndex` to the\n    // end of the selected array. Then we can re-insert the candidate with\n    // the updated score and suppressBeginIndex back in the candidate list.\n    // If on the other hand, `candidate.score` has dropped below the score\n    // threshold, we will not add it back to the candidates list.\n    candidate.suppressBeginIndex = selectedIndices.length;\n\n    if (!ignoreCandidate) {\n      // Candidate has passed all the tests, and is not suppressed, so\n      // select the candidate.\n      if (candidate.score === originalScore) {\n        selectedIndices.push(boxIndex);\n        selectedScores.push(candidate.score);\n      } else if (candidate.score > scoreThreshold) {\n        // Candidate's score is suppressed but is still high enough to be\n        // considered, so add back to the candidates list.\n        binaryInsert(candidates, candidate, ascendingComparator);\n      }\n    }\n  }\n\n  // NonMaxSuppressionV4 feature: padding output to maxOutputSize.\n  const validOutputs = selectedIndices.length;\n  const elemsToPad = maxOutputSize - validOutputs;\n\n  if (padToMaxOutputSize && elemsToPad > 0) {\n    selectedIndices.push(...new Array(elemsToPad).fill(0));\n    selectedScores.push(...new Array(elemsToPad).fill(0.0));\n  }\n\n  const result: NonMaxSuppressionResult = {selectedIndices};\n\n  if (returnScoresTensor) {\n    result['selectedScores'] = selectedScores;\n  }\n\n  if (returnValidOutputs) {\n    result['validOutputs'] = validOutputs;\n  }\n\n  return result;\n}\n\nfunction intersectionOverUnion(boxes: TypedArray, i: number, j: number) {\n  const iCoord = boxes.subarray(i * 4, i * 4 + 4);\n  const jCoord = boxes.subarray(j * 4, j * 4 + 4);\n  const yminI = Math.min(iCoord[0], iCoord[2]);\n  const xminI = Math.min(iCoord[1], iCoord[3]);\n  const ymaxI = Math.max(iCoord[0], iCoord[2]);\n  const xmaxI = Math.max(iCoord[1], iCoord[3]);\n  const yminJ = Math.min(jCoord[0], jCoord[2]);\n  const xminJ = Math.min(jCoord[1], jCoord[3]);\n  const ymaxJ = Math.max(jCoord[0], jCoord[2]);\n  const xmaxJ = Math.max(jCoord[1], jCoord[3]);\n  const areaI = (ymaxI - yminI) * (xmaxI - xminI);\n  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);\n  if (areaI <= 0 || areaJ <= 0) {\n    return 0.0;\n  }\n  const intersectionYmin = Math.max(yminI, yminJ);\n  const intersectionXmin = Math.max(xminI, xminJ);\n  const intersectionYmax = Math.min(ymaxI, ymaxJ);\n  const intersectionXmax = Math.min(xmaxI, xmaxJ);\n  const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0) *\n      Math.max(intersectionXmax - intersectionXmin, 0.0);\n  return intersectionArea / (areaI + areaJ - intersectionArea);\n}\n\n// A Gaussian penalty function, this method always returns values in [0, 1].\n// The weight is a function of similarity, the more overlap two boxes are, the\n// smaller the weight is, meaning highly overlapping boxe will be significantly\n// penalized. On the other hand, a non-overlapping box will not be penalized.\nfunction suppressWeight(iouThreshold: number, scale: number, iou: number) {\n  const weight = Math.exp(scale * iou * iou);\n  return iou <= iouThreshold ? weight : 0.0;\n}\n\nfunction ascendingComparator(c1: Candidate, c2: Candidate) {\n  // For objects with same scores, we make the object with the larger index go\n  // first. In an array that pops from the end, this means that the object with\n  // the smaller index will be popped first. This ensures the same output as\n  // the TensorFlow python version.\n  return (c1.score - c2.score) ||\n      ((c1.score === c2.score) && (c2.boxIndex - c1.boxIndex));\n}\n"]}","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv2D } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes a 2D convolution over the input x.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter, rank 4, of shape\n * `[filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n * - `same` and stride 1: output will be of same size as input,\n * regardless of filter size.\n * - `valid`: output will be smaller than input if filter is larger\n * than 1x1.\n * - For more info, see this guide:\n * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](\n * https://www.tensorflow.org/api_docs/python/tf/nn/convolution)\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n * \"NHWC\". Specify the data format of the input and output data. With the\n * default format \"NHWC\", the data is stored in the order of: [batch,\n * height, width, channels].\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n * in which we sample input values across the height and width dimensions\n * in atrous convolution. Defaults to `[1, 1]`. If `dilations` is a single\n * number, then `dilationHeight == dilationWidth`. If it is greater than\n * 1, then all values of `strides` must be 1.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n * provided, it will default to truncate.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv2d_(x, filter, strides, pad, dataFormat = 'NHWC', dilations = [1, 1], dimRoundingMode) {\n const $x = convertToTensor(x, 'x', 'conv2d', 'float32');\n const $filter = convertToTensor(filter, 'filter', 'conv2d', 'float32');\n let x4D = $x;\n let reshapedTo4D = false;\n if ($x.rank === 3) {\n reshapedTo4D = true;\n x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n }\n util.assert(x4D.rank === 4, () => `Error in conv2d: input must be rank 4, but got rank ${x4D.rank}.`);\n util.assert($filter.rank === 4, () => `Error in conv2d: filter must be rank 4, but got rank ` +\n `${$filter.rank}.`);\n conv_util.checkPadOnDimRoundingMode('conv2d', pad, dimRoundingMode);\n const inDepth = dataFormat === 'NHWC' ? x4D.shape[3] : x4D.shape[1];\n util.assert(inDepth === $filter.shape[2], () => `Error in conv2d: depth of input (${inDepth}) must match ` +\n `input depth for filter ${$filter.shape[2]}.`);\n util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in conv2D: Either strides or dilations must be 1. ' +\n `Got strides ${strides} and dilations '${dilations}'`);\n const inputs = { x: x4D, filter: $filter };\n const attrs = { strides, pad, dataFormat, dilations, dimRoundingMode };\n // tslint:disable-next-line: no-unnecessary-type-assertion\n const res = ENGINE.runKernel(Conv2D, inputs, attrs);\n if (reshapedTo4D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n }\n return res;\n}\nexport const conv2d = op({ conv2d_ });\n//# sourceMappingURL=data:application/json;base64,{"version":3,"file":"conv2d.js","sourceRoot":"","sources":["../../../../../../tfjs-core/src/ops/conv2d.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AACH,OAAO,EAAC,MAAM,EAAC,MAAM,WAAW,CAAC;AACjC,OAAO,EAAC,MAAM,EAA4B,MAAM,iBAAiB,CAAC;AAIlE,OAAO,EAAC,eAAe,EAAC,MAAM,oBAAoB,CAAC;AAEnD,OAAO,KAAK,IAAI,MAAM,SAAS,CAAC;AAEhC,OAAO,KAAK,SAAS,MAAM,aAAa,CAAC;AACzC,OAAO,EAAC,EAAE,EAAC,MAAM,aAAa,CAAC;AAC/B,OAAO,EAAC,OAAO,EAAC,MAAM,WAAW,CAAC;AAElC;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;GA+BG;AACH,SAAS,OAAO,CACZ,CAAe,EAAE,MAA2B,EAC5C,OAAgC,EAChC,GAAoD,EACpD,aAA4B,MAAM,EAClC,YAAqC,CAAC,CAAC,EAAE,CAAC,CAAC,EAC3C,eAAwC;IAC1C,MAAM,EAAE,GAAG,eAAe,CAAC,CAAC,EAAE,GAAG,EAAE,QAAQ,EAAE,SAAS,CAAC,CAAC;IACxD,MAAM,OAAO,GAAG,eAAe,CAAC,MAAM,EAAE,QAAQ,EAAE,QAAQ,EAAE,SAAS,CAAC,CAAC;IAEvE,IAAI,GAAG,GAAG,EAAc,CAAC;IACzB,IAAI,YAAY,GAAG,KAAK,CAAC;IAEzB,IAAI,EAAE,CAAC,IAAI,KAAK,CAAC,EAAE;QACjB,YAAY,GAAG,IAAI,CAAC;QACpB,GAAG,GAAG,OAAO,CAAC,EAAE,EAAE,CAAC,CAAC,EAAE,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;KAC/D;IAED,IAAI,CAAC,MAAM,CACP,GAAG,CAAC,IAAI,KAAK,CAAC,EACd,GAAG,EAAE,CAAC,uDAAuD,GAAG,CAAC,IAAI,GAAG,CAAC,CAAC;IAC9E,IAAI,CAAC,MAAM,CACP,OAAO,CAAC,IAAI,KAAK,CAAC,EAClB,GAAG,EAAE,CAAC,uDAAuD;QACzD,GAAG,OAAO,CAAC,IAAI,GAAG,CAAC,CAAC;IAC5B,SAAS,CAAC,yBAAyB,CAAC,QAAQ,EAAE,GAAG,EAAE,eAAe,CAAC,CAAC;IACpE,MAAM,OAAO,GAAG,UAAU,KAAK,MAAM,CAAC,CAAC,CAAC,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC;IACpE,IAAI,CAAC,MAAM,CACP,OAAO,KAAK,OAAO,CAAC,KAAK,CAAC,CAAC,CAAC,EAC5B,GAAG,EAAE,CAAC,oCAAoC,OAAO,eAAe;QAC5D,0BAA0B,OAAO,CAAC,KAAK,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC;IACvD,IAAI,CAAC,MAAM,CACP,SAAS,CAAC,8BAA8B,CAAC,OAAO,EAAE,SAAS,CAAC,EAC5D,GAAG,EAAE,CAAC,0DAA0D;QAC5D,eAAe,OAAO,mBAAmB,SAAS,GAAG,CAAC,CAAC;IAE/D,MAAM,MAAM,GAAiB,EAAC,CAAC,EAAE,GAAG,EAAE,MAAM,EAAE,OAAO,EAAC,CAAC;IACvD,MAAM,KAAK,GACO,EAAC,OAAO,EAAE,GAAG,EAAE,UAAU,EAAE,SAAS,EAAE,eAAe,EAAC,CAAC;IAEzE,0DAA0D;IAC1D,MAAM,GAAG,GAAG,MAAM,CAAC,SAAS,CACZ,MAAM,EAAE,MAA8B,EACtC,KAA2B,CAAM,CAAC;IAElD,IAAI,YAAY,EAAE;QAChB,OAAO,OAAO,CAAC,GAAG,EAAE,CAAC,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAM,CAAC;KACtE;IACD,OAAO,GAAG,CAAC;AACb,CAAC;AAED,MAAM,CAAC,MAAM,MAAM,GAAG,EAAE,CAAC,EAAC,OAAO,EAAC,CAAC,CAAC","sourcesContent":["/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\nimport {Conv2D, Conv2DAttrs, Conv2DInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor3D, Tensor4D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport * as conv_util from './conv_util';\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Computes a 2D convolution over the input x.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](\n *          https://www.tensorflow.org/api_docs/python/tf/nn/convolution)\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels].\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `dilations` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n *     provided, it will default to truncate.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv2d_<T extends Tensor3D|Tensor4D>(\n    x: T|TensorLike, filter: Tensor4D|TensorLike,\n    strides: [number, number]|number,\n    pad: 'valid'|'same'|number|conv_util.ExplicitPadding,\n    dataFormat: 'NHWC'|'NCHW' = 'NHWC',\n    dilations: [number, number]|number = [1, 1],\n    dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  const $x = convertToTensor(x, 'x', 'conv2d', 'float32');\n  const $filter = convertToTensor(filter, 'filter', 'conv2d', 'float32');\n\n  let x4D = $x as Tensor4D;\n  let reshapedTo4D = false;\n\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n\n  util.assert(\n      x4D.rank === 4,\n      () => `Error in conv2d: input must be rank 4, but got rank ${x4D.rank}.`);\n  util.assert(\n      $filter.rank === 4,\n      () => `Error in conv2d: filter must be rank 4, but got rank ` +\n          `${$filter.rank}.`);\n  conv_util.checkPadOnDimRoundingMode('conv2d', pad, dimRoundingMode);\n  const inDepth = dataFormat === 'NHWC' ? x4D.shape[3] : x4D.shape[1];\n  util.assert(\n      inDepth === $filter.shape[2],\n      () => `Error in conv2d: depth of input (${inDepth}) must match ` +\n          `input depth for filter ${$filter.shape[2]}.`);\n  util.assert(\n      conv_util.eitherStridesOrDilationsAreOne(strides, dilations),\n      () => 'Error in conv2D: Either strides or dilations must be 1. ' +\n          `Got strides ${strides} and dilations '${dilations}'`);\n\n  const inputs: Conv2DInputs = {x: x4D, filter: $filter};\n  const attrs:\n      Conv2DAttrs = {strides, pad, dataFormat, dilations, dimRoundingMode};\n\n  // tslint:disable-next-line: no-unnecessary-type-assertion\n  const res = ENGINE.runKernel(\n                  Conv2D, inputs as {} as NamedTensorMap,\n                  attrs as {} as NamedAttrMap) as T;\n\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as T;\n  }\n  return res;\n}\n\nexport const conv2d = op({conv2d_});\n"]}","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { GreaterEqual } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a >= b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.greaterEqual(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction greaterEqual_(a, b) {\n let $a = convertToTensor(a, 'a', 'greaterEqual', 'string_or_numeric');\n let $b = convertToTensor(b, 'b', 'greaterEqual', 'string_or_numeric');\n [$a, $b] = makeTypesMatch($a, $b);\n assertAndGetBroadcastShape($a.shape, $b.shape);\n const inputs = { a: $a, b: $b };\n return ENGINE.runKernel(GreaterEqual, inputs);\n}\nexport const greaterEqual = op({ greaterEqual_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { makeZerosTypedArray, sizeFromShape } from '../util';\nimport { complex } from './complex';\n/**\n * Creates a `tf.Tensor` with all elements set to 0.\n *\n * ```js\n * tf.zeros([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The type of an element in the resulting tensor. Can\n * be 'float32', 'int32' or 'bool'. Defaults to 'float'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function zeros(shape, dtype = 'float32') {\n if (dtype === 'complex64') {\n const real = zeros(shape, 'float32');\n const imag = zeros(shape, 'float32');\n return complex(real, imag);\n }\n const values = makeZerosTypedArray(sizeFromShape(shape), dtype);\n return ENGINE.makeTensor(values, shape, dtype);\n}\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Step } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes step of the input `tf.Tensor` element-wise: `x > 0 ? 1 : alpha * x`\n *\n * ```js\n * const x = tf.tensor1d([0, 2, -1, -3]);\n *\n * x.step(.5).print(); // or tf.step(x, .5)\n * ```\n * @param x The input tensor.\n * @param alpha The gradient when input is negative.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction step_(x, alpha = 0.0) {\n const $x = convertToTensor(x, 'x', 'step');\n const inputs = { x: $x };\n const attrs = { alpha };\n return ENGINE.runKernel(Step, inputs, attrs);\n}\nexport const step = op({ step_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates a `tf.Tensor` with the provided values, shape and dtype.\n *\n * ```js\n * // Pass an array of values to create a vector.\n * tf.tensor([1, 2, 3, 4]).print();\n * ```\n *\n * ```js\n * // Pass a nested array of values to make a matrix or a higher\n * // dimensional tensor.\n * tf.tensor([[1, 2], [3, 4]]).print();\n * ```\n *\n * ```js\n * // Pass a flat array and specify a shape yourself.\n * tf.tensor([1, 2, 3, 4], [2, 2]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n * or a flat array, or a `TypedArray`. If the values are strings,\n * they will be encoded as utf-8 and kept as `Uint8Array[]`.\n * @param shape The shape of the tensor. Optional. If not provided,\n * it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor(values, shape, dtype) {\n const inferredShape = inferShape(values, dtype);\n return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Tile } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Construct a tensor by repeating it the number of times given by reps.\n *\n * This operation creates a new tensor by replicating `input` `reps`\n * times. The output tensor's i'th dimension has `input.shape[i] *\n * reps[i]` elements, and the values of `input` are replicated\n * `reps[i]` times along the i'th dimension. For example, tiling\n * `[a, b, c, d]` by `[2]` produces `[a, b, c, d, a, b, c, d]`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n *\n * a.tile([2]).print(); // or a.tile([2])\n * ```\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * a.tile([1, 2]).print(); // or a.tile([1, 2])\n * ```\n * @param x The tensor to tile.\n * @param reps Determines the number of replications per dimension.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction tile_(x, reps) {\n const $x = convertToTensor(x, 'x', 'tile', 'string_or_numeric');\n util.assert($x.rank === reps.length, () => `Error in transpose: rank of input ${$x.rank} ` +\n `must match length of reps ${reps}.`);\n const inputs = { x: $x };\n const attrs = { reps };\n return ENGINE.runKernel(Tile, inputs, attrs);\n}\nexport const tile = op({ tile_ });\n//# 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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Max } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the maximum of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and an\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.max().print(); // or tf.max(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.max(axis).print(); // or tf.max(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension(s) to reduce. By default it reduces\n * all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction max_(x, axis = null, keepDims = false) {\n const $x = convertToTensor(x, 'x', 'max');\n const inputs = { x: $x };\n const attrs = { reductionIndices: axis, keepDims };\n return ENGINE.runKernel(Max, inputs, attrs);\n}\nexport const max = op({ max_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport var Rank;\n(function (Rank) {\n Rank[\"R0\"] = \"R0\";\n Rank[\"R1\"] = \"R1\";\n Rank[\"R2\"] = \"R2\";\n Rank[\"R3\"] = \"R3\";\n Rank[\"R4\"] = \"R4\";\n Rank[\"R5\"] = \"R5\";\n Rank[\"R6\"] = \"R6\";\n})(Rank || (Rank = {}));\n// Looks for upcasting types. Used, for example, in operations with mixed dtype\n// inputs.\nvar UpcastInt32AndMap;\n(function (UpcastInt32AndMap) {\n UpcastInt32AndMap[\"float32\"] = \"float32\";\n UpcastInt32AndMap[\"int32\"] = \"int32\";\n UpcastInt32AndMap[\"bool\"] = \"int32\";\n UpcastInt32AndMap[\"complex64\"] = \"complex64\";\n})(UpcastInt32AndMap || (UpcastInt32AndMap = {}));\nvar UpcastBoolAndMap;\n(function (UpcastBoolAndMap) {\n UpcastBoolAndMap[\"float32\"] = \"float32\";\n UpcastBoolAndMap[\"int32\"] = \"int32\";\n UpcastBoolAndMap[\"bool\"] = \"bool\";\n UpcastBoolAndMap[\"complex64\"] = \"complex64\";\n})(UpcastBoolAndMap || (UpcastBoolAndMap = {}));\nvar UpcastFloat32AndMap;\n(function (UpcastFloat32AndMap) {\n UpcastFloat32AndMap[\"float32\"] = \"float32\";\n UpcastFloat32AndMap[\"int32\"] = \"float32\";\n UpcastFloat32AndMap[\"bool\"] = \"float32\";\n UpcastFloat32AndMap[\"complex64\"] = \"complex64\";\n})(UpcastFloat32AndMap || (UpcastFloat32AndMap = {}));\nvar UpcastComplex64AndMap;\n(function (UpcastComplex64AndMap) {\n UpcastComplex64AndMap[\"float32\"] = \"complex64\";\n UpcastComplex64AndMap[\"int32\"] = \"complex64\";\n UpcastComplex64AndMap[\"bool\"] = \"complex64\";\n UpcastComplex64AndMap[\"complex64\"] = \"complex64\";\n})(UpcastComplex64AndMap || (UpcastComplex64AndMap = {}));\nconst upcastTypeMap = {\n 'float32': UpcastFloat32AndMap,\n 'int32': UpcastInt32AndMap,\n 'bool': UpcastBoolAndMap,\n 'complex64': UpcastComplex64AndMap\n};\nexport function upcastType(typeA, typeB) {\n if (typeA === 'string' || typeB === 'string') {\n if (typeA === 'string' && typeB === 'string') {\n return 'string';\n }\n throw new Error(`Can not upcast ${typeA} with ${typeB}`);\n }\n return upcastTypeMap[typeA][typeB];\n}\n/** Returns the output type after summation. */\nexport function sumOutType(type) {\n return upcastType(type, 'int32');\n}\n//# sourceMappingURL=data:application/json;base64,{"version":3,"file":"types.js","sourceRoot":"","sources":["../../../../../tfjs-core/src/types.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAiDH,MAAM,CAAN,IAAY,IAQX;AARD,WAAY,IAAI;IACd,iBAAS,CAAA;IACT,iBAAS,CAAA;IACT,iBAAS,CAAA;IACT,iBAAS,CAAA;IACT,iBAAS,CAAA;IACT,iBAAS,CAAA;IACT,iBAAS,CAAA;AACX,CAAC,EARW,IAAI,KAAJ,IAAI,QAQf;AAWD,+EAA+E;AAC/E,UAAU;AACV,IAAK,iBAKJ;AALD,WAAK,iBAAiB;IACpB,wCAAqB,CAAA;IACrB,oCAAiB,CAAA;IACjB,mCAAgB,CAAA;IAChB,4CAAyB,CAAA;AAC3B,CAAC,EALI,iBAAiB,KAAjB,iBAAiB,QAKrB;AAED,IAAK,gBAKJ;AALD,WAAK,gBAAgB;IACnB,uCAAqB,CAAA;IACrB,mCAAiB,CAAA;IACjB,iCAAe,CAAA;IACf,2CAAyB,CAAA;AAC3B,CAAC,EALI,gBAAgB,KAAhB,gBAAgB,QAKpB;AAED,IAAK,mBAKJ;AALD,WAAK,mBAAmB;IACtB,0CAAqB,CAAA;IACrB,wCAAmB,CAAA;IACnB,uCAAkB,CAAA;IAClB,8CAAyB,CAAA;AAC3B,CAAC,EALI,mBAAmB,KAAnB,mBAAmB,QAKvB;AAED,IAAK,qBAKJ;AALD,WAAK,qBAAqB;IACxB,8CAAuB,CAAA;IACvB,4CAAqB,CAAA;IACrB,2CAAoB,CAAA;IACpB,gDAAyB,CAAA;AAC3B,CAAC,EALI,qBAAqB,KAArB,qBAAqB,QAKzB;AAED,MAAM,aAAa,GAAG;IACpB,SAAS,EAAE,mBAAmB;IAC9B,OAAO,EAAE,iBAAiB;IAC1B,MAAM,EAAE,gBAAgB;IACxB,WAAW,EAAE,qBAAqB;CACnC,CAAC;AAEF,MAAM,UAAU,UAAU,CAAC,KAAe,EAAE,KAAe;IACzD,IAAI,KAAK,KAAK,QAAQ,IAAI,KAAK,KAAK,QAAQ,EAAE;QAC5C,IAAI,KAAK,KAAK,QAAQ,IAAI,KAAK,KAAK,QAAQ,EAAE;YAC5C,OAAO,QAAQ,CAAC;SACjB;QACD,MAAM,IAAI,KAAK,CAAC,kBAAkB,KAAK,SAAS,KAAK,EAAE,CAAC,CAAC;KAC1D;IACD,OAAO,aAAa,CAAC,KAAK,CAAC,CAAC,KAAK,CAAC,CAAC;AACrC,CAAC;AAED,+CAA+C;AAC/C,MAAM,UAAU,UAAU,CAAC,IAAc;IACvC,OAAO,UAAU,CAAC,IAAI,EAAE,OAAO,CAAC,CAAC;AACnC,CAAC","sourcesContent":["/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/** @docalias number[] */\nexport interface ShapeMap {\n  R0: number[];\n  R1: [number];\n  R2: [number, number];\n  R3: [number, number, number];\n  R4: [number, number, number, number];\n  R5: [number, number, number, number, number];\n  R6: [number, number, number, number, number, number];\n}\n\n/** @docalias number[] */\nexport interface ArrayMap {\n  R0: number;\n  R1: number[];\n  R2: number[][];\n  R3: number[][][];\n  R4: number[][][][];\n  R5: number[][][][][];\n  R6: number[][][][][][];\n}\n\nexport interface DataTypeMap {\n  float32: Float32Array;\n  int32: Int32Array;\n  bool: Uint8Array;\n  complex64: Float32Array;\n  string: string[];\n}\n\nexport interface SingleValueMap {\n  bool: boolean;\n  int32: number;\n  float32: number;\n  complex64: number;\n  string: string;\n}\n\n/** @docalias 'float32'|'int32'|'bool'|'complex64'|'string' */\nexport type DataType = keyof DataTypeMap;\nexport type NumericDataType = 'float32'|'int32'|'bool'|'complex64';\nexport type TypedArray = Float32Array|Int32Array|Uint8Array;\n/** Tensor data used in tensor creation and user-facing API. */\nexport type DataValues = DataTypeMap[DataType];\n/** The underlying tensor data that gets stored in a backend. */\nexport type BackendValues = Float32Array|Int32Array|Uint8Array|Uint8Array[];\n\nexport enum Rank {\n  R0 = 'R0',\n  R1 = 'R1',\n  R2 = 'R2',\n  R3 = 'R3',\n  R4 = 'R4',\n  R5 = 'R5',\n  R6 = 'R6'\n}\n\nexport type FlatVector = boolean[]|number[]|TypedArray;\nexport type RegularArray<T> =\n    T[]|T[][]|T[][][]|T[][][][]|T[][][][][]|T[][][][][][];\n\n// tslint:disable-next-line:no-any\nexport interface RecursiveArray<T extends any> {\n  [index: number]: T|RecursiveArray<T>;\n}\n\n// Looks for upcasting types. Used, for example, in operations with mixed dtype\n// inputs.\nenum UpcastInt32AndMap {\n  'float32' = 'float32',\n  'int32' = 'int32',\n  'bool' = 'int32',\n  'complex64' = 'complex64'\n}\n\nenum UpcastBoolAndMap {\n  'float32' = 'float32',\n  'int32' = 'int32',\n  'bool' = 'bool',\n  'complex64' = 'complex64'\n}\n\nenum UpcastFloat32AndMap {\n  'float32' = 'float32',\n  'int32' = 'float32',\n  'bool' = 'float32',\n  'complex64' = 'complex64'\n}\n\nenum UpcastComplex64AndMap {\n  'float32' = 'complex64',\n  'int32' = 'complex64',\n  'bool' = 'complex64',\n  'complex64' = 'complex64'\n}\n\nconst upcastTypeMap = {\n  'float32': UpcastFloat32AndMap,\n  'int32': UpcastInt32AndMap,\n  'bool': UpcastBoolAndMap,\n  'complex64': UpcastComplex64AndMap\n};\n\nexport function upcastType(typeA: DataType, typeB: DataType): DataType {\n  if (typeA === 'string' || typeB === 'string') {\n    if (typeA === 'string' && typeB === 'string') {\n      return 'string';\n    }\n    throw new Error(`Can not upcast ${typeA} with ${typeB}`);\n  }\n  return upcastTypeMap[typeA][typeB];\n}\n\n/** Returns the output type after summation. */\nexport function sumOutType(type: DataType): DataType {\n  return upcastType(type, 'int32');\n}\n\n/** @docalias TypedArray|Array */\nexport type TensorLike =\n    TypedArray|number|boolean|string|RecursiveArray<number|number[]|TypedArray>|\n    RecursiveArray<boolean>|RecursiveArray<string>|Uint8Array[];\nexport type ScalarLike = number|boolean|string|Uint8Array;\n/** @docalias TypedArray|Array */\nexport type TensorLike1D = TypedArray|number[]|boolean[]|string[]|Uint8Array[];\n/** @docalias TypedArray|Array */\nexport type TensorLike2D = TypedArray|number[]|number[][]|boolean[]|boolean[][]|\n    string[]|string[][]|Uint8Array[]|Uint8Array[][];\n/** @docalias TypedArray|Array */\nexport type TensorLike3D = TypedArray|number[]|number[][][]|boolean[]|\n    boolean[][][]|string[]|string[][][]|Uint8Array[]|Uint8Array[][][];\n/** @docalias TypedArray|Array */\nexport type TensorLike4D = TypedArray|number[]|number[][][][]|boolean[]|\n    boolean[][][][]|string[]|string[][][][]|Uint8Array[]|Uint8Array[][][][];\n/** @docalias TypedArray|Array */\nexport type TensorLike5D =\n    TypedArray|number[]|number[][][][][]|boolean[]|boolean[][][][][]|string[]|\n    string[][][][][]|Uint8Array[]|Uint8Array[][][][][];\n/** @docalias TypedArray|Array */\nexport type TensorLike6D =\n    TypedArray|number[]|number[][][][][][]|boolean[]|boolean[][][][][][]|\n    string[]|string[][][][][][]|Uint8Array[]|Uint8Array[][][][][];\n\n/** Type for representing image data in Uint8Array type. */\nexport interface PixelData {\n  width: number;\n  height: number;\n  data: Uint8Array;\n}\n"]}","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Fill } from '../kernel_names';\n/**\n * Creates a `tf.Tensor` filled with a scalar value.\n *\n * ```js\n * tf.fill([2, 2], 4).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param value The scalar value to fill the tensor with.\n * @param dtype The type of an element in the resulting tensor. Defaults to\n * 'float'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction fill(shape, value, dtype) {\n const attrs = { shape, value, dtype };\n return ENGINE.runKernel(Fill, {}, attrs);\n}\nexport { fill };\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Relu } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes rectified linear element-wise: `max(x, 0)`.\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.relu().print(); // or tf.relu(x)\n * ```\n * @param x The input tensor. If the dtype is `bool`, the output dtype will be\n * `int32'.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction relu_(x) {\n const $x = convertToTensor(x, 'x', 'relu');\n const inputs = { x: $x };\n return ENGINE.runKernel(Relu, inputs);\n}\nexport const relu = op({ relu_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Unpack } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Unstacks a `tf.Tensor` of rank-`R` into a list of rank-`(R-1)` `tf.Tensor`s.\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * tf.unstack(a).forEach(tensor => tensor.print());\n * ```\n *\n * @param x A tensor object.\n * @param axis The axis to unstack along. Defaults to 0 (the first dim).\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction unstack_(x, axis = 0) {\n const $x = convertToTensor(x, 'x', 'unstack', 'string_or_numeric');\n util.assert(axis >= -$x.shape.length && axis < $x.shape.length, () => `Axis = ${axis} is not in [-${$x.shape.length}, ${$x.shape.length})`);\n const inputs = { value: $x };\n const attrs = { axis };\n return ENGINE.runKernel(Unpack, inputs, attrs);\n}\nexport const unstack = op({ unstack_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer, util } from '@tensorflow/tfjs-core';\nexport function bincountImpl(xVals, weightsVals, weightsDtype, weightsShape, size) {\n const weightsSize = util.sizeFromShape(weightsShape);\n const outVals = util.makeZerosTypedArray(size, weightsDtype);\n for (let i = 0; i < xVals.length; i++) {\n const value = xVals[i];\n if (value < 0) {\n throw new Error('Input x must be non-negative!');\n }\n if (value >= size) {\n continue;\n }\n if (weightsSize > 0) {\n outVals[value] += weightsVals[i];\n }\n else {\n outVals[value] += 1;\n }\n }\n return outVals;\n}\nexport function bincountReduceImpl(xBuf, weightsBuf, size, binaryOutput = false) {\n const numRows = xBuf.shape[0];\n const numCols = xBuf.shape[1];\n const outBuf = buffer([numRows, size], weightsBuf.dtype);\n for (let i = 0; i < numRows; i++) {\n for (let j = 0; j < numCols; j++) {\n const value = xBuf.get(i, j);\n if (value < 0) {\n throw new Error('Input x must be non-negative!');\n }\n if (value >= size) {\n continue;\n }\n if (binaryOutput) {\n outBuf.set(1, i, value);\n }\n else {\n if (weightsBuf.size > 0) {\n outBuf.set(outBuf.get(i, value) + weightsBuf.get(i, j), i, value);\n }\n else {\n outBuf.set(outBuf.get(i, value) + 1, i, value);\n }\n }\n }\n }\n return outBuf;\n}\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sub } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc, createComplexBinaryKernelImpl } from '../utils/binary_utils';\nexport const subImpl = createSimpleBinaryKernelImpl(((aValue, bValue) => aValue - bValue));\nexport const subComplexImpl = createComplexBinaryKernelImpl(((aReal, aImag, bReal, bImag) => {\n return { real: aReal - bReal, imag: aImag - bImag };\n}));\nexport const sub = binaryKernelFunc(Sub, subImpl, subComplexImpl);\nexport const subConfig = {\n kernelName: Sub,\n backendName: 'cpu',\n kernelFunc: sub\n};\n//# 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{ reshape } from './reshape';\nexport function xAs4D(x) {\n let x4D;\n if (x.rank === 0 || x.rank === 1) {\n x4D = reshape(x, [1, 1, 1, x.size]);\n }\n else if (x.rank === 2) {\n x4D = reshape(x, [1, 1, x.shape[0], x.shape[1]]);\n }\n else if (x.rank === 3) {\n x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n }\n else {\n x4D = x;\n }\n return x4D;\n}\n//# 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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { FusedBatchNorm } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { xAs4D } from './batchnorm_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Batch normalization.\n *\n * As described in\n * [http://arxiv.org/abs/1502.03167](http://arxiv.org/abs/1502.03167).\n *\n * Mean, variance, scale, and offset can be of two shapes:\n * - The same shape as the input.\n * - In the common case, the depth dimension is the last dimension of x, so\n * the values would be an `tf.Tensor1D` of shape [depth].\n *\n * Also available are stricter rank-specific methods with the same signature\n * as this method that assert that parameters passed are of given rank\n * - `tf.batchNorm2d`\n * - `tf.batchNorm3d`\n * - `tf.batchNorm4d`\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction batchNorm_(x, mean, variance, offset, scale, varianceEpsilon) {\n if (varianceEpsilon == null) {\n varianceEpsilon = 0.001;\n }\n const $x = convertToTensor(x, 'x', 'batchNorm');\n const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n let $scale;\n if (scale != null) {\n $scale = convertToTensor(scale, 'scale', 'batchNorm');\n }\n let $offset;\n if (offset != null) {\n $offset = convertToTensor(offset, 'offset', 'batchNorm');\n }\n util.assert($mean.rank === $variance.rank, () => 'Batch normalization gradient requires mean and variance to have ' +\n 'equal ranks.');\n util.assert($offset == null || $mean.rank === $offset.rank, () => 'Batch normalization gradient requires mean and offset to have ' +\n 'equal ranks.');\n util.assert($scale == null || $mean.rank === $scale.rank, () => 'Batch normalization gradient requires mean and scale to have ' +\n 'equal ranks.');\n const x4D = xAs4D($x);\n const inputs = {\n x: x4D,\n scale: $scale,\n offset: $offset,\n mean: $mean,\n variance: $variance\n };\n const attrs = { varianceEpsilon };\n // tslint:disable-next-line: no-unnecessary-type-assertion\n const res = ENGINE.runKernel(FusedBatchNorm, inputs, attrs);\n return reshape(res, $x.shape);\n}\nexport const batchNorm = op({ batchNorm_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as seedrandom from 'seedrandom';\nimport { expectNumbersClose, testEpsilon } from '../test_util';\n// https://en.wikipedia.org/wiki/Marsaglia_polar_method\nexport class MPRandGauss {\n constructor(mean, stdDeviation, dtype, truncated, seed) {\n this.mean = mean;\n this.stdDev = stdDeviation;\n this.dtype = dtype;\n this.nextVal = NaN;\n this.truncated = truncated;\n if (this.truncated) {\n this.upper = this.mean + this.stdDev * 2;\n this.lower = this.mean - this.stdDev * 2;\n }\n const seedValue = seed ? seed : Math.random();\n this.random = seedrandom.alea(seedValue.toString());\n }\n /** Returns next sample from a Gaussian distribution. */\n nextValue() {\n if (!isNaN(this.nextVal)) {\n const value = this.nextVal;\n this.nextVal = NaN;\n return value;\n }\n let resultX, resultY;\n let isValid = false;\n while (!isValid) {\n let v1, v2, s;\n do {\n v1 = 2 * this.random() - 1;\n v2 = 2 * this.random() - 1;\n s = v1 * v1 + v2 * v2;\n } while (s >= 1 || s === 0);\n const mul = Math.sqrt(-2.0 * Math.log(s) / s);\n resultX = this.mean + this.stdDev * v1 * mul;\n resultY = this.mean + this.stdDev * v2 * mul;\n if (!this.truncated || this.isValidTruncated(resultX)) {\n isValid = true;\n }\n }\n if (!this.truncated || this.isValidTruncated(resultY)) {\n this.nextVal = this.convertValue(resultY);\n }\n return this.convertValue(resultX);\n }\n /** Handles proper rounding for non-floating-point numbers. */\n convertValue(value) {\n if (this.dtype == null || this.dtype === 'float32') {\n return value;\n }\n return Math.round(value);\n }\n /** Returns true if less than 2-standard-deviations from the mean. */\n isValidTruncated(value) {\n return value <= this.upper && value >= this.lower;\n }\n}\n// Marsaglia, George, and Wai Wan Tsang. 2000. \"A Simple Method for Generating\n// Gamma Variables.\"\nexport class RandGamma {\n constructor(alpha, beta, dtype, seed) {\n this.alpha = alpha;\n this.beta = 1 / beta; // convert rate to scale parameter\n this.dtype = dtype;\n const seedValue = seed ? seed : Math.random();\n this.randu = seedrandom.alea(seedValue.toString());\n this.randn = new MPRandGauss(0, 1, dtype, false, this.randu());\n if (alpha < 1) {\n this.d = alpha + (2 / 3);\n }\n else {\n this.d = alpha - (1 / 3);\n }\n this.c = 1 / Math.sqrt(9 * this.d);\n }\n /** Returns next sample from a gamma distribution. */\n nextValue() {\n let x2, v0, v1, x, u, v;\n while (true) {\n do {\n x = this.randn.nextValue();\n v = 1 + (this.c * x);\n } while (v <= 0);\n v *= v * v;\n x2 = x * x;\n v0 = 1 - (0.331 * x2 * x2);\n v1 = (0.5 * x2) + (this.d * (1 - v + Math.log(v)));\n u = this.randu();\n if (u < v0 || Math.log(u) < v1) {\n break;\n }\n }\n v = (1 / this.beta) * this.d * v;\n if (this.alpha < 1) {\n v *= Math.pow(this.randu(), 1 / this.alpha);\n }\n return this.convertValue(v);\n }\n /** Handles proper rounding for non-floating-point numbers. */\n convertValue(value) {\n if (this.dtype === 'float32') {\n return value;\n }\n return Math.round(value);\n }\n}\nexport class UniformRandom {\n constructor(min = 0, max = 1, dtype, seed) {\n /** Handles proper rounding for non floating point numbers. */\n this.canReturnFloat = () => (this.dtype == null || this.dtype === 'float32');\n this.min = min;\n this.range = max - min;\n this.dtype = dtype;\n if (seed == null) {\n seed = Math.random();\n }\n if (typeof seed === 'number') {\n seed = seed.toString();\n }\n if (!this.canReturnFloat() && this.range <= 1) {\n throw new Error(`The difference between ${min} - ${max} <= 1 and dtype is not float`);\n }\n this.random = seedrandom.alea(seed);\n }\n convertValue(value) {\n if (this.canReturnFloat()) {\n return value;\n }\n return Math.round(value);\n }\n nextValue() {\n return this.convertValue(this.min + this.range * this.random());\n }\n}\nexport function jarqueBeraNormalityTest(values) {\n // https://en.wikipedia.org/wiki/Jarque%E2%80%93Bera_test\n const n = values.length;\n const s = skewness(values);\n const k = kurtosis(values);\n const jb = n / 6 * (Math.pow(s, 2) + 0.25 * Math.pow(k - 3, 2));\n // JB test requires 2-degress of freedom from Chi-Square @ 0.95:\n // http://www.itl.nist.gov/div898/handbook/eda/section3/eda3674.htm\n const CHI_SQUARE_2DEG = 5.991;\n if (jb > CHI_SQUARE_2DEG) {\n throw new Error(`Invalid p-value for JB: ${jb}`);\n }\n}\nexport function expectArrayInMeanStdRange(actual, expectedMean, expectedStdDev, epsilon) {\n if (epsilon == null) {\n epsilon = testEpsilon();\n }\n const actualMean = mean(actual);\n expectNumbersClose(actualMean, expectedMean, epsilon);\n expectNumbersClose(standardDeviation(actual, actualMean), expectedStdDev, epsilon);\n}\nfunction mean(values) {\n let sum = 0;\n for (let i = 0; i < values.length; i++) {\n sum += values[i];\n }\n return sum / values.length;\n}\nfunction standardDeviation(values, mean) {\n let squareDiffSum = 0;\n for (let i = 0; i < values.length; i++) {\n const diff = values[i] - mean;\n squareDiffSum += diff * diff;\n }\n return Math.sqrt(squareDiffSum / values.length);\n}\nfunction kurtosis(values) {\n // https://en.wikipedia.org/wiki/Kurtosis\n const valuesMean = mean(values);\n const n = values.length;\n let sum2 = 0;\n let sum4 = 0;\n for (let i = 0; i < n; i++) {\n const v = values[i] - valuesMean;\n sum2 += Math.pow(v, 2);\n sum4 += Math.pow(v, 4);\n }\n return (1 / n) * sum4 / Math.pow((1 / n) * sum2, 2);\n}\nfunction skewness(values) {\n // https://en.wikipedia.org/wiki/Skewness\n const valuesMean = mean(values);\n const n = values.length;\n let sum2 = 0;\n let sum3 = 0;\n for (let i = 0; i < n; i++) {\n const v = values[i] - valuesMean;\n sum2 += Math.pow(v, 2);\n sum3 += Math.pow(v, 3);\n }\n return (1 / n) * sum3 / Math.pow((1 / (n - 1)) * sum2, 3 / 2);\n}\n//# 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* @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as seedrandom from 'seedrandom';\n\nimport {expectNumbersClose, testEpsilon} from '../test_util';\nimport {TypedArray} from '../types';\n\nexport interface RandomBase {\n  nextValue(): number;\n}\n\nexport interface RandomGamma {\n  nextValue(): number;\n}\n\nexport interface RandNormalDataTypes {\n  float32: Float32Array;\n  int32: Int32Array;\n}\n\nexport interface RandGammaDataTypes {\n  float32: Float32Array;\n  int32: Int32Array;\n}\n\n// https://en.wikipedia.org/wiki/Marsaglia_polar_method\nexport class MPRandGauss implements RandomBase {\n  private mean: number;\n  private stdDev: number;\n  private nextVal: number;\n  private dtype?: keyof RandNormalDataTypes;\n  private truncated?: boolean;\n  private upper?: number;\n  private lower?: number;\n  private random: seedrandom.prng;\n\n  constructor(\n      mean: number, stdDeviation: number, dtype?: keyof RandNormalDataTypes,\n      truncated?: boolean, seed?: number) {\n    this.mean = mean;\n    this.stdDev = stdDeviation;\n    this.dtype = dtype;\n    this.nextVal = NaN;\n    this.truncated = truncated;\n    if (this.truncated) {\n      this.upper = this.mean + this.stdDev * 2;\n      this.lower = this.mean - this.stdDev * 2;\n    }\n    const seedValue = seed ? seed : Math.random();\n    this.random = seedrandom.alea(seedValue.toString());\n  }\n\n  /** Returns next sample from a Gaussian distribution. */\n  public nextValue(): number {\n    if (!isNaN(this.nextVal)) {\n      const value = this.nextVal;\n      this.nextVal = NaN;\n      return value;\n    }\n\n    let resultX: number, resultY: number;\n    let isValid = false;\n    while (!isValid) {\n      let v1: number, v2: number, s: number;\n      do {\n        v1 = 2 * this.random() - 1;\n        v2 = 2 * this.random() - 1;\n        s = v1 * v1 + v2 * v2;\n      } while (s >= 1 || s === 0);\n\n      const mul = Math.sqrt(-2.0 * Math.log(s) / s);\n      resultX = this.mean + this.stdDev * v1 * mul;\n      resultY = this.mean + this.stdDev * v2 * mul;\n\n      if (!this.truncated || this.isValidTruncated(resultX)) {\n        isValid = true;\n      }\n    }\n\n    if (!this.truncated || this.isValidTruncated(resultY)) {\n      this.nextVal = this.convertValue(resultY);\n    }\n    return this.convertValue(resultX);\n  }\n\n  /** Handles proper rounding for non-floating-point numbers. */\n  private convertValue(value: number): number {\n    if (this.dtype == null || this.dtype === 'float32') {\n      return value;\n    }\n    return Math.round(value);\n  }\n\n  /** Returns true if less than 2-standard-deviations from the mean. */\n  private isValidTruncated(value: number): boolean {\n    return value <= this.upper && value >= this.lower;\n  }\n}\n\n// Marsaglia, George, and Wai Wan Tsang. 2000. \"A Simple Method for Generating\n// Gamma Variables.\"\nexport class RandGamma implements RandomGamma {\n  private alpha: number;\n  private beta: number;\n  private d: number;\n  private c: number;\n  private dtype?: keyof RandGammaDataTypes;\n  private randu: seedrandom.prng;\n  private randn: MPRandGauss;\n\n  constructor(\n      alpha: number, beta: number, dtype: keyof RandGammaDataTypes,\n      seed?: number) {\n    this.alpha = alpha;\n    this.beta = 1 / beta;  // convert rate to scale parameter\n    this.dtype = dtype;\n\n    const seedValue = seed ? seed : Math.random();\n    this.randu = seedrandom.alea(seedValue.toString());\n    this.randn = new MPRandGauss(0, 1, dtype, false, this.randu());\n\n    if (alpha < 1) {\n      this.d = alpha + (2 / 3);\n    } else {\n      this.d = alpha - (1 / 3);\n    }\n    this.c = 1 / Math.sqrt(9 * this.d);\n  }\n\n  /** Returns next sample from a gamma distribution. */\n  public nextValue(): number {\n    let x2: number, v0: number, v1: number, x: number, u: number, v: number;\n    while (true) {\n      do {\n        x = this.randn.nextValue();\n        v = 1 + (this.c * x);\n      } while (v <= 0);\n      v *= v * v;\n      x2 = x * x;\n      v0 = 1 - (0.331 * x2 * x2);\n      v1 = (0.5 * x2) + (this.d * (1 - v + Math.log(v)));\n      u = this.randu();\n      if (u < v0 || Math.log(u) < v1) {\n        break;\n      }\n    }\n    v = (1 / this.beta) * this.d * v;\n    if (this.alpha < 1) {\n      v *= Math.pow(this.randu(), 1 / this.alpha);\n    }\n    return this.convertValue(v);\n  }\n  /** Handles proper rounding for non-floating-point numbers. */\n  private convertValue(value: number): number {\n    if (this.dtype === 'float32') {\n      return value;\n    }\n    return Math.round(value);\n  }\n}\n\nexport class UniformRandom implements RandomBase {\n  private min: number;\n  private range: number;\n  private random: seedrandom.prng;\n  private dtype?: keyof RandNormalDataTypes;\n\n  constructor(\n      min = 0, max = 1, dtype?: keyof RandNormalDataTypes,\n      seed?: string|number) {\n    this.min = min;\n    this.range = max - min;\n    this.dtype = dtype;\n    if (seed == null) {\n      seed = Math.random();\n    }\n    if (typeof seed === 'number') {\n      seed = seed.toString();\n    }\n\n    if (!this.canReturnFloat() && this.range <= 1) {\n      throw new Error(\n          `The difference between ${min} - ${max} <= 1 and dtype is not float`);\n    }\n    this.random = seedrandom.alea(seed);\n  }\n\n  /** Handles proper rounding for non floating point numbers. */\n  private canReturnFloat = () =>\n      (this.dtype == null || this.dtype === 'float32');\n\n  private convertValue(value: number): number {\n    if (this.canReturnFloat()) {\n      return value;\n    }\n    return Math.round(value);\n  }\n\n  nextValue() {\n    return this.convertValue(this.min + this.range * this.random());\n  }\n}\n\nexport function jarqueBeraNormalityTest(values: TypedArray|number[]) {\n  // https://en.wikipedia.org/wiki/Jarque%E2%80%93Bera_test\n  const n = values.length;\n  const s = skewness(values);\n  const k = kurtosis(values);\n  const jb = n / 6 * (Math.pow(s, 2) + 0.25 * Math.pow(k - 3, 2));\n  // JB test requires 2-degress of freedom from Chi-Square @ 0.95:\n  // http://www.itl.nist.gov/div898/handbook/eda/section3/eda3674.htm\n  const CHI_SQUARE_2DEG = 5.991;\n  if (jb > CHI_SQUARE_2DEG) {\n    throw new Error(`Invalid p-value for JB: ${jb}`);\n  }\n}\n\nexport function expectArrayInMeanStdRange(\n    actual: TypedArray|number[], expectedMean: number, expectedStdDev: number,\n    epsilon?: number) {\n  if (epsilon == null) {\n    epsilon = testEpsilon();\n  }\n  const actualMean = mean(actual);\n  expectNumbersClose(actualMean, expectedMean, epsilon);\n  expectNumbersClose(\n      standardDeviation(actual, actualMean), expectedStdDev, epsilon);\n}\n\nfunction mean(values: TypedArray|number[]) {\n  let sum = 0;\n  for (let i = 0; i < values.length; i++) {\n    sum += values[i];\n  }\n  return sum / values.length;\n}\n\nfunction standardDeviation(values: TypedArray|number[], mean: number) {\n  let squareDiffSum = 0;\n  for (let i = 0; i < values.length; i++) {\n    const diff = values[i] - mean;\n    squareDiffSum += diff * diff;\n  }\n  return Math.sqrt(squareDiffSum / values.length);\n}\n\nfunction kurtosis(values: TypedArray|number[]) {\n  // https://en.wikipedia.org/wiki/Kurtosis\n  const valuesMean = mean(values);\n  const n = values.length;\n  let sum2 = 0;\n  let sum4 = 0;\n  for (let i = 0; i < n; i++) {\n    const v = values[i] - valuesMean;\n    sum2 += Math.pow(v, 2);\n    sum4 += Math.pow(v, 4);\n  }\n  return (1 / n) * sum4 / Math.pow((1 / n) * sum2, 2);\n}\n\nfunction skewness(values: TypedArray|number[]) {\n  // https://en.wikipedia.org/wiki/Skewness\n  const valuesMean = mean(values);\n  const n = values.length;\n  let sum2 = 0;\n  let sum3 = 0;\n  for (let i = 0; i < n; i++) {\n    const v = values[i] - valuesMean;\n    sum2 += Math.pow(v, 2);\n    sum3 += Math.pow(v, 3);\n  }\n  return (1 / n) * sum3 / Math.pow((1 / (n - 1)) * sum2, 3 / 2);\n}\n"]}","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Range } from '../kernel_names';\n/**\n * Creates a new `tf.Tensor1D` filled with the numbers in the range provided.\n *\n * The tensor is a is half-open interval meaning it includes start, but\n * excludes stop. Decrementing ranges and negative step values are also\n * supported.sv\n *\n *\n * ```js\n * tf.range(0, 9, 2).print();\n * ```\n *\n * @param start An integer start value\n * @param stop An integer stop value\n * @param step An integer increment (will default to 1 or -1)\n * @param dtype The data type of the output tensor. Defaults to 'float32'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function range(start, stop, step = 1, dtype = 'float32') {\n if (step === 0) {\n throw new Error('Cannot have a step of zero');\n }\n const attrs = { start, stop, step, dtype };\n return ENGINE.runKernel(Range, {} /* inputs */, attrs);\n}\n//# sourceMappingURL=data:application/json;base64,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","var g;\n\n// This works in non-strict mode\ng = (function() {\n\treturn this;\n})();\n\ntry {\n\t// This works if eval is allowed (see CSP)\n\tg = g || new Function(\"return this\")();\n} catch (e) {\n\t// This works if the window reference is available\n\tif (typeof window === \"object\") g = window;\n}\n\n// g can still be undefined, but nothing to do about it...\n// We return undefined, instead of nothing here, so it's\n// easier to handle this case. if(!global) { ...}\n\nmodule.exports = g;\n","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst CUSTOM_OPS = {};\n/**\n * Register an Op for graph model executor. This allow you to register\n * TensorFlow custom op or override existing op.\n *\n * Here is an example of registering a new MatMul Op.\n * ```js\n * const customMatmul = (node) =>\n * tf.matMul(\n * node.inputs[0], node.inputs[1],\n * node.attrs['transpose_a'], node.attrs['transpose_b']);\n *\n * tf.registerOp('MatMul', customMatmul);\n * ```\n * The inputs and attrs of the node object is based on the TensorFlow op\n * registry.\n *\n * @param name The Tensorflow Op name.\n * @param opFunc An op function which is called with the current graph node\n * during execution and needs to return a tensor or a list of tensors. The node\n * has the following attributes:\n * - attr: A map from attribute name to its value\n * - inputs: A list of input tensors\n *\n * @doc {heading: 'Models', subheading: 'Op Registry'}\n */\nexport function registerOp(name, opFunc) {\n const opMapper = {\n tfOpName: name,\n category: 'custom',\n inputs: [],\n attrs: [],\n customExecutor: opFunc\n };\n CUSTOM_OPS[name] = opMapper;\n}\n/**\n * Retrieve the OpMapper object for the registered op.\n *\n * @param name The Tensorflow Op name.\n *\n * @doc {heading: 'Models', subheading: 'Op Registry'}\n */\nexport function getRegisteredOp(name) {\n return CUSTOM_OPS[name];\n}\n/**\n * Deregister the Op for graph model executor.\n *\n * @param name The Tensorflow Op name.\n *\n * @doc {heading: 'Models', subheading: 'Op Registry'}\n */\nexport function deregisterOp(name) {\n delete CUSTOM_OPS[name];\n}\n//# 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* @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Mean } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the mean of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces `x` along the dimensions given in `axis`. Unless `keepDims` is\n * true, the rank of the `tf.Tensor` is reduced by 1 for each entry in `axis`.\n * If `keepDims` is true, the reduced dimensions are retained with length 1.\n * If `axis` has no entries, all dimensions are reduced, and a `tf.Tensor` with\n * a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.mean().print(); // or tf.mean(a)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.mean(axis).print(); // or tf.mean(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension(s) to reduce. By default it reduces\n * all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction mean_(x, axis = null, keepDims = false) {\n const $x = convertToTensor(x, 'x', 'mean');\n const inputs = { x: $x };\n const attrs = { axis, keepDims };\n return ENGINE.runKernel(Mean, inputs, attrs);\n}\nexport const mean = op({ mean_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Real } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns the real part of a complex (or real) tensor.\n *\n * Given a tensor input, this operation returns a tensor of type float that is\n * the real part of each element in input considered as a complex number.\n *\n * If the input is real, it simply makes a clone.\n *\n * ```js\n * const x = tf.complex([-2.25, 3.25], [4.75, 5.75]);\n * tf.real(x).print();\n * ```\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction real_(input) {\n const $input = convertToTensor(input, 'input', 'real');\n const inputs = { input: $input };\n return ENGINE.runKernel(Real, inputs);\n}\nexport const real = op({ real_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { IFFT } from '../../kernel_names';\nimport { assert } from '../../util';\nimport { op } from '../operation';\n/**\n * Inverse fast Fourier transform.\n *\n * Computes the inverse 1-dimensional discrete Fourier transform over the\n * inner-most dimension of input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n * const imag = tf.tensor1d([1, 2, 3]);\n * const x = tf.complex(real, imag);\n *\n * x.ifft().print(); // tf.spectral.ifft(x).print();\n * ```\n * @param input The complex input to compute an ifft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction ifft_(input) {\n assert(input.dtype === 'complex64', () => `The dtype for tf.spectral.ifft() must be complex64 ` +\n `but got ${input.dtype}.`);\n const inputs = { input };\n return ENGINE.runKernel(IFFT, inputs);\n}\nexport const ifft = op({ ifft_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Note that the identifier globalNameSpace is scoped to this module, but will\n// always resolve to the same global object regardless of how the module is\n// resolved.\n// tslint:disable-next-line:no-any\nlet globalNameSpace;\n// tslint:disable-next-line:no-any\nexport function getGlobalNamespace() {\n if (globalNameSpace == null) {\n // tslint:disable-next-line:no-any\n let ns;\n if (typeof (window) !== 'undefined') {\n ns = window;\n }\n else if (typeof (global) !== 'undefined') {\n ns = global;\n }\n else if (typeof (process) !== 'undefined') {\n ns = process;\n }\n else if (typeof (self) !== 'undefined') {\n ns = self;\n }\n else {\n throw new Error('Could not find a global object');\n }\n globalNameSpace = ns;\n }\n return globalNameSpace;\n}\n// tslint:disable-next-line:no-any\nfunction getGlobalMap() {\n const ns = getGlobalNamespace();\n if (ns._tfGlobals == null) {\n ns._tfGlobals = new Map();\n }\n return ns._tfGlobals;\n}\n/**\n * Returns a globally accessible 'singleton' object.\n *\n * @param key the name of the object\n * @param init a function to initialize to initialize this object\n * the first time it is fetched.\n */\nexport function getGlobal(key, init) {\n const globalMap = getGlobalMap();\n if (globalMap.has(key)) {\n return globalMap.get(key);\n }\n else {\n const singleton = init();\n globalMap.set(key, singleton);\n return globalMap.get(key);\n }\n}\n//# 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{ computeStrides, sizeFromShape } from '../util';\n/**\n * Check whether updates.shape = indices.shape[:batchDim] +\n * shape[sliceDim:]\n *\n * @param x The input tensor.\n */\nexport function validateUpdateShape(shape, indices, updates) {\n const sliceDim = (indices.rank > 1) ? indices.shape[indices.rank - 1] : 1;\n const batchDim = (indices.rank > 1) ? indices.rank - 1 : 1;\n const shapeError = 'Must have updates.shape = indices.shape[:batchDim] + ' +\n `shape[sliceDim:], got updates.shape: ${updates.shape}` +\n `, indices.shape: ${indices.shape}, shape: ${shape}` +\n `, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`;\n if (updates.rank < batchDim) {\n throw new Error(shapeError + ` update.rank < ${batchDim}. `);\n }\n if (shape.length < sliceDim + (updates.rank - batchDim)) {\n throw new Error(shapeError +\n ` Output shape length < ${sliceDim + (updates.rank - batchDim)}`);\n }\n if (updates.rank !== batchDim + shape.length - sliceDim) {\n throw new Error(shapeError + ` update.rank != ${batchDim + shape.length - sliceDim}`);\n }\n for (let d = 0; d < batchDim; ++d) {\n if (updates.shape[d] !== indices.shape[d]) {\n throw new Error(shapeError +\n ` updates.shape[${d}] (${updates.shape[d]}) != indices.shape[${d}] (${indices.shape[d]}).`);\n }\n }\n for (let d = 0; d < updates.rank - batchDim; ++d) {\n if (updates.shape[d + batchDim] !== shape[d + sliceDim]) {\n throw new Error(shapeError +\n ` updates.shape[${d + batchDim}] (${updates.shape[d + batchDim]}) != shape[${d + batchDim}] (${shape[d + batchDim]})`);\n }\n }\n}\n/**\n * Validate scatter nd inputs.\n *\n * @param update The tensor contains the update values.\n * @param indices The tensor contains the indices for the update values.\n * @param shape The shape of the output tensor.\n */\nexport function validateInput(updates, indices, shape) {\n if (indices.rank < 1) {\n throw new Error('tf.scatterND() expects the indices to be rank 1 or higher,' +\n ` but the rank was ${indices.rank}.`);\n }\n if 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All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {TensorInfo} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {computeStrides, sizeFromShape} from '../util';\n\n/**\n * Check whether updates.shape = indices.shape[:batchDim] +\n * shape[sliceDim:]\n *\n * @param x The input tensor.\n */\nexport function validateUpdateShape(\n    shape: number[], indices: Tensor, updates: Tensor) {\n  const sliceDim = (indices.rank > 1) ? indices.shape[indices.rank - 1] : 1;\n  const batchDim = (indices.rank > 1) ? indices.rank - 1 : 1;\n\n  const shapeError = 'Must have updates.shape = indices.shape[:batchDim] + ' +\n      `shape[sliceDim:], got updates.shape: ${updates.shape}` +\n      `, indices.shape: ${indices.shape}, shape: ${shape}` +\n      `, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`;\n\n  if (updates.rank < batchDim) {\n    throw new Error(shapeError + ` update.rank < ${batchDim}. `);\n  }\n  if (shape.length < sliceDim + (updates.rank - batchDim)) {\n    throw new Error(\n        shapeError +\n        ` Output shape length < ${sliceDim + (updates.rank - batchDim)}`);\n  }\n  if (updates.rank !== batchDim + shape.length - sliceDim) {\n    throw new Error(\n        shapeError + ` update.rank != ${batchDim + shape.length - sliceDim}`);\n  }\n  for (let d = 0; d < batchDim; ++d) {\n    if (updates.shape[d] !== indices.shape[d]) {\n      throw new Error(\n          shapeError +\n          ` updates.shape[${d}] (${updates.shape[d]}) != indices.shape[${d}] (${\n              indices.shape[d]}).`);\n    }\n  }\n  for (let d = 0; d < updates.rank - batchDim; ++d) {\n    if (updates.shape[d + batchDim] !== shape[d + sliceDim]) {\n      throw new Error(\n          shapeError +\n          ` updates.shape[${d + batchDim}] (${\n              updates.shape[d + batchDim]}) != shape[${d + batchDim}] (${\n              shape[d + batchDim]})`);\n    }\n  }\n}\n\nexport interface ScatterShapeInfo {\n  sliceRank: number;\n  numUpdates: number;\n  sliceSize: number;\n  strides: number[];\n  outputSize: number;\n}\n/**\n * Validate scatter nd inputs.\n *\n * @param update The tensor contains the update values.\n * @param indices The tensor contains the indices for the update values.\n * @param shape The shape of the output tensor.\n */\nexport function validateInput(\n    updates: Tensor, indices: Tensor, shape: number[]) {\n  if (indices.rank < 1) {\n    throw new Error(\n        'tf.scatterND() expects the indices to be rank 1 or higher,' +\n        ` but the rank was ${indices.rank}.`);\n  }\n  if (updates.rank < 1) {\n    throw new Error(\n        'tf.scatterND() expects the updates to be rank 1 or higher,' +\n        ` but the rank was ${updates.rank}.`);\n  }\n  if (indices.dtype !== 'int32') {\n    throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${\n        indices.dtype}`);\n  }\n  if (shape.length < 1) {\n    throw new Error(\n        `Output rank must be greater or equal to 1, but got shape: ${shape}`);\n  }\n\n  if (shape.length === 0) {\n    if (indices.size === 0) {\n      throw new Error(`Indices specified for empty output. indices shape: ${\n          indices.shape}`);\n    }\n    if (updates.size === 0) {\n      throw new Error(`Updates specified for empty output. updates shape: ${\n          updates.shape}`);\n    }\n  }\n\n  validateUpdateShape(shape, indices, updates);\n}\n\n/**\n * Calculate the shape information for the output.\n *\n * @param update The tensor contains the update values.\n * @param indices The tensor contains the indices for the update values.\n * @param shape The shape of the output tensor.\n *\n * @returns ScatterShapeInfo\n */\nexport function calculateShapes(\n    updates: TensorInfo, indices: TensorInfo,\n    shape: number[]): ScatterShapeInfo {\n  // Calculate the number of dimensions in indices\n  const indicesRank = indices.shape.length;\n  const sliceRank = (indicesRank > 1) ? indices.shape[indicesRank - 1] : 1;\n\n  // Calculate the number of elements that make up each slice of our updated\n  // tensor. This allows us to work with flattened tensors and copy over whole\n  // slices at a time.\n  const totalNd = shape.length;\n\n  let sliceSize = 1;\n  for (let i = sliceRank; i < totalNd; ++i) {\n    sliceSize *= shape[i];\n  }\n\n  const safeSliceDim = (sliceRank < 1) ? 1 : sliceRank;\n  const numUpdates = sizeFromShape(indices.shape) / safeSliceDim;\n\n  const strides = [...computeStrides(shape.slice(0, sliceRank)), 1];\n  const outputSize = sizeFromShape(shape);\n  return {sliceRank, numUpdates, sliceSize, strides, outputSize};\n}\n"]}","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\nconst NEW_AXIS = -2;\nconst SHRINK_AXIS = -1;\nexport function assertParamsValid(input, begin, size) {\n const inputRank = input.shape.length;\n util.assert(inputRank === begin.length, () => `Error in slice${inputRank}D: Length of begin ${begin} must ` +\n `match the rank of the array (${inputRank}).`);\n util.assert(inputRank === size.length, () => `Error in slice${inputRank}D: Length of size ${size} must ` +\n `match the rank of the array (${inputRank}).`);\n for (let i = 0; i < inputRank; ++i) {\n util.assert(begin[i] + size[i] <= input.shape[i], () => `Error in slice${inputRank}D: begin[${i}] + size[${i}] ` +\n `(${begin[i] + size[i]}) would overflow input.shape[${i}] (${input.shape[i]})`);\n }\n}\n/** Converts a binary mask to an array of axes. Used in stridedSlice(). */\nexport function maskToAxes(mask) {\n const axes = [];\n let axis = 0;\n while (mask > 0) {\n if (mask & 1) {\n axes.push(axis);\n }\n mask /= 2;\n axis++;\n }\n return axes;\n}\n/** Computes the output shape given the strided slice params. */\nexport function computeOutShape(begin, end, strides) {\n const size = [];\n for (let axis = 0; axis < begin.length; axis++) {\n size[axis] = Math.ceil((end[axis] - begin[axis]) / strides[axis]);\n }\n return size;\n}\n// Creates full selection at the elided dimensions. If the dimension matches\n// the ellipsis mask, override the current stride value. Otherwise, insert.\nexport function stridesWithElidedDims(strides, ellipsisInsertionIndex, numElidedAxes, inputShape) {\n const newStrides = [...strides];\n for (let i = newStrides.length; i < inputShape.length; i++) {\n newStrides.push(1);\n }\n for (let i = 0; i < numElidedAxes; i++) {\n if (i === 0) {\n newStrides[ellipsisInsertionIndex] = 1;\n }\n else {\n newStrides.splice(ellipsisInsertionIndex, 0 /* num elements to delete */, 1 /* element to add */);\n newStrides.pop();\n }\n }\n return newStrides;\n}\nfunction unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, normalizedAxis) {\n if (normalizedAxis <= ellipsisInsertionIndex) {\n return normalizedAxis;\n }\n return normalizedAxis - (numElidedAxes - 1);\n}\nfunction getElidedAxes(numElidedAxes, ellipsisInsertionIndex) {\n const elidedAxes = [];\n for (let i = 0; i < numElidedAxes; i++) {\n elidedAxes.push(ellipsisInsertionIndex + i);\n }\n return elidedAxes;\n}\n// Normalize the start, end and strides.\nexport function getNormalizedAxes(inputShape, ellipsisAxes, numInterpolatedAxes, begin, end, strides, beginMask, endMask, ellipsisMask) {\n const inputRank = inputShape.length;\n let normalizedBegin = new Array(inputRank), normalizedEnd = new Array(inputRank), normalizedStrides = new Array(inputRank);\n if (ellipsisAxes.length && numInterpolatedAxes > 0) {\n const fullIndex = ellipsisAxes[0];\n // The ellipsis applies to the masked index as well as any dimensions\n // that are interpolated.\n const numElidedAxes = numInterpolatedAxes + 1;\n normalizedBegin = startIndicesWithElidedDims(beginMask, fullIndex, numElidedAxes, begin, inputShape);\n normalizedEnd = stopIndicesWithElidedDims(endMask, fullIndex, numElidedAxes, end, inputShape);\n normalizedStrides =\n stridesWithElidedDims(strides, fullIndex, numElidedAxes, inputShape);\n }\n else {\n for (let axis = 0; axis < inputRank; axis++) {\n normalizedBegin[axis] = startForAxis(beginMask, begin, strides, inputShape, axis, ellipsisMask);\n normalizedEnd[axis] =\n stopForAxis(endMask, end, strides, inputShape, axis, ellipsisMask);\n normalizedStrides[axis] = stridesForAxis(strides, axis, ellipsisMask);\n }\n }\n return {\n begin: normalizedBegin,\n end: normalizedEnd,\n strides: normalizedStrides\n };\n}\n// Creates full selection at the elided dimensions. If the dimension matches\n// the ellipsis mask, override the current start value. Otherwise, insert.\nexport function startIndicesWithElidedDims(beginMask, ellipsisInsertionIndex, numElidedAxes, originalBegin, inputShape) {\n const newIndices = [...inputShape];\n const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);\n for (let axis = 0; axis < newIndices.length; axis++) {\n if (elidedAxes.indexOf(axis) > -1) {\n newIndices[axis] = 0;\n }\n else {\n const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);\n let originalValue = originalBegin[originalAxis];\n if (beginMask & 1 << originalAxis) {\n originalValue = 0;\n }\n newIndices[axis] = originalValue;\n }\n }\n return newIndices;\n}\n// Creates full selection at the elided dimensions. If the dimension matches\n// the ellipsis mask, override the current stop value. Otherwise, insert.\nexport function stopIndicesWithElidedDims(endMask, ellipsisInsertionIndex, numElidedAxes, originalEnd, inputShape) {\n const newIndices = [...inputShape];\n const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);\n for (let axis = 0; axis < newIndices.length; axis++) {\n if (elidedAxes.indexOf(axis) > -1) {\n newIndices[axis] = Number.MAX_SAFE_INTEGER;\n }\n else {\n const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);\n let originalValue = originalEnd[originalAxis];\n if (endMask & 1 << originalAxis) {\n originalValue = Number.MAX_SAFE_INTEGER;\n }\n newIndices[axis] = originalValue;\n }\n }\n for (let i = 0; i < newIndices.length; i++) {\n // Handle negative indices\n const axisSize = inputShape[i];\n if (newIndices[i] < 0) {\n newIndices[i] += axisSize;\n }\n newIndices[i] = util.clamp(0, newIndices[i], inputShape[i]);\n }\n return newIndices;\n}\nexport function stridesForAxis(strides, axis, ellipsisMask) {\n let stride = strides[axis];\n if (ellipsisMask & (1 << axis) || stride == null) {\n stride = 1;\n }\n return stride;\n}\nexport function startForAxis(beginMask, startIndices, strides, inputShape, axis, ellipsisMask) {\n // Begin with the specified index\n let start = startIndices[axis];\n const stride = strides[axis] || 1;\n // Check the axis bit from right of masked axes, or the begin index is not set\n // for the axis.\n if (beginMask & 1 << axis || ellipsisMask & 1 << axis || start == null) {\n if (stride > 0) {\n // Forward iteration - use the first element. These values will get\n // clamped below (Note: We could have set them to 0 and axis_size-1, but\n // use lowest() and max() to maintain symmetry with StopForAxis())\n start = Number.MIN_SAFE_INTEGER;\n }\n else {\n // Backward iteration - use the last element.\n start = Number.MAX_SAFE_INTEGER;\n }\n }\n // Handle negative indices\n const axisSize = inputShape[axis];\n if (start < 0) {\n start += axisSize;\n }\n // Clamping\n start = util.clamp(0, start, axisSize - 1);\n return start;\n}\nexport function stopForAxis(endMask, stopIndices, strides, inputShape, axis, ellipsisMask) {\n // Begin with the specified index\n let stop = stopIndices[axis];\n const stride = strides[axis] || 1;\n // Check the axis bit from right of masked axes, or if the stop index is not\n // set for this axis.\n if (endMask & (1 << axis) || ellipsisMask & (1 << axis) || stop == null) {\n if (stride > 0) {\n // Forward iteration - use the last element. These values will get\n // clamped below\n stop = Number.MAX_SAFE_INTEGER;\n }\n else {\n // Backward iteration - use the first element.\n stop = Number.MIN_SAFE_INTEGER;\n }\n }\n // Handle negative indices\n const axisSize = inputShape[axis];\n if (stop < 0) {\n stop += axisSize;\n }\n // Clamping\n // Because the end index points one past the last element, we need slightly\n // different clamping ranges depending on the direction.\n if (stride > 0) {\n // Forward iteration\n stop = util.clamp(0, stop, axisSize);\n }\n else {\n // Backward iteration\n stop = util.clamp(-1, stop, axisSize - 1);\n }\n return stop;\n}\n/**\n * Returns true if the slice occupies a continous set of elements in the\n * 'flat' space.\n */\nexport function isSliceContinous(shape, begin, size) {\n // Index of the first axis that has size > 1.\n let firstNonOneAxis = size.length;\n for (let i = 0; i < size.length; i++) {\n if (size[i] > 1) {\n firstNonOneAxis = i;\n break;\n }\n }\n for (let i = firstNonOneAxis + 1; i < size.length; i++) {\n if (begin[i] > 0 || size[i] !== shape[i]) {\n return false;\n }\n }\n return true;\n}\nexport function computeFlatOffset(begin, strides) {\n let flatOffset = begin.length > 0 ? begin[begin.length - 1] : 1;\n for (let i = 0; i < begin.length - 1; i++) {\n flatOffset += begin[i] * strides[i];\n }\n return flatOffset;\n}\nexport function parseSliceParams(x, begin, size) {\n // The following logic allows for more ergonomic calls.\n let begin_;\n const xRank = x.shape.length;\n if (typeof begin === 'number') {\n begin_ = [begin, ...new Array(xRank - 1).fill(0)];\n }\n else if (begin.length < xRank) {\n begin_ = begin.concat(new Array(xRank - begin.length).fill(0));\n }\n else {\n begin_ = begin.slice();\n }\n begin_.forEach(d => {\n util.assert(d !== -1, () => 'slice() does not support negative begin indexing.');\n });\n let size_;\n if (size == null) {\n size_ = new Array(xRank).fill(-1);\n }\n else if (typeof size === 'number') {\n size_ = [size, ...new Array(xRank - 1).fill(-1)];\n }\n else if (size.length < xRank) {\n size_ = size.concat(new Array(xRank - size.length).fill(-1));\n }\n else {\n size_ = size;\n }\n size_ = size_.map((d, i) => {\n if (d >= 0) {\n return d;\n }\n else {\n util.assert(d === -1, () => `Negative size values should be exactly -1 but got ` +\n `${d} for the slice() size at index ${i}.`);\n return x.shape[i] - begin_[i];\n }\n });\n return [begin_, size_];\n}\n// Convert the slicing specification from a sparse representation to a dense\n// representation. This means that all ellipses and newaxis are expanded out.\nexport function sliceInfo(xShape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask) {\n let stridesNonNull;\n if (strides == null) {\n stridesNonNull = new Array(begin.length);\n stridesNonNull.fill(1);\n }\n else {\n stridesNonNull = strides;\n }\n // Only one non-zero bit is allowed in ellipsisMask, which means ellipsisMask\n // is a power of 2. Use bit compares to ensure ellipsisMask is 0 or a power\n // of 2. When i is a power of 2, i & (i - 1) is always 0.\n // Also ref:\n // https://stackoverflow.com/questions/600293/how-to-check-if-a-number-is-a-power-of-2\n if (ellipsisMask != null && (ellipsisMask & (ellipsisMask - 1)) !== 0) {\n throw new Error('Multiple ellipses in slice is not allowed.');\n }\n // Step 1: Account for ellipsis and new axis.\n // Check for ellipsis and count how many non-newaxis there are after.\n let ellipsisSeen = false;\n const sparseSpec = {\n dims: stridesNonNull.length,\n numAddAxisAfterEllipsis: 0,\n begin: begin.slice(),\n end: end.slice(),\n strides: stridesNonNull.slice(),\n beginMask,\n endMask,\n ellipsisMask,\n newAxisMask,\n shrinkAxisMask\n };\n for (let i = 0; i < sparseSpec.dims; i++) {\n if (ellipsisSeen && ((1 << i) & newAxisMask) !== 0) {\n sparseSpec.numAddAxisAfterEllipsis++;\n }\n if ((1 << i) & ellipsisMask) {\n ellipsisSeen = true;\n }\n }\n // If no ellipsis insert one at the end.\n if (!ellipsisSeen) {\n sparseSpec.ellipsisMask |= (1 << sparseSpec.dims);\n sparseSpec.dims++; // this effects loop iteration below\n }\n // Step 2: Make a sparse spec into a full index spec.\n //\n // The sparse spec deos not correspond to the number of dimensions.\n // Make a dense spec that cooresponds to the number of dimensions.\n //\n // For example suppose foo[...,3:] on foo.shape = [2, 2, 3] then we need to\n // produce the missing beginMask for the first two dimensions i.e. from\n // beginMaskSpec = 0, endMaskSpec = 2, we achieve beginMask = 6 (110),\n // endMask = 7 (111).\n const denseSpec = {\n dims: xShape.length,\n beginMask: 0,\n endMask: 0,\n beginValid: false,\n endValid: false\n };\n buildDenseSpec(sparseSpec, denseSpec);\n // Step 3: Make implicit ranges (non-zero beginMasks and endMasks) explicit\n // and bounds check.\n let isIdentity = true;\n let sliceDim0 = true;\n let isSimpleSlice = true;\n const processingShape = [];\n const finalShape = [];\n for (let i = 0; i < xShape.length; ++i) {\n if (denseSpec.strides[i] === 0) {\n throw Error(`strides[${i}] must be non-zero`);\n }\n const shrinkI = !!(denseSpec.shrinkAxisMask & (1 << i));\n const dimI = xShape[i];\n if (dimI === -1) {\n processingShape.push(shrinkI ? 1 : -1);\n continue;\n }\n const masks = [denseSpec.beginMask & (1 << i), denseSpec.endMask & (1 << i)];\n const validRange = [\n denseSpec.strides[i] > 0 ? 0 : -1,\n denseSpec.strides[i] > 0 ? dimI : dimI - 1\n ];\n if (shrinkI && denseSpec.strides[i] <= 0) {\n throw Error('only stride 1 allowed on non-range indexing.');\n }\n isSimpleSlice = isSimpleSlice && (denseSpec.strides[i] === 1);\n const beginAndEndMasked = !!((denseSpec.beginMask & (1 << i)) && (denseSpec.endMask & (1 << i)));\n if (denseSpec.beginValid && denseSpec.endValid) {\n if (shrinkI) {\n // If we are shrinking, the end index is now possibly incorrect. In\n // particular foo[-1] produces sparseBegin = -1, sparseEnd = 0.\n // and canonical puts these to n-1 and 0, which implies a degenerate\n // interval. Fortunately, it is now safe to re-create end as begin + 1.\n const xFwd = denseSpec.begin[i] < 0 ? dimI + denseSpec.begin[i] :\n denseSpec.begin[i];\n denseSpec.begin[i] = xFwd;\n denseSpec.end[i] = denseSpec.begin[i] + 1;\n if (xFwd < 0 || xFwd >= dimI) {\n throw Error(`slice index ${denseSpec.begin[i]} of dimension ${i} out of bounds.`);\n }\n }\n else {\n denseSpec.begin[i] = canonical(denseSpec.begin[i], 0, denseSpec.strides[i], dimI, masks, validRange);\n denseSpec.end[i] = canonical(denseSpec.end[i], 1, denseSpec.strides[i], dimI, masks, validRange);\n }\n // Update optimization values\n const takeAllInDimension = denseSpec.strides[i] === 1 &&\n denseSpec.begin[i] === 0 && denseSpec.end[i] === dimI;\n isIdentity = isIdentity && takeAllInDimension;\n sliceDim0 = sliceDim0 &&\n ((i === 0 && denseSpec.strides[i] === 1) || takeAllInDimension);\n }\n else {\n isIdentity =\n isIdentity && ((denseSpec.strides[i] === 1) && beginAndEndMasked);\n sliceDim0 = sliceDim0 &&\n ((i === 0 && denseSpec.strides[i] === 1) || beginAndEndMasked);\n }\n // Compute the processing shape (the intermediate Eigen will produce)\n let intervalLength;\n let knownInterval = false;\n if (denseSpec.beginValid && denseSpec.endValid) {\n intervalLength = denseSpec.end[i] - denseSpec.begin[i];\n knownInterval = true;\n }\n else if (shrinkI) {\n // The dimension is still known as 1 for the processingShape, but will be\n // discarded for the final shape.\n intervalLength = 1;\n knownInterval = true;\n }\n else if (beginAndEndMasked) {\n // Even if we don't have values for begin or end, we do know that this\n // dimension covers the whole interval. If we have shape information for\n // this dimension, that tells us the interval length.\n if (dimI >= 0) {\n if (denseSpec.strides[i] < 0) {\n intervalLength = -dimI;\n }\n else {\n intervalLength = dimI;\n }\n knownInterval = true;\n }\n }\n if (knownInterval) {\n let sizeI;\n // Hold zero if the interval is degenerate, otherwise account for\n // remainder\n if (intervalLength === 0 ||\n ((intervalLength < 0) !== (denseSpec.strides[i] < 0))) {\n sizeI = 0;\n }\n else {\n sizeI = Math.trunc(intervalLength / denseSpec.strides[i]) +\n (intervalLength % denseSpec.strides[i] !== 0 ? 1 : 0);\n }\n processingShape.push(sizeI);\n }\n else {\n processingShape.push(-1);\n }\n }\n // Step 4: Compute the final shape\n //\n // newAxis will increase dimension by 1 (with a one-size dimension)\n // slices like foo[3, ...] will reduce dimension by 1.\n // This cannot be done earlier, because it depends on Step 3.\n for (let denseDim = 0; denseDim < denseSpec.finalShapeGatherIndices.length; ++denseDim) {\n const gatherIndex = denseSpec.finalShapeGatherIndices[denseDim];\n if (gatherIndex >= 0) {\n finalShape.push(processingShape[gatherIndex]);\n }\n else if (gatherIndex === NEW_AXIS) {\n finalShape.push(1);\n }\n }\n const finalShapeSparse = finalShape.filter((dim, i) => denseSpec.finalShapeGatherIndices[i] !== NEW_AXIS);\n return {\n finalShapeSparse,\n finalShape,\n isIdentity,\n sliceDim0,\n isSimpleSlice,\n begin: denseSpec.begin,\n end: denseSpec.end,\n strides: denseSpec.strides\n };\n}\nfunction buildDenseSpec(sparse, dense) {\n dense.beginMask = 0;\n dense.endMask = 0;\n dense.shrinkAxisMask = 0;\n let fullIndex = 0;\n dense.beginValid = sparse.begin != null;\n dense.endValid = sparse.end != null;\n dense.begin = new Array(dense.dims);\n dense.end = new Array(dense.dims);\n dense.strides = new Array(dense.dims);\n dense.finalShapeGatherIndices = [];\n dense.finalShapeGatherIndicesSparse = [];\n dense.inputShapeGatherIndicesSparse = new Array(dense.dims);\n for (let i = 0; i < sparse.dims; i++) {\n if ((1 << i) & sparse.ellipsisMask) {\n // Only the bit that has ellipsis will fall in this condition.\n // Expand the ellipsis into the appropriate indices\n // Note: this only works because we guaranteed one ellipsis.\n const nextIndex = Math.min(dense.dims - (sparse.dims - i) + 1 + sparse.numAddAxisAfterEllipsis, dense.dims);\n for (; fullIndex < nextIndex; fullIndex++) {\n // newAxis aren't real axis so you have to skip.\n dense.begin[fullIndex] = 0;\n dense.end[fullIndex] = 0;\n dense.strides[fullIndex] = 1;\n dense.beginMask |= (1 << fullIndex);\n dense.endMask |= (1 << fullIndex);\n dense.finalShapeGatherIndices.push(fullIndex);\n dense.finalShapeGatherIndicesSparse.push(-1);\n dense.inputShapeGatherIndicesSparse[fullIndex] = i;\n }\n }\n else if ((1 << i) & sparse.newAxisMask) {\n // Only the bit that has newAxis will fall in this condition.\n dense.finalShapeGatherIndices.push(NEW_AXIS);\n dense.finalShapeGatherIndicesSparse.push(-1);\n }\n else {\n if (fullIndex === dense.begin.length) {\n throw Error(`Index out of range using input dim ${fullIndex}; input ` +\n `has only ${dense.dims} dims, ${dense.begin.length}.`);\n }\n // Gather slicing spec into appropriate index.\n if (sparse.begin != null) {\n dense.begin[fullIndex] = sparse.begin[i];\n }\n if (sparse.end != null) {\n dense.end[fullIndex] = sparse.end[i];\n }\n dense.strides[fullIndex] = sparse.strides[i];\n if (sparse.beginMask & (1 << i)) {\n dense.beginMask |= (1 << fullIndex);\n }\n if (sparse.endMask & (1 << i)) {\n dense.endMask |= (1 << fullIndex);\n }\n // If shrink, record where to get the dimensionality from (i.e. newAxis)\n // creates a fake 1 size dimension. Also remember shrink axis (now in\n // dense form) so we can ignore dense.end below.\n if (sparse.shrinkAxisMask & (1 << i)) {\n dense.finalShapeGatherIndices.push(SHRINK_AXIS);\n dense.finalShapeGatherIndicesSparse.push(-1);\n dense.shrinkAxisMask |= (1 << fullIndex);\n }\n else {\n dense.finalShapeGatherIndices.push(fullIndex);\n // Remember that where in the sparse shape the dense dim comes from.\n dense.finalShapeGatherIndicesSparse.push(i);\n }\n dense.inputShapeGatherIndicesSparse[fullIndex] = i;\n fullIndex++;\n }\n }\n}\nfunction canonical(x, c, strideI, dimI, masks, validRange) {\n if (masks[c]) {\n return strideI > 0 ? validRange[c] : validRange[(c + 1) & 1];\n }\n else {\n const xFwd = x < 0 ? dimI + x : x; // make negative indices positive\n return xFwd < validRange[0] ? validRange[0] :\n xFwd > validRange[1] ? validRange[1] : xFwd;\n }\n}\n//# 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* @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {TensorInfo} from '../kernel_registry';\nimport * as util from '../util';\n\nconst NEW_AXIS = -2;\nconst SHRINK_AXIS = -1;\n\n// Sparse slicing specification\n// if one does foo[3:5, ..., -3], the begin, end and strides will have length\n// of 3.\ninterface StridedSliceSparseSpec {\n  dims: number;\n  numAddAxisAfterEllipsis: number;\n  begin: number[];\n  end: number[];\n  strides: number[];\n  beginMask: number;\n  endMask: number;\n  ellipsisMask: number;\n  newAxisMask: number;\n  shrinkAxisMask: number;\n}\n\n// Dense slicing specification\n// all ellipses and newaxis are expanded out. So if foo[3:5, ..., -3] where foo\n// is 10 dimensional, each array of begin, end, strides will have 10 entries\n// where as the sparse can have length less than the rank of foo.\ninterface StridedSliceDenseSpec {\n  dims: number;\n  beginMask?: number;\n  endMask?: number;\n  beginValid: boolean;\n  endValid: boolean;\n  begin?: number[];\n  end?: number[];\n  strides?: number[];\n  // This array helps construct the final shape of the slice.\n  // The final tensor is reduced in rank whenever a single index e.g. foo[3]\n  // is called for. The final tensor increases in rank with newAxis entries.\n  // If an index in this array is positive, the size of the dimension is\n  // obtained from canonical end-begin.  Otherwise, if it is a NEW_AXIS, it will\n  // be 1. A shrunk dimension is skipped.\n  finalShapeGatherIndices?: number[];\n  // This array has the same size as finalShapeGatherIndices, but it remembers\n  // the sparse index that a dimension comes from, instead of dense index.\n  // A -1 in this vector means the index is not from the sparse input.\n  finalShapeGatherIndicesSparse?: number[];\n  inputShapeGatherIndicesSparse?: number[];\n  // The dense indexed shrink mask is which processing dimensions should be\n  // shrunk. For example, if foo.shape = [10, 10, 10, 10], foo[3, ..., 5] has\n  // sparseShrinkAxisMask of 5 (0101) and denseShrinkAxisMask of 9 (1001),\n  // yielding a final shape [10, 10].\n  shrinkAxisMask?: number;\n}\n\nexport type SliceInfo = {\n  finalShapeSparse: number[],\n  finalShape: number[],\n  isIdentity: boolean,\n  sliceDim0: boolean,\n  isSimpleSlice: boolean,\n  begin: number[],\n  end: number[],\n  strides: number[]\n};\n\nexport function assertParamsValid(\n    input: TensorInfo, begin: number[], size: number[]): void {\n  const inputRank = input.shape.length;\n  util.assert(\n      inputRank === begin.length,\n      () => `Error in slice${inputRank}D: Length of begin ${begin} must ` +\n          `match the rank of the array (${inputRank}).`);\n  util.assert(\n      inputRank === size.length,\n      () => `Error in slice${inputRank}D: Length of size ${size} must ` +\n          `match the rank of the array (${inputRank}).`);\n\n  for (let i = 0; i < inputRank; ++i) {\n    util.assert(\n        begin[i] + size[i] <= input.shape[i],\n        () => `Error in slice${inputRank}D: begin[${i}] + size[${i}] ` +\n            `(${begin[i] + size[i]}) would overflow input.shape[${i}] (${\n                  input.shape[i]})`);\n  }\n}\n\n/** Converts a binary mask to an array of axes. Used in stridedSlice(). */\nexport function maskToAxes(mask: number): number[] {\n  const axes = [];\n  let axis = 0;\n  while (mask > 0) {\n    if (mask & 1) {\n      axes.push(axis);\n    }\n    mask /= 2;\n    axis++;\n  }\n  return axes;\n}\n\n/** Computes the output shape given the strided slice params. */\nexport function computeOutShape(\n    begin: number[], end: number[], strides: number[]): number[] {\n  const size = [];\n  for (let axis = 0; axis < begin.length; axis++) {\n    size[axis] = Math.ceil((end[axis] - begin[axis]) / strides[axis]);\n  }\n  return size;\n}\n\n// Creates full selection at the elided dimensions. If the dimension matches\n// the ellipsis mask, override the current stride value. Otherwise, insert.\nexport function stridesWithElidedDims(\n    strides: number[], ellipsisInsertionIndex: number, numElidedAxes: number,\n    inputShape: number[]): number[] {\n  const newStrides = [...strides];\n  for (let i = newStrides.length; i < inputShape.length; i++) {\n    newStrides.push(1);\n  }\n  for (let i = 0; i < numElidedAxes; i++) {\n    if (i === 0) {\n      newStrides[ellipsisInsertionIndex] = 1;\n    } else {\n      newStrides.splice(\n          ellipsisInsertionIndex, 0 /* num elements to delete */,\n          1 /* element to add */);\n      newStrides.pop();\n    }\n  }\n  return newStrides;\n}\n\nfunction unnormalizeAxis(\n    ellipsisInsertionIndex: number, numElidedAxes: number,\n    normalizedAxis: number): number {\n  if (normalizedAxis <= ellipsisInsertionIndex) {\n    return normalizedAxis;\n  }\n\n  return normalizedAxis - (numElidedAxes - 1);\n}\n\nfunction getElidedAxes(numElidedAxes: number, ellipsisInsertionIndex: number) {\n  const elidedAxes = [];\n  for (let i = 0; i < numElidedAxes; i++) {\n    elidedAxes.push(ellipsisInsertionIndex + i);\n  }\n  return elidedAxes;\n}\n\n// Normalize the start, end and strides.\nexport function getNormalizedAxes(\n    inputShape: number[], ellipsisAxes: number[], numInterpolatedAxes: number,\n    begin: number[], end: number[], strides: number[], beginMask: number,\n    endMask: number,\n    ellipsisMask: number): {begin: number[], end: number[], strides: number[]} {\n  const inputRank = inputShape.length;\n  let normalizedBegin = new Array(inputRank),\n      normalizedEnd = new Array(inputRank),\n      normalizedStrides = new Array(inputRank);\n  if (ellipsisAxes.length && numInterpolatedAxes > 0) {\n    const fullIndex = ellipsisAxes[0];\n\n    // The ellipsis applies to the masked index as well as any dimensions\n    // that are interpolated.\n    const numElidedAxes = numInterpolatedAxes + 1;\n    normalizedBegin = startIndicesWithElidedDims(\n        beginMask, fullIndex, numElidedAxes, begin, inputShape);\n    normalizedEnd = stopIndicesWithElidedDims(\n        endMask, fullIndex, numElidedAxes, end, inputShape);\n    normalizedStrides =\n        stridesWithElidedDims(strides, fullIndex, numElidedAxes, inputShape);\n  } else {\n    for (let axis = 0; axis < inputRank; axis++) {\n      normalizedBegin[axis] = startForAxis(\n          beginMask, begin, strides, inputShape, axis, ellipsisMask);\n      normalizedEnd[axis] =\n          stopForAxis(endMask, end, strides, inputShape, axis, ellipsisMask);\n      normalizedStrides[axis] = stridesForAxis(strides, axis, ellipsisMask);\n    }\n  }\n\n  return {\n    begin: normalizedBegin,\n    end: normalizedEnd,\n    strides: normalizedStrides\n  };\n}\n\n// Creates full selection at the elided dimensions. If the dimension matches\n// the ellipsis mask, override the current start value. Otherwise, insert.\nexport function startIndicesWithElidedDims(\n    beginMask: number, ellipsisInsertionIndex: number, numElidedAxes: number,\n    originalBegin: number[], inputShape: number[]): number[] {\n  const newIndices = [...inputShape];\n  const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);\n\n  for (let axis = 0; axis < newIndices.length; axis++) {\n    if (elidedAxes.indexOf(axis) > -1) {\n      newIndices[axis] = 0;\n    } else {\n      const originalAxis =\n          unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);\n      let originalValue = originalBegin[originalAxis];\n      if (beginMask & 1 << originalAxis) {\n        originalValue = 0;\n      }\n\n      newIndices[axis] = originalValue;\n    }\n  }\n  return newIndices;\n}\n\n// Creates full selection at the elided dimensions. If the dimension matches\n// the ellipsis mask, override the current stop value. Otherwise, insert.\nexport function stopIndicesWithElidedDims(\n    endMask: number, ellipsisInsertionIndex: number, numElidedAxes: number,\n    originalEnd: number[], inputShape: number[]): number[] {\n  const newIndices = [...inputShape];\n  const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);\n\n  for (let axis = 0; axis < newIndices.length; axis++) {\n    if (elidedAxes.indexOf(axis) > -1) {\n      newIndices[axis] = Number.MAX_SAFE_INTEGER;\n    } else {\n      const originalAxis =\n          unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);\n      let originalValue = originalEnd[originalAxis];\n      if (endMask & 1 << originalAxis) {\n        originalValue = Number.MAX_SAFE_INTEGER;\n      }\n      newIndices[axis] = originalValue;\n    }\n  }\n\n  for (let i = 0; i < newIndices.length; i++) {\n    // Handle negative indices\n    const axisSize = inputShape[i];\n    if (newIndices[i] < 0) {\n      newIndices[i] += axisSize;\n    }\n    newIndices[i] = util.clamp(0, newIndices[i], inputShape[i]);\n  }\n  return newIndices;\n}\n\nexport function stridesForAxis(\n    strides: number[], axis: number, ellipsisMask: number): number {\n  let stride = strides[axis];\n  if (ellipsisMask & (1 << axis) || stride == null) {\n    stride = 1;\n  }\n\n  return stride;\n}\n\nexport function startForAxis(\n    beginMask: number, startIndices: number[], strides: number[],\n    inputShape: number[], axis: number, ellipsisMask: number): number {\n  // Begin with the specified index\n  let start = startIndices[axis];\n  const stride = strides[axis] || 1;\n\n  // Check the axis bit from right of masked axes, or the begin index is not set\n  // for the axis.\n  if (beginMask & 1 << axis || ellipsisMask & 1 << axis || start == null) {\n    if (stride > 0) {\n      // Forward iteration - use the first element. These values will get\n      // clamped below (Note: We could have set them to 0 and axis_size-1, but\n      // use lowest() and max() to maintain symmetry with StopForAxis())\n      start = Number.MIN_SAFE_INTEGER;\n    } else {\n      // Backward iteration - use the last element.\n      start = Number.MAX_SAFE_INTEGER;\n    }\n  }\n\n  // Handle negative indices\n  const axisSize = inputShape[axis];\n  if (start < 0) {\n    start += axisSize;\n  }\n\n  // Clamping\n  start = util.clamp(0, start, axisSize - 1);\n\n  return start;\n}\n\nexport function stopForAxis(\n    endMask: number, stopIndices: number[], strides: number[],\n    inputShape: number[], axis: number, ellipsisMask: number): number {\n  // Begin with the specified index\n  let stop = stopIndices[axis];\n  const stride = strides[axis] || 1;\n\n  // Check the axis bit from right of masked axes, or if the stop index is not\n  // set for this axis.\n  if (endMask & (1 << axis) || ellipsisMask & (1 << axis) || stop == null) {\n    if (stride > 0) {\n      // Forward iteration - use the last element. These values will get\n      // clamped below\n      stop = Number.MAX_SAFE_INTEGER;\n    } else {\n      // Backward iteration - use the first element.\n      stop = Number.MIN_SAFE_INTEGER;\n    }\n  }\n\n  // Handle negative indices\n  const axisSize = inputShape[axis];\n  if (stop < 0) {\n    stop += axisSize;\n  }\n\n  // Clamping\n  // Because the end index points one past the last element, we need slightly\n  // different clamping ranges depending on the direction.\n  if (stride > 0) {\n    // Forward iteration\n    stop = util.clamp(0, stop, axisSize);\n  } else {\n    // Backward iteration\n    stop = util.clamp(-1, stop, axisSize - 1);\n  }\n\n  return stop;\n}\n\n/**\n * Returns true if the slice occupies a continous set of elements in the\n * 'flat' space.\n */\nexport function isSliceContinous(\n    shape: number[], begin: number[], size: number[]) {\n  // Index of the first axis that has size > 1.\n  let firstNonOneAxis = size.length;\n  for (let i = 0; i < size.length; i++) {\n    if (size[i] > 1) {\n      firstNonOneAxis = i;\n      break;\n    }\n  }\n\n  for (let i = firstNonOneAxis + 1; i < size.length; i++) {\n    if (begin[i] > 0 || size[i] !== shape[i]) {\n      return false;\n    }\n  }\n  return true;\n}\n\nexport function computeFlatOffset(begin: number[], strides: number[]): number {\n  let flatOffset = begin.length > 0 ? begin[begin.length - 1] : 1;\n  for (let i = 0; i < begin.length - 1; i++) {\n    flatOffset += begin[i] * strides[i];\n  }\n  return flatOffset;\n}\n\nexport function parseSliceParams(\n    x: TensorInfo, begin: number|number[], size?: number|number[]) {\n  // The following logic allows for more ergonomic calls.\n  let begin_: number[];\n  const xRank = x.shape.length;\n  if (typeof begin === 'number') {\n    begin_ = [begin, ...new Array(xRank - 1).fill(0)];\n  } else if (begin.length < xRank) {\n    begin_ = begin.concat(new Array(xRank - begin.length).fill(0));\n  } else {\n    begin_ = begin.slice();\n  }\n  begin_.forEach(d => {\n    util.assert(\n        d !== -1, () => 'slice() does not support negative begin indexing.');\n  });\n  let size_: number[];\n  if (size == null) {\n    size_ = new Array(xRank).fill(-1);\n  } else if (typeof size === 'number') {\n    size_ = [size, ...new Array(xRank - 1).fill(-1)];\n  } else if (size.length < xRank) {\n    size_ = size.concat(new Array(xRank - size.length).fill(-1));\n  } else {\n    size_ = size;\n  }\n  size_ = size_.map((d, i) => {\n    if (d >= 0) {\n      return d;\n    } else {\n      util.assert(\n          d === -1,\n          () => `Negative size values should be exactly -1 but got ` +\n              `${d} for the slice() size at index ${i}.`);\n      return x.shape[i] - begin_[i];\n    }\n  });\n  return [begin_, size_];\n}\n\n// Convert the slicing specification from a sparse representation to a dense\n// representation. This means that all ellipses and newaxis are expanded out.\nexport function sliceInfo(\n    xShape: number[], begin: number[], end: number[], strides: number[],\n    beginMask: number, endMask: number, ellipsisMask: number,\n    newAxisMask: number, shrinkAxisMask: number): SliceInfo {\n  let stridesNonNull;\n  if (strides == null) {\n    stridesNonNull = new Array(begin.length);\n    stridesNonNull.fill(1);\n  } else {\n    stridesNonNull = strides;\n  }\n\n  // Only one non-zero bit is allowed in ellipsisMask, which means ellipsisMask\n  // is a power of 2. Use bit compares to ensure ellipsisMask is 0 or a power\n  // of 2. When i is a power of 2, i & (i - 1) is always 0.\n  // Also ref:\n  // https://stackoverflow.com/questions/600293/how-to-check-if-a-number-is-a-power-of-2\n  if (ellipsisMask != null && (ellipsisMask & (ellipsisMask - 1)) !== 0) {\n    throw new Error('Multiple ellipses in slice is not allowed.');\n  }\n\n  // Step 1: Account for ellipsis and new axis.\n  // Check for ellipsis and count how many non-newaxis there are after.\n  let ellipsisSeen = false;\n\n  const sparseSpec: StridedSliceSparseSpec = {\n    dims: stridesNonNull.length,\n    numAddAxisAfterEllipsis: 0,\n    begin: begin.slice(),\n    end: end.slice(),\n    strides: stridesNonNull.slice(),\n    beginMask,\n    endMask,\n    ellipsisMask,\n    newAxisMask,\n    shrinkAxisMask\n  };\n\n  for (let i = 0; i < sparseSpec.dims; i++) {\n    if (ellipsisSeen && ((1 << i) & newAxisMask) !== 0) {\n      sparseSpec.numAddAxisAfterEllipsis++;\n    }\n    if ((1 << i) & ellipsisMask) {\n      ellipsisSeen = true;\n    }\n  }\n  // If no ellipsis insert one at the end.\n  if (!ellipsisSeen) {\n    sparseSpec.ellipsisMask |= (1 << sparseSpec.dims);\n    sparseSpec.dims++;  // this effects loop iteration below\n  }\n\n  // Step 2: Make a sparse spec into a full index spec.\n  //\n  // The sparse spec deos not correspond to the number of dimensions.\n  // Make a dense spec that cooresponds to the number of dimensions.\n  //\n  // For example suppose foo[...,3:] on foo.shape = [2, 2, 3] then we need to\n  // produce the missing beginMask for the first two dimensions i.e. from\n  // beginMaskSpec = 0, endMaskSpec = 2, we achieve beginMask = 6 (110),\n  // endMask = 7 (111).\n  const denseSpec: StridedSliceDenseSpec = {\n    dims: xShape.length,\n    beginMask: 0,\n    endMask: 0,\n    beginValid: false,\n    endValid: false\n  };\n\n  buildDenseSpec(sparseSpec, denseSpec);\n\n  // Step 3: Make implicit ranges (non-zero beginMasks and endMasks) explicit\n  // and bounds check.\n  let isIdentity = true;\n  let sliceDim0 = true;\n  let isSimpleSlice = true;\n  const processingShape = [];\n  const finalShape = [];\n\n  for (let i = 0; i < xShape.length; ++i) {\n    if (denseSpec.strides[i] === 0) {\n      throw Error(`strides[${i}] must be non-zero`);\n    }\n    const shrinkI = !!(denseSpec.shrinkAxisMask & (1 << i));\n    const dimI = xShape[i];\n    if (dimI === -1) {\n      processingShape.push(shrinkI ? 1 : -1);\n      continue;\n    }\n\n    const masks =\n        [denseSpec.beginMask & (1 << i), denseSpec.endMask & (1 << i)];\n    const validRange = [\n      denseSpec.strides[i] > 0 ? 0 : -1,\n      denseSpec.strides[i] > 0 ? dimI : dimI - 1\n    ];\n\n    if (shrinkI && denseSpec.strides[i] <= 0) {\n      throw Error('only stride 1 allowed on non-range indexing.');\n    }\n\n    isSimpleSlice = isSimpleSlice && (denseSpec.strides[i] === 1);\n\n    const beginAndEndMasked =\n        !!((denseSpec.beginMask & (1 << i)) && (denseSpec.endMask & (1 << i)));\n\n    if (denseSpec.beginValid && denseSpec.endValid) {\n      if (shrinkI) {\n        // If we are shrinking, the end index is now possibly incorrect. In\n        // particular foo[-1] produces sparseBegin = -1, sparseEnd = 0.\n        // and canonical puts these to n-1 and 0, which implies a degenerate\n        // interval. Fortunately, it is now safe to re-create end as begin + 1.\n        const xFwd = denseSpec.begin[i] < 0 ? dimI + denseSpec.begin[i] :\n                                              denseSpec.begin[i];\n        denseSpec.begin[i] = xFwd;\n        denseSpec.end[i] = denseSpec.begin[i] + 1;\n        if (xFwd < 0 || xFwd >= dimI) {\n          throw Error(`slice index ${denseSpec.begin[i]} of dimension ${\n              i} out of bounds.`);\n        }\n      } else {\n        denseSpec.begin[i] = canonical(\n            denseSpec.begin[i], 0, denseSpec.strides[i], dimI, masks,\n            validRange);\n        denseSpec.end[i] = canonical(\n            denseSpec.end[i], 1, denseSpec.strides[i], dimI, masks, validRange);\n      }\n      // Update optimization values\n      const takeAllInDimension = denseSpec.strides[i] === 1 &&\n          denseSpec.begin[i] === 0 && denseSpec.end[i] === dimI;\n      isIdentity = isIdentity && takeAllInDimension;\n      sliceDim0 = sliceDim0 &&\n          ((i === 0 && denseSpec.strides[i] === 1) || takeAllInDimension);\n    } else {\n      isIdentity =\n          isIdentity && ((denseSpec.strides[i] === 1) && beginAndEndMasked);\n      sliceDim0 = sliceDim0 &&\n          ((i === 0 && denseSpec.strides[i] === 1) || beginAndEndMasked);\n    }\n    // Compute the processing shape (the intermediate Eigen will produce)\n    let intervalLength;\n    let knownInterval = false;\n    if (denseSpec.beginValid && denseSpec.endValid) {\n      intervalLength = denseSpec.end[i] - denseSpec.begin[i];\n      knownInterval = true;\n    } else if (shrinkI) {\n      // The dimension is still known as 1 for the processingShape, but will be\n      // discarded for the final shape.\n      intervalLength = 1;\n      knownInterval = true;\n    } else if (beginAndEndMasked) {\n      // Even if we don't have values for begin or end, we do know that this\n      // dimension covers the whole interval. If we have shape information for\n      // this dimension, that tells us the interval length.\n      if (dimI >= 0) {\n        if (denseSpec.strides[i] < 0) {\n          intervalLength = -dimI;\n        } else {\n          intervalLength = dimI;\n        }\n        knownInterval = true;\n      }\n    }\n    if (knownInterval) {\n      let sizeI;\n      // Hold zero if the interval is degenerate, otherwise account for\n      // remainder\n      if (intervalLength === 0 ||\n          ((intervalLength < 0) !== (denseSpec.strides[i] < 0))) {\n        sizeI = 0;\n      } else {\n        sizeI = Math.trunc(intervalLength / denseSpec.strides[i]) +\n            (intervalLength % denseSpec.strides[i] !== 0 ? 1 : 0);\n      }\n      processingShape.push(sizeI);\n    } else {\n      processingShape.push(-1);\n    }\n  }\n\n  // Step 4: Compute the final shape\n  //\n  // newAxis will increase dimension by 1 (with a one-size dimension)\n  // slices like foo[3, ...] will reduce dimension by 1.\n  // This cannot be done earlier, because it depends on Step 3.\n  for (let denseDim = 0; denseDim < denseSpec.finalShapeGatherIndices.length;\n       ++denseDim) {\n    const gatherIndex = denseSpec.finalShapeGatherIndices[denseDim];\n    if (gatherIndex >= 0) {\n      finalShape.push(processingShape[gatherIndex]);\n    } else if (gatherIndex === NEW_AXIS) {\n      finalShape.push(1);\n    }\n  }\n\n  const finalShapeSparse = finalShape.filter(\n      (dim, i) => denseSpec.finalShapeGatherIndices[i] !== NEW_AXIS);\n\n  return {\n    finalShapeSparse,\n    finalShape,\n    isIdentity,\n    sliceDim0,\n    isSimpleSlice,\n    begin: denseSpec.begin,\n    end: denseSpec.end,\n    strides: denseSpec.strides\n  };\n}\n\nfunction buildDenseSpec(\n    sparse: StridedSliceSparseSpec, dense: StridedSliceDenseSpec) {\n  dense.beginMask = 0;\n  dense.endMask = 0;\n  dense.shrinkAxisMask = 0;\n\n  let fullIndex = 0;\n  dense.beginValid = sparse.begin != null;\n  dense.endValid = sparse.end != null;\n\n  dense.begin = new Array(dense.dims);\n  dense.end = new Array(dense.dims);\n  dense.strides = new Array(dense.dims);\n  dense.finalShapeGatherIndices = [];\n  dense.finalShapeGatherIndicesSparse = [];\n  dense.inputShapeGatherIndicesSparse = new Array(dense.dims);\n\n  for (let i = 0; i < sparse.dims; i++) {\n    if ((1 << i) & sparse.ellipsisMask) {\n      // Only the bit that has ellipsis will fall in this condition.\n      // Expand the ellipsis into the appropriate indices\n      // Note: this only works because we guaranteed one ellipsis.\n      const nextIndex = Math.min(\n          dense.dims - (sparse.dims - i) + 1 + sparse.numAddAxisAfterEllipsis,\n          dense.dims);\n      for (; fullIndex < nextIndex; fullIndex++) {\n        // newAxis aren't real axis so you have to skip.\n        dense.begin[fullIndex] = 0;\n        dense.end[fullIndex] = 0;\n        dense.strides[fullIndex] = 1;\n        dense.beginMask |= (1 << fullIndex);\n        dense.endMask |= (1 << fullIndex);\n        dense.finalShapeGatherIndices.push(fullIndex);\n        dense.finalShapeGatherIndicesSparse.push(-1);\n        dense.inputShapeGatherIndicesSparse[fullIndex] = i;\n      }\n    } else if ((1 << i) & sparse.newAxisMask) {\n      // Only the bit that has newAxis will fall in this condition.\n      dense.finalShapeGatherIndices.push(NEW_AXIS);\n      dense.finalShapeGatherIndicesSparse.push(-1);\n    } else {\n      if (fullIndex === dense.begin.length) {\n        throw Error(\n            `Index out of range using input dim ${fullIndex}; input ` +\n            `has only ${dense.dims} dims, ${dense.begin.length}.`);\n      }\n\n      // Gather slicing spec into appropriate index.\n      if (sparse.begin != null) {\n        dense.begin[fullIndex] = sparse.begin[i];\n      }\n      if (sparse.end != null) {\n        dense.end[fullIndex] = sparse.end[i];\n      }\n      dense.strides[fullIndex] = sparse.strides[i];\n      if (sparse.beginMask & (1 << i)) {\n        dense.beginMask |= (1 << fullIndex);\n      }\n      if (sparse.endMask & (1 << i)) {\n        dense.endMask |= (1 << fullIndex);\n      }\n      // If shrink, record where to get the dimensionality from (i.e. newAxis)\n      // creates a fake 1 size dimension. Also remember shrink axis (now in\n      // dense form) so we can ignore dense.end below.\n      if (sparse.shrinkAxisMask & (1 << i)) {\n        dense.finalShapeGatherIndices.push(SHRINK_AXIS);\n        dense.finalShapeGatherIndicesSparse.push(-1);\n        dense.shrinkAxisMask |= (1 << fullIndex);\n      } else {\n        dense.finalShapeGatherIndices.push(fullIndex);\n        // Remember that where in the sparse shape the dense dim comes from.\n        dense.finalShapeGatherIndicesSparse.push(i);\n      }\n      dense.inputShapeGatherIndicesSparse[fullIndex] = i;\n      fullIndex++;\n    }\n  }\n}\n\nfunction canonical(\n    x: number, c: number, strideI: number, dimI: number, masks: number[],\n    validRange: number[]) {\n  if (masks[c]) {\n    return strideI > 0 ? validRange[c] : validRange[(c + 1) & 1];\n  } else {\n    const xFwd = x < 0 ? dimI + x : x;  // make negative indices positive\n    return xFwd < validRange[0] ? validRange[0] :\n                                  xFwd > validRange[1] ? validRange[1] : xFwd;\n  }\n}\n"]}","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Maximum } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { cast } from './cast';\nimport { op } from './operation';\n/**\n * Returns the max of a and b (`a > b ? a : b`) element-wise.\n * Supports broadcasting.\n *\n * We also expose `tf.maximumStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.maximum(b).print(); // or tf.maximum(a, b)\n * ```\n *\n * ```js\n * // Broadcast maximum a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.maximum(b).print(); // or tf.maximum(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction maximum_(a, b) {\n let $a = convertToTensor(a, 'a', 'maximum');\n let $b = convertToTensor(b, 'b', 'maximum');\n [$a, $b] = makeTypesMatch($a, $b);\n if ($a.dtype === 'bool') {\n $a = cast($a, 'int32');\n $b = cast($b, 'int32');\n }\n assertAndGetBroadcastShape($a.shape, $b.shape);\n const inputs = { a: $a, b: $b };\n return ENGINE.runKernel(Maximum, inputs);\n}\nexport const maximum = op({ maximum_ });\n//# 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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { BatchToSpaceND } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * This operation reshapes the \"batch\" dimension 0 into `M + 1` dimensions of\n * shape `blockShape + [batch]`, interleaves these blocks back into the grid\n * defined by the spatial dimensions `[1, ..., M]`, to obtain a result with\n * the same rank as the input. The spatial dimensions of this intermediate\n * result are then optionally cropped according to `crops` to produce the\n * output. This is the reverse of `tf.spaceToBatchND`. See below for a precise\n * description.\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [4, 1, 1, 1]);\n * const blockShape = [2, 2];\n * const crops = [[0, 0], [0, 0]];\n *\n * x.batchToSpaceND(blockShape, crops).print();\n * ```\n *\n * @param x A `tf.Tensor`. N-D with `x.shape` = `[batch] + spatialShape +\n * remainingShape`, where spatialShape has `M` dimensions.\n * @param blockShape A 1-D array. Must have shape `[M]`, all values must\n * be >= 1.\n * @param crops A 2-D array. Must have shape `[M, 2]`, all values must be >= 0.\n * `crops[i] = [cropStart, cropEnd]` specifies the amount to crop from input\n * dimension `i + 1`, which corresponds to spatial dimension `i`. It is required\n * that `cropStart[i] + cropEnd[i] <= blockShape[i] * inputShape[i + 1]`\n *\n * This operation is equivalent to the following steps:\n *\n * 1. Reshape `x` to `reshaped` of shape: `[blockShape[0], ...,\n * blockShape[M-1], batch / prod(blockShape), x.shape[1], ...,\n * x.shape[N-1]]`\n *\n * 2. Permute dimensions of `reshaped`to produce `permuted` of shape `[batch /\n * prod(blockShape),x.shape[1], blockShape[0], ..., x.shape[M],\n * blockShape[M-1],x.shape[M+1], ..., x.shape[N-1]]`\n *\n * 3. Reshape `permuted` to produce `reshapedPermuted` of shape `[batch /\n * prod(blockShape),x.shape[1] * blockShape[0], ..., x.shape[M] *\n * blockShape[M-1],x.shape[M+1], ..., x.shape[N-1]]`\n *\n * 4. Crop the start and end of dimensions `[1, ..., M]` of `reshapedPermuted`\n * according to `crops` to produce the output of shape: `[batch /\n * prod(blockShape),x.shape[1] * blockShape[0] - crops[0,0] - crops[0,1],\n * ..., x.shape[M] * blockShape[M-1] - crops[M-1,0] -\n * crops[M-1,1],x.shape[M+1], ..., x.shape[N-1]]`\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction batchToSpaceND_(x, blockShape, crops) {\n const $x = convertToTensor(x, 'x', 'batchToSpaceND');\n const prod = blockShape.reduce((a, b) => a * b);\n util.assert($x.rank >= 1 + blockShape.length, () => `input rank is ${$x.rank} but should be > than blockShape.length ${blockShape.length}`);\n util.assert(crops.length === blockShape.length, () => `crops.length is ${crops.length} but should be equal to blockShape.length ${blockShape.length}`);\n util.assert($x.shape[0] % prod === 0, () => `input tensor batch is ${$x.shape[0]} but is not divisible by the product of ` +\n `the elements of blockShape ${blockShape.join(' * ')} === ${prod}`);\n const inputs = { x: $x };\n const attrs = { blockShape, crops };\n return ENGINE.runKernel(BatchToSpaceND, inputs, attrs);\n}\nexport const batchToSpaceND = op({ batchToSpaceND_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Cos } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes cos of the input `tf.Tensor` element-wise: `cos(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);\n *\n * x.cos().print(); // or tf.cos(x)\n * ```\n * @param x The input tensor. Must be float32 type.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction cos_(x) {\n const $x = convertToTensor(x, 'x', 'cos', 'float32');\n const inputs = { x: $x };\n return ENGINE.runKernel(Cos, inputs);\n}\nexport const cos = op({ cos_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { DepthwiseConv2dNative } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Depthwise 2D convolution.\n *\n * Given a 4D `input` array and a `filter` array of shape\n * `[filterHeight, filterWidth, inChannels, channelMultiplier]` containing\n * `inChannels` convolutional filters of depth 1, this op applies a\n * different filter to each input channel (expanding from 1 channel to\n * `channelMultiplier` channels for each), then concatenates the results\n * together. The output has `inChannels * channelMultiplier` channels.\n *\n * See\n * [https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d](\n * https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d)\n * for more details.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter tensor, rank 4, of shape\n * `[filterHeight, filterWidth, inChannels, channelMultiplier]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`. If strides is a single number, then `strideHeight ==\n * strideWidth`.\n * @param pad The type of padding algorithm.\n * - `same` and stride 1: output will be of same size as input,\n * regardless of filter size.\n * - `valid`: output will be smaller than input if filter is larger\n * than 1x1.\n * - For more info, see this guide:\n * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](\n * https://www.tensorflow.org/api_docs/python/tf/nn/convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n * in which we sample input values across the height and width dimensions\n * in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single\n * number, then `dilationHeight == dilationWidth`. If it is greater than\n * 1, then all values of `strides` must be 1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n * \"NHWC\". Specify the data format of the input and output data. With the\n * default format \"NHWC\", the data is stored in the order of: [batch,\n * height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n * provided, it will default to truncate.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction depthwiseConv2d_(x, filter, strides, pad, dataFormat = 'NHWC', dilations = [1, 1], dimRoundingMode) {\n const $x = convertToTensor(x, 'x', 'depthwiseConv2d', 'float32');\n const $filter = convertToTensor(filter, 'filter', 'depthwiseConv2d', 'float32');\n let x4D = $x;\n let reshapedTo4D = false;\n if ($x.rank === 3) {\n reshapedTo4D = true;\n x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n }\n util.assert(x4D.rank === 4, () => `Error in depthwiseConv2d: input must be rank 4, but got ` +\n `rank ${x4D.rank}.`);\n util.assert($filter.rank === 4, () => `Error in depthwiseConv2d: filter must be rank 4, but got rank ` +\n `${$filter.rank}.`);\n util.assert(x4D.shape[3] === $filter.shape[2], () => `Error in depthwiseConv2d: number of input channels ` +\n `(${x4D.shape[3]}) must match the inChannels dimension in ` +\n `filter ${$filter.shape[2]}.`);\n conv_util.checkPadOnDimRoundingMode('depthwiseConv2d', pad, dimRoundingMode);\n const inputs = { x: x4D, filter: $filter };\n const attrs = { strides, pad, dataFormat, dilations, dimRoundingMode };\n // tslint:disable-next-line: no-unnecessary-type-assertion\n const res = ENGINE.runKernel(DepthwiseConv2dNative, inputs, attrs);\n if (reshapedTo4D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n }\n return res;\n}\nexport const depthwiseConv2d = op({ depthwiseConv2d_ });\n//# sourceMappingURL=data:application/json;base64,{"version":3,"file":"depthwise_conv2d.js","sourceRoot":"","sources":["../../../../../../tfjs-core/src/ops/depthwise_conv2d.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AACH,OAAO,EAAC,MAAM,EAAC,MAAM,WAAW,CAAC;AACjC,OAAO,EAAC,qBAAqB,EAA0D,MAAM,iBAAiB,CAAC;AAI/G,OAAO,EAAC,eAAe,EAAC,MAAM,oBAAoB,CAAC;AAEnD,OAAO,KAAK,IAAI,MAAM,SAAS,CAAC;AAEhC,OAAO,KAAK,SAAS,MAAM,aAAa,CAAC;AACzC,OAAO,EAAC,EAAE,EAAC,MAAM,aAAa,CAAC;AAC/B,OAAO,EAAC,OAAO,EAAC,MAAM,WAAW,CAAC;AAElC;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;GA4CG;AACH,SAAS,gBAAgB,CACrB,CAAe,EAAE,MAA2B,EAC5C,OAAgC,EAChC,GAAoD,EACpD,aAA4B,MAAM,EAClC,YAAqC,CAAC,CAAC,EAAE,CAAC,CAAC,EAC3C,eAAwC;IAC1C,MAAM,EAAE,GAAG,eAAe,CAAC,CAAC,EAAE,GAAG,EAAE,iBAAiB,EAAE,SAAS,CAAC,CAAC;IACjE,MAAM,OAAO,GACT,eAAe,CAAC,MAAM,EAAE,QAAQ,EAAE,iBAAiB,EAAE,SAAS,CAAC,CAAC;IAEpE,IAAI,GAAG,GAAG,EAAc,CAAC;IACzB,IAAI,YAAY,GAAG,KAAK,CAAC;IACzB,IAAI,EAAE,CAAC,IAAI,KAAK,CAAC,EAAE;QACjB,YAAY,GAAG,IAAI,CAAC;QACpB,GAAG,GAAG,OAAO,CAAC,EAAE,EAAE,CAAC,CAAC,EAAE,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;KAC/D;IACD,IAAI,CAAC,MAAM,CACP,GAAG,CAAC,IAAI,KAAK,CAAC,EACd,GAAG,EAAE,CAAC,0DAA0D;QAC5D,QAAQ,GAAG,CAAC,IAAI,GAAG,CAAC,CAAC;IAC7B,IAAI,CAAC,MAAM,CACP,OAAO,CAAC,IAAI,KAAK,CAAC,EAClB,GAAG,EAAE,CAAC,gEAAgE;QAClE,GAAG,OAAO,CAAC,IAAI,GAAG,CAAC,CAAC;IAC5B,IAAI,CAAC,MAAM,CACP,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,KAAK,OAAO,CAAC,KAAK,CAAC,CAAC,CAAC,EACjC,GAAG,EAAE,CAAC,qDAAqD;QACvD,IAAI,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,2CAA2C;QAC3D,UAAU,OAAO,CAAC,KAAK,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC;IACvC,SAAS,CAAC,yBAAyB,CAAC,iBAAiB,EAAE,GAAG,EAAE,eAAe,CAAC,CAAC;IAC7E,MAAM,MAAM,GAAgC,EAAC,CAAC,EAAE,GAAG,EAAE,MAAM,EAAE,OAAO,EAAC,CAAC;IACtE,MAAM,KAAK,GACP,EAAC,OAAO,EAAE,GAAG,EAAE,UAAU,EAAE,SAAS,EAAE,eAAe,EAAC,CAAC;IAE3D,0DAA0D;IAC1D,MAAM,GAAG,GAAG,MAAM,CAAC,SAAS,CACZ,qBAAqB,EAAE,MAA8B,EACrD,KAA2B,CAAM,CAAC;IAElD,IAAI,YAAY,EAAE;QAChB,OAAO,OAAO,CAAC,GAAG,EAAE,CAAC,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAM,CAAC;KACtE;IACD,OAAO,GAAG,CAAC;AACb,CAAC;AAED,MAAM,CAAC,MAAM,eAAe,GAAG,EAAE,CAAC,EAAC,gBAAgB,EAAC,CAAC,CAAC","sourcesContent":["/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\nimport {DepthwiseConv2dNative, DepthwiseConv2dNativeAttrs, DepthwiseConv2dNativeInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor3D, Tensor4D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport * as conv_util from './conv_util';\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Depthwise 2D convolution.\n *\n * Given a 4D `input` array and a `filter` array of shape\n * `[filterHeight, filterWidth, inChannels, channelMultiplier]` containing\n * `inChannels` convolutional filters of depth 1, this op applies a\n * different filter to each input channel (expanding from 1 channel to\n * `channelMultiplier` channels for each), then concatenates the results\n * together. The output has `inChannels * channelMultiplier` channels.\n *\n * See\n * [https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d](\n *     https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d)\n * for more details.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter tensor, rank 4, of shape\n *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`. If strides is a single number, then `strideHeight ==\n * strideWidth`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](\n *          https://www.tensorflow.org/api_docs/python/tf/nn/convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n *     provided, it will default to truncate.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction depthwiseConv2d_<T extends Tensor3D|Tensor4D>(\n    x: T|TensorLike, filter: Tensor4D|TensorLike,\n    strides: [number, number]|number,\n    pad: 'valid'|'same'|number|conv_util.ExplicitPadding,\n    dataFormat: 'NHWC'|'NCHW' = 'NHWC',\n    dilations: [number, number]|number = [1, 1],\n    dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  const $x = convertToTensor(x, 'x', 'depthwiseConv2d', 'float32');\n  const $filter =\n      convertToTensor(filter, 'filter', 'depthwiseConv2d', 'float32');\n\n  let x4D = $x as Tensor4D;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n  util.assert(\n      x4D.rank === 4,\n      () => `Error in depthwiseConv2d: input must be rank 4, but got ` +\n          `rank ${x4D.rank}.`);\n  util.assert(\n      $filter.rank === 4,\n      () => `Error in depthwiseConv2d: filter must be rank 4, but got rank ` +\n          `${$filter.rank}.`);\n  util.assert(\n      x4D.shape[3] === $filter.shape[2],\n      () => `Error in depthwiseConv2d: number of input channels ` +\n          `(${x4D.shape[3]}) must match the inChannels dimension in ` +\n          `filter ${$filter.shape[2]}.`);\n  conv_util.checkPadOnDimRoundingMode('depthwiseConv2d', pad, dimRoundingMode);\n  const inputs: DepthwiseConv2dNativeInputs = {x: x4D, filter: $filter};\n  const attrs: DepthwiseConv2dNativeAttrs =\n      {strides, pad, dataFormat, dilations, dimRoundingMode};\n\n  // tslint:disable-next-line: no-unnecessary-type-assertion\n  const res = ENGINE.runKernel(\n                  DepthwiseConv2dNative, inputs as {} as NamedTensorMap,\n                  attrs as {} as NamedAttrMap) as T;\n\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as T;\n  }\n  return res;\n}\n\nexport const depthwiseConv2d = op({depthwiseConv2d_});\n"]}","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Equal } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a == b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.equal(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction equal_(a, b) {\n let $a = convertToTensor(a, 'a', 'equal', 'string_or_numeric');\n let $b = convertToTensor(b, 'b', 'equal', 'string_or_numeric');\n [$a, $b] = makeTypesMatch($a, $b);\n assertAndGetBroadcastShape($a.shape, $b.shape);\n const inputs = { a: $a, b: $b };\n return ENGINE.runKernel(Equal, inputs);\n}\nexport const equal = op({ equal_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Floor } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes floor of input `tf.Tensor` element-wise: `floor(x)`.\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3]);\n *\n * x.floor().print(); // or tf.floor(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction floor_(x) {\n const $x = convertToTensor(x, 'x', 'floor', 'float32');\n const inputs = { x: $x };\n return ENGINE.runKernel(Floor, inputs);\n}\nexport const floor = op({ floor_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { GatherV2 } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Gather slices from tensor `x`'s axis `axis` according to `indices`.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * const indices = tf.tensor1d([1, 3, 3], 'int32');\n *\n * x.gather(indices).print();\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const indices = tf.tensor1d([1, 1, 0], 'int32');\n *\n * x.gather(indices).print();\n * ```\n * @param x The input tensor whose slices to be gathered.\n * @param indices The indices of the values to extract.\n * @param axis The axis over which to select values. Defaults to 0.\n * @param batchDims Optional. The number of batch dimensions. It must be less\n * than or equal to rank(indices). Defaults to 0.\n * The output tensor will have shape of\n * `x.shape[:axis] + indices.shape[batchDims:] + x.shape[axis + 1:]`\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction gather_(x, indices, axis = 0, batchDims = 0) {\n const $x = convertToTensor(x, 'x', 'gather');\n const $indices = convertToTensor(indices, 'indices', 'gather', 'int32');\n const inputs = { x: $x, indices: $indices };\n const attrs = { axis, batchDims };\n return ENGINE.runKernel(GatherV2, inputs, attrs);\n}\nexport const gather = op({ gather_ });\n//# 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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Imag } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns the imaginary part of a complex (or real) tensor.\n *\n * Given a tensor input, this operation returns a tensor of type float that is\n * the imaginary part of each element in input considered as a complex number.\n * If input is real, a tensor of all zeros is returned.\n *\n * ```js\n * const x = tf.complex([-2.25, 3.25], [4.75, 5.75]);\n * tf.imag(x).print();\n * ```\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction imag_(input) {\n const $input = convertToTensor(input, 'input', 'imag');\n const inputs = { input: $input };\n return ENGINE.runKernel(Imag, inputs);\n}\nexport const imag = op({ imag_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LogicalNot } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns the truth value of `NOT x` element-wise.\n *\n * ```js\n * const a = tf.tensor1d([false, true], 'bool');\n *\n * a.logicalNot().print();\n * ```\n *\n * @param x The input tensor. Must be of dtype 'bool'.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalNot_(x) {\n const $x = convertToTensor(x, 'x', 'logicalNot', 'bool');\n const inputs = { x: $x };\n return ENGINE.runKernel(LogicalNot, inputs);\n}\nexport const logicalNot = op({ logicalNot_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { SpaceToBatchND } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * This operation divides \"spatial\" dimensions `[1, ..., M]` of the input into\n * a grid of blocks of shape `blockShape`, and interleaves these blocks with\n * the \"batch\" dimension (0) such that in the output, the spatial\n * dimensions `[1, ..., M]` correspond to the position within the grid,\n * and the batch dimension combines both the position within a spatial block\n * and the original batch position. Prior to division into blocks,\n * the spatial dimensions of the input are optionally zero padded\n * according to `paddings`. See below for a precise description.\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);\n * const blockShape = [2, 2];\n * const paddings = [[0, 0], [0, 0]];\n *\n * x.spaceToBatchND(blockShape, paddings).print();\n * ```\n *\n * @param x A `tf.Tensor`. N-D with `x.shape` = `[batch] + spatialShape +\n * remainingShape`, where spatialShape has `M` dimensions.\n * @param blockShape A 1-D array. Must have shape `[M]`, all values must\n * be >= 1.\n * @param paddings A 2-D array. Must have shape `[M, 2]`, all values must be >=\n * 0. `paddings[i] = [padStart, padEnd]` specifies the amount to zero-pad\n * from input dimension `i + 1`, which corresponds to spatial dimension `i`. It\n * is required that\n * `(inputShape[i + 1] + padStart + padEnd) % blockShape[i] === 0`\n *\n * This operation is equivalent to the following steps:\n *\n * 1. Zero-pad the start and end of dimensions `[1, ..., M]` of the input\n * according to `paddings` to produce `padded` of shape paddedShape.\n *\n * 2. Reshape `padded` to `reshapedPadded` of shape:\n * `[batch] + [paddedShape[1] / blockShape[0], blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1], blockShape[M-1]] + remainingShape`\n *\n * 3. Permute dimensions of `reshapedPadded` to produce `permutedReshapedPadded`\n * of shape: `blockShape + [batch] + [paddedShape[1] / blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1]] + remainingShape`\n *\n * 4. Reshape `permutedReshapedPadded` to flatten `blockShape` into the\n * batch dimension, producing an output tensor of shape:\n * `[batch * prod(blockShape)] + [paddedShape[1] / blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1]] + remainingShape`\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction spaceToBatchND_(x, blockShape, paddings) {\n const $x = convertToTensor(x, 'x', 'spaceToBatchND');\n util.assert($x.rank >= 1 + blockShape.length, () => `input rank ${$x.rank} should be > than [blockShape] ${blockShape.length}`);\n util.assert(paddings.length === blockShape.length, () => `paddings.shape[0] ${paddings.length} must be equal to [blockShape] ${blockShape.length}`);\n util.assert($x.shape.reduce((a, b, i) => {\n if (i > 0 && i <= blockShape.length) {\n return a &&\n ((b + paddings[i - 1][0] + paddings[i - 1][1]) %\n blockShape[i - 1] ===\n 0);\n }\n return a;\n }, true), () => `input spatial dimensions ${$x.shape.slice(1)} with paddings ${paddings.toString()} must be divisible by blockShapes ${blockShape.toString()}`);\n const inputs = { x: $x };\n const attrs = { blockShape, paddings };\n return ENGINE.runKernel(SpaceToBatchND, inputs, attrs);\n}\nexport const spaceToBatchND = op({ spaceToBatchND_ });\n//# sourceMappingURL=data:application/json;base64,{"version":3,"file":"space_to_batch_nd.js","sourceRoot":"","sources":["../../../../../../tfjs-core/src/ops/space_to_batch_nd.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAEH,OAAO,EAAC,MAAM,EAAC,MAAM,WAAW,CAAC;AACjC,OAAO,EAAC,cAAc,EAA4C,MAAM,iBAAiB,CAAC;AAI1F,OAAO,EAAC,eAAe,EAAC,MAAM,oBAAoB,CAAC;AAEnD,OAAO,KAAK,IAAI,MAAM,SAAS,CAAC;AAEhC,OAAO,EAAC,EAAE,EAAC,MAAM,aAAa,CAAC;AAE/B;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;GA+CG;AACH,SAAS,eAAe,CACpB,CAAe,EAAE,UAAoB,EAAE,QAAoB;IAC7D,MAAM,EAAE,GAAG,eAAe,CAAC,CAAC,EAAE,GAAG,EAAE,gBAAgB,CAAC,CAAC;IAErD,IAAI,CAAC,MAAM,CACP,EAAE,CAAC,IAAI,IAAI,CAAC,GAAG,UAAU,CAAC,MAAM,EAChC,GAAG,EAAE,CAAC,cAAc,EAAE,CAAC,IAAI,kCACvB,UAAU,CAAC,MAAM,EAAE,CAAC,CAAC;IAE7B,IAAI,CAAC,MAAM,CACP,QAAQ,CAAC,MAAM,KAAK,UAAU,CAAC,MAAM,EACrC,GAAG,EAAE,CAAC,qBACF,QAAQ,CAAC,MAAM,kCAAkC,UAAU,CAAC,MAAM,EAAE,CAAC,CAAC;IAE9E,IAAI,CAAC,MAAM,CACP,EAAE,CAAC,KAAK,CAAC,MAAM,CACX,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,EAAE;QACV,IAAI,CAAC,GAAG,CAAC,IAAI,CAAC,IAAI,UAAU,CAAC,MAAM,EAAE;YACnC,OAAO,CAAC;gBACJ,CAAC,CAAC,CAAC,GAAG,QAAQ,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,QAAQ,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;oBACzC,UAAU,CAAC,CAAC,GAAG,CAAC,CAAC;oBACrB,CAAC,CAAC,CAAC;SACT;QACD,OAAO,CAAC,CAAC;IACX,CAAC,EACD,IAAI,CAAC,EACT,GAAG,EAAE,CAAC,4BAA4B,EAAE,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,CAAC,kBAC/C,QAAQ,CAAC,QAAQ,EAAE,qCACnB,UAAU,CAAC,QAAQ,EAAE,EAAE,CAAC,CAAC;IAEjC,MAAM,MAAM,GAAyB,EAAC,CAAC,EAAE,EAAE,EAAC,CAAC;IAC7C,MAAM,KAAK,GAAwB,EAAC,UAAU,EAAE,QAAQ,EAAC,CAAC;IAE1D,OAAO,MAAM,CAAC,SAAS,CACnB,cAAc,EAAE,MAA8B,EAC9C,KAA2B,CAAC,CAAC;AACnC,CAAC;AAED,MAAM,CAAC,MAAM,cAAc,GAAG,EAAE,CAAC,EAAC,eAAe,EAAC,CAAC,CAAC","sourcesContent":["/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {SpaceToBatchND, SpaceToBatchNDAttrs, SpaceToBatchNDInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\n\n/**\n * This operation divides \"spatial\" dimensions `[1, ..., M]` of the input into\n * a grid of blocks of shape `blockShape`, and interleaves these blocks with\n * the \"batch\" dimension (0) such that in the output, the spatial\n * dimensions `[1, ..., M]` correspond to the position within the grid,\n * and the batch dimension combines both the position within a spatial block\n * and the original batch position. Prior to division into blocks,\n * the spatial dimensions of the input are optionally zero padded\n * according to `paddings`. See below for a precise description.\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);\n * const blockShape = [2, 2];\n * const paddings = [[0, 0], [0, 0]];\n *\n * x.spaceToBatchND(blockShape, paddings).print();\n * ```\n *\n * @param x A `tf.Tensor`. N-D with `x.shape` = `[batch] + spatialShape +\n * remainingShape`, where spatialShape has `M` dimensions.\n * @param blockShape A 1-D array. Must have shape `[M]`, all values must\n * be >= 1.\n * @param paddings A 2-D array. Must have shape `[M, 2]`, all values must be >=\n *     0. `paddings[i] = [padStart, padEnd]` specifies the amount to zero-pad\n * from input dimension `i + 1`, which corresponds to spatial dimension `i`. It\n * is required that\n * `(inputShape[i + 1] + padStart + padEnd) % blockShape[i] === 0`\n *\n * This operation is equivalent to the following steps:\n *\n * 1. Zero-pad the start and end of dimensions `[1, ..., M]` of the input\n * according to `paddings` to produce `padded` of shape paddedShape.\n *\n * 2. Reshape `padded` to `reshapedPadded` of shape:\n * `[batch] + [paddedShape[1] / blockShape[0], blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1], blockShape[M-1]] + remainingShape`\n *\n * 3. Permute dimensions of `reshapedPadded` to produce `permutedReshapedPadded`\n * of shape: `blockShape + [batch] + [paddedShape[1] / blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1]] + remainingShape`\n *\n * 4. Reshape `permutedReshapedPadded` to flatten `blockShape` into the\n * batch dimension, producing an output tensor of shape:\n * `[batch * prod(blockShape)] + [paddedShape[1] / blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1]] + remainingShape`\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction spaceToBatchND_<T extends Tensor>(\n    x: T|TensorLike, blockShape: number[], paddings: number[][]): T {\n  const $x = convertToTensor(x, 'x', 'spaceToBatchND');\n\n  util.assert(\n      $x.rank >= 1 + blockShape.length,\n      () => `input rank ${$x.rank} should be > than [blockShape] ${\n          blockShape.length}`);\n\n  util.assert(\n      paddings.length === blockShape.length,\n      () => `paddings.shape[0] ${\n          paddings.length} must be equal to [blockShape] ${blockShape.length}`);\n\n  util.assert(\n      $x.shape.reduce(\n          (a, b, i) => {\n            if (i > 0 && i <= blockShape.length) {\n              return a &&\n                  ((b + paddings[i - 1][0] + paddings[i - 1][1]) %\n                       blockShape[i - 1] ===\n                   0);\n            }\n            return a;\n          },\n          true),\n      () => `input spatial dimensions ${$x.shape.slice(1)} with paddings ${\n          paddings.toString()} must be divisible by blockShapes ${\n          blockShape.toString()}`);\n\n  const inputs: SpaceToBatchNDInputs = {x: $x};\n  const attrs: SpaceToBatchNDAttrs = {blockShape, paddings};\n\n  return ENGINE.runKernel(\n      SpaceToBatchND, inputs as {} as NamedTensorMap,\n      attrs as {} as NamedAttrMap);\n}\n\nexport const spaceToBatchND = op({spaceToBatchND_});\n"]}","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { FFT } from '../../kernel_names';\nimport { assert } from '../../util';\nimport { op } from '../operation';\n/**\n * Fast Fourier transform.\n *\n * Computes the 1-dimensional discrete Fourier transform over the inner-most\n * dimension of input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n * const imag = tf.tensor1d([1, 2, 3]);\n * const x = tf.complex(real, imag);\n *\n * x.fft().print(); // tf.spectral.fft(x).print();\n * ```\n * @param input The complex input to compute an fft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction fft_(input) {\n assert(input.dtype === 'complex64', () => `The dtype for tf.spectral.fft() must be complex64 ` +\n `but got ${input.dtype}.`);\n const inputs = { input };\n return ENGINE.runKernel(FFT, inputs);\n}\nexport const fft = op({ fft_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { assert } from '../../util';\nimport { complex } from '../complex';\nimport { concat } from '../concat';\nimport { imag } from '../imag';\nimport { op } from '../operation';\nimport { real } from '../real';\nimport { reshape } from '../reshape';\nimport { slice } from '../slice';\nimport { split } from '../split';\nimport { zeros } from '../zeros';\nimport { zerosLike } from '../zeros_like';\nimport { fft } from './fft';\n/**\n * Real value input fast Fourier transform.\n *\n * Computes the 1-dimensional discrete Fourier transform over the\n * inner-most dimension of the real input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n *\n * real.rfft().print();\n * ```\n * @param input The real value input to compute an rfft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction rfft_(input, fftLength) {\n assert(input.dtype === 'float32', () => `The dtype for rfft() must be real value but got ${input.dtype}`);\n let innerDimensionSize = input.shape[input.shape.length - 1];\n const batch = input.size / innerDimensionSize;\n let adjustedInput;\n if (fftLength != null && fftLength < innerDimensionSize) {\n // Need to crop\n const begin = input.shape.map(v => 0);\n const size = input.shape.map(v => v);\n size[input.shape.length - 1] = fftLength;\n adjustedInput = slice(input, begin, size);\n innerDimensionSize = fftLength;\n }\n else if (fftLength != null && fftLength > innerDimensionSize) {\n // Need to pad with zeros\n const zerosShape = input.shape.map(v => v);\n zerosShape[input.shape.length - 1] = fftLength - innerDimensionSize;\n adjustedInput = concat([input, zeros(zerosShape)], input.shape.length - 1);\n innerDimensionSize = fftLength;\n }\n else {\n adjustedInput = input;\n }\n // Complement the input with zero imaginary numbers.\n const zerosInput = zerosLike(adjustedInput);\n const complexInput = reshape(complex(adjustedInput, zerosInput), [batch, innerDimensionSize]);\n const ret = fft(complexInput);\n // Exclude complex conjugations. 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All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../../tensor';\nimport {assert} from '../../util';\nimport {complex} from '../complex';\nimport {concat} from '../concat';\nimport {imag} from '../imag';\nimport {op} from '../operation';\nimport {real} from '../real';\nimport {reshape} from '../reshape';\nimport {slice} from '../slice';\nimport {split} from '../split';\nimport {zeros} from '../zeros';\nimport {zerosLike} from '../zeros_like';\n\nimport {fft} from './fft';\n\n/**\n * Real value input fast Fourier transform.\n *\n * Computes the 1-dimensional discrete Fourier transform over the\n * inner-most dimension of the real input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n *\n * real.rfft().print();\n * ```\n * @param input The real value input to compute an rfft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction rfft_(input: Tensor, fftLength?: number): Tensor {\n  assert(\n      input.dtype === 'float32',\n      () => `The dtype for rfft() must be real value but got ${input.dtype}`);\n\n  let innerDimensionSize = input.shape[input.shape.length - 1];\n  const batch = input.size / innerDimensionSize;\n\n  let adjustedInput: Tensor;\n  if (fftLength != null && fftLength < innerDimensionSize) {\n    // Need to crop\n    const begin = input.shape.map(v => 0);\n    const size = input.shape.map(v => v);\n    size[input.shape.length - 1] = fftLength;\n    adjustedInput = slice(input, begin, size);\n    innerDimensionSize = fftLength;\n  } else if (fftLength != null && fftLength > innerDimensionSize) {\n    // Need to pad with zeros\n    const zerosShape = input.shape.map(v => v);\n    zerosShape[input.shape.length - 1] = fftLength - innerDimensionSize;\n    adjustedInput = concat([input, zeros(zerosShape)], input.shape.length - 1);\n    innerDimensionSize = fftLength;\n  } else {\n    adjustedInput = input;\n  }\n\n  // Complement the input with zero imaginary numbers.\n  const zerosInput = zerosLike(adjustedInput);\n  const complexInput =\n      reshape(complex(adjustedInput, zerosInput), [batch, innerDimensionSize]);\n\n  const ret = fft(complexInput);\n\n  // Exclude complex conjugations. These conjugations are put symmetrically.\n  const half = Math.floor(innerDimensionSize / 2) + 1;\n  const realValues = real(ret);\n  const imagValues = imag(ret);\n  const realComplexConjugate = split(\n      realValues, [half, innerDimensionSize - half],\n      realValues.shape.length - 1);\n  const imagComplexConjugate = split(\n      imagValues, [half, innerDimensionSize - half],\n      imagValues.shape.length - 1);\n\n  const outputShape = adjustedInput.shape.slice();\n  outputShape[adjustedInput.shape.length - 1] = half;\n\n  return reshape(\n      complex(realComplexConjugate[0], imagComplexConjugate[0]), outputShape);\n}\n\nexport const rfft = op({rfft_});\n"]}","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { squeezeShape } from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Removes dimensions of size 1 from the shape of a `tf.Tensor`.\n *\n * ```js\n * const x = tf.tensor([1, 2, 3, 4], [1, 1, 4]);\n * x.squeeze().print();\n * ```\n *\n * @param x The input tensor to be squeezed.\n * @param axis An optional list of numbers. If specified, only\n * squeezes the dimensions listed. The dimension index starts at 0. It\n * is an error to squeeze a dimension that is not 1.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction squeeze_(x, axis) {\n const $x = convertToTensor(x, 'x', 'squeeze');\n return reshape($x, squeezeShape($x.shape, axis).newShape);\n}\nexport const squeeze = op({ squeeze_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Equal } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc } from '../utils/binary_utils';\nexport const equalImpl = createSimpleBinaryKernelImpl((a, b) => (a === b) ? 1 : 0);\nexport const equal = binaryKernelFunc(Equal, equalImpl, null /* complexImpl */, 'bool');\nexport const equalConfig = {\n kernelName: Equal,\n backendName: 'cpu',\n kernelFunc: equal\n};\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Exp } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const expImpl = createSimpleUnaryImpl((xi) => Math.exp(xi));\nexport const exp = unaryKernelFuncFromImpl(Exp, expImpl, 'float32');\nexport const expConfig = {\n kernelName: Exp,\n backendName: 'cpu',\n kernelFunc: exp,\n};\n//# sourceMappingURL=data:application/json;base64,eyJ2ZXJzaW9uIjozLCJmaWxlIjoiRXhwLmpzIiwic291cmNlUm9vdCI6IiIsInNvdXJjZXMiOlsiLi4vLi4vLi4vLi4vLi4vLi4vdGZqcy1iYWNrZW5kLWNwdS9zcmMva2VybmVscy9FeHAudHMiXSwibmFtZXMiOltdLCJtYXBwaW5ncyI6IkFBQUE7Ozs7Ozs7Ozs7Ozs7OztHQWVHO0FBRUgsT0FBTyxFQUFDLEdBQUcsRUFBZSxNQUFNLHVCQUF1QixDQUFDO0FBRXhELE9BQU8sRUFBQyxxQkFBcUIsRUFBQyxNQUFNLHFCQUFxQixDQUFDO0FBQzFELE9BQU8sRUFBQyx1QkFBdUIsRUFBQyxNQUFNLHNCQUFzQixDQUFDO0FBRTdELE1BQU0sQ0FBQyxNQUFNLE9BQU8sR0FBRyxxQkFBcUIsQ0FBQyxDQUFDLEVBQUUsRUFBRSxFQUFFLENBQUMsSUFBSSxDQUFDLEdBQUcsQ0FBQyxFQUFFLENBQUMsQ0FBQyxDQUFDO0FBQ25FLE1BQU0sQ0FBQyxNQUFNLEdBQUcsR0FBRyx1QkFBdUIsQ0FBQyxHQUFHLEVBQUUsT0FBTyxFQUFFLFNBQVMsQ0FBQyxDQUFDO0FBRXBFLE1BQU0sQ0FBQyxNQUFNLFNBQVMsR0FBaUI7SUFDckMsVUFBVSxFQUFFLEdBQUc7SUFDZixXQUFXLEVBQUUsS0FBSztJQUNsQixVQUFVLEVBQUUsR0FBRztDQUNoQixDQUFDIiwic291cmNlc0NvbnRlbnQiOlsiLyoqXG4gKiBAbGljZW5zZVxuICogQ29weXJpZ2h0IDIwMjAgR29vZ2xlIExMQy4gQWxsIFJpZ2h0cyBSZXNlcnZlZC5cbiAqIExpY2Vuc2VkIHVuZGVyIHRoZSBBcGFjaGUgTGljZW5zZSwgVmVyc2lvbiAyLjAgKHRoZSBMaWNlbnNlKTtcbiAqIHlvdSBtYXkgbm90IHVzZSB0aGlzIGZpbGUgZXhjZXB0IGluIGNvbXBsaWFuY2Ugd2l0aCB0aGUgTGljZW5zZS5cbiAqIFlvdSBtYXkgb2J0YWluIGEgY29weSBvZiB0aGUgTGljZW5zZSBhdFxuICpcbiAqIGh0dHA6Ly93d3cuYXBhY2hlLm9yZy9saWNlbnNlcy9MSUNFTlNFLTIuMFxuICpcbiAqIFVubGVzcyByZXF1aXJlZCBieSBhcHBsaWNhYmxlIGxhdyBvciBhZ3JlZWQgdG8gaW4gd3JpdGluZywgc29mdHdhcmVcbiAqIGRpc3RyaWJ1dGVkIHVuZGVyIHRoZSBMaWNlbnNlIGlzIGRpc3RyaWJ1dGVkIG9uIGFuIEFTIElTIEJBU0lTLFxuICogV0lUSE9VVCBXQVJSQU5USUVTIE9SIENPTkRJVElPTlMgT0YgQU5ZIEtJTkQsIGVpdGhlciBleHByZXNzIG9yIGltcGxpZWQuXG4gKiBTZWUgdGhlIExpY2Vuc2UgZm9yIHRoZSBzcGVjaWZpYyBsYW5ndWFnZSBnb3Zlcm5pbmcgcGVybWlzc2lvbnMgYW5kXG4gKiBsaW1pdGF0aW9ucyB1bmRlciB0aGUgTGljZW5zZS5cbiAqID09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09XG4gKi9cblxuaW1wb3J0IHtFeHAsIEtlcm5lbENvbmZpZ30gZnJvbSAnQHRlbnNvcmZsb3cvdGZqcy1jb3JlJztcblxuaW1wb3J0IHtjcmVhdGVTaW1wbGVVbmFyeUltcGx9IGZyb20gJy4uL3V0aWxzL3VuYXJ5X2ltcGwnO1xuaW1wb3J0IHt1bmFyeUtlcm5lbEZ1bmNGcm9tSW1wbH0gZnJvbSAnLi4vdXRpbHMvdW5hcnlfdXRpbHMnO1xuXG5leHBvcnQgY29uc3QgZXhwSW1wbCA9IGNyZWF0ZVNpbXBsZVVuYXJ5SW1wbCgoeGkpID0+IE1hdGguZXhwKHhpKSk7XG5leHBvcnQgY29uc3QgZXhwID0gdW5hcnlLZXJuZWxGdW5jRnJvbUltcGwoRXhwLCBleHBJbXBsLCAnZmxvYXQzMicpO1xuXG5leHBvcnQgY29uc3QgZXhwQ29uZmlnOiBLZXJuZWxDb25maWcgPSB7XG4gIGtlcm5lbE5hbWU6IEV4cCxcbiAgYmFja2VuZE5hbWU6ICdjcHUnLFxuICBrZXJuZWxGdW5jOiBleHAsXG59O1xuIl19","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport function transposeImpl(xVals, xShape, dtype, perm, newShape) {\n const xRank = xShape.length;\n const xSize = util.sizeFromShape(xShape);\n const xStrides = util.computeStrides(xShape);\n const newStrides = util.computeStrides(newShape);\n const result = util.getTypedArrayFromDType(dtype, util.sizeFromShape(newShape));\n for (let i = 0; i < xSize; ++i) {\n const loc = util.indexToLoc(i, xRank, xStrides);\n // Permute location.\n const newLoc = new Array(loc.length);\n for (let i = 0; i < newLoc.length; i++) {\n newLoc[i] = loc[perm[i]];\n }\n const newIndex = util.locToIndex(newLoc, xRank, newStrides);\n result[newIndex] = xVals[i];\n }\n return result;\n}\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sigmoid } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const sigmoidImpl = createSimpleUnaryImpl((xi) => 1 / (1 + Math.exp(-xi)));\nexport const sigmoid = unaryKernelFunc(Sigmoid, (xi) => 1 / (1 + Math.exp(-xi)));\nexport const sigmoidConfig = {\n kernelName: Sigmoid,\n backendName: 'cpu',\n kernelFunc: sigmoid,\n};\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nexport function sparseSegmentReductionImpl(input, inputShape, inputDType, indices, segmentIds, isMean = false, defaultValue = 0) {\n const numIndices = indices.length;\n // Flatten the array to two dimensions\n const inputFlat = [inputShape[0], input.length / inputShape[0]];\n const numCol = inputFlat[1];\n // Note that the current implementation assumes that segmentIds values are\n // sorted.\n const lastSegmentIdPlusOne = numIndices > 0 ? segmentIds[numIndices - 1] + 1 : 0;\n const outputRows = lastSegmentIdPlusOne;\n if (outputRows < 0) {\n throw new Error(backend_util.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());\n }\n const outputShape = inputShape.slice();\n outputShape[0] = outputRows;\n const outputLength = outputShape.reduce((product, value) => product * value, 1);\n // Output array is initialized with the value 0 by default.\n const output = util.getArrayFromDType(inputDType, outputLength);\n // Note that we do not initialize the output buffer with a default value, so\n // we need to explicitly set missing indices to the default value.\n if (numIndices === 0) {\n if (outputRows > 0) {\n output.fill(defaultValue);\n }\n return [output, outputShape];\n }\n if (outputRows <= 0) {\n throw new Error(backend_util.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());\n }\n let start = 0, end = 1;\n // Index from which the output is not initialized.\n let uninitializedIndex = 0;\n let outIndex = segmentIds[start];\n while (true) {\n // We initialize nextIndex to 0 to avoid may be uninitialized warning\n let nextIndex = 0;\n if (end < numIndices) {\n nextIndex = segmentIds[end];\n if (outIndex === nextIndex) {\n ++end;\n continue;\n }\n // We have a new segment here. Verify that the segment ids are growing.\n if (outIndex >= nextIndex) {\n throw new Error(backend_util\n .getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage());\n }\n }\n if (outIndex < 0 || outIndex >= outputRows) {\n throw new Error(backend_util.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(outIndex, outputRows));\n }\n // If there is a gap between two indices, we need to set that gap to the\n // default value.\n if (outIndex > uninitializedIndex) {\n output.fill(defaultValue, uninitializedIndex * numCol, outIndex * numCol);\n }\n for (let i = start; i < end; ++i) {\n const index = indices[i];\n if (index < 0 || index >= inputFlat[0]) {\n throw new Error(backend_util.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(i, indices[i], inputFlat[0]));\n }\n for (let j = 0; j < numCol; j++) {\n output[outIndex * numCol + j] += input[index * numCol + j];\n }\n }\n if (isMean) {\n for (let j = 0; j < numCol; j++) {\n output[outIndex * numCol + j] /= end - start;\n }\n }\n start = end;\n ++end;\n uninitializedIndex = outIndex + 1;\n outIndex = nextIndex;\n if (end > numIndices) {\n break;\n }\n }\n // Fill the gap at the end with the default value.\n if (uninitializedIndex < outputRows) {\n output.fill(defaultValue, uninitializedIndex * numCol, outputRows * numCol);\n }\n return [output, outputShape];\n}\n//# sourceMappingURL=data:application/json;base64,{"version":3,"file":"SparseSegmentReduction_impl.js","sourceRoot":"","sources":["../../../../../../tfjs-backend-cpu/src/kernels/SparseSegmentReduction_impl.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAEH,OAAO,EAAC,YAAY,EAAwB,IAAI,EAAC,MAAM,uBAAuB,CAAC;AAE/E,MAAM,UAAU,0BAA0B,CACtC,KAAiB,EAAE,UAAoB,EAAE,UAAoB,EAC7D,OAAmB,EAAE,UAAsB,EAAE,MAAM,GAAG,KAAK,EAC3D,YAAY,GAAG,CAAC;IAClB,MAAM,UAAU,GAAG,OAAO,CAAC,MAAM,CAAC;IAElC,sCAAsC;IACtC,MAAM,SAAS,GAAa,CAAC,UAAU,CAAC,CAAC,CAAC,EAAE,KAAK,CAAC,MAAM,GAAG,UAAU,CAAC,CAAC,CAAC,CAAC,CAAC;IAC1E,MAAM,MAAM,GAAG,SAAS,CAAC,CAAC,CAAC,CAAC;IAC5B,0EAA0E;IAC1E,UAAU;IACV,MAAM,oBAAoB,GACtB,UAAU,GAAG,CAAC,CAAC,CAAC,CAAC,UAAU,CAAC,UAAU,GAAG,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;IACxD,MAAM,UAAU,GAAG,oBAAoB,CAAC;IAExC,IAAI,UAAU,GAAG,CAAC,EAAE;QAClB,MAAM,IAAI,KAAK,CACX,YAAY,CAAC,uDAAuD,EAAE,CAAC,CAAC;KAC7E;IAED,MAAM,WAAW,GAAG,UAAU,CAAC,KAAK,EAAE,CAAC;IACvC,WAAW,CAAC,CAAC,CAAC,GAAG,UAAU,CAAC;IAE5B,MAAM,YAAY,GACd,WAAW,CAAC,MAAM,CAAC,CAAC,OAAO,EAAE,KAAK,EAAE,EAAE,CAAC,OAAO,GAAG,KAAK,EAAE,CAAC,CAAC,CAAC;IAC/D,2DAA2D;IAC3D,MAAM,MAAM,GAAG,IAAI,CAAC,iBAAiB,CAAC,UAAU,EAAE,YAAY,CAAe,CAAC;IAE9E,4EAA4E;IAC5E,kEAAkE;IAClE,IAAI,UAAU,KAAK,CAAC,EAAE;QACpB,IAAI,UAAU,GAAG,CAAC,EAAE;YAClB,MAAM,CAAC,IAAI,CAAC,YAAY,CAAC,CAAC;SAC3B;QACD,OAAO,CAAC,MAAM,EAAE,WAAW,CAAC,CAAC;KAC9B;IAED,IAAI,UAAU,IAAI,CAAC,EAAE;QACnB,MAAM,IAAI,KAAK,CACX,YAAY,CAAC,uDAAuD,EAAE,CAAC,CAAC;KAC7E;IAED,IAAI,KAAK,GAAG,CAAC,EAAE,GAAG,GAAG,CAAC,CAAC;IACvB,kDAAkD;IAClD,IAAI,kBAAkB,GAAG,CAAC,CAAC;IAC3B,IAAI,QAAQ,GAAG,UAAU,CAAC,KAAK,CAAC,CAAC;IAEjC,OAAO,IAAI,EAAE;QACX,qEAAqE;QACrE,IAAI,SAAS,GAAG,CAAC,CAAC;QAClB,IAAI,GAAG,GAAG,UAAU,EAAE;YACpB,SAAS,GAAG,UAAU,CAAC,GAAG,CAAC,CAAC;YAC5B,IAAI,QAAQ,KAAK,SAAS,EAAE;gBAC1B,EAAE,GAAG,CAAC;gBACN,SAAS;aACV;YACD,wEAAwE;YACxE,IAAI,QAAQ,IAAI,SAAS,EAAE;gBACzB,MAAM,IAAI,KAAK,CAAC,YAAY;qBACvB,4DAA4D,EAAE,CAAC,CAAC;aACtE;SACF;QAED,IAAI,QAAQ,GAAG,CAAC,IAAI,QAAQ,IAAI,UAAU,EAAE;YAC1C,MAAM,IAAI,KAAK,CACX,YAAY,CAAC,wDAAwD,CACjE,QAAQ,EAAE,UAAU,CAAC,CAAC,CAAC;SAChC;QAED,wEAAwE;QACxE,iBAAiB;QACjB,IAAI,QAAQ,GAAG,kBAAkB,EAAE;YACjC,MAAM,CAAC,IAAI,CAAC,YAAY,EAAE,kBAAkB,GAAG,MAAM,EAAE,QAAQ,GAAG,MAAM,CAAC,CAAC;SAC3E;QAED,KAAK,IAAI,CAAC,GAAG,KAAK,EAAE,CAAC,GAAG,GAAG,EAAE,EAAE,CAAC,EAAE;YAChC,MAAM,KAAK,GAAG,OAAO,CAAC,CAAC,CAAC,CAAC;YACzB,IAAI,KAAK,GAAG,CAAC,IAAI,KAAK,IAAI,SAAS,CAAC,CAAC,CAAC,EAAE;gBACtC,MAAM,IAAI,KAAK,CACX,YAAY,CAAC,sDAAsD,CAC/D,CAAC,EAAE,OAAO,CAAC,CAAC,CAAC,EAAE,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;aACvC;YACD,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,MAAM,EAAE,CAAC,EAAE,EAAE;gBAC/B,MAAM,CAAC,QAAQ,GAAG,MAAM,GAAG,CAAC,CAAC,IAAI,KAAK,CAAC,KAAK,GAAG,MAAM,GAAG,CAAC,CAAC,CAAC;aAC5D;SACF;QAED,IAAI,MAAM,EAAE;YACV,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,MAAM,EAAE,CAAC,EAAE,EAAE;gBAC/B,MAAM,CAAC,QAAQ,GAAG,MAAM,GAAG,CAAC,CAAC,IAAI,GAAG,GAAG,KAAK,CAAC;aAC9C;SACF;QAED,KAAK,GAAG,GAAG,CAAC;QACZ,EAAE,GAAG,CAAC;QACN,kBAAkB,GAAG,QAAQ,GAAG,CAAC,CAAC;QAClC,QAAQ,GAAG,SAAS,CAAC;QACrB,IAAI,GAAG,GAAG,UAAU,EAAE;YACpB,MAAM;SACP;KACF;IAED,kDAAkD;IAClD,IAAI,kBAAkB,GAAG,UAAU,EAAE;QACnC,MAAM,CAAC,IAAI,CAAC,YAAY,EAAE,kBAAkB,GAAG,MAAM,EAAE,UAAU,GAAG,MAAM,CAAC,CAAC;KAC7E;IAED,OAAO,CAAC,MAAM,EAAE,WAAW,CAAC,CAAC;AAC/B,CAAC","sourcesContent":["/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, DataType, TypedArray, util} from '@tensorflow/tfjs-core';\n\nexport function sparseSegmentReductionImpl(\n    input: TypedArray, inputShape: number[], inputDType: DataType,\n    indices: TypedArray, segmentIds: TypedArray, isMean = false,\n    defaultValue = 0): [TypedArray, number[]] {\n  const numIndices = indices.length;\n\n  // Flatten the array to two dimensions\n  const inputFlat: number[] = [inputShape[0], input.length / inputShape[0]];\n  const numCol = inputFlat[1];\n  // Note that the current implementation assumes that segmentIds values are\n  // sorted.\n  const lastSegmentIdPlusOne =\n      numIndices > 0 ? segmentIds[numIndices - 1] + 1 : 0;\n  const outputRows = lastSegmentIdPlusOne;\n\n  if (outputRows < 0) {\n    throw new Error(\n        backend_util.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());\n  }\n\n  const outputShape = inputShape.slice();\n  outputShape[0] = outputRows;\n\n  const outputLength =\n      outputShape.reduce((product, value) => product * value, 1);\n  // Output array is initialized with the value 0 by default.\n  const output = util.getArrayFromDType(inputDType, outputLength) as TypedArray;\n\n  // Note that we do not initialize the output buffer with a default value, so\n  // we need to explicitly set missing indices to the default value.\n  if (numIndices === 0) {\n    if (outputRows > 0) {\n      output.fill(defaultValue);\n    }\n    return [output, outputShape];\n  }\n\n  if (outputRows <= 0) {\n    throw new Error(\n        backend_util.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());\n  }\n\n  let start = 0, end = 1;\n  // Index from which the output is not initialized.\n  let uninitializedIndex = 0;\n  let outIndex = segmentIds[start];\n\n  while (true) {\n    // We initialize nextIndex to 0 to avoid may be uninitialized warning\n    let nextIndex = 0;\n    if (end < numIndices) {\n      nextIndex = segmentIds[end];\n      if (outIndex === nextIndex) {\n        ++end;\n        continue;\n      }\n      // We have a new segment here.  Verify that the segment ids are growing.\n      if (outIndex >= nextIndex) {\n        throw new Error(backend_util\n            .getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage());\n      }\n    }\n\n    if (outIndex < 0 || outIndex >= outputRows) {\n      throw new Error(\n          backend_util.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(\n              outIndex, outputRows));\n    }\n\n    // If there is a gap between two indices, we need to set that gap to the\n    // default value.\n    if (outIndex > uninitializedIndex) {\n      output.fill(defaultValue, uninitializedIndex * numCol, outIndex * numCol);\n    }\n\n    for (let i = start; i < end; ++i) {\n      const index = indices[i];\n      if (index < 0 || index >= inputFlat[0]) {\n        throw new Error(\n            backend_util.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(\n                i, indices[i], inputFlat[0]));\n      }\n      for (let j = 0; j < numCol; j++) {\n        output[outIndex * numCol + j] += input[index * numCol + j];\n      }\n    }\n\n    if (isMean) {\n      for (let j = 0; j < numCol; j++) {\n        output[outIndex * numCol + j] /= end - start;\n      }\n    }\n\n    start = end;\n    ++end;\n    uninitializedIndex = outIndex + 1;\n    outIndex = nextIndex;\n    if (end > numIndices) {\n      break;\n    }\n  }\n\n  // Fill the gap at the end with the default value.\n  if (uninitializedIndex < outputRows) {\n    output.fill(defaultValue, uninitializedIndex * numCol, outputRows * numCol);\n  }\n\n  return [output, outputShape];\n}\n"]}","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Tile } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { clone } from './clone';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Broadcast an array to a compatible shape NumPy-style.\n *\n * The tensor's shape is compared to the broadcast shape from end to beginning.\n * Ones are prepended to the tensor's shape until is has the same length as\n * the broadcast shape. If input.shape[i]==shape[i], the (i+1)-th axis is\n * already broadcast-compatible. If input.shape[i]==1 and shape[i]==N, then\n * the input tensor is tiled N times along that axis (using tf.tile).\n *\n * @param input The tensor that is to be broadcasted.\n * @param shape The input is to be broadcast to this shape.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction broadcastTo_(x, shape) {\n let input = convertToTensor(x, 'broadcastTo', 'x');\n const xShape = input.shape;\n if (shape.some(d => !(d > 0) || d % 1 !== 0)) {\n throw new Error(`broadcastTo(): Invalid broadcast shape [${shape}].`);\n }\n if (shape.length < input.rank) {\n throw new Error(`broadcastTo(): shape.length=${shape.length} < input.rank=${input.rank}.`);\n }\n if (shape.length > input.rank) {\n const newShape = input.shape.slice();\n while (newShape.length < shape.length) {\n newShape.unshift(1);\n }\n input = reshape(input, newShape);\n }\n const inputShape = input.shape;\n const reps = Array.from(shape);\n for (let i = shape.length - 1; i >= 0; i--) {\n if (inputShape[i] === shape[i]) {\n reps[i] = 1;\n }\n else if (input.shape[i] !== 1) {\n throw new Error(`broadcastTo(): [${xShape}] cannot be broadcast to [${shape}].`);\n }\n }\n const axes = reps.map((n, i) => n > 1 ? i : -1).filter(i => i >= 0);\n if (axes.length === 0) {\n return clone(input);\n }\n // TODO call broadcastTo kernel directly once backends implement broadcstTo\n const inputs = { x: input };\n const attrs = { reps };\n return ENGINE.runKernel(Tile, inputs, attrs);\n}\nexport const broadcastTo = op({ broadcastTo_ });\n//# 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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv2DBackpropInput } from '../kernel_names';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the derivative of the input of a 2D convolution.\n *\n * @param xShape The shape of the input: [batch, height, width, inDepth].\n * If length of 3, batch of 1 is assumed.\n * @param dy The derivative of the output, of rank 4 or rank 3 of shape\n * `[batch, outHeight, outWidth, outDepth]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter, rank 4, of shape\n * `[filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm used:\n * - `same` and stride 1: output will be of same size as input,\n * regardless of filter size.\n * - `valid`: output will be smaller than input if filter is larger\n * than 1x1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n * \"NHWC\". Specify the data format of the input and output data. With the\n * default format \"NHWC\", the data is stored in the order of: [batch,\n * height, width, channels].\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n * provided, it will default to truncate.\n */\nfunction conv2DBackpropInput_(xShape, dy, filter, strides, pad, dataFormat = 'NHWC', dimRoundingMode) {\n util.assert(xShape.length === dy.rank, () => `Length of inShape ` +\n `(${xShape.length}) and rank of dy (${dy.rank}) must match`);\n let xShape4D = xShape;\n let dy4D = dy;\n let reshapedTo4D = false;\n if (dy.rank === 3) {\n reshapedTo4D = true;\n dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n xShape4D = [1, xShape[0], xShape[1], xShape[2]];\n }\n util.assert(xShape4D.length === 4, () => `Error in conv2dDerInput: inShape must be length 4, but got length ` +\n `${xShape4D.length}.`);\n util.assert(dy4D.rank === 4, () => `Error in conv2dDerInput: dy must be rank 4, but got ` +\n `rank ${dy4D.rank}`);\n util.assert(filter.rank === 4, () => `Error in conv2dDerInput: filter must be rank 4, but got ` +\n `rank ${filter.rank}`);\n const inDepth = dataFormat === 'NHWC' ? xShape4D[3] : xShape4D[1];\n const outDepth = dataFormat === 'NHWC' ? dy4D.shape[3] : dy4D.shape[1];\n util.assert(inDepth === filter.shape[2], () => `Error in conv2dDerInput: depth of input (${inDepth}) must ` +\n `match input depth for filter ${filter.shape[2]}.`);\n util.assert(outDepth === filter.shape[3], () => `Error in conv2dDerInput: depth of output (${outDepth}) must ` +\n `match output depth for filter ${filter.shape[3]}.`);\n conv_util.checkPadOnDimRoundingMode('conv2dDerInput', pad, dimRoundingMode);\n const inputs = { dy: dy4D, filter };\n const attrs = { strides, pad, dataFormat, dimRoundingMode, inputShape: xShape4D };\n // tslint:disable-next-line: no-unnecessary-type-assertion\n const res = ENGINE.runKernel(Conv2DBackpropInput, inputs, attrs);\n if (reshapedTo4D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n }\n return res;\n}\nexport const conv2DBackpropInput = op({ conv2DBackpropInput_ });\n//# sourceMappingURL=data:application/json;base64,{"version":3,"file":"conv2d_backprop_input.js","sourceRoot":"","sources":["../../../../../../tfjs-core/src/ops/conv2d_backprop_input.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AACH,OAAO,EAAC,MAAM,EAAC,MAAM,WAAW,CAAC;AACjC,OAAO,EAAC,mBAAmB,EAAsD,MAAM,iBAAiB,CAAC;AAIzG,OAAO,KAAK,IAAI,MAAM,SAAS,CAAC;AAEhC,OAAO,KAAK,SAAS,MAAM,aAAa,CAAC;AACzC,OAAO,EAAC,EAAE,EAAC,MAAM,aAAa,CAAC;AAC/B,OAAO,EAAC,OAAO,EAAC,MAAM,WAAW,CAAC;AAElC;;;;;;;;;;;;;;;;;;;;;;;GAuBG;AACH,SAAS,oBAAoB,CACzB,MAAiE,EAAE,EAAK,EACxE,MAAgB,EAAE,OAAgC,EAClD,GAAoD,EACpD,aAA4B,MAAM,EAClC,eAAwC;IAC1C,IAAI,CAAC,MAAM,CACP,MAAM,CAAC,MAAM,KAAK,EAAE,CAAC,IAAI,EACzB,GAAG,EAAE,CAAC,oBAAoB;QACtB,IAAI,MAAM,CAAC,MAAM,qBAAqB,EAAE,CAAC,IAAI,cAAc,CAAC,CAAC;IAErE,IAAI,QAAQ,GAAG,MAA0C,CAAC;IAC1D,IAAI,IAAI,GAAG,EAAc,CAAC;IAC1B,IAAI,YAAY,GAAG,KAAK,CAAC;IACzB,IAAI,EAAE,CAAC,IAAI,KAAK,CAAC,EAAE;QACjB,YAAY,GAAG,IAAI,CAAC;QACpB,IAAI,GAAG,OAAO,CAAC,EAAE,EAAE,CAAC,CAAC,EAAE,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;QAC/D,QAAQ,GAAG,CAAC,CAAC,EAAE,MAAM,CAAC,CAAC,CAAC,EAAE,MAAM,CAAC,CAAC,CAAC,EAAE,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC;KACjD;IAED,IAAI,CAAC,MAAM,CACP,QAAQ,CAAC,MAAM,KAAK,CAAC,EACrB,GAAG,EAAE,CACD,oEAAoE;QACpE,GAAG,QAAQ,CAAC,MAAM,GAAG,CAAC,CAAC;IAC/B,IAAI,CAAC,MAAM,CACP,IAAI,CAAC,IAAI,KAAK,CAAC,EACf,GAAG,EAAE,CAAC,sDAAsD;QACxD,QAAQ,IAAI,CAAC,IAAI,EAAE,CAAC,CAAC;IAC7B,IAAI,CAAC,MAAM,CACP,MAAM,CAAC,IAAI,KAAK,CAAC,EACjB,GAAG,EAAE,CAAC,0DAA0D;QAC5D,QAAQ,MAAM,CAAC,IAAI,EAAE,CAAC,CAAC;IAC/B,MAAM,OAAO,GAAG,UAAU,KAAK,MAAM,CAAC,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC;IAClE,MAAM,QAAQ,GAAG,UAAU,KAAK,MAAM,CAAC,CAAC,CAAC,IAAI,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC;IACvE,IAAI,CAAC,MAAM,CACP,OAAO,KAAK,MAAM,CAAC,KAAK,CAAC,CAAC,CAAC,EAC3B,GAAG,EAAE,CAAC,4CAA4C,OAAO,SAAS;QAC9D,gCAAgC,MAAM,CAAC,KAAK,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC;IAC5D,IAAI,CAAC,MAAM,CACP,QAAQ,KAAK,MAAM,CAAC,KAAK,CAAC,CAAC,CAAC,EAC5B,GAAG,EAAE,CAAC,6CAA6C,QAAQ,SAAS;QAChE,iCAAiC,MAAM,CAAC,KAAK,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC;IAC7D,SAAS,CAAC,yBAAyB,CAAC,gBAAgB,EAAE,GAAG,EAAE,eAAe,CAAC,CAAC;IAC5E,MAAM,MAAM,GAA8B,EAAC,EAAE,EAAE,IAAI,EAAE,MAAM,EAAC,CAAC;IAC7D,MAAM,KAAK,GACP,EAAC,OAAO,EAAE,GAAG,EAAE,UAAU,EAAE,eAAe,EAAE,UAAU,EAAE,QAAQ,EAAC,CAAC;IAEtE,0DAA0D;IAC1D,MAAM,GAAG,GAAG,MAAM,CAAC,SAAS,CACZ,mBAAmB,EAAE,MAA8B,EACnD,KAA2B,CAAM,CAAC;IAElD,IAAI,YAAY,EAAE;QAChB,OAAO,OAAO,CAAC,GAAG,EAAE,CAAC,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAM,CAAC;KACtE;IACD,OAAO,GAAG,CAAC;AACb,CAAC;AAED,MAAM,CAAC,MAAM,mBAAmB,GAAG,EAAE,CAAC,EAAC,oBAAoB,EAAC,CAAC,CAAC","sourcesContent":["/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\nimport {Conv2DBackpropInput, Conv2DBackpropInputAttrs, Conv2DBackpropInputInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor3D, Tensor4D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport * as util from '../util';\n\nimport * as conv_util from './conv_util';\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Computes the derivative of the input of a 2D convolution.\n *\n * @param xShape The shape of the input: [batch, height, width, inDepth].\n * If length of 3, batch of 1 is assumed.\n * @param dy The derivative of the output, of rank 4 or rank 3 of shape\n *   `[batch, outHeight, outWidth, outDepth]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm used:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels].\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n *     provided, it will default to truncate.\n */\nfunction conv2DBackpropInput_<T extends Tensor3D|Tensor4D>(\n    xShape: [number, number, number, number]|[number, number, number], dy: T,\n    filter: Tensor4D, strides: [number, number]|number,\n    pad: 'valid'|'same'|number|conv_util.ExplicitPadding,\n    dataFormat: 'NHWC'|'NCHW' = 'NHWC',\n    dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  util.assert(\n      xShape.length === dy.rank,\n      () => `Length of inShape ` +\n          `(${xShape.length}) and rank of dy (${dy.rank}) must match`);\n\n  let xShape4D = xShape as [number, number, number, number];\n  let dy4D = dy as Tensor4D;\n  let reshapedTo4D = false;\n  if (dy.rank === 3) {\n    reshapedTo4D = true;\n    dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n    xShape4D = [1, xShape[0], xShape[1], xShape[2]];\n  }\n\n  util.assert(\n      xShape4D.length === 4,\n      () =>\n          `Error in conv2dDerInput: inShape must be length 4, but got length ` +\n          `${xShape4D.length}.`);\n  util.assert(\n      dy4D.rank === 4,\n      () => `Error in conv2dDerInput: dy must be rank 4, but got ` +\n          `rank ${dy4D.rank}`);\n  util.assert(\n      filter.rank === 4,\n      () => `Error in conv2dDerInput: filter must be rank 4, but got ` +\n          `rank ${filter.rank}`);\n  const inDepth = dataFormat === 'NHWC' ? xShape4D[3] : xShape4D[1];\n  const outDepth = dataFormat === 'NHWC' ? dy4D.shape[3] : dy4D.shape[1];\n  util.assert(\n      inDepth === filter.shape[2],\n      () => `Error in conv2dDerInput: depth of input (${inDepth}) must ` +\n          `match input depth for filter ${filter.shape[2]}.`);\n  util.assert(\n      outDepth === filter.shape[3],\n      () => `Error in conv2dDerInput: depth of output (${outDepth}) must ` +\n          `match output depth for filter ${filter.shape[3]}.`);\n  conv_util.checkPadOnDimRoundingMode('conv2dDerInput', pad, dimRoundingMode);\n  const inputs: Conv2DBackpropInputInputs = {dy: dy4D, filter};\n  const attrs: Conv2DBackpropInputAttrs =\n      {strides, pad, dataFormat, dimRoundingMode, inputShape: xShape4D};\n\n  // tslint:disable-next-line: no-unnecessary-type-assertion\n  const res = ENGINE.runKernel(\n                  Conv2DBackpropInput, inputs as {} as NamedTensorMap,\n                  attrs as {} as NamedAttrMap) as T;\n\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as T;\n  }\n  return res;\n}\n\nexport const conv2DBackpropInput = op({conv2DBackpropInput_});\n"]}","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from './engine';\nimport { inferShape } from './tensor_util_env';\nimport { arraysEqual, encodeString, flatten, isString, isTypedArray } from './util';\nconst TEST_EPSILON_FLOAT32 = 1e-3;\nexport const TEST_EPSILON_FLOAT16 = 1e-1;\nexport function expectArraysClose(actual, expected, epsilon) {\n if (epsilon == null) {\n epsilon = testEpsilon();\n }\n return expectArraysPredicate(actual, expected, (a, b) => areClose(a, b, epsilon));\n}\nexport function testEpsilon() {\n return ENGINE.backend.floatPrecision() === 32 ? TEST_EPSILON_FLOAT32 :\n TEST_EPSILON_FLOAT16;\n}\nfunction expectArraysPredicate(actual, expected, predicate) {\n let checkClassType = true;\n if (isTypedArray(actual) || isTypedArray(expected)) {\n checkClassType = false;\n }\n if (isTypedArray(actual) && isTypedArray(expected)) {\n checkClassType = true;\n }\n if (checkClassType) {\n const aType = actual.constructor.name;\n const bType = expected.constructor.name;\n if (aType !== bType) {\n throw new Error(`Arrays are of different type. Actual: ${aType}. ` +\n `Expected: ${bType}`);\n }\n }\n if (Array.isArray(actual) && Array.isArray(expected)) {\n const actualShape = inferShape(actual);\n const expectedShape = inferShape(expected);\n if (!arraysEqual(actualShape, expectedShape)) {\n throw new Error(`Arrays have different shapes. ` +\n `Actual: [${actualShape}]. Expected: [${expectedShape}]`);\n }\n }\n const actualFlat = isTypedArray(actual) ? actual : flatten(actual);\n const expectedFlat = isTypedArray(expected) ?\n expected :\n flatten(expected);\n if (actualFlat.length !== expectedFlat.length) {\n throw new Error(`Arrays have different lengths actual: ${actualFlat.length} vs ` +\n `expected: ${expectedFlat.length}.\\n` +\n `Actual: ${actualFlat}.\\n` +\n `Expected: ${expectedFlat}.`);\n }\n for (let i = 0; i < expectedFlat.length; ++i) {\n const a = actualFlat[i];\n const e = expectedFlat[i];\n if (!predicate(a, e)) {\n throw new Error(`Arrays differ: actual[${i}] = ${a}, expected[${i}] = ${e}.\\n` +\n `Actual: ${actualFlat}.\\n` +\n `Expected: ${expectedFlat}.`);\n }\n }\n}\nexport function expectPromiseToFail(fn, done) {\n fn().then(() => done.fail(), () => done());\n}\nexport function expectArraysEqual(actual, expected) {\n const exp = typeof expected === 'string' || typeof expected === 'number' ||\n typeof expected === 'boolean' ?\n [expected] :\n expected;\n if (isString(actual) || isString(actual[0]) ||\n isString(expected) || isString(expected[0])) {\n // tslint:disable-next-line: triple-equals\n return expectArraysPredicate(actual, exp, (a, b) => a == b);\n }\n return expectArraysPredicate(actual, expected, (a, b) => areClose(a, b, 0));\n}\nexport function expectNumbersClose(a, e, epsilon) {\n if (epsilon == null) {\n epsilon = testEpsilon();\n }\n if (!areClose(a, e, epsilon)) {\n throw new Error(`Numbers differ: actual === ${a}, expected === ${e}`);\n }\n}\nfunction areClose(a, e, epsilon) {\n if (!isFinite(a) && !isFinite(e)) {\n return true;\n }\n if (isNaN(a) || isNaN(e) || Math.abs(a - e) > epsilon) {\n return false;\n }\n return true;\n}\nexport function expectValuesInRange(actual, low, high) {\n for (let i = 0; i < actual.length; i++) {\n if (actual[i] < low || actual[i] > high) {\n throw new Error(`Value out of range:${actual[i]} low: ${low}, high: ${high}`);\n }\n }\n}\nexport function expectArrayBuffersEqual(actual, expected) {\n // Safari does not like comparing ArrayBuffers directly. Wrapping in\n // a Float32Array solves this issue.\n const actualArray = new Float32Array(actual);\n const expectedArray = new Float32Array(expected);\n if (actualArray.length !== expectedArray.length) {\n throw new Error('Expected ArrayBuffer to be of length ' +\n `${expectedArray.length}, but it was ${actualArray.length}`);\n }\n for (let i = 0; i < expectedArray.length; i++) {\n if (actualArray[i] !== expectedArray[i]) {\n throw new Error(`Expected ArrayBuffer value at ${i} to be ` +\n `${expectedArray[i]} but got ${actualArray[i]} instead`);\n }\n }\n}\n/** Encodes strings into utf-8 bytes. */\nexport function encodeStrings(a) {\n for (let i = 0; i < a.length; i++) {\n const val = a[i];\n if (Array.isArray(val)) {\n encodeStrings(val);\n }\n else {\n a[i] = encodeString(val);\n }\n }\n return a;\n}\n//# 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* @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from './engine';\nimport {inferShape} from './tensor_util_env';\nimport {RecursiveArray, TensorLike, TypedArray} from './types';\nimport {arraysEqual, encodeString, flatten, isString, isTypedArray} from './util';\n\nconst TEST_EPSILON_FLOAT32 = 1e-3;\nexport const TEST_EPSILON_FLOAT16 = 1e-1;\n\nexport function expectArraysClose(\n    actual: TypedArray|number|RecursiveArray<number>,\n    expected: TypedArray|number|RecursiveArray<number>, epsilon?: number) {\n  if (epsilon == null) {\n    epsilon = testEpsilon();\n  }\n  return expectArraysPredicate(\n      actual, expected, (a, b) => areClose(a as number, b as number, epsilon));\n}\n\nexport function testEpsilon() {\n  return ENGINE.backend.floatPrecision() === 32 ? TEST_EPSILON_FLOAT32 :\n                                                  TEST_EPSILON_FLOAT16;\n}\n\nfunction expectArraysPredicate(\n    actual: TensorLike, expected: TensorLike,\n    predicate: (a: {}, b: {}) => boolean) {\n  let checkClassType = true;\n  if (isTypedArray(actual) || isTypedArray(expected)) {\n    checkClassType = false;\n  }\n  if (isTypedArray(actual) && isTypedArray(expected)) {\n    checkClassType = true;\n  }\n  if (checkClassType) {\n    const aType = actual.constructor.name;\n    const bType = expected.constructor.name;\n\n    if (aType !== bType) {\n      throw new Error(\n          `Arrays are of different type. Actual: ${aType}. ` +\n          `Expected: ${bType}`);\n    }\n  }\n\n  if (Array.isArray(actual) && Array.isArray(expected)) {\n    const actualShape = inferShape(actual);\n    const expectedShape = inferShape(expected);\n    if (!arraysEqual(actualShape, expectedShape)) {\n      throw new Error(\n          `Arrays have different shapes. ` +\n          `Actual: [${actualShape}]. Expected: [${expectedShape}]`);\n    }\n  }\n\n  const actualFlat =\n      isTypedArray(actual) ? actual : flatten(actual as RecursiveArray<number>);\n  const expectedFlat = isTypedArray(expected) ?\n      expected :\n      flatten(expected as RecursiveArray<number>);\n\n  if (actualFlat.length !== expectedFlat.length) {\n    throw new Error(\n        `Arrays have different lengths actual: ${actualFlat.length} vs ` +\n        `expected: ${expectedFlat.length}.\\n` +\n        `Actual:   ${actualFlat}.\\n` +\n        `Expected: ${expectedFlat}.`);\n  }\n  for (let i = 0; i < expectedFlat.length; ++i) {\n    const a = actualFlat[i];\n    const e = expectedFlat[i];\n\n    if (!predicate(a, e)) {\n      throw new Error(\n          `Arrays differ: actual[${i}] = ${a}, expected[${i}] = ${e}.\\n` +\n          `Actual:   ${actualFlat}.\\n` +\n          `Expected: ${expectedFlat}.`);\n    }\n  }\n}\n\nexport interface DoneFn {\n  (): void;\n  fail: (message?: Error|string) => void;\n}\n\nexport function expectPromiseToFail(fn: () => Promise<{}>, done: DoneFn): void {\n  fn().then(() => done.fail(), () => done());\n}\n\nexport function expectArraysEqual(actual: TensorLike, expected: TensorLike) {\n  const exp = typeof expected === 'string' || typeof expected === 'number' ||\n          typeof expected === 'boolean' ?\n      [expected] as number[] :\n      expected as number[];\n  if (isString(actual) || isString((actual as string[])[0]) ||\n      isString(expected) || isString((expected as string[])[0])) {\n    // tslint:disable-next-line: triple-equals\n    return expectArraysPredicate(actual, exp, (a, b) => a == b);\n  }\n  return expectArraysPredicate(\n      actual, expected, (a, b) => areClose(a as number, b as number, 0));\n}\n\nexport function expectNumbersClose(a: number, e: number, epsilon?: number) {\n  if (epsilon == null) {\n    epsilon = testEpsilon();\n  }\n  if (!areClose(a, e, epsilon)) {\n    throw new Error(`Numbers differ: actual === ${a}, expected === ${e}`);\n  }\n}\n\nfunction areClose(a: number, e: number, epsilon: number): boolean {\n  if (!isFinite(a) && !isFinite(e)) {\n    return true;\n  }\n  if (isNaN(a) || isNaN(e) || Math.abs(a - e) > epsilon) {\n    return false;\n  }\n  return true;\n}\n\nexport function expectValuesInRange(\n    actual: TypedArray|number[], low: number, high: number) {\n  for (let i = 0; i < actual.length; i++) {\n    if (actual[i] < low || actual[i] > high) {\n      throw new Error(\n          `Value out of range:${actual[i]} low: ${low}, high: ${high}`);\n    }\n  }\n}\n\nexport function expectArrayBuffersEqual(\n    actual: ArrayBuffer, expected: ArrayBuffer) {\n  // Safari does not like comparing ArrayBuffers directly. Wrapping in\n  // a Float32Array solves this issue.\n  const actualArray = new Float32Array(actual);\n  const expectedArray = new Float32Array(expected);\n  if (actualArray.length !== expectedArray.length) {\n    throw new Error(\n        'Expected ArrayBuffer to be of length ' +\n        `${expectedArray.length}, but it was ${actualArray.length}`);\n  }\n\n  for (let i = 0; i < expectedArray.length; i++) {\n    if (actualArray[i] !== expectedArray[i]) {\n      throw new Error(\n          `Expected ArrayBuffer value at ${i} to be ` +\n          `${expectedArray[i]} but got ${actualArray[i]} instead`);\n    }\n  }\n}\n\n/** Encodes strings into utf-8 bytes. */\nexport function encodeStrings(a: RecursiveArray<{}>):\n    RecursiveArray<Uint8Array> {\n  for (let i = 0; i < (a as Array<{}>).length; i++) {\n    const val = a[i];\n    if (Array.isArray(val)) {\n      encodeStrings(val);\n    } else {\n      a[i] = encodeString(val as string);\n    }\n  }\n  return a as RecursiveArray<Uint8Array>;\n}\n"]}","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv2DBackpropFilter } from '../kernel_names';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the derivative of the filter of a 2D convolution.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n * [batch, height, width, inChannels]. If rank 3, batch of 1 is assumed.\n * @param dy The dy image, of rank 4 or rank 3, of shape\n * [batch, height, width, outDepth]. If rank 3, batch of 1 is assumed.\n * @param filterShape The shape of the filter, length 4,\n * [filterHeight, filterWidth, inDepth, outDepth].\n * @param strides The strides of the convolution: [strideHeight,\n * strideWidth].\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n * used in the forward prop of the op.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n * \"NHWC\". Specify the data format of the input and output data. With the\n * default format \"NHWC\", the data is stored in the order of: [batch,\n * height, width, channels].\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n * provided, it will default to truncate.\n */\nfunction conv2DBackpropFilter_(x, dy, filterShape, strides, pad, dataFormat = 'NHWC', dimRoundingMode) {\n let x4D = x;\n if (x.rank === 3) {\n x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n }\n let dy4D = dy;\n if (dy4D.rank === 3) {\n dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n }\n util.assert(x4D.rank === 4, () => `Error in conv2dDerFilter: input must be rank 4, but got shape ` +\n `${x4D.shape}.`);\n util.assert(dy4D.rank === 4, () => `Error in conv2dDerFilter: dy must be rank 4, but got shape ` +\n `${dy4D.shape}.`);\n util.assert(filterShape.length === 4, () => `Error in conv2dDerFilter: filterShape must be length 4, but got ` +\n `${filterShape}.`);\n const inDepth = dataFormat === 'NHWC' ? x4D.shape[3] : x4D.shape[1];\n const outDepth = dataFormat === 'NHWC' ? dy4D.shape[3] : dy4D.shape[1];\n util.assert(inDepth === filterShape[2], () => `Error in conv2dDerFilter: depth of input ${inDepth}) must ` +\n `match input depth in filter (${filterShape[2]}.`);\n util.assert(outDepth === filterShape[3], () => `Error in conv2dDerFilter: depth of dy (${outDepth}) must ` +\n `match output depth for filter (${filterShape[3]}).`);\n conv_util.checkPadOnDimRoundingMode('conv2dDerFilter', pad, dimRoundingMode);\n const inputs = { x: x4D, dy: dy4D };\n const attrs = { strides, pad, dataFormat, dimRoundingMode, filterShape };\n // tslint:disable-next-line: no-unnecessary-type-assertion\n return ENGINE.runKernel(Conv2DBackpropFilter, inputs, attrs);\n}\nexport const conv2DBackpropFilter = op({ conv2DBackpropFilter_ });\n//# sourceMappingURL=data:application/json;base64,{"version":3,"file":"conv2d_backprop_filter.js","sourceRoot":"","sources":["../../../../../../tfjs-core/src/ops/conv2d_backprop_filter.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AACH,OAAO,EAAC,MAAM,EAAC,MAAM,WAAW,CAAC;AACjC,OAAO,EAAC,oBAAoB,EAAwD,MAAM,iBAAiB,CAAC;AAI5G,OAAO,KAAK,IAAI,MAAM,SAAS,CAAC;AAEhC,OAAO,KAAK,SAAS,MAAM,aAAa,CAAC;AACzC,OAAO,EAAC,EAAE,EAAC,MAAM,aAAa,CAAC;AAC/B,OAAO,EAAC,OAAO,EAAC,MAAM,WAAW,CAAC;AAElC;;;;;;;;;;;;;;;;;;;GAmBG;AACH,SAAS,qBAAqB,CAC1B,CAAI,EAAE,EAAK,EAAE,WAA6C,EAC1D,OAAgC,EAChC,GAAoD,EACpD,aAA4B,MAAM,EAClC,eAAwC;IAC1C,IAAI,GAAG,GAAG,CAAa,CAAC;IACxB,IAAI,CAAC,CAAC,IAAI,KAAK,CAAC,EAAE;QAChB,GAAG,GAAG,OAAO,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;KAC3D;IACD,IAAI,IAAI,GAAG,EAAc,CAAC;IAC1B,IAAI,IAAI,CAAC,IAAI,KAAK,CAAC,EAAE;QACnB,IAAI,GAAG,OAAO,CAAC,EAAE,EAAE,CAAC,CAAC,EAAE,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;KAChE;IACD,IAAI,CAAC,MAAM,CACP,GAAG,CAAC,IAAI,KAAK,CAAC,EACd,GAAG,EAAE,CAAC,gEAAgE;QAClE,GAAG,GAAG,CAAC,KAAK,GAAG,CAAC,CAAC;IACzB,IAAI,CAAC,MAAM,CACP,IAAI,CAAC,IAAI,KAAK,CAAC,EACf,GAAG,EAAE,CAAC,6DAA6D;QAC/D,GAAG,IAAI,CAAC,KAAK,GAAG,CAAC,CAAC;IAC1B,IAAI,CAAC,MAAM,CACP,WAAW,CAAC,MAAM,KAAK,CAAC,EACxB,GAAG,EAAE,CAAC,kEAAkE;QACpE,GAAG,WAAW,GAAG,CAAC,CAAC;IAC3B,MAAM,OAAO,GAAG,UAAU,KAAK,MAAM,CAAC,CAAC,CAAC,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC;IACpE,MAAM,QAAQ,GAAG,UAAU,KAAK,MAAM,CAAC,CAAC,CAAC,IAAI,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC;IACvE,IAAI,CAAC,MAAM,CACP,OAAO,KAAK,WAAW,CAAC,CAAC,CAAC,EAC1B,GAAG,EAAE,CAAC,4CAA4C,OAAO,SAAS;QAC9D,gCAAgC,WAAW,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC;IAC3D,IAAI,CAAC,MAAM,CACP,QAAQ,KAAK,WAAW,CAAC,CAAC,CAAC,EAC3B,GAAG,EAAE,CAAC,0CAA0C,QAAQ,SAAS;QAC7D,kCAAkC,WAAW,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC;IAC9D,SAAS,CAAC,yBAAyB,CAAC,iBAAiB,EAAE,GAAG,EAAE,eAAe,CAAC,CAAC;IAC7E,MAAM,MAAM,GAA+B,EAAC,CAAC,EAAE,GAAG,EAAE,EAAE,EAAE,IAAI,EAAC,CAAC;IAC9D,MAAM,KAAK,GACP,EAAC,OAAO,EAAE,GAAG,EAAE,UAAU,EAAE,eAAe,EAAE,WAAW,EAAC,CAAC;IAE7D,0DAA0D;IAC1D,OAAO,MAAM,CAAC,SAAS,CACZ,oBAAoB,EAAE,MAA8B,EACpD,KAA2B,CAAa,CAAC;AACtD,CAAC;AAED,MAAM,CAAC,MAAM,oBAAoB,GAAG,EAAE,CAAC,EAAC,qBAAqB,EAAC,CAAC,CAAC","sourcesContent":["/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\nimport {Conv2DBackpropFilter, Conv2DBackpropFilterAttrs, Conv2DBackpropFilterInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor3D, Tensor4D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport * as util from '../util';\n\nimport * as conv_util from './conv_util';\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Computes the derivative of the filter of a 2D convolution.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     [batch, height, width, inChannels]. If rank 3, batch of 1 is assumed.\n * @param dy The dy image, of rank 4 or rank 3, of shape\n *     [batch, height, width, outDepth]. If rank 3, batch of 1 is assumed.\n * @param filterShape The shape of the filter, length 4,\n *     [filterHeight, filterWidth, inDepth, outDepth].\n * @param strides The strides of the convolution: [strideHeight,\n * strideWidth].\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels].\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n *     provided, it will default to truncate.\n */\nfunction conv2DBackpropFilter_<T extends Tensor3D|Tensor4D>(\n    x: T, dy: T, filterShape: [number, number, number, number],\n    strides: [number, number]|number,\n    pad: 'valid'|'same'|number|conv_util.ExplicitPadding,\n    dataFormat: 'NHWC'|'NCHW' = 'NHWC',\n    dimRoundingMode?: 'floor'|'round'|'ceil'): Tensor4D {\n  let x4D = x as Tensor4D;\n  if (x.rank === 3) {\n    x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n  }\n  let dy4D = dy as Tensor4D;\n  if (dy4D.rank === 3) {\n    dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n  }\n  util.assert(\n      x4D.rank === 4,\n      () => `Error in conv2dDerFilter: input must be rank 4, but got shape ` +\n          `${x4D.shape}.`);\n  util.assert(\n      dy4D.rank === 4,\n      () => `Error in conv2dDerFilter: dy must be rank 4, but got shape ` +\n          `${dy4D.shape}.`);\n  util.assert(\n      filterShape.length === 4,\n      () => `Error in conv2dDerFilter: filterShape must be length 4, but got ` +\n          `${filterShape}.`);\n  const inDepth = dataFormat === 'NHWC' ? x4D.shape[3] : x4D.shape[1];\n  const outDepth = dataFormat === 'NHWC' ? dy4D.shape[3] : dy4D.shape[1];\n  util.assert(\n      inDepth === filterShape[2],\n      () => `Error in conv2dDerFilter: depth of input ${inDepth}) must ` +\n          `match input depth in filter (${filterShape[2]}.`);\n  util.assert(\n      outDepth === filterShape[3],\n      () => `Error in conv2dDerFilter: depth of dy (${outDepth}) must ` +\n          `match output depth for filter (${filterShape[3]}).`);\n  conv_util.checkPadOnDimRoundingMode('conv2dDerFilter', pad, dimRoundingMode);\n  const inputs: Conv2DBackpropFilterInputs = {x: x4D, dy: dy4D};\n  const attrs: Conv2DBackpropFilterAttrs =\n      {strides, pad, dataFormat, dimRoundingMode, filterShape};\n\n  // tslint:disable-next-line: no-unnecessary-type-assertion\n  return ENGINE.runKernel(\n             Conv2DBackpropFilter, inputs as {} as NamedTensorMap,\n             attrs as {} as NamedAttrMap) as Tensor4D;\n}\n\nexport const conv2DBackpropFilter = op({conv2DBackpropFilter_});\n"]}","/*!\n localForage -- Offline Storage, Improved\n Version 1.7.3\n https://localforage.github.io/localForage\n (c) 2013-2017 Mozilla, Apache License 2.0\n*/\n(function(f){if(typeof exports===\"object\"&&typeof module!==\"undefined\"){module.exports=f()}else if(typeof define===\"function\"&&define.amd){define([],f)}else{var g;if(typeof window!==\"undefined\"){g=window}else if(typeof global!==\"undefined\"){g=global}else if(typeof self!==\"undefined\"){g=self}else{g=this}g.localforage = f()}})(function(){var define,module,exports;return (function e(t,n,r){function s(o,u){if(!n[o]){if(!t[o]){var a=typeof require==\"function\"&&require;if(!u&&a)return a(o,!0);if(i)return i(o,!0);var f=new Error(\"Cannot find module '\"+o+\"'\");throw (f.code=\"MODULE_NOT_FOUND\", f)}var l=n[o]={exports:{}};t[o][0].call(l.exports,function(e){var n=t[o][1][e];return s(n?n:e)},l,l.exports,e,t,n,r)}return n[o].exports}var i=typeof require==\"function\"&&require;for(var o=0;o element; its readystatechange event will be fired asynchronously once it is inserted\n // into the document. 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Oh the lulz...\n var isSafari = typeof openDatabase !== 'undefined' && /(Safari|iPhone|iPad|iPod)/.test(navigator.userAgent) && !/Chrome/.test(navigator.userAgent) && !/BlackBerry/.test(navigator.platform);\n\n var hasFetch = typeof fetch === 'function' && fetch.toString().indexOf('[native code') !== -1;\n\n // Safari <10.1 does not meet our requirements for IDB support (#5572)\n // since Safari 10.1 shipped with fetch, we can use that to detect it\n return (!isSafari || hasFetch) && typeof indexedDB !== 'undefined' &&\n // some outdated implementations of IDB that appear on Samsung\n // and HTC Android devices <4.4 are missing IDBKeyRange\n // See: https://github.com/mozilla/localForage/issues/128\n // See: https://github.com/mozilla/localForage/issues/272\n typeof IDBKeyRange !== 'undefined';\n } catch (e) {\n return false;\n }\n}\n\n// Abstracts constructing a Blob object, so it also works in older\n// browsers that don't support the native Blob constructor. (i.e.\n// old QtWebKit versions, at least).\n// Abstracts constructing a Blob object, so it also works in older\n// browsers that don't support the native Blob constructor. (i.e.\n// old QtWebKit versions, at least).\nfunction createBlob(parts, properties) {\n /* global BlobBuilder,MSBlobBuilder,MozBlobBuilder,WebKitBlobBuilder */\n parts = parts || [];\n properties = properties || {};\n try {\n return new Blob(parts, properties);\n } catch (e) {\n if (e.name !== 'TypeError') {\n throw e;\n }\n var Builder = typeof BlobBuilder !== 'undefined' ? BlobBuilder : typeof MSBlobBuilder !== 'undefined' ? MSBlobBuilder : typeof MozBlobBuilder !== 'undefined' ? MozBlobBuilder : WebKitBlobBuilder;\n var builder = new Builder();\n for (var i = 0; i < parts.length; i += 1) {\n builder.append(parts[i]);\n }\n return builder.getBlob(properties.type);\n }\n}\n\n// This is CommonJS because lie is an external dependency, so Rollup\n// can just ignore it.\nif (typeof Promise === 'undefined') {\n // In the \"nopromises\" build this will just throw if you don't have\n // a global promise object, but it would throw anyway later.\n _dereq_(3);\n}\nvar Promise$1 = Promise;\n\nfunction executeCallback(promise, callback) {\n if (callback) {\n promise.then(function (result) {\n callback(null, result);\n }, function (error) {\n callback(error);\n });\n }\n}\n\nfunction executeTwoCallbacks(promise, callback, errorCallback) {\n if (typeof callback === 'function') {\n promise.then(callback);\n }\n\n if (typeof errorCallback === 'function') {\n promise[\"catch\"](errorCallback);\n }\n}\n\nfunction normalizeKey(key) {\n // Cast the key to a string, as that's all we can set as a key.\n if (typeof key !== 'string') {\n console.warn(key + ' used as a key, but it is not a string.');\n key = String(key);\n }\n\n return key;\n}\n\nfunction getCallback() {\n if (arguments.length && typeof arguments[arguments.length - 1] === 'function') {\n return arguments[arguments.length - 1];\n }\n}\n\n// Some code originally from async_storage.js in\n// [Gaia](https://github.com/mozilla-b2g/gaia).\n\nvar DETECT_BLOB_SUPPORT_STORE = 'local-forage-detect-blob-support';\nvar supportsBlobs = void 0;\nvar dbContexts = {};\nvar toString = Object.prototype.toString;\n\n// Transaction Modes\nvar READ_ONLY = 'readonly';\nvar READ_WRITE = 'readwrite';\n\n// Transform a binary string to an array buffer, because otherwise\n// weird stuff happens when you try to work with the binary string directly.\n// It is known.\n// From http://stackoverflow.com/questions/14967647/ (continues on next line)\n// encode-decode-image-with-base64-breaks-image (2013-04-21)\nfunction _binStringToArrayBuffer(bin) {\n var length = bin.length;\n var buf = new ArrayBuffer(length);\n var arr = new Uint8Array(buf);\n for (var i = 0; i < length; i++) {\n arr[i] = bin.charCodeAt(i);\n }\n return buf;\n}\n\n//\n// Blobs are not supported in all versions of IndexedDB, notably\n// Chrome <37 and Android <5. In those versions, storing a blob will throw.\n//\n// Various other blob bugs exist in Chrome v37-42 (inclusive).\n// Detecting them is expensive and confusing to users, and Chrome 37-42\n// is at very low usage worldwide, so we do a hacky userAgent check instead.\n//\n// content-type bug: https://code.google.com/p/chromium/issues/detail?id=408120\n// 404 bug: https://code.google.com/p/chromium/issues/detail?id=447916\n// FileReader bug: https://code.google.com/p/chromium/issues/detail?id=447836\n//\n// Code borrowed from PouchDB. See:\n// https://github.com/pouchdb/pouchdb/blob/master/packages/node_modules/pouchdb-adapter-idb/src/blobSupport.js\n//\nfunction _checkBlobSupportWithoutCaching(idb) {\n return new Promise$1(function (resolve) {\n var txn = idb.transaction(DETECT_BLOB_SUPPORT_STORE, READ_WRITE);\n var blob = createBlob(['']);\n txn.objectStore(DETECT_BLOB_SUPPORT_STORE).put(blob, 'key');\n\n txn.onabort = function (e) {\n // If the transaction aborts now its due to not being able to\n // write to the database, likely due to the disk being full\n e.preventDefault();\n e.stopPropagation();\n resolve(false);\n };\n\n txn.oncomplete = function () {\n var matchedChrome = navigator.userAgent.match(/Chrome\\/(\\d+)/);\n var matchedEdge = navigator.userAgent.match(/Edge\\//);\n // MS Edge pretends to be Chrome 42:\n // https://msdn.microsoft.com/en-us/library/hh869301%28v=vs.85%29.aspx\n resolve(matchedEdge || !matchedChrome || parseInt(matchedChrome[1], 10) >= 43);\n };\n })[\"catch\"](function () {\n return false; // error, so assume unsupported\n });\n}\n\nfunction _checkBlobSupport(idb) {\n if (typeof supportsBlobs === 'boolean') {\n return Promise$1.resolve(supportsBlobs);\n }\n return _checkBlobSupportWithoutCaching(idb).then(function (value) {\n supportsBlobs = value;\n return supportsBlobs;\n });\n}\n\nfunction _deferReadiness(dbInfo) {\n var dbContext = dbContexts[dbInfo.name];\n\n // Create a deferred object representing the current database operation.\n var deferredOperation = {};\n\n deferredOperation.promise = new Promise$1(function (resolve, reject) {\n deferredOperation.resolve = resolve;\n deferredOperation.reject = reject;\n });\n\n // Enqueue the deferred operation.\n dbContext.deferredOperations.push(deferredOperation);\n\n // Chain its promise to the database readiness.\n if (!dbContext.dbReady) {\n dbContext.dbReady = deferredOperation.promise;\n } else {\n dbContext.dbReady = dbContext.dbReady.then(function () {\n return deferredOperation.promise;\n });\n }\n}\n\nfunction _advanceReadiness(dbInfo) {\n var dbContext = dbContexts[dbInfo.name];\n\n // Dequeue a deferred operation.\n var deferredOperation = dbContext.deferredOperations.pop();\n\n // Resolve its promise (which is part of the database readiness\n // chain of promises).\n if (deferredOperation) {\n deferredOperation.resolve();\n return deferredOperation.promise;\n }\n}\n\nfunction _rejectReadiness(dbInfo, err) {\n var dbContext = dbContexts[dbInfo.name];\n\n // Dequeue a deferred operation.\n var deferredOperation = dbContext.deferredOperations.pop();\n\n // Reject its promise (which is part of the database readiness\n // chain of promises).\n if (deferredOperation) {\n deferredOperation.reject(err);\n return deferredOperation.promise;\n }\n}\n\nfunction _getConnection(dbInfo, upgradeNeeded) {\n return new Promise$1(function (resolve, reject) {\n dbContexts[dbInfo.name] = dbContexts[dbInfo.name] || createDbContext();\n\n if (dbInfo.db) {\n if (upgradeNeeded) {\n _deferReadiness(dbInfo);\n dbInfo.db.close();\n } else {\n return resolve(dbInfo.db);\n }\n }\n\n var dbArgs = [dbInfo.name];\n\n if (upgradeNeeded) {\n dbArgs.push(dbInfo.version);\n }\n\n var openreq = idb.open.apply(idb, dbArgs);\n\n if (upgradeNeeded) {\n openreq.onupgradeneeded = function (e) {\n var db = openreq.result;\n try {\n db.createObjectStore(dbInfo.storeName);\n if (e.oldVersion <= 1) {\n // Added when support for blob shims was added\n db.createObjectStore(DETECT_BLOB_SUPPORT_STORE);\n }\n } catch (ex) {\n if (ex.name === 'ConstraintError') {\n console.warn('The database \"' + dbInfo.name + '\"' + ' has been upgraded from version ' + e.oldVersion + ' to version ' + e.newVersion + ', but the storage \"' + dbInfo.storeName + '\" already exists.');\n } else {\n throw ex;\n }\n }\n };\n }\n\n openreq.onerror = function (e) {\n e.preventDefault();\n reject(openreq.error);\n };\n\n openreq.onsuccess = function () {\n resolve(openreq.result);\n _advanceReadiness(dbInfo);\n };\n });\n}\n\nfunction _getOriginalConnection(dbInfo) {\n return _getConnection(dbInfo, false);\n}\n\nfunction _getUpgradedConnection(dbInfo) {\n return _getConnection(dbInfo, true);\n}\n\nfunction _isUpgradeNeeded(dbInfo, defaultVersion) {\n if (!dbInfo.db) {\n return true;\n }\n\n var isNewStore = !dbInfo.db.objectStoreNames.contains(dbInfo.storeName);\n var isDowngrade = dbInfo.version < dbInfo.db.version;\n var isUpgrade = dbInfo.version > dbInfo.db.version;\n\n if (isDowngrade) {\n // If the version is not the default one\n // then warn for impossible downgrade.\n if (dbInfo.version !== defaultVersion) {\n console.warn('The database \"' + dbInfo.name + '\"' + \" can't be downgraded from version \" + dbInfo.db.version + ' to version ' + dbInfo.version + '.');\n }\n // Align the versions to prevent errors.\n dbInfo.version = dbInfo.db.version;\n }\n\n if (isUpgrade || isNewStore) {\n // If the store is new then increment the version (if needed).\n // This will trigger an \"upgradeneeded\" event which is required\n // for creating a store.\n if (isNewStore) {\n var incVersion = dbInfo.db.version + 1;\n if (incVersion > dbInfo.version) {\n dbInfo.version = incVersion;\n }\n }\n\n return true;\n }\n\n return false;\n}\n\n// encode a blob for indexeddb engines that don't support blobs\nfunction _encodeBlob(blob) {\n return new Promise$1(function (resolve, reject) {\n var reader = new FileReader();\n reader.onerror = reject;\n reader.onloadend = function (e) {\n var base64 = btoa(e.target.result || '');\n resolve({\n __local_forage_encoded_blob: true,\n data: base64,\n type: blob.type\n });\n };\n reader.readAsBinaryString(blob);\n });\n}\n\n// decode an encoded blob\nfunction _decodeBlob(encodedBlob) {\n var arrayBuff = _binStringToArrayBuffer(atob(encodedBlob.data));\n return createBlob([arrayBuff], { type: encodedBlob.type });\n}\n\n// is this one of our fancy encoded blobs?\nfunction _isEncodedBlob(value) {\n return value && value.__local_forage_encoded_blob;\n}\n\n// Specialize the default `ready()` function by making it dependent\n// on the current database operations. Thus, the driver will be actually\n// ready when it's been initialized (default) *and* there are no pending\n// operations on the database (initiated by some other instances).\nfunction _fullyReady(callback) {\n var self = this;\n\n var promise = self._initReady().then(function () {\n var dbContext = dbContexts[self._dbInfo.name];\n\n if (dbContext && dbContext.dbReady) {\n return dbContext.dbReady;\n }\n });\n\n executeTwoCallbacks(promise, callback, callback);\n return promise;\n}\n\n// Try to establish a new db connection to replace the\n// current one which is broken (i.e. experiencing\n// InvalidStateError while creating a transaction).\nfunction _tryReconnect(dbInfo) {\n _deferReadiness(dbInfo);\n\n var dbContext = dbContexts[dbInfo.name];\n var forages = dbContext.forages;\n\n for (var i = 0; i < forages.length; i++) {\n var forage = forages[i];\n if (forage._dbInfo.db) {\n forage._dbInfo.db.close();\n forage._dbInfo.db = null;\n }\n }\n dbInfo.db = null;\n\n return _getOriginalConnection(dbInfo).then(function (db) {\n dbInfo.db = db;\n if (_isUpgradeNeeded(dbInfo)) {\n // Reopen the database for upgrading.\n return _getUpgradedConnection(dbInfo);\n }\n return db;\n }).then(function (db) {\n // store the latest db reference\n // in case the db was upgraded\n dbInfo.db = dbContext.db = db;\n for (var i = 0; i < forages.length; i++) {\n forages[i]._dbInfo.db = db;\n }\n })[\"catch\"](function (err) {\n _rejectReadiness(dbInfo, err);\n throw err;\n });\n}\n\n// FF doesn't like Promises (micro-tasks) and IDDB store operations,\n// so we have to do it with callbacks\nfunction createTransaction(dbInfo, mode, callback, retries) {\n if (retries === undefined) {\n retries = 1;\n }\n\n try {\n var tx = dbInfo.db.transaction(dbInfo.storeName, mode);\n callback(null, tx);\n } catch (err) {\n if (retries > 0 && (!dbInfo.db || err.name === 'InvalidStateError' || err.name === 'NotFoundError')) {\n return Promise$1.resolve().then(function () {\n if (!dbInfo.db || err.name === 'NotFoundError' && !dbInfo.db.objectStoreNames.contains(dbInfo.storeName) && dbInfo.version <= dbInfo.db.version) {\n // increase the db version, to create the new ObjectStore\n if (dbInfo.db) {\n dbInfo.version = dbInfo.db.version + 1;\n }\n // Reopen the database for upgrading.\n return _getUpgradedConnection(dbInfo);\n }\n }).then(function () {\n return _tryReconnect(dbInfo).then(function () {\n createTransaction(dbInfo, mode, callback, retries - 1);\n });\n })[\"catch\"](callback);\n }\n\n callback(err);\n }\n}\n\nfunction createDbContext() {\n return {\n // Running localForages sharing a database.\n forages: [],\n // Shared database.\n db: null,\n // Database readiness (promise).\n dbReady: null,\n // Deferred operations on the database.\n deferredOperations: []\n };\n}\n\n// Open the IndexedDB database (automatically creates one if one didn't\n// previously exist), using any options set in the config.\nfunction _initStorage(options) {\n var self = this;\n var dbInfo = {\n db: null\n };\n\n if (options) {\n for (var i in options) {\n dbInfo[i] = options[i];\n }\n }\n\n // Get the current context of the database;\n var dbContext = dbContexts[dbInfo.name];\n\n // ...or create a new context.\n if (!dbContext) {\n dbContext = createDbContext();\n // Register the new context in the global container.\n dbContexts[dbInfo.name] = dbContext;\n }\n\n // Register itself as a running localForage in the current context.\n dbContext.forages.push(self);\n\n // Replace the default `ready()` function with the specialized one.\n if (!self._initReady) {\n self._initReady = self.ready;\n self.ready = _fullyReady;\n }\n\n // Create an array of initialization states of the related localForages.\n var initPromises = [];\n\n function ignoreErrors() {\n // Don't handle errors here,\n // just makes sure related localForages aren't pending.\n return Promise$1.resolve();\n }\n\n for (var j = 0; j < dbContext.forages.length; j++) {\n var forage = dbContext.forages[j];\n if (forage !== self) {\n // Don't wait for itself...\n initPromises.push(forage._initReady()[\"catch\"](ignoreErrors));\n }\n }\n\n // Take a snapshot of the related localForages.\n var forages = dbContext.forages.slice(0);\n\n // Initialize the connection process only when\n // all the related localForages aren't pending.\n return Promise$1.all(initPromises).then(function () {\n dbInfo.db = dbContext.db;\n // Get the connection or open a new one without upgrade.\n return _getOriginalConnection(dbInfo);\n }).then(function (db) {\n dbInfo.db = db;\n if (_isUpgradeNeeded(dbInfo, self._defaultConfig.version)) {\n // Reopen the database for upgrading.\n return _getUpgradedConnection(dbInfo);\n }\n return db;\n }).then(function (db) {\n dbInfo.db = dbContext.db = db;\n self._dbInfo = dbInfo;\n // Share the final connection amongst related localForages.\n for (var k = 0; k < forages.length; k++) {\n var forage = forages[k];\n if (forage !== self) {\n // Self is already up-to-date.\n forage._dbInfo.db = dbInfo.db;\n forage._dbInfo.version = dbInfo.version;\n }\n }\n });\n}\n\nfunction getItem(key, callback) {\n var self = this;\n\n key = normalizeKey(key);\n\n var promise = new Promise$1(function (resolve, reject) {\n self.ready().then(function () {\n createTransaction(self._dbInfo, READ_ONLY, function (err, transaction) {\n if (err) {\n return reject(err);\n }\n\n try {\n var store = transaction.objectStore(self._dbInfo.storeName);\n var req = store.get(key);\n\n req.onsuccess = function () {\n var value = req.result;\n if (value === undefined) {\n value = null;\n }\n if (_isEncodedBlob(value)) {\n value = _decodeBlob(value);\n }\n resolve(value);\n };\n\n req.onerror = function () {\n reject(req.error);\n };\n } catch (e) {\n reject(e);\n }\n });\n })[\"catch\"](reject);\n });\n\n executeCallback(promise, callback);\n return promise;\n}\n\n// Iterate over all items stored in database.\nfunction iterate(iterator, callback) {\n var self = this;\n\n var promise = new Promise$1(function (resolve, reject) {\n self.ready().then(function () {\n createTransaction(self._dbInfo, READ_ONLY, function (err, transaction) {\n if (err) {\n return reject(err);\n }\n\n try {\n var store = transaction.objectStore(self._dbInfo.storeName);\n var req = store.openCursor();\n var iterationNumber = 1;\n\n req.onsuccess = function () {\n var cursor = req.result;\n\n if (cursor) {\n var value = cursor.value;\n if (_isEncodedBlob(value)) {\n value = _decodeBlob(value);\n }\n var result = iterator(value, cursor.key, iterationNumber++);\n\n // when the iterator callback retuns any\n // (non-`undefined`) value, then we stop\n // the iteration immediately\n if (result !== void 0) {\n resolve(result);\n } else {\n cursor[\"continue\"]();\n }\n } else {\n resolve();\n }\n };\n\n req.onerror = function () {\n reject(req.error);\n };\n } catch (e) {\n reject(e);\n }\n });\n })[\"catch\"](reject);\n });\n\n executeCallback(promise, callback);\n\n return promise;\n}\n\nfunction setItem(key, value, callback) {\n var self = this;\n\n key = normalizeKey(key);\n\n var promise = new Promise$1(function (resolve, reject) {\n var dbInfo;\n self.ready().then(function () {\n dbInfo = self._dbInfo;\n if (toString.call(value) === '[object Blob]') {\n return _checkBlobSupport(dbInfo.db).then(function (blobSupport) {\n if (blobSupport) {\n return value;\n }\n return _encodeBlob(value);\n });\n }\n return value;\n }).then(function (value) {\n createTransaction(self._dbInfo, READ_WRITE, function (err, transaction) {\n if (err) {\n return reject(err);\n }\n\n try {\n var store = transaction.objectStore(self._dbInfo.storeName);\n\n // The reason we don't _save_ null is because IE 10 does\n // not support saving the `null` type in IndexedDB. How\n // ironic, given the bug below!\n // See: https://github.com/mozilla/localForage/issues/161\n if (value === null) {\n value = undefined;\n }\n\n var req = store.put(value, key);\n\n transaction.oncomplete = function () {\n // Cast to undefined so the value passed to\n // callback/promise is the same as what one would get out\n // of `getItem()` later. This leads to some weirdness\n // (setItem('foo', undefined) will return `null`), but\n // it's not my fault localStorage is our baseline and that\n // it's weird.\n if (value === undefined) {\n value = null;\n }\n\n resolve(value);\n };\n transaction.onabort = transaction.onerror = function () {\n var err = req.error ? req.error : req.transaction.error;\n reject(err);\n };\n } catch (e) {\n reject(e);\n }\n });\n })[\"catch\"](reject);\n });\n\n executeCallback(promise, callback);\n return promise;\n}\n\nfunction removeItem(key, callback) {\n var self = this;\n\n key = normalizeKey(key);\n\n var promise = new Promise$1(function (resolve, reject) {\n self.ready().then(function () {\n createTransaction(self._dbInfo, READ_WRITE, function (err, transaction) {\n if (err) {\n return reject(err);\n }\n\n try {\n var store = transaction.objectStore(self._dbInfo.storeName);\n // We use a Grunt task to make this safe for IE and some\n // versions of Android (including those used by Cordova).\n // Normally IE won't like `.delete()` and will insist on\n // using `['delete']()`, but we have a build step that\n // fixes this for us now.\n var req = store[\"delete\"](key);\n transaction.oncomplete = function () {\n resolve();\n };\n\n transaction.onerror = function () {\n reject(req.error);\n };\n\n // The request will be also be aborted if we've exceeded our storage\n // space.\n transaction.onabort = function () {\n var err = req.error ? req.error : req.transaction.error;\n reject(err);\n };\n } catch (e) {\n reject(e);\n }\n });\n })[\"catch\"](reject);\n });\n\n executeCallback(promise, callback);\n return promise;\n}\n\nfunction clear(callback) {\n var self = this;\n\n var promise = new Promise$1(function (resolve, reject) {\n self.ready().then(function () {\n createTransaction(self._dbInfo, READ_WRITE, function (err, transaction) {\n if (err) {\n return reject(err);\n }\n\n try {\n var store = transaction.objectStore(self._dbInfo.storeName);\n var req = store.clear();\n\n transaction.oncomplete = function () {\n resolve();\n };\n\n transaction.onabort = transaction.onerror = function () {\n var err = req.error ? req.error : req.transaction.error;\n reject(err);\n };\n } catch (e) {\n reject(e);\n }\n });\n })[\"catch\"](reject);\n });\n\n executeCallback(promise, callback);\n return promise;\n}\n\nfunction length(callback) {\n var self = this;\n\n var promise = new Promise$1(function (resolve, reject) {\n self.ready().then(function () {\n createTransaction(self._dbInfo, READ_ONLY, function (err, transaction) {\n if (err) {\n return reject(err);\n }\n\n try {\n var store = transaction.objectStore(self._dbInfo.storeName);\n var req = store.count();\n\n req.onsuccess = function () {\n resolve(req.result);\n };\n\n req.onerror = function () {\n reject(req.error);\n };\n } catch (e) {\n reject(e);\n }\n });\n })[\"catch\"](reject);\n });\n\n executeCallback(promise, callback);\n return promise;\n}\n\nfunction key(n, callback) {\n var self = this;\n\n var promise = new Promise$1(function (resolve, reject) {\n if (n < 0) {\n resolve(null);\n\n return;\n }\n\n self.ready().then(function () {\n createTransaction(self._dbInfo, READ_ONLY, function (err, transaction) {\n if (err) {\n return reject(err);\n }\n\n try {\n var store = transaction.objectStore(self._dbInfo.storeName);\n var advanced = false;\n var req = store.openCursor();\n\n req.onsuccess = function () {\n var cursor = req.result;\n if (!cursor) {\n // this means there weren't enough keys\n resolve(null);\n\n return;\n }\n\n if (n === 0) {\n // We have the first key, return it if that's what they\n // wanted.\n resolve(cursor.key);\n } else {\n if (!advanced) {\n // Otherwise, ask the cursor to skip ahead n\n // records.\n advanced = true;\n cursor.advance(n);\n } else {\n // When we get here, we've got the nth key.\n resolve(cursor.key);\n }\n }\n };\n\n req.onerror = function () {\n reject(req.error);\n };\n } catch (e) {\n reject(e);\n }\n });\n })[\"catch\"](reject);\n });\n\n executeCallback(promise, callback);\n return promise;\n}\n\nfunction keys(callback) {\n var self = this;\n\n var promise = new Promise$1(function (resolve, reject) {\n self.ready().then(function () {\n createTransaction(self._dbInfo, READ_ONLY, function (err, transaction) {\n if (err) {\n return reject(err);\n }\n\n try {\n var store = transaction.objectStore(self._dbInfo.storeName);\n var req = store.openCursor();\n var keys = [];\n\n req.onsuccess = function () {\n var cursor = req.result;\n\n if (!cursor) {\n resolve(keys);\n return;\n }\n\n keys.push(cursor.key);\n cursor[\"continue\"]();\n };\n\n req.onerror = function () {\n reject(req.error);\n };\n } catch (e) {\n reject(e);\n }\n });\n })[\"catch\"](reject);\n });\n\n executeCallback(promise, callback);\n return promise;\n}\n\nfunction dropInstance(options, callback) {\n callback = getCallback.apply(this, arguments);\n\n var currentConfig = this.config();\n options = typeof options !== 'function' && options || {};\n if (!options.name) {\n options.name = options.name || currentConfig.name;\n options.storeName = options.storeName || currentConfig.storeName;\n }\n\n var self = this;\n var promise;\n if (!options.name) {\n promise = Promise$1.reject('Invalid arguments');\n } else {\n var isCurrentDb = options.name === currentConfig.name && self._dbInfo.db;\n\n var dbPromise = isCurrentDb ? Promise$1.resolve(self._dbInfo.db) : _getOriginalConnection(options).then(function (db) {\n var dbContext = dbContexts[options.name];\n var forages = dbContext.forages;\n dbContext.db = db;\n for (var i = 0; i < forages.length; i++) {\n forages[i]._dbInfo.db = db;\n }\n return db;\n });\n\n if (!options.storeName) {\n promise = dbPromise.then(function (db) {\n _deferReadiness(options);\n\n var dbContext = dbContexts[options.name];\n var forages = dbContext.forages;\n\n db.close();\n for (var i = 0; i < forages.length; i++) {\n var forage = forages[i];\n forage._dbInfo.db = null;\n }\n\n var dropDBPromise = new Promise$1(function (resolve, reject) {\n var req = idb.deleteDatabase(options.name);\n\n req.onerror = req.onblocked = function (err) {\n var db = req.result;\n if (db) {\n db.close();\n }\n reject(err);\n };\n\n req.onsuccess = function () {\n var db = req.result;\n if (db) {\n db.close();\n }\n resolve(db);\n };\n });\n\n return dropDBPromise.then(function (db) {\n dbContext.db = db;\n for (var i = 0; i < forages.length; i++) {\n var _forage = forages[i];\n _advanceReadiness(_forage._dbInfo);\n }\n })[\"catch\"](function (err) {\n (_rejectReadiness(options, err) || Promise$1.resolve())[\"catch\"](function () {});\n throw err;\n });\n });\n } else {\n promise = dbPromise.then(function (db) {\n if (!db.objectStoreNames.contains(options.storeName)) {\n return;\n }\n\n var newVersion = db.version + 1;\n\n _deferReadiness(options);\n\n var dbContext = dbContexts[options.name];\n var forages = dbContext.forages;\n\n db.close();\n for (var i = 0; i < forages.length; i++) {\n var forage = forages[i];\n forage._dbInfo.db = null;\n forage._dbInfo.version = newVersion;\n }\n\n var dropObjectPromise = new Promise$1(function (resolve, reject) {\n var req = idb.open(options.name, newVersion);\n\n req.onerror = function (err) {\n var db = req.result;\n db.close();\n reject(err);\n };\n\n req.onupgradeneeded = function () {\n var db = req.result;\n db.deleteObjectStore(options.storeName);\n };\n\n req.onsuccess = function () {\n var db = req.result;\n db.close();\n resolve(db);\n };\n });\n\n return dropObjectPromise.then(function (db) {\n dbContext.db = db;\n for (var j = 0; j < forages.length; j++) {\n var _forage2 = forages[j];\n _forage2._dbInfo.db = db;\n _advanceReadiness(_forage2._dbInfo);\n }\n })[\"catch\"](function (err) {\n (_rejectReadiness(options, err) || Promise$1.resolve())[\"catch\"](function () {});\n throw err;\n });\n });\n }\n }\n\n executeCallback(promise, callback);\n return promise;\n}\n\nvar asyncStorage = {\n _driver: 'asyncStorage',\n _initStorage: _initStorage,\n _support: isIndexedDBValid(),\n iterate: iterate,\n getItem: getItem,\n setItem: setItem,\n removeItem: removeItem,\n clear: clear,\n length: length,\n key: key,\n keys: keys,\n dropInstance: dropInstance\n};\n\nfunction isWebSQLValid() {\n return typeof openDatabase === 'function';\n}\n\n// Sadly, the best way to save binary data in WebSQL/localStorage is serializing\n// it to Base64, so this is how we store it to prevent very strange errors with less\n// verbose ways of binary <-> string data storage.\nvar BASE_CHARS = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/';\n\nvar BLOB_TYPE_PREFIX = '~~local_forage_type~';\nvar BLOB_TYPE_PREFIX_REGEX = /^~~local_forage_type~([^~]+)~/;\n\nvar SERIALIZED_MARKER = '__lfsc__:';\nvar SERIALIZED_MARKER_LENGTH = SERIALIZED_MARKER.length;\n\n// OMG the serializations!\nvar TYPE_ARRAYBUFFER = 'arbf';\nvar TYPE_BLOB = 'blob';\nvar TYPE_INT8ARRAY = 'si08';\nvar TYPE_UINT8ARRAY = 'ui08';\nvar TYPE_UINT8CLAMPEDARRAY = 'uic8';\nvar TYPE_INT16ARRAY = 'si16';\nvar TYPE_INT32ARRAY = 'si32';\nvar TYPE_UINT16ARRAY = 'ur16';\nvar TYPE_UINT32ARRAY = 'ui32';\nvar TYPE_FLOAT32ARRAY = 'fl32';\nvar TYPE_FLOAT64ARRAY = 'fl64';\nvar TYPE_SERIALIZED_MARKER_LENGTH = SERIALIZED_MARKER_LENGTH + TYPE_ARRAYBUFFER.length;\n\nvar toString$1 = Object.prototype.toString;\n\nfunction stringToBuffer(serializedString) {\n // Fill the string into a ArrayBuffer.\n var bufferLength = serializedString.length * 0.75;\n var len = serializedString.length;\n var i;\n var p = 0;\n var encoded1, encoded2, encoded3, encoded4;\n\n if (serializedString[serializedString.length - 1] === '=') {\n bufferLength--;\n if (serializedString[serializedString.length - 2] === '=') {\n bufferLength--;\n }\n }\n\n var buffer = new ArrayBuffer(bufferLength);\n var bytes = new Uint8Array(buffer);\n\n for (i = 0; i < len; i += 4) {\n encoded1 = BASE_CHARS.indexOf(serializedString[i]);\n encoded2 = BASE_CHARS.indexOf(serializedString[i + 1]);\n encoded3 = BASE_CHARS.indexOf(serializedString[i + 2]);\n encoded4 = BASE_CHARS.indexOf(serializedString[i + 3]);\n\n /*jslint bitwise: true */\n bytes[p++] = encoded1 << 2 | encoded2 >> 4;\n bytes[p++] = (encoded2 & 15) << 4 | encoded3 >> 2;\n bytes[p++] = (encoded3 & 3) << 6 | encoded4 & 63;\n }\n return buffer;\n}\n\n// Converts a buffer to a string to store, serialized, in the backend\n// storage library.\nfunction bufferToString(buffer) {\n // base64-arraybuffer\n var bytes = new Uint8Array(buffer);\n var base64String = '';\n var i;\n\n for (i = 0; i < bytes.length; i += 3) {\n /*jslint bitwise: true */\n base64String += BASE_CHARS[bytes[i] >> 2];\n base64String += BASE_CHARS[(bytes[i] & 3) << 4 | bytes[i + 1] >> 4];\n base64String += BASE_CHARS[(bytes[i + 1] & 15) << 2 | bytes[i + 2] >> 6];\n base64String += BASE_CHARS[bytes[i + 2] & 63];\n }\n\n if (bytes.length % 3 === 2) {\n base64String = base64String.substring(0, base64String.length - 1) + '=';\n } else if (bytes.length % 3 === 1) {\n base64String = base64String.substring(0, base64String.length - 2) + '==';\n }\n\n return base64String;\n}\n\n// Serialize a value, afterwards executing a callback (which usually\n// instructs the `setItem()` callback/promise to be executed). This is how\n// we store binary data with localStorage.\nfunction serialize(value, callback) {\n var valueType = '';\n if (value) {\n valueType = toString$1.call(value);\n }\n\n // Cannot use `value instanceof ArrayBuffer` or such here, as these\n // checks fail when running the tests using casper.js...\n //\n // TODO: See why those tests fail and use a better solution.\n if (value && (valueType === '[object ArrayBuffer]' || value.buffer && toString$1.call(value.buffer) === '[object ArrayBuffer]')) {\n // Convert binary arrays to a string and prefix the string with\n // a special marker.\n var buffer;\n var marker = SERIALIZED_MARKER;\n\n if (value instanceof ArrayBuffer) {\n buffer = value;\n marker += TYPE_ARRAYBUFFER;\n } else {\n buffer = value.buffer;\n\n if (valueType === '[object Int8Array]') {\n marker += TYPE_INT8ARRAY;\n } else if (valueType === '[object Uint8Array]') {\n marker += TYPE_UINT8ARRAY;\n } else if (valueType === '[object Uint8ClampedArray]') {\n marker += TYPE_UINT8CLAMPEDARRAY;\n } else if (valueType === '[object Int16Array]') {\n marker += TYPE_INT16ARRAY;\n } else if (valueType === '[object Uint16Array]') {\n marker += TYPE_UINT16ARRAY;\n } else if (valueType === '[object Int32Array]') {\n marker += TYPE_INT32ARRAY;\n } else if (valueType === '[object Uint32Array]') {\n marker += TYPE_UINT32ARRAY;\n } else if (valueType === '[object Float32Array]') {\n marker += TYPE_FLOAT32ARRAY;\n } else if (valueType === '[object Float64Array]') {\n marker += TYPE_FLOAT64ARRAY;\n } else {\n callback(new Error('Failed to get type for BinaryArray'));\n }\n }\n\n callback(marker + bufferToString(buffer));\n } else if (valueType === '[object Blob]') {\n // Conver the blob to a binaryArray and then to a string.\n var fileReader = new FileReader();\n\n fileReader.onload = function () {\n // Backwards-compatible prefix for the blob type.\n var str = BLOB_TYPE_PREFIX + value.type + '~' + bufferToString(this.result);\n\n callback(SERIALIZED_MARKER + TYPE_BLOB + str);\n };\n\n fileReader.readAsArrayBuffer(value);\n } else {\n try {\n callback(JSON.stringify(value));\n } catch (e) {\n console.error(\"Couldn't convert value into a JSON string: \", value);\n\n callback(null, e);\n }\n }\n}\n\n// Deserialize data we've inserted into a value column/field. We place\n// special markers into our strings to mark them as encoded; this isn't\n// as nice as a meta field, but it's the only sane thing we can do whilst\n// keeping localStorage support intact.\n//\n// Oftentimes this will just deserialize JSON content, but if we have a\n// special marker (SERIALIZED_MARKER, defined above), we will extract\n// some kind of arraybuffer/binary data/typed array out of the string.\nfunction deserialize(value) {\n // If we haven't marked this string as being specially serialized (i.e.\n // something other than serialized JSON), we can just return it and be\n // done with it.\n if (value.substring(0, SERIALIZED_MARKER_LENGTH) !== SERIALIZED_MARKER) {\n return JSON.parse(value);\n }\n\n // The following code deals with deserializing some kind of Blob or\n // TypedArray. First we separate out the type of data we're dealing\n // with from the data itself.\n var serializedString = value.substring(TYPE_SERIALIZED_MARKER_LENGTH);\n var type = value.substring(SERIALIZED_MARKER_LENGTH, TYPE_SERIALIZED_MARKER_LENGTH);\n\n var blobType;\n // Backwards-compatible blob type serialization strategy.\n // DBs created with older versions of localForage will simply not have the blob type.\n if (type === TYPE_BLOB && BLOB_TYPE_PREFIX_REGEX.test(serializedString)) {\n var matcher = serializedString.match(BLOB_TYPE_PREFIX_REGEX);\n blobType = matcher[1];\n serializedString = serializedString.substring(matcher[0].length);\n }\n var buffer = stringToBuffer(serializedString);\n\n // Return the right type based on the code/type set during\n // serialization.\n switch (type) {\n case TYPE_ARRAYBUFFER:\n return buffer;\n case TYPE_BLOB:\n return createBlob([buffer], { type: blobType });\n case TYPE_INT8ARRAY:\n return new Int8Array(buffer);\n case TYPE_UINT8ARRAY:\n return new Uint8Array(buffer);\n case TYPE_UINT8CLAMPEDARRAY:\n return new Uint8ClampedArray(buffer);\n case TYPE_INT16ARRAY:\n return new Int16Array(buffer);\n case TYPE_UINT16ARRAY:\n return new Uint16Array(buffer);\n case TYPE_INT32ARRAY:\n return new Int32Array(buffer);\n case TYPE_UINT32ARRAY:\n return new Uint32Array(buffer);\n case TYPE_FLOAT32ARRAY:\n return new Float32Array(buffer);\n case TYPE_FLOAT64ARRAY:\n return new Float64Array(buffer);\n default:\n throw new Error('Unkown type: ' + type);\n }\n}\n\nvar localforageSerializer = {\n serialize: serialize,\n deserialize: deserialize,\n stringToBuffer: stringToBuffer,\n bufferToString: bufferToString\n};\n\n/*\n * Includes code from:\n *\n * base64-arraybuffer\n * https://github.com/niklasvh/base64-arraybuffer\n *\n * Copyright (c) 2012 Niklas von Hertzen\n * Licensed under the MIT license.\n */\n\nfunction createDbTable(t, dbInfo, callback, errorCallback) {\n t.executeSql('CREATE TABLE IF NOT EXISTS ' + dbInfo.storeName + ' ' + '(id INTEGER PRIMARY KEY, key unique, value)', [], callback, errorCallback);\n}\n\n// Open the WebSQL database (automatically creates one if one didn't\n// previously exist), using any options set in the config.\nfunction _initStorage$1(options) {\n var self = this;\n var dbInfo = {\n db: null\n };\n\n if (options) {\n for (var i in options) {\n dbInfo[i] = typeof options[i] !== 'string' ? options[i].toString() : options[i];\n }\n }\n\n var dbInfoPromise = new Promise$1(function (resolve, reject) {\n // Open the database; the openDatabase API will automatically\n // create it for us if it doesn't exist.\n try {\n dbInfo.db = openDatabase(dbInfo.name, String(dbInfo.version), dbInfo.description, dbInfo.size);\n } catch (e) {\n return reject(e);\n }\n\n // Create our key/value table if it doesn't exist.\n dbInfo.db.transaction(function (t) {\n createDbTable(t, dbInfo, function () {\n self._dbInfo = dbInfo;\n resolve();\n }, function (t, error) {\n reject(error);\n });\n }, reject);\n });\n\n dbInfo.serializer = localforageSerializer;\n return dbInfoPromise;\n}\n\nfunction tryExecuteSql(t, dbInfo, sqlStatement, args, callback, errorCallback) {\n t.executeSql(sqlStatement, args, callback, function (t, error) {\n if (error.code === error.SYNTAX_ERR) {\n t.executeSql('SELECT name FROM sqlite_master ' + \"WHERE type='table' AND name = ?\", [dbInfo.storeName], function (t, results) {\n if (!results.rows.length) {\n // if the table is missing (was deleted)\n // re-create it table and retry\n createDbTable(t, dbInfo, function () {\n t.executeSql(sqlStatement, args, callback, errorCallback);\n }, errorCallback);\n } else {\n errorCallback(t, error);\n }\n }, errorCallback);\n } else {\n errorCallback(t, error);\n }\n }, errorCallback);\n}\n\nfunction getItem$1(key, callback) {\n var self = this;\n\n key = normalizeKey(key);\n\n var promise = new Promise$1(function (resolve, reject) {\n self.ready().then(function () {\n var dbInfo = self._dbInfo;\n dbInfo.db.transaction(function (t) {\n tryExecuteSql(t, dbInfo, 'SELECT * FROM ' + dbInfo.storeName + ' WHERE key = ? LIMIT 1', [key], function (t, results) {\n var result = results.rows.length ? results.rows.item(0).value : null;\n\n // Check to see if this is serialized content we need to\n // unpack.\n if (result) {\n result = dbInfo.serializer.deserialize(result);\n }\n\n resolve(result);\n }, function (t, error) {\n reject(error);\n });\n });\n })[\"catch\"](reject);\n });\n\n executeCallback(promise, callback);\n return promise;\n}\n\nfunction iterate$1(iterator, callback) {\n var self = this;\n\n var promise = new Promise$1(function (resolve, reject) {\n self.ready().then(function () {\n var dbInfo = self._dbInfo;\n\n dbInfo.db.transaction(function (t) {\n tryExecuteSql(t, dbInfo, 'SELECT * FROM ' + dbInfo.storeName, [], function (t, results) {\n var rows = results.rows;\n var length = rows.length;\n\n for (var i = 0; i < length; i++) {\n var item = rows.item(i);\n var result = item.value;\n\n // Check to see if this is serialized content\n // we need to unpack.\n if (result) {\n result = dbInfo.serializer.deserialize(result);\n }\n\n result = iterator(result, item.key, i + 1);\n\n // void(0) prevents problems with redefinition\n // of `undefined`.\n if (result !== void 0) {\n resolve(result);\n return;\n }\n }\n\n resolve();\n }, function (t, error) {\n reject(error);\n });\n });\n })[\"catch\"](reject);\n });\n\n executeCallback(promise, callback);\n return promise;\n}\n\nfunction _setItem(key, value, callback, retriesLeft) {\n var self = this;\n\n key = normalizeKey(key);\n\n var promise = new Promise$1(function (resolve, reject) {\n self.ready().then(function () {\n // The localStorage API doesn't return undefined values in an\n // \"expected\" way, so undefined is always cast to null in all\n // drivers. See: https://github.com/mozilla/localForage/pull/42\n if (value === undefined) {\n value = null;\n }\n\n // Save the original value to pass to the callback.\n var originalValue = value;\n\n var dbInfo = self._dbInfo;\n dbInfo.serializer.serialize(value, function (value, error) {\n if (error) {\n reject(error);\n } else {\n dbInfo.db.transaction(function (t) {\n tryExecuteSql(t, dbInfo, 'INSERT OR REPLACE INTO ' + dbInfo.storeName + ' ' + '(key, value) VALUES (?, ?)', [key, value], function () {\n resolve(originalValue);\n }, function (t, error) {\n reject(error);\n });\n }, function (sqlError) {\n // The transaction failed; check\n // to see if it's a quota error.\n if (sqlError.code === sqlError.QUOTA_ERR) {\n // We reject the callback outright for now, but\n // it's worth trying to re-run the transaction.\n // Even if the user accepts the prompt to use\n // more storage on Safari, this error will\n // be called.\n //\n // Try to re-run the transaction.\n if (retriesLeft > 0) {\n resolve(_setItem.apply(self, [key, originalValue, callback, retriesLeft - 1]));\n return;\n }\n reject(sqlError);\n }\n });\n }\n });\n })[\"catch\"](reject);\n });\n\n executeCallback(promise, callback);\n return promise;\n}\n\nfunction setItem$1(key, value, callback) {\n return _setItem.apply(this, [key, value, callback, 1]);\n}\n\nfunction removeItem$1(key, callback) {\n var self = this;\n\n key = normalizeKey(key);\n\n var promise = new Promise$1(function (resolve, reject) {\n self.ready().then(function () {\n var dbInfo = self._dbInfo;\n dbInfo.db.transaction(function (t) {\n tryExecuteSql(t, dbInfo, 'DELETE FROM ' + dbInfo.storeName + ' WHERE key = ?', [key], function () {\n resolve();\n }, function (t, error) {\n reject(error);\n });\n });\n })[\"catch\"](reject);\n });\n\n executeCallback(promise, callback);\n return promise;\n}\n\n// Deletes every item in the table.\n// TODO: Find out if this resets the AUTO_INCREMENT number.\nfunction clear$1(callback) {\n var self = this;\n\n var promise = new Promise$1(function (resolve, reject) {\n self.ready().then(function () {\n var dbInfo = self._dbInfo;\n dbInfo.db.transaction(function (t) {\n tryExecuteSql(t, dbInfo, 'DELETE FROM ' + dbInfo.storeName, [], function () {\n resolve();\n }, function (t, error) {\n reject(error);\n });\n });\n })[\"catch\"](reject);\n });\n\n executeCallback(promise, callback);\n return promise;\n}\n\n// Does a simple `COUNT(key)` to get the number of items stored in\n// localForage.\nfunction length$1(callback) {\n var self = this;\n\n var promise = new Promise$1(function (resolve, reject) {\n self.ready().then(function () {\n var dbInfo = self._dbInfo;\n dbInfo.db.transaction(function (t) {\n // Ahhh, SQL makes this one soooooo easy.\n tryExecuteSql(t, dbInfo, 'SELECT COUNT(key) as c FROM ' + dbInfo.storeName, [], function (t, results) {\n var result = results.rows.item(0).c;\n resolve(result);\n }, function (t, error) {\n reject(error);\n });\n });\n })[\"catch\"](reject);\n });\n\n executeCallback(promise, callback);\n return promise;\n}\n\n// Return the key located at key index X; essentially gets the key from a\n// `WHERE id = ?`. This is the most efficient way I can think to implement\n// this rarely-used (in my experience) part of the API, but it can seem\n// inconsistent, because we do `INSERT OR REPLACE INTO` on `setItem()`, so\n// the ID of each key will change every time it's updated. Perhaps a stored\n// procedure for the `setItem()` SQL would solve this problem?\n// TODO: Don't change ID on `setItem()`.\nfunction key$1(n, callback) {\n var self = this;\n\n var promise = new Promise$1(function (resolve, reject) {\n self.ready().then(function () {\n var dbInfo = self._dbInfo;\n dbInfo.db.transaction(function (t) {\n tryExecuteSql(t, dbInfo, 'SELECT key FROM ' + dbInfo.storeName + ' WHERE id = ? LIMIT 1', [n + 1], function (t, results) {\n var result = results.rows.length ? results.rows.item(0).key : null;\n resolve(result);\n }, function (t, error) {\n reject(error);\n });\n });\n })[\"catch\"](reject);\n });\n\n executeCallback(promise, callback);\n return promise;\n}\n\nfunction keys$1(callback) {\n var self = this;\n\n var promise = new Promise$1(function (resolve, reject) {\n self.ready().then(function () {\n var dbInfo = self._dbInfo;\n dbInfo.db.transaction(function (t) {\n tryExecuteSql(t, dbInfo, 'SELECT key FROM ' + dbInfo.storeName, [], function (t, results) {\n var keys = [];\n\n for (var i = 0; i < results.rows.length; i++) {\n keys.push(results.rows.item(i).key);\n }\n\n resolve(keys);\n }, function (t, error) {\n reject(error);\n });\n });\n })[\"catch\"](reject);\n });\n\n executeCallback(promise, callback);\n return promise;\n}\n\n// https://www.w3.org/TR/webdatabase/#databases\n// > There is no way to enumerate or delete the databases available for an origin from this API.\nfunction getAllStoreNames(db) {\n return new Promise$1(function (resolve, reject) {\n db.transaction(function (t) {\n t.executeSql('SELECT name FROM sqlite_master ' + \"WHERE type='table' AND name <> '__WebKitDatabaseInfoTable__'\", [], function (t, results) {\n var storeNames = [];\n\n for (var i = 0; i < results.rows.length; i++) {\n storeNames.push(results.rows.item(i).name);\n }\n\n resolve({\n db: db,\n storeNames: storeNames\n });\n }, function (t, error) {\n reject(error);\n });\n }, function (sqlError) {\n reject(sqlError);\n });\n });\n}\n\nfunction dropInstance$1(options, callback) {\n callback = getCallback.apply(this, arguments);\n\n var currentConfig = this.config();\n options = typeof options !== 'function' && options || {};\n if (!options.name) {\n options.name = options.name || currentConfig.name;\n options.storeName = options.storeName || currentConfig.storeName;\n }\n\n var self = this;\n var promise;\n if (!options.name) {\n promise = Promise$1.reject('Invalid arguments');\n } else {\n promise = new Promise$1(function (resolve) {\n var db;\n if (options.name === currentConfig.name) {\n // use the db reference of the current instance\n db = self._dbInfo.db;\n } else {\n db = openDatabase(options.name, '', '', 0);\n }\n\n if (!options.storeName) {\n // drop all database tables\n resolve(getAllStoreNames(db));\n } else {\n resolve({\n db: db,\n storeNames: [options.storeName]\n });\n }\n }).then(function (operationInfo) {\n return new Promise$1(function (resolve, reject) {\n operationInfo.db.transaction(function (t) {\n function dropTable(storeName) {\n return new Promise$1(function (resolve, reject) {\n t.executeSql('DROP TABLE IF EXISTS ' + storeName, [], function () {\n resolve();\n }, function (t, error) {\n reject(error);\n });\n });\n }\n\n var operations = [];\n for (var i = 0, len = operationInfo.storeNames.length; i < len; i++) {\n operations.push(dropTable(operationInfo.storeNames[i]));\n }\n\n Promise$1.all(operations).then(function () {\n resolve();\n })[\"catch\"](function (e) {\n reject(e);\n });\n }, function (sqlError) {\n reject(sqlError);\n });\n });\n });\n }\n\n executeCallback(promise, callback);\n return promise;\n}\n\nvar webSQLStorage = {\n _driver: 'webSQLStorage',\n _initStorage: _initStorage$1,\n _support: isWebSQLValid(),\n iterate: iterate$1,\n getItem: getItem$1,\n setItem: setItem$1,\n removeItem: removeItem$1,\n clear: clear$1,\n length: length$1,\n key: key$1,\n keys: keys$1,\n dropInstance: dropInstance$1\n};\n\nfunction isLocalStorageValid() {\n try {\n return typeof localStorage !== 'undefined' && 'setItem' in localStorage &&\n // in IE8 typeof localStorage.setItem === 'object'\n !!localStorage.setItem;\n } catch (e) {\n return false;\n }\n}\n\nfunction _getKeyPrefix(options, defaultConfig) {\n var keyPrefix = options.name + '/';\n\n if (options.storeName !== defaultConfig.storeName) {\n keyPrefix += options.storeName + '/';\n }\n return keyPrefix;\n}\n\n// Check if localStorage throws when saving an item\nfunction checkIfLocalStorageThrows() {\n var localStorageTestKey = '_localforage_support_test';\n\n try {\n localStorage.setItem(localStorageTestKey, true);\n localStorage.removeItem(localStorageTestKey);\n\n return false;\n } catch (e) {\n return true;\n }\n}\n\n// Check if localStorage is usable and allows to save an item\n// This method checks if localStorage is usable in Safari Private Browsing\n// mode, or in any other case where the available quota for localStorage\n// is 0 and there wasn't any saved items yet.\nfunction _isLocalStorageUsable() {\n return !checkIfLocalStorageThrows() || localStorage.length > 0;\n}\n\n// Config the localStorage backend, using options set in the config.\nfunction _initStorage$2(options) {\n var self = this;\n var dbInfo = {};\n if (options) {\n for (var i in options) {\n dbInfo[i] = options[i];\n }\n }\n\n dbInfo.keyPrefix = _getKeyPrefix(options, self._defaultConfig);\n\n if (!_isLocalStorageUsable()) {\n return Promise$1.reject();\n }\n\n self._dbInfo = dbInfo;\n dbInfo.serializer = localforageSerializer;\n\n return Promise$1.resolve();\n}\n\n// Remove all keys from the datastore, effectively destroying all data in\n// the app's key/value store!\nfunction clear$2(callback) {\n var self = this;\n var promise = self.ready().then(function () {\n var keyPrefix = self._dbInfo.keyPrefix;\n\n for (var i = localStorage.length - 1; i >= 0; i--) {\n var key = localStorage.key(i);\n\n if (key.indexOf(keyPrefix) === 0) {\n localStorage.removeItem(key);\n }\n }\n });\n\n executeCallback(promise, callback);\n return promise;\n}\n\n// Retrieve an item from the store. Unlike the original async_storage\n// library in Gaia, we don't modify return values at all. If a key's value\n// is `undefined`, we pass that value to the callback function.\nfunction getItem$2(key, callback) {\n var self = this;\n\n key = normalizeKey(key);\n\n var promise = self.ready().then(function () {\n var dbInfo = self._dbInfo;\n var result = localStorage.getItem(dbInfo.keyPrefix + key);\n\n // If a result was found, parse it from the serialized\n // string into a JS object. If result isn't truthy, the key\n // is likely undefined and we'll pass it straight to the\n // callback.\n if (result) {\n result = dbInfo.serializer.deserialize(result);\n }\n\n return result;\n });\n\n executeCallback(promise, callback);\n return promise;\n}\n\n// Iterate over all items in the store.\nfunction iterate$2(iterator, callback) {\n var self = this;\n\n var promise = self.ready().then(function () {\n var dbInfo = self._dbInfo;\n var keyPrefix = dbInfo.keyPrefix;\n var keyPrefixLength = keyPrefix.length;\n var length = localStorage.length;\n\n // We use a dedicated iterator instead of the `i` variable below\n // so other keys we fetch in localStorage aren't counted in\n // the `iterationNumber` argument passed to the `iterate()`\n // callback.\n //\n // See: github.com/mozilla/localForage/pull/435#discussion_r38061530\n var iterationNumber = 1;\n\n for (var i = 0; i < length; i++) {\n var key = localStorage.key(i);\n if (key.indexOf(keyPrefix) !== 0) {\n continue;\n }\n var value = localStorage.getItem(key);\n\n // If a result was found, parse it from the serialized\n // string into a JS object. If result isn't truthy, the\n // key is likely undefined and we'll pass it straight\n // to the iterator.\n if (value) {\n value = dbInfo.serializer.deserialize(value);\n }\n\n value = iterator(value, key.substring(keyPrefixLength), iterationNumber++);\n\n if (value !== void 0) {\n return value;\n }\n }\n });\n\n executeCallback(promise, callback);\n return promise;\n}\n\n// Same as localStorage's key() method, except takes a callback.\nfunction key$2(n, callback) {\n var self = this;\n var promise = self.ready().then(function () {\n var dbInfo = self._dbInfo;\n var result;\n try {\n result = localStorage.key(n);\n } catch (error) {\n result = null;\n }\n\n // Remove the prefix from the key, if a key is found.\n if (result) {\n result = result.substring(dbInfo.keyPrefix.length);\n }\n\n return result;\n });\n\n executeCallback(promise, callback);\n return promise;\n}\n\nfunction keys$2(callback) {\n var self = this;\n var promise = self.ready().then(function () {\n var dbInfo = self._dbInfo;\n var length = localStorage.length;\n var keys = [];\n\n for (var i = 0; i < length; i++) {\n var itemKey = localStorage.key(i);\n if (itemKey.indexOf(dbInfo.keyPrefix) === 0) {\n keys.push(itemKey.substring(dbInfo.keyPrefix.length));\n }\n }\n\n return keys;\n });\n\n executeCallback(promise, callback);\n return promise;\n}\n\n// Supply the number of keys in the datastore to the callback function.\nfunction length$2(callback) {\n var self = this;\n var promise = self.keys().then(function (keys) {\n return keys.length;\n });\n\n executeCallback(promise, callback);\n return promise;\n}\n\n// Remove an item from the store, nice and simple.\nfunction removeItem$2(key, callback) {\n var self = this;\n\n key = normalizeKey(key);\n\n var promise = self.ready().then(function () {\n var dbInfo = self._dbInfo;\n localStorage.removeItem(dbInfo.keyPrefix + key);\n });\n\n executeCallback(promise, callback);\n return promise;\n}\n\n// Set a key's value and run an optional callback once the value is set.\n// Unlike Gaia's implementation, the callback function is passed the value,\n// in case you want to operate on that value only after you're sure it\n// saved, or something like that.\nfunction setItem$2(key, value, callback) {\n var self = this;\n\n key = normalizeKey(key);\n\n var promise = self.ready().then(function () {\n // Convert undefined values to null.\n // https://github.com/mozilla/localForage/pull/42\n if (value === undefined) {\n value = null;\n }\n\n // Save the original value to pass to the callback.\n var originalValue = value;\n\n return new Promise$1(function (resolve, reject) {\n var dbInfo = self._dbInfo;\n dbInfo.serializer.serialize(value, function (value, error) {\n if (error) {\n reject(error);\n } else {\n try {\n localStorage.setItem(dbInfo.keyPrefix + key, value);\n resolve(originalValue);\n } catch (e) {\n // localStorage capacity exceeded.\n // TODO: Make this a specific error/event.\n if (e.name === 'QuotaExceededError' || e.name === 'NS_ERROR_DOM_QUOTA_REACHED') {\n reject(e);\n }\n reject(e);\n }\n }\n });\n });\n });\n\n executeCallback(promise, callback);\n return promise;\n}\n\nfunction dropInstance$2(options, callback) {\n callback = getCallback.apply(this, arguments);\n\n options = typeof options !== 'function' && options || {};\n if (!options.name) {\n var currentConfig = this.config();\n options.name = options.name || currentConfig.name;\n options.storeName = options.storeName || currentConfig.storeName;\n }\n\n var self = this;\n var promise;\n if (!options.name) {\n promise = Promise$1.reject('Invalid arguments');\n } else {\n promise = new Promise$1(function (resolve) {\n if (!options.storeName) {\n resolve(options.name + '/');\n } else {\n resolve(_getKeyPrefix(options, self._defaultConfig));\n }\n }).then(function (keyPrefix) {\n for (var i = localStorage.length - 1; i >= 0; i--) {\n var key = localStorage.key(i);\n\n if (key.indexOf(keyPrefix) === 0) {\n localStorage.removeItem(key);\n }\n }\n });\n }\n\n executeCallback(promise, callback);\n return promise;\n}\n\nvar localStorageWrapper = {\n _driver: 'localStorageWrapper',\n _initStorage: _initStorage$2,\n _support: isLocalStorageValid(),\n iterate: iterate$2,\n getItem: getItem$2,\n setItem: setItem$2,\n removeItem: removeItem$2,\n clear: clear$2,\n length: length$2,\n key: key$2,\n keys: keys$2,\n dropInstance: dropInstance$2\n};\n\nvar sameValue = function sameValue(x, y) {\n return x === y || typeof x === 'number' && typeof y === 'number' && isNaN(x) && isNaN(y);\n};\n\nvar includes = function includes(array, searchElement) {\n var len = array.length;\n var i = 0;\n while (i < len) {\n if (sameValue(array[i], searchElement)) {\n return true;\n }\n i++;\n }\n\n return false;\n};\n\nvar isArray = Array.isArray || function (arg) {\n return Object.prototype.toString.call(arg) === '[object Array]';\n};\n\n// Drivers are stored here when `defineDriver()` is called.\n// They are shared across all instances of localForage.\nvar DefinedDrivers = {};\n\nvar DriverSupport = {};\n\nvar DefaultDrivers = {\n INDEXEDDB: asyncStorage,\n WEBSQL: webSQLStorage,\n LOCALSTORAGE: localStorageWrapper\n};\n\nvar DefaultDriverOrder = [DefaultDrivers.INDEXEDDB._driver, DefaultDrivers.WEBSQL._driver, DefaultDrivers.LOCALSTORAGE._driver];\n\nvar OptionalDriverMethods = ['dropInstance'];\n\nvar LibraryMethods = ['clear', 'getItem', 'iterate', 'key', 'keys', 'length', 'removeItem', 'setItem'].concat(OptionalDriverMethods);\n\nvar DefaultConfig = {\n description: '',\n driver: DefaultDriverOrder.slice(),\n name: 'localforage',\n // Default DB size is _JUST UNDER_ 5MB, as it's the highest size\n // we can use without a prompt.\n size: 4980736,\n storeName: 'keyvaluepairs',\n version: 1.0\n};\n\nfunction callWhenReady(localForageInstance, libraryMethod) {\n localForageInstance[libraryMethod] = function () {\n var _args = arguments;\n return localForageInstance.ready().then(function () {\n return localForageInstance[libraryMethod].apply(localForageInstance, _args);\n });\n };\n}\n\nfunction extend() {\n for (var i = 1; i < arguments.length; i++) {\n var arg = arguments[i];\n\n if (arg) {\n for (var _key in arg) {\n if (arg.hasOwnProperty(_key)) {\n if (isArray(arg[_key])) {\n arguments[0][_key] = arg[_key].slice();\n } else {\n arguments[0][_key] = arg[_key];\n }\n }\n }\n }\n }\n\n return arguments[0];\n}\n\nvar LocalForage = function () {\n function LocalForage(options) {\n _classCallCheck(this, LocalForage);\n\n for (var driverTypeKey in DefaultDrivers) {\n if (DefaultDrivers.hasOwnProperty(driverTypeKey)) {\n var driver = DefaultDrivers[driverTypeKey];\n var driverName = driver._driver;\n this[driverTypeKey] = driverName;\n\n if (!DefinedDrivers[driverName]) {\n // we don't need to wait for the promise,\n // since the default drivers can be defined\n // in a blocking manner\n this.defineDriver(driver);\n }\n }\n }\n\n this._defaultConfig = extend({}, DefaultConfig);\n this._config = extend({}, this._defaultConfig, options);\n this._driverSet = null;\n this._initDriver = null;\n this._ready = false;\n this._dbInfo = null;\n\n this._wrapLibraryMethodsWithReady();\n this.setDriver(this._config.driver)[\"catch\"](function () {});\n }\n\n // Set any config values for localForage; can be called anytime before\n // the first API call (e.g. `getItem`, `setItem`).\n // We loop through options so we don't overwrite existing config\n // values.\n\n\n LocalForage.prototype.config = function config(options) {\n // If the options argument is an object, we use it to set values.\n // Otherwise, we return either a specified config value or all\n // config values.\n if ((typeof options === 'undefined' ? 'undefined' : _typeof(options)) === 'object') {\n // If localforage is ready and fully initialized, we can't set\n // any new configuration values. Instead, we return an error.\n if (this._ready) {\n return new Error(\"Can't call config() after localforage \" + 'has been used.');\n }\n\n for (var i in options) {\n if (i === 'storeName') {\n options[i] = options[i].replace(/\\W/g, '_');\n }\n\n if (i === 'version' && typeof options[i] !== 'number') {\n return new Error('Database version must be a number.');\n }\n\n this._config[i] = options[i];\n }\n\n // after all config options are set and\n // the driver option is used, try setting it\n if ('driver' in options && options.driver) {\n return this.setDriver(this._config.driver);\n }\n\n return true;\n } else if (typeof options === 'string') {\n return this._config[options];\n } else {\n return this._config;\n }\n };\n\n // Used to define a custom driver, shared across all instances of\n // localForage.\n\n\n LocalForage.prototype.defineDriver = function defineDriver(driverObject, callback, errorCallback) {\n var promise = new Promise$1(function (resolve, reject) {\n try {\n var driverName = driverObject._driver;\n var complianceError = new Error('Custom driver not compliant; see ' + 'https://mozilla.github.io/localForage/#definedriver');\n\n // A driver name should be defined and not overlap with the\n // library-defined, default drivers.\n if (!driverObject._driver) {\n reject(complianceError);\n return;\n }\n\n var driverMethods = LibraryMethods.concat('_initStorage');\n for (var i = 0, len = driverMethods.length; i < len; i++) {\n var driverMethodName = driverMethods[i];\n\n // when the property is there,\n // it should be a method even when optional\n var isRequired = !includes(OptionalDriverMethods, driverMethodName);\n if ((isRequired || driverObject[driverMethodName]) && typeof driverObject[driverMethodName] !== 'function') {\n reject(complianceError);\n return;\n }\n }\n\n var configureMissingMethods = function configureMissingMethods() {\n var methodNotImplementedFactory = function methodNotImplementedFactory(methodName) {\n return function () {\n var error = new Error('Method ' + methodName + ' is not implemented by the current driver');\n var promise = Promise$1.reject(error);\n executeCallback(promise, arguments[arguments.length - 1]);\n return promise;\n };\n };\n\n for (var _i = 0, _len = OptionalDriverMethods.length; _i < _len; _i++) {\n var optionalDriverMethod = OptionalDriverMethods[_i];\n if (!driverObject[optionalDriverMethod]) {\n driverObject[optionalDriverMethod] = methodNotImplementedFactory(optionalDriverMethod);\n }\n }\n };\n\n configureMissingMethods();\n\n var setDriverSupport = function setDriverSupport(support) {\n if (DefinedDrivers[driverName]) {\n console.info('Redefining LocalForage driver: ' + driverName);\n }\n DefinedDrivers[driverName] = driverObject;\n DriverSupport[driverName] = support;\n // don't use a then, so that we can define\n // drivers that have simple _support methods\n // in a blocking manner\n resolve();\n };\n\n if ('_support' in driverObject) {\n if (driverObject._support && typeof driverObject._support === 'function') {\n driverObject._support().then(setDriverSupport, reject);\n } else {\n setDriverSupport(!!driverObject._support);\n }\n } else {\n setDriverSupport(true);\n }\n } catch (e) {\n reject(e);\n }\n });\n\n executeTwoCallbacks(promise, callback, errorCallback);\n return promise;\n };\n\n LocalForage.prototype.driver = function driver() {\n return this._driver || null;\n };\n\n LocalForage.prototype.getDriver = function getDriver(driverName, callback, errorCallback) {\n var getDriverPromise = DefinedDrivers[driverName] ? Promise$1.resolve(DefinedDrivers[driverName]) : Promise$1.reject(new Error('Driver not found.'));\n\n executeTwoCallbacks(getDriverPromise, callback, errorCallback);\n return getDriverPromise;\n };\n\n LocalForage.prototype.getSerializer = function getSerializer(callback) {\n var serializerPromise = Promise$1.resolve(localforageSerializer);\n executeTwoCallbacks(serializerPromise, callback);\n return serializerPromise;\n };\n\n LocalForage.prototype.ready = function ready(callback) {\n var self = this;\n\n var promise = self._driverSet.then(function () {\n if (self._ready === null) {\n self._ready = self._initDriver();\n }\n\n return self._ready;\n });\n\n executeTwoCallbacks(promise, callback, callback);\n return promise;\n };\n\n LocalForage.prototype.setDriver = function setDriver(drivers, callback, errorCallback) {\n var self = this;\n\n if (!isArray(drivers)) {\n drivers = [drivers];\n }\n\n var supportedDrivers = this._getSupportedDrivers(drivers);\n\n function setDriverToConfig() {\n self._config.driver = self.driver();\n }\n\n function extendSelfWithDriver(driver) {\n self._extend(driver);\n setDriverToConfig();\n\n self._ready = self._initStorage(self._config);\n return self._ready;\n }\n\n function initDriver(supportedDrivers) {\n return function () {\n var currentDriverIndex = 0;\n\n function driverPromiseLoop() {\n while (currentDriverIndex < supportedDrivers.length) {\n var driverName = supportedDrivers[currentDriverIndex];\n currentDriverIndex++;\n\n self._dbInfo = null;\n self._ready = null;\n\n return self.getDriver(driverName).then(extendSelfWithDriver)[\"catch\"](driverPromiseLoop);\n }\n\n setDriverToConfig();\n var error = new Error('No available storage method found.');\n self._driverSet = Promise$1.reject(error);\n return self._driverSet;\n }\n\n return driverPromiseLoop();\n };\n }\n\n // There might be a driver initialization in progress\n // so wait for it to finish in order to avoid a possible\n // race condition to set _dbInfo\n var oldDriverSetDone = this._driverSet !== null ? this._driverSet[\"catch\"](function () {\n return Promise$1.resolve();\n }) : Promise$1.resolve();\n\n this._driverSet = oldDriverSetDone.then(function () {\n var driverName = supportedDrivers[0];\n self._dbInfo = null;\n self._ready = null;\n\n return self.getDriver(driverName).then(function (driver) {\n self._driver = driver._driver;\n setDriverToConfig();\n self._wrapLibraryMethodsWithReady();\n self._initDriver = initDriver(supportedDrivers);\n });\n })[\"catch\"](function () {\n setDriverToConfig();\n var error = new Error('No available storage method found.');\n self._driverSet = Promise$1.reject(error);\n return self._driverSet;\n });\n\n executeTwoCallbacks(this._driverSet, callback, errorCallback);\n return this._driverSet;\n };\n\n LocalForage.prototype.supports = function supports(driverName) {\n return !!DriverSupport[driverName];\n };\n\n LocalForage.prototype._extend = function _extend(libraryMethodsAndProperties) {\n extend(this, libraryMethodsAndProperties);\n };\n\n LocalForage.prototype._getSupportedDrivers = function _getSupportedDrivers(drivers) {\n var supportedDrivers = [];\n for (var i = 0, len = drivers.length; i < len; i++) {\n var driverName = drivers[i];\n if (this.supports(driverName)) {\n supportedDrivers.push(driverName);\n }\n }\n return supportedDrivers;\n };\n\n LocalForage.prototype._wrapLibraryMethodsWithReady = function _wrapLibraryMethodsWithReady() {\n // Add a stub for each driver API method that delays the call to the\n // corresponding driver method until localForage is ready. These stubs\n // will be replaced by the driver methods as soon as the driver is\n // loaded, so there is no performance impact.\n for (var i = 0, len = LibraryMethods.length; i < len; i++) {\n callWhenReady(this, LibraryMethods[i]);\n }\n };\n\n LocalForage.prototype.createInstance = function createInstance(options) {\n return new LocalForage(options);\n };\n\n return LocalForage;\n}();\n\n// The actual localForage object that we expose as a module or via a\n// global. It's extended by pulling in one of our other libraries.\n\n\nvar localforage_js = new LocalForage();\n\nmodule.exports = localforage_js;\n\n},{\"3\":3}]},{},[4])(4)\n});\n","// shim for using process in browser\nvar process = module.exports = {};\n\n// cached from whatever global is present so that test runners that stub it\n// don't break things. But we need to wrap it in a try catch in case it is\n// wrapped in strict mode code which doesn't define any globals. It's inside a\n// function because try/catches deoptimize in certain engines.\n\nvar cachedSetTimeout;\nvar cachedClearTimeout;\n\nfunction defaultSetTimout() {\n throw new Error('setTimeout has not been defined');\n}\nfunction defaultClearTimeout () {\n throw new Error('clearTimeout has not been defined');\n}\n(function () {\n try {\n if (typeof setTimeout === 'function') {\n cachedSetTimeout = setTimeout;\n } else {\n cachedSetTimeout = defaultSetTimout;\n }\n } catch (e) {\n cachedSetTimeout = defaultSetTimout;\n }\n try {\n if (typeof clearTimeout === 'function') {\n cachedClearTimeout = clearTimeout;\n } else {\n cachedClearTimeout = defaultClearTimeout;\n }\n } catch (e) {\n cachedClearTimeout = defaultClearTimeout;\n }\n} ())\nfunction runTimeout(fun) {\n if (cachedSetTimeout === setTimeout) {\n //normal enviroments in sane situations\n return setTimeout(fun, 0);\n }\n // if setTimeout wasn't available but was latter defined\n if ((cachedSetTimeout === defaultSetTimout || !cachedSetTimeout) && setTimeout) {\n cachedSetTimeout = setTimeout;\n return setTimeout(fun, 0);\n }\n try {\n // when when somebody has screwed with setTimeout but no I.E. maddness\n return cachedSetTimeout(fun, 0);\n } catch(e){\n try {\n // When we are in I.E. but the script has been evaled so I.E. doesn't trust the global object when called normally\n return cachedSetTimeout.call(null, fun, 0);\n } catch(e){\n // same as above but when it's a version of I.E. that must have the global object for 'this', hopfully our context correct otherwise it will throw a global error\n return cachedSetTimeout.call(this, fun, 0);\n }\n }\n\n\n}\nfunction runClearTimeout(marker) {\n if (cachedClearTimeout === clearTimeout) {\n //normal enviroments in sane situations\n return clearTimeout(marker);\n }\n // if clearTimeout wasn't available but was latter defined\n if ((cachedClearTimeout === defaultClearTimeout || !cachedClearTimeout) && clearTimeout) {\n cachedClearTimeout = clearTimeout;\n return clearTimeout(marker);\n }\n try {\n // when when somebody has screwed with setTimeout but no I.E. maddness\n return cachedClearTimeout(marker);\n } catch (e){\n try {\n // When we are in I.E. but the script has been evaled so I.E. doesn't trust the global object when called normally\n return cachedClearTimeout.call(null, marker);\n } catch (e){\n // same as above but when it's a version of I.E. that must have the global object for 'this', hopfully our context correct otherwise it will throw a global error.\n // Some versions of I.E. have different rules for clearTimeout vs setTimeout\n return cachedClearTimeout.call(this, marker);\n }\n }\n\n\n\n}\nvar queue = [];\nvar draining = false;\nvar currentQueue;\nvar queueIndex = -1;\n\nfunction cleanUpNextTick() {\n if (!draining || !currentQueue) {\n return;\n }\n draining = false;\n if (currentQueue.length) {\n queue = currentQueue.concat(queue);\n } else {\n queueIndex = -1;\n }\n if (queue.length) {\n drainQueue();\n }\n}\n\nfunction drainQueue() {\n if (draining) {\n return;\n }\n var timeout = runTimeout(cleanUpNextTick);\n draining = true;\n\n var len = queue.length;\n while(len) {\n currentQueue = queue;\n queue = [];\n while (++queueIndex < len) {\n if (currentQueue) {\n currentQueue[queueIndex].run();\n }\n }\n queueIndex = -1;\n len = queue.length;\n }\n currentQueue = null;\n draining = false;\n runClearTimeout(timeout);\n}\n\nprocess.nextTick = function (fun) {\n var args = new Array(arguments.length - 1);\n if (arguments.length > 1) {\n for (var i = 1; i < arguments.length; i++) {\n args[i - 1] = arguments[i];\n }\n }\n queue.push(new Item(fun, args));\n if (queue.length === 1 && !draining) {\n runTimeout(drainQueue);\n }\n};\n\n// v8 likes predictible objects\nfunction Item(fun, array) {\n this.fun = fun;\n this.array = array;\n}\nItem.prototype.run = function () {\n this.fun.apply(null, this.array);\n};\nprocess.title = 'browser';\nprocess.browser = true;\nprocess.env = {};\nprocess.argv = [];\nprocess.version = ''; // empty string to avoid regexp issues\nprocess.versions = {};\n\nfunction noop() {}\n\nprocess.on = noop;\nprocess.addListener = noop;\nprocess.once = noop;\nprocess.off = noop;\nprocess.removeListener = noop;\nprocess.removeAllListeners = noop;\nprocess.emit = noop;\nprocess.prependListener = noop;\nprocess.prependOnceListener = noop;\n\nprocess.listeners = function (name) { return [] }\n\nprocess.binding = function (name) {\n throw new Error('process.binding is not supported');\n};\n\nprocess.cwd = function () { return '/' };\nprocess.chdir = function (dir) {\n throw new Error('process.chdir is not supported');\n};\nprocess.umask = function() { return 0; };\n","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport './engine';\nimport * as device_util from './device_util';\nimport { env } from './environment';\nconst ENV = env();\n/**\n * This file contains environment-related flag registrations.\n */\n/** Whether to enable debug mode. */\nENV.registerFlag('DEBUG', () => false, debugValue => {\n if (debugValue) {\n console.warn('Debugging mode is ON. The output of every math call will ' +\n 'be downloaded to CPU and checked for NaNs. ' +\n 'This significantly impacts performance.');\n }\n});\n/** Whether we are in a browser (as versus, say, node.js) environment. */\nENV.registerFlag('IS_BROWSER', () => device_util.isBrowser());\n/** Whether we are in a browser (as versus, say, node.js) environment. */\nENV.registerFlag('IS_NODE', () => (typeof process !== 'undefined') &&\n (typeof process.versions !== 'undefined') &&\n (typeof process.versions.node !== 'undefined'));\n/** Whether this browser is Chrome. */\nENV.registerFlag('IS_CHROME', () => typeof navigator !== 'undefined' && navigator != null &&\n navigator.userAgent != null && /Chrome/.test(navigator.userAgent) &&\n /Google Inc/.test(navigator.vendor));\n/**\n * True when the environment is \"production\" where we disable safety checks\n * to gain performance.\n */\nENV.registerFlag('PROD', () => false);\n/**\n * Whether to do sanity checks when inferring a shape from user-provided\n * values, used when creating a new tensor.\n */\nENV.registerFlag('TENSORLIKE_CHECK_SHAPE_CONSISTENCY', () => ENV.getBool('DEBUG'));\n/** Whether deprecation warnings are enabled. */\nENV.registerFlag('DEPRECATION_WARNINGS_ENABLED', () => true);\n/** True if running unit tests. */\nENV.registerFlag('IS_TEST', () => false);\n/** Whether to check computation result for errors. */\nENV.registerFlag('CHECK_COMPUTATION_FOR_ERRORS', () => true);\n/** Whether the backend needs to wrap input to imageBitmap. */\nENV.registerFlag('WRAP_TO_IMAGEBITMAP', () => false);\n/** Experimental flag, whether enter compile only phase. */\nENV.registerFlag('ENGINE_COMPILE_ONLY', () => false);\n//# 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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const EPSILON_FLOAT32 = 1e-7;\nexport const EPSILON_FLOAT16 = 1e-4;\n/** Convenient class for storing tensor-related data. */\nexport class DataStorage {\n constructor(backend, dataMover) {\n this.backend = backend;\n this.dataMover = dataMover;\n this.data = new WeakMap();\n this.dataIdsCount = 0;\n }\n get(dataId) {\n if (!this.data.has(dataId)) {\n this.dataMover.moveData(this.backend, dataId);\n }\n return this.data.get(dataId);\n }\n set(dataId, value) {\n this.dataIdsCount++;\n this.data.set(dataId, value);\n }\n has(dataId) {\n return this.data.has(dataId);\n }\n delete(dataId) {\n this.dataIdsCount--;\n return this.data.delete(dataId);\n }\n numDataIds() {\n return this.dataIdsCount;\n }\n}\n/**\n * The interface that defines the kernels that should be implemented when\n * adding a new backend. New backends don't need to implement every one of the\n * methods, this can be done gradually (throw an error for unimplemented\n * methods).\n */\nexport class KernelBackend {\n refCount(dataId) {\n return notYetImplemented('refCount');\n }\n incRef(dataId) {\n return notYetImplemented('incRef');\n }\n timerAvailable() {\n return true;\n }\n time(f) {\n return notYetImplemented('time');\n }\n read(dataId) {\n return notYetImplemented('read');\n }\n readSync(dataId) {\n return notYetImplemented('readSync');\n }\n readToGPU(dataId, options) {\n return notYetImplemented('readToGPU');\n }\n numDataIds() {\n return notYetImplemented('numDataIds');\n }\n disposeData(dataId, force) {\n return notYetImplemented('disposeData');\n }\n write(values, shape, dtype) {\n return notYetImplemented('write');\n }\n move(dataId, values, shape, dtype, refCount) {\n return notYetImplemented('move');\n }\n memory() {\n return notYetImplemented('memory');\n }\n /** Returns the highest precision for floats in bits (e.g. 16 or 32) */\n floatPrecision() {\n return notYetImplemented('floatPrecision');\n }\n /** Returns the smallest representable number. */\n epsilon() {\n return this.floatPrecision() === 32 ? 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All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Backend, DataId, DataToGPUOptions, GPUData} from '../tensor';\nimport {BackendValues, DataType} from '../types';\n\nexport const EPSILON_FLOAT32 = 1e-7;\nexport const EPSILON_FLOAT16 = 1e-4;\n\n// Required information for all backends.\nexport interface BackendTimingInfo {\n  kernelMs: number|{error: string};\n  getExtraProfileInfo?(): string;  // a field for additional timing information\n                                   // e.g. packing / unpacking for WebGL backend\n}\n\nexport interface TensorStorage {\n  read(dataId: DataId): Promise<BackendValues>;\n  readSync(dataId: DataId): BackendValues;\n  disposeData(dataId: DataId, force?: boolean): boolean;\n  write(values: BackendValues, shape: number[], dtype: DataType): DataId;\n  move(\n      dataId: DataId, values: BackendValues, shape: number[], dtype: DataType,\n      refCount: number): void;\n  memory(): {unreliable: boolean;};  // Backend-specific information.\n  /** Returns number of data ids currently in the storage. */\n  numDataIds(): number;\n  refCount(dataId: DataId): number;\n}\n\n/** Convenient class for storing tensor-related data. */\nexport class DataStorage<T> {\n  private data = new WeakMap<DataId, T>();\n  private dataIdsCount = 0;\n\n  constructor(private backend: KernelBackend, private dataMover: DataMover) {}\n\n  get(dataId: DataId) {\n    if (!this.data.has(dataId)) {\n      this.dataMover.moveData(this.backend, dataId);\n    }\n    return this.data.get(dataId);\n  }\n\n  set(dataId: DataId, value: T): void {\n    this.dataIdsCount++;\n    this.data.set(dataId, value);\n  }\n\n  has(dataId: DataId): boolean {\n    return this.data.has(dataId);\n  }\n\n  delete(dataId: DataId): boolean {\n    this.dataIdsCount--;\n    return this.data.delete(dataId);\n  }\n\n  numDataIds(): number {\n    return this.dataIdsCount;\n  }\n}\n\nexport interface DataMover {\n  /**\n   * To be called by backends whenever they see a dataId that they don't own.\n   * Upon calling this method, the mover will fetch the tensor from another\n   * backend and register it with the current active backend.\n   */\n  moveData(backend: KernelBackend, dataId: DataId): void;\n}\n\nexport interface BackendTimer {\n  // check if backend timer is available\n  timerAvailable(): boolean;\n  time(f: () => void): Promise<BackendTimingInfo>;\n}\n\n/**\n * The interface that defines the kernels that should be implemented when\n * adding a new backend. New backends don't need to implement every one of the\n * methods, this can be done gradually (throw an error for unimplemented\n * methods).\n */\nexport class KernelBackend implements TensorStorage, Backend, BackendTimer {\n  refCount(dataId: DataId): number {\n    return notYetImplemented('refCount');\n  }\n  incRef(dataId: DataId): void {\n    return notYetImplemented('incRef');\n  }\n  timerAvailable(): boolean {\n    return true;\n  }\n  time(f: () => void): Promise<BackendTimingInfo> {\n    return notYetImplemented('time');\n  }\n  read(dataId: object): Promise<BackendValues> {\n    return notYetImplemented('read');\n  }\n  readSync(dataId: object): BackendValues {\n    return notYetImplemented('readSync');\n  }\n  readToGPU(dataId: object, options?: DataToGPUOptions): GPUData {\n    return notYetImplemented('readToGPU');\n  }\n  numDataIds(): number {\n    return notYetImplemented('numDataIds');\n  }\n  disposeData(dataId: object, force?: boolean): boolean {\n    return notYetImplemented('disposeData');\n  }\n  write(values: BackendValues, shape: number[], dtype: DataType): DataId {\n    return notYetImplemented('write');\n  }\n  move(\n      dataId: DataId, values: BackendValues, shape: number[], dtype: DataType,\n      refCount: number): void {\n    return notYetImplemented('move');\n  }\n  memory(): {unreliable: boolean; reasons?: string[]} {\n    return notYetImplemented('memory');\n  }\n  /** Returns the highest precision for floats in bits (e.g. 16 or 32) */\n  floatPrecision(): 16|32 {\n    return notYetImplemented('floatPrecision');\n  }\n  /** Returns the smallest representable number.  */\n  epsilon(): number {\n    return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16;\n  }\n  dispose(): void {\n    return notYetImplemented('dispose');\n  }\n}\n\nfunction notYetImplemented(kernelName: string): never {\n  throw new Error(\n      `'${kernelName}' not yet implemented or not found in the registry. ` +\n      `This kernel may not be supported by the tfjs backend you have chosen`);\n}\n"]}","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Tanh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes hyperbolic tangent of the input `tf.Tensor` element-wise: `tanh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, 70]);\n *\n * x.tanh().print(); // or tf.tanh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction tanh_(x) {\n const $x = convertToTensor(x, 'x', 'tanh', 'float32');\n const inputs = { x: $x };\n return ENGINE.runKernel(Tanh, inputs);\n}\nexport const tanh = op({ tanh_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Min } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the minimum value from the input.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the array is reduced by 1 for each entry in `axes`.\n * If `keepDims` is true, the reduced dimensions are retained with length 1.\n * If `axes` has no entries, all dimensions are reduced, and an array with a\n * single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.min().print(); // or tf.min(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.min(axis).print(); // or tf.min(x, axis)\n * ```\n *\n * @param x The input Tensor.\n * @param axis The dimension(s) to reduce. By default it reduces\n * all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction min_(x, axis = null, keepDims = false) {\n const $x = convertToTensor(x, 'x', 'min');\n const inputs = { x: $x };\n const attrs = { axis, keepDims };\n // tslint:disable-next-line: no-unnecessary-type-assertion\n return ENGINE.runKernel(Min, inputs, attrs);\n}\nexport const min = op({ min_ });\n//# sourceMappingURL=data:application/json;base64,eyJ2ZXJzaW9uIjozLCJmaWxlIjoibWluLmpzIiwic291cmNlUm9vdCI6IiIsInNvdXJjZXMiOlsiLi4vLi4vLi4vLi4vLi4vLi4vdGZqcy1jb3JlL3NyYy9vcHMvbWluLnRzIl0sIm5hbWVzIjpbXSwibWFwcGluZ3MiOiJBQUFBOzs7Ozs7Ozs7Ozs7Ozs7R0FlRztBQUNILE9BQU8sRUFBQyxNQUFNLEVBQUMsTUFBTSxXQUFXLENBQUM7QUFDakMsT0FBTyxFQUFDLEdBQUcsRUFBc0IsTUFBTSxpQkFBaUIsQ0FBQztBQUl6RCxPQUFPLEVBQUMsZUFBZSxFQUFDLE1BQU0sb0JBQW9CLENBQUM7QUFHbkQsT0FBTyxFQUFDLEVBQUUsRUFBQyxNQUFNLGFBQWEsQ0FBQztBQUUvQjs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7OztHQTRCRztBQUNILFNBQVMsSUFBSSxDQUNULENBQW9CLEVBQUUsT0FBd0IsSUFBSSxFQUFFLFFBQVEsR0FBRyxLQUFLO0lBQ3RFLE1BQU0sRUFBRSxHQUFHLGVBQWUsQ0FBQyxDQUFDLEVBQUUsR0FBRyxFQUFFLEtBQUssQ0FBQyxDQUFDO0lBRTFDLE1BQU0sTUFBTSxHQUFjLEVBQUMsQ0FBQyxFQUFFLEVBQUUsRUFBQyxDQUFDO0lBQ2xDLE1BQU0sS0FBSyxHQUFhLEVBQUMsSUFBSSxFQUFFLFFBQVEsRUFBQyxDQUFDO0lBRXpDLDBEQUEwRDtJQUMxRCxPQUFPLE1BQU0sQ0FBQyxTQUFTLENBQ1osR0FBRyxFQUFFLE1BQThCLEVBQ25DLEtBQTJCLENBQU0sQ0FBQztBQUMvQyxDQUFDO0FBRUQsTUFBTSxDQUFDLE1BQU0sR0FBRyxHQUFHLEVBQUUsQ0FBQyxFQUFDLElBQUksRUFBQyxDQUFDLENBQUMiLCJzb3VyY2VzQ29udGVudCI6WyIvKipcbiAqIEBsaWNlbnNlXG4gKiBDb3B5cmlnaHQgMjAyMCBHb29nbGUgSW5jLiBBbGwgUmlnaHRzIFJlc2VydmVkLlxuICogTGljZW5zZWQgdW5kZXIgdGhlIEFwYWNoZSBMaWNlbnNlLCBWZXJzaW9uIDIuMCAodGhlIFwiTGljZW5zZVwiKTtcbiAqIHlvdSBtYXkgbm90IHVzZSB0aGlzIGZpbGUgZXhjZXB0IGluIGNvbXBsaWFuY2Ugd2l0aCB0aGUgTGljZW5zZS5cbiAqIFlvdSBtYXkgb2J0YWluIGEgY29weSBvZiB0aGUgTGljZW5zZSBhdFxuICpcbiAqIGh0dHA6Ly93d3cuYXBhY2hlLm9yZy9saWNlbnNlcy9MSUNFTlNFLTIuMFxuICpcbiAqIFVubGVzcyByZXF1aXJlZCBieSBhcHBsaWNhYmxlIGxhdyBvciBhZ3JlZWQgdG8gaW4gd3JpdGluZywgc29mdHdhcmVcbiAqIGRpc3RyaWJ1dGVkIHVuZGVyIHRoZSBMaWNlbnNlIGlzIGRpc3RyaWJ1dGVkIG9uIGFuIFwiQVMgSVNcIiBCQVNJUyxcbiAqIFdJVEhPVVQgV0FSUkFOVElFUyBPUiBDT05ESVRJT05TIE9GIEFOWSBLSU5ELCBlaXRoZXIgZXhwcmVzcyBvciBpbXBsaWVkLlxuICogU2VlIHRoZSBMaWNlbnNlIGZvciB0aGUgc3BlY2lmaWMgbGFuZ3VhZ2UgZ292ZXJuaW5nIHBlcm1pc3Npb25zIGFuZFxuICogbGltaXRhdGlvbnMgdW5kZXIgdGhlIExpY2Vuc2UuXG4gKiA9PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PVxuICovXG5pbXBvcnQge0VOR0lORX0gZnJvbSAnLi4vZW5naW5lJztcbmltcG9ydCB7TWluLCBNaW5BdHRycywgTWluSW5wdXRzfSBmcm9tICcuLi9rZXJuZWxfbmFtZXMnO1xuaW1wb3J0IHtOYW1lZEF0dHJNYXB9IGZyb20gJy4uL2tlcm5lbF9yZWdpc3RyeSc7XG5pbXBvcnQge1RlbnNvcn0gZnJvbSAnLi4vdGVuc29yJztcbmltcG9ydCB7TmFtZWRUZW5zb3JNYXB9IGZyb20gJy4uL3RlbnNvcl90eXBlcyc7XG5pbXBvcnQge2NvbnZlcnRUb1RlbnNvcn0gZnJvbSAnLi4vdGVuc29yX3V0aWxfZW52JztcbmltcG9ydCB7VGVuc29yTGlrZX0gZnJvbSAnLi4vdHlwZXMnO1xuXG5pbXBvcnQge29wfSBmcm9tICcuL29wZXJhdGlvbic7XG5cbi8qKlxuICogQ29tcHV0ZXMgdGhlIG1pbmltdW0gdmFsdWUgZnJvbSB0aGUgaW5wdXQuXG4gKlxuICogUmVkdWNlcyB0aGUgaW5wdXQgYWxvbmcgdGhlIGRpbWVuc2lvbnMgZ2l2ZW4gaW4gYGF4ZXNgLiBVbmxlc3MgYGtlZXBEaW1zYFxuICogaXMgdHJ1ZSwgdGhlIHJhbmsgb2YgdGhlIGFycmF5IGlzIHJlZHVjZWQgYnkgMSBmb3IgZWFjaCBlbnRyeSBpbiBgYXhlc2AuXG4gKiBJZiBga2VlcERpbXNgIGlzIHRydWUsIHRoZSByZWR1Y2VkIGRpbWVuc2lvbnMgYXJlIHJldGFpbmVkIHdpdGggbGVuZ3RoIDEuXG4gKiBJZiBgYXhlc2AgaGFzIG5vIGVudHJpZXMsIGFsbCBkaW1lbnNpb25zIGFyZSByZWR1Y2VkLCBhbmQgYW4gYXJyYXkgd2l0aCBhXG4gKiBzaW5nbGUgZWxlbWVudCBpcyByZXR1cm5lZC5cbiAqXG4gKiBgYGBqc1xuICogY29uc3QgeCA9IHRmLnRlbnNvcjFkKFsxLCAyLCAzXSk7XG4gKlxuICogeC5taW4oKS5wcmludCgpOyAgLy8gb3IgdGYubWluKHgpXG4gKiBgYGBcbiAqXG4gKiBgYGBqc1xuICogY29uc3QgeCA9IHRmLnRlbnNvcjJkKFsxLCAyLCAzLCA0XSwgWzIsIDJdKTtcbiAqXG4gKiBjb25zdCBheGlzID0gMTtcbiAqIHgubWluKGF4aXMpLnByaW50KCk7ICAvLyBvciB0Zi5taW4oeCwgYXhpcylcbiAqIGBgYFxuICpcbiAqIEBwYXJhbSB4IFRoZSBpbnB1dCBUZW5zb3IuXG4gKiBAcGFyYW0gYXhpcyBUaGUgZGltZW5zaW9uKHMpIHRvIHJlZHVjZS4gQnkgZGVmYXVsdCBpdCByZWR1Y2VzXG4gKiAgICAgYWxsIGRpbWVuc2lvbnMuXG4gKiBAcGFyYW0ga2VlcERpbXMgSWYgdHJ1ZSwgcmV0YWlucyByZWR1Y2VkIGRpbWVuc2lvbnMgd2l0aCBzaXplIDEuXG4gKlxuICogQGRvYyB7aGVhZGluZzogJ09wZXJhdGlvbnMnLCBzdWJoZWFkaW5nOiAnUmVkdWN0aW9uJ31cbiAqL1xuZnVuY3Rpb24gbWluXzxUIGV4dGVuZHMgVGVuc29yPihcbiAgICB4OiBUZW5zb3J8VGVuc29yTGlrZSwgYXhpczogbnVtYmVyfG51bWJlcltdID0gbnVsbCwga2VlcERpbXMgPSBmYWxzZSk6IFQge1xuICBjb25zdCAkeCA9IGNvbnZlcnRUb1RlbnNvcih4LCAneCcsICdtaW4nKTtcblxuICBjb25zdCBpbnB1dHM6IE1pbklucHV0cyA9IHt4OiAkeH07XG4gIGNvbnN0IGF0dHJzOiBNaW5BdHRycyA9IHtheGlzLCBrZWVwRGltc307XG5cbiAgLy8gdHNsaW50OmRpc2FibGUtbmV4dC1saW5lOiBuby11bm5lY2Vzc2FyeS10eXBlLWFzc2VydGlvblxuICByZXR1cm4gRU5HSU5FLnJ1bktlcm5lbChcbiAgICAgICAgICAgICBNaW4sIGlucHV0cyBhcyB7fSBhcyBOYW1lZFRlbnNvck1hcCxcbiAgICAgICAgICAgICBhdHRycyBhcyB7fSBhcyBOYW1lZEF0dHJNYXApIGFzIFQ7XG59XG5cbmV4cG9ydCBjb25zdCBtaW4gPSBvcCh7bWluX30pO1xuIl19","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { OneHot } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Creates a one-hot `tf.Tensor`. The locations represented by `indices` take\n * value `onValue` (defaults to 1), while all other locations take value\n * `offValue` (defaults to 0). If `indices` is rank `R`, the output has rank\n * `R+1` with the last axis of size `depth`.\n * `indices` used to encode prediction class must start from 0. For example,\n * if you have 3 classes of data, class 1 should be encoded as 0, class 2\n * should be 1, and class 3 should be 2.\n *\n * ```js\n * tf.oneHot(tf.tensor1d([0, 1], 'int32'), 3).print();\n * ```\n *\n * @param indices `tf.Tensor` of indices with dtype `int32`. Indices must\n * start from 0.\n * @param depth The depth of the one hot dimension.\n * @param onValue A number used to fill in the output when the index matches\n * the location.\n * @param offValue A number used to fill in the output when the index does\n * not match the location.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction oneHot_(indices, depth, onValue = 1, offValue = 0) {\n if (depth < 2) {\n throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);\n }\n const $indices = convertToTensor(indices, 'indices', 'oneHot', 'int32');\n const inputs = { indices: $indices };\n const attrs = { depth, onValue, offValue };\n return ENGINE.runKernel(OneHot, inputs, attrs);\n}\nexport const oneHot = op({ oneHot_ });\n//# 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* @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const SELU_SCALEALPHA = 1.7580993408473768599402175208123;\nexport const SELU_SCALE = 1.0507009873554804934193349852946;\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nconst ENV = env();\n/** Whether to keep intermediate tensors. */\nENV.registerFlag('KEEP_INTERMEDIATE_TENSORS', () => false, debugValue => {\n if (debugValue) {\n console.warn('Keep intermediate tensors is ON. This will print the values of all ' +\n 'intermediate tensors during model inference. Not all models ' +\n 'support this mode. For details, check e2e/benchmarks/ ' +\n 'model_config.js. This significantly impacts performance.');\n }\n});\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getTensor } from '../executors/utils';\nimport { getBoolArrayParam, getBoolParam, getDtypeArrayParam, getDtypeParam, getNumberParam, getNumericArrayParam, getStringArrayParam, getStringParam, getTensorShapeArrayParam, getTensorShapeParam } from '../operation_mapper';\n/**\n * Helper class for lookup inputs and params for nodes in the model graph.\n */\nexport class NodeValueImpl {\n constructor(node, tensorMap, context) {\n this.node = node;\n this.tensorMap = tensorMap;\n this.context = context;\n this.inputs = [];\n this.attrs = {};\n this.inputs = node.inputNames.map(name => this.getInput(name));\n if (node.rawAttrs != null) {\n this.attrs = Object.keys(node.rawAttrs)\n .reduce((attrs, key) => {\n attrs[key] = this.getAttr(key);\n return attrs;\n }, {});\n }\n }\n /**\n * Return the value of the attribute or input param.\n * @param name String: name of attribute or input param.\n */\n getInput(name) {\n return getTensor(name, this.tensorMap, this.context);\n }\n /**\n * Return the value of the attribute or input param.\n * @param name String: name of attribute or input param.\n */\n getAttr(name, defaultValue) {\n const value = this.node.rawAttrs[name];\n if (value.tensor != null) {\n return getTensor(name, this.tensorMap, this.context);\n }\n if (value.i != null || value.f != null) {\n return getNumberParam(this.node.rawAttrs, name, defaultValue);\n }\n if (value.s != null) {\n return getStringParam(this.node.rawAttrs, name, defaultValue);\n }\n if (value.b != null) {\n return getBoolParam(this.node.rawAttrs, name, defaultValue);\n }\n if (value.shape != null) {\n return getTensorShapeParam(this.node.rawAttrs, name, defaultValue);\n }\n if (value.type != null) {\n return getDtypeParam(this.node.rawAttrs, name, defaultValue);\n }\n if (value.list != null) {\n if (value.list.i != null || value.list.f != null) {\n return getNumericArrayParam(this.node.rawAttrs, name, defaultValue);\n }\n if (value.list.s != null) {\n return getStringArrayParam(this.node.rawAttrs, name, defaultValue);\n }\n if (value.list.shape != null) {\n return getTensorShapeArrayParam(this.node.rawAttrs, name, defaultValue);\n }\n if (value.list.b != null) {\n return getBoolArrayParam(this.node.rawAttrs, name, defaultValue);\n }\n if (value.list.type != null) {\n return getDtypeArrayParam(this.node.rawAttrs, name, defaultValue);\n }\n }\n return defaultValue;\n }\n}\n//# 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* @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {DataType, Tensor} from '@tensorflow/tfjs-core';\n\nimport {NamedTensorsMap} from '../../data/types';\nimport {ExecutionContext} from '../../executor/execution_context';\nimport {getTensor} from '../executors/utils';\nimport {getBoolArrayParam, getBoolParam, getDtypeArrayParam, getDtypeParam, getNumberParam, getNumericArrayParam, getStringArrayParam, getStringParam, getTensorShapeArrayParam, getTensorShapeParam} from '../operation_mapper';\nimport {GraphNode, Node, ValueType} from '../types';\n\n/**\n * Helper class for lookup inputs and params for nodes in the model graph.\n */\nexport class NodeValueImpl implements GraphNode {\n  public readonly inputs: Tensor[] = [];\n  public readonly attrs: {[key: string]: ValueType} = {};\n  constructor(\n      private node: Node, private tensorMap: NamedTensorsMap,\n      private context: ExecutionContext) {\n    this.inputs = node.inputNames.map(name => this.getInput(name));\n    if (node.rawAttrs != null) {\n      this.attrs = Object.keys(node.rawAttrs)\n                       .reduce((attrs: {[key: string]: ValueType}, key) => {\n                         attrs[key] = this.getAttr(key);\n                         return attrs;\n                       }, {});\n    }\n  }\n\n  /**\n   * Return the value of the attribute or input param.\n   * @param name String: name of attribute or input param.\n   */\n  private getInput(name: string): Tensor {\n    return getTensor(name, this.tensorMap, this.context);\n  }\n\n  /**\n   * Return the value of the attribute or input param.\n   * @param name String: name of attribute or input param.\n   */\n  private getAttr(name: string, defaultValue?: ValueType): ValueType {\n    const value = this.node.rawAttrs[name];\n    if (value.tensor != null) {\n      return getTensor(name, this.tensorMap, this.context);\n    }\n    if (value.i != null || value.f != null) {\n      return getNumberParam(this.node.rawAttrs, name, defaultValue as number);\n    }\n    if (value.s != null) {\n      return getStringParam(this.node.rawAttrs, name, defaultValue as string);\n    }\n    if (value.b != null) {\n      return getBoolParam(this.node.rawAttrs, name, defaultValue as boolean);\n    }\n    if (value.shape != null) {\n      return getTensorShapeParam(\n          this.node.rawAttrs, name, defaultValue as number[]);\n    }\n    if (value.type != null) {\n      return getDtypeParam(this.node.rawAttrs, name, defaultValue as DataType);\n    }\n    if (value.list != null) {\n      if (value.list.i != null || value.list.f != null) {\n        return getNumericArrayParam(\n            this.node.rawAttrs, name, defaultValue as number[]);\n      }\n      if (value.list.s != null) {\n        return getStringArrayParam(\n            this.node.rawAttrs, name, defaultValue as string[]);\n      }\n      if (value.list.shape != null) {\n        return getTensorShapeArrayParam(\n            this.node.rawAttrs, name, defaultValue as number[][]);\n      }\n      if (value.list.b != null) {\n        return getBoolArrayParam(\n            this.node.rawAttrs, name, defaultValue as boolean[]);\n      }\n      if (value.list.type != null) {\n        return getDtypeArrayParam(\n            this.node.rawAttrs, name, defaultValue as DataType[]);\n      }\n    }\n\n    return defaultValue;\n  }\n}\n"]}","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n switch (node.op) {\n case 'BiasAdd':\n case 'AddV2':\n case 'Add': {\n return [tfOps.add(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n }\n case 'AddN': {\n return [tfOps.addN(getParamValue('tensors', node, tensorMap, context))];\n }\n case 'FloorMod':\n case 'Mod':\n return [tfOps.mod(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n case 'Mul':\n return [tfOps.mul(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n case 'RealDiv':\n case 'Div': {\n return [tfOps.div(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n }\n case 'DivNoNan': {\n return [tfOps.divNoNan(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n }\n case 'FloorDiv': {\n return [tfOps.floorDiv(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n }\n case 'Sub': {\n return [tfOps.sub(getParamValue('a', node, tensorMap, 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* @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '@tensorflow/tfjs-core';\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\n\nimport {NamedTensorsMap} from '../../data/types';\nimport {ExecutionContext} from '../../executor/execution_context';\nimport {InternalOpExecutor, Node} from '../types';\n\nimport {getParamValue} from './utils';\n\nexport const executeOp: InternalOpExecutor =\n    (node: Node, tensorMap: NamedTensorsMap,\n     context: ExecutionContext): Tensor[] => {\n      switch (node.op) {\n        case 'BiasAdd':\n        case 'AddV2':\n        case 'Add': {\n          return [tfOps.add(\n              (getParamValue('a', node, tensorMap, context) as Tensor),\n              getParamValue('b', node, tensorMap, context) as Tensor)];\n        }\n        case 'AddN': {\n          return [tfOps.addN((\n              getParamValue('tensors', node, tensorMap, context) as Tensor[]))];\n        }\n        case 'FloorMod':\n        case 'Mod':\n          return [tfOps.mod(\n              getParamValue('a', node, tensorMap, context) as Tensor,\n              getParamValue('b', node, tensorMap, context) as Tensor)];\n        case 'Mul':\n          return [tfOps.mul(\n              getParamValue('a', node, tensorMap, context) as Tensor,\n              getParamValue('b', node, tensorMap, context) as Tensor)];\n        case 'RealDiv':\n        case 'Div': {\n          return [tfOps.div(\n              getParamValue('a', node, tensorMap, context) as Tensor,\n              getParamValue('b', node, tensorMap, context) as Tensor)];\n        }\n        case 'DivNoNan': {\n          return [tfOps.divNoNan(\n              getParamValue('a', node, tensorMap, context) as Tensor,\n              getParamValue('b', node, tensorMap, context) as Tensor)];\n        }\n        case 'FloorDiv': {\n          return [tfOps.floorDiv(\n              getParamValue('a', node, tensorMap, context) as Tensor,\n              getParamValue('b', node, tensorMap, context) as Tensor)];\n        }\n        case 'Sub': {\n          return [tfOps.sub(\n              getParamValue('a', node, tensorMap, context) as Tensor,\n              getParamValue('b', node, tensorMap, context) as Tensor)];\n        }\n        case 'Minimum': {\n          return [tfOps.minimum(\n              getParamValue('a', node, tensorMap, context) as Tensor,\n              getParamValue('b', node, tensorMap, context) as Tensor)];\n        }\n        case 'Maximum': {\n          return [tfOps.maximum(\n              getParamValue('a', node, tensorMap, context) as Tensor,\n              getParamValue('b', node, tensorMap, context) as Tensor)];\n        }\n        case 'Pow': {\n          return [tfOps.pow(\n              getParamValue('a', node, tensorMap, context) as Tensor,\n              getParamValue('b', node, tensorMap, context) as Tensor)];\n        }\n        case 'SquaredDifference': {\n          return [tfOps.squaredDifference(\n              getParamValue('a', node, tensorMap, context) as Tensor,\n              getParamValue('b', node, tensorMap, context) as Tensor)];\n        }\n        default:\n          throw TypeError(`Node type ${node.op} is not implemented`);\n      }\n    };\n\nexport const CATEGORY = 'arithmetic';\n"]}","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue, getTensor } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n switch (node.op) {\n case 'Abs':\n case 'ComplexAbs':\n return [tfOps.abs(getParamValue('x', node, tensorMap, context))];\n case 'Acos':\n return [tfOps.acos(getParamValue('x', node, tensorMap, context))];\n case 'Acosh':\n return [tfOps.acosh(getParamValue('x', node, tensorMap, context))];\n case 'Asin':\n return [tfOps.asin(getParamValue('x', node, tensorMap, context))];\n case 'Asinh':\n return [tfOps.asinh(getParamValue('x', node, tensorMap, context))];\n case 'Atan':\n return [tfOps.atan(getParamValue('x', node, tensorMap, context))];\n case 'Atan2':\n return [tfOps.atan2(getParamValue('x', node, tensorMap, context), getParamValue('y', node, tensorMap, context))];\n case 'Atanh':\n return [tfOps.atanh(getParamValue('x', node, tensorMap, context))];\n case 'Ceil':\n return [tfOps.ceil(getParamValue('x', node, tensorMap, context))];\n case 'Complex':\n return [tfOps.complex(getParamValue('real', node, tensorMap, context), getParamValue('imag', node, tensorMap, context))];\n case 'Cos':\n return [tfOps.cos(getParamValue('x', node, tensorMap, context))];\n case 'Cosh':\n return [tfOps.cosh(getParamValue('x', node, tensorMap, context))];\n case 'Elu':\n return [tfOps.elu(getParamValue('x', node, tensorMap, context))];\n case 'Erf':\n return [tfOps.erf(getParamValue('x', node, tensorMap, context))];\n case 'Exp':\n return [tfOps.exp(getParamValue('x', node, tensorMap, context))];\n case 'Expm1': {\n return [tfOps.expm1(getParamValue('x', node, tensorMap, context))];\n }\n case 'Floor':\n return [tfOps.floor(getParamValue('x', node, tensorMap, context))];\n case 'Log':\n return [tfOps.log(getParamValue('x', node, tensorMap, context))];\n case 'Log1p': {\n return [tfOps.log1p(getParamValue('x', node, tensorMap, context))];\n }\n case 'Imag':\n return [tfOps.imag(getParamValue('x', node, tensorMap, context))];\n case 'Neg':\n return [tfOps.neg(getParamValue('x', node, tensorMap, context))];\n case 'Reciprocal': {\n return [tfOps.reciprocal(getParamValue('x', node, tensorMap, context))];\n }\n case 'Real':\n return [tfOps.real(getParamValue('x', node, tensorMap, context))];\n case 'Relu':\n return [tfOps.relu(getParamValue('x', node, tensorMap, context))];\n case 'Round': {\n return [tfOps.round(getParamValue('x', node, tensorMap, context))];\n }\n case 'Selu':\n return [tfOps.selu(getParamValue('x', node, tensorMap, context))];\n case 'Sigmoid':\n return [tfOps.sigmoid(getParamValue('x', node, tensorMap, context))];\n case 'Sin':\n return [tfOps.sin(getParamValue('x', node, tensorMap, context))];\n case 'Sign': {\n return [tfOps.sign(getParamValue('x', node, tensorMap, context))];\n }\n case 'Sinh': {\n return [tfOps.sinh(getParamValue('x', node, tensorMap, context))];\n }\n case 'Softplus': {\n return [tfOps.softplus(getParamValue('x', node, tensorMap, context))];\n }\n case 'Sqrt': {\n return [tfOps.sqrt(getParamValue('x', node, tensorMap, context))];\n }\n case 'Square': {\n return [tfOps.square(getParamValue('x', node, tensorMap, context))];\n }\n case 'Tanh': {\n return [tfOps.tanh(getParamValue('x', node, tensorMap, context))];\n }\n case 'Tan':\n return [tfOps.tan(getParamValue('x', node, tensorMap, context))];\n case 'ClipByValue':\n return [tfOps.clipByValue(getParamValue('x', node, tensorMap, context), getParamValue('clipValueMin', node, tensorMap, context), getParamValue('clipValueMax', node, tensorMap, context))];\n case 'Relu6':\n return [tfOps.relu6(getParamValue('x', node, tensorMap, context))];\n case 'Rsqrt':\n return [tfOps.rsqrt(getTensor(node.inputNames[0], tensorMap, context))];\n case 'Prod':\n return [tfOps.prod(getParamValue('x', node, tensorMap, context), getParamValue('axes', node, tensorMap, context))];\n case 'LeakyRelu':\n return [tfOps.leakyRelu(getParamValue('x', node, tensorMap, context), getParamValue('alpha', node, tensorMap, context))];\n case 'Prelu':\n return [tfOps.prelu(getParamValue('x', node, tensorMap, context), getParamValue('alpha', node, tensorMap, context))];\n case 'IsNan':\n return [tfOps.isNaN(getTensor(node.inputNames[0], tensorMap, context))];\n default:\n throw TypeError(`Node type ${node.op} is not implemented`);\n }\n};\nexport const CATEGORY = 'basic_math';\n//# 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* @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '@tensorflow/tfjs-core';\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\n\nimport {NamedTensorsMap} from '../../data/types';\nimport {ExecutionContext} from '../../executor/execution_context';\nimport {InternalOpExecutor, Node} from '../types';\n\nimport {getParamValue, getTensor} from './utils';\n\nexport const executeOp: InternalOpExecutor =\n    (node: Node, tensorMap: NamedTensorsMap,\n     context: ExecutionContext): Tensor[] => {\n      switch (node.op) {\n        case 'Abs':\n        case 'ComplexAbs':\n          return [tfOps.abs(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        case 'Acos':\n          return [tfOps.acos(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        case 'Acosh':\n          return [tfOps.acosh(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        case 'Asin':\n          return [tfOps.asin(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        case 'Asinh':\n          return [tfOps.asinh(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        case 'Atan':\n          return [tfOps.atan(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        case 'Atan2':\n          return [tfOps.atan2(\n              getParamValue('x', node, tensorMap, context) as Tensor,\n              getParamValue('y', node, tensorMap, context) as Tensor)];\n        case 'Atanh':\n          return [tfOps.atanh(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        case 'Ceil':\n          return [tfOps.ceil(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        case 'Complex':\n          return [tfOps.complex(\n              getParamValue('real', node, tensorMap, context) as Tensor,\n              getParamValue('imag', node, tensorMap, context) as Tensor)];\n        case 'Cos':\n          return [tfOps.cos(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        case 'Cosh':\n          return [tfOps.cosh(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        case 'Elu':\n          return [tfOps.elu(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        case 'Erf':\n          return [tfOps.erf(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        case 'Exp':\n          return [tfOps.exp(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        case 'Expm1': {\n          return [tfOps.expm1(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        }\n        case 'Floor':\n          return [tfOps.floor(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        case 'Log':\n          return [tfOps.log(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        case 'Log1p': {\n          return [tfOps.log1p(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        }\n        case 'Imag':\n          return [tfOps.imag(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n\n        case 'Neg':\n          return [tfOps.neg(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        case 'Reciprocal': {\n          return [tfOps.reciprocal(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        }\n        case 'Real':\n          return [tfOps.real(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        case 'Relu':\n          return [tfOps.relu(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        case 'Round': {\n          return [tfOps.round(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        }\n        case 'Selu':\n          return [tfOps.selu(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        case 'Sigmoid':\n          return [tfOps.sigmoid(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        case 'Sin':\n          return [tfOps.sin(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        case 'Sign': {\n          return [tfOps.sign(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        }\n        case 'Sinh': {\n          return [tfOps.sinh(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        }\n        case 'Softplus': {\n          return [tfOps.softplus(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        }\n        case 'Sqrt': {\n          return [tfOps.sqrt(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        }\n        case 'Square': {\n          return [tfOps.square(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        }\n        case 'Tanh': {\n          return [tfOps.tanh(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        }\n        case 'Tan':\n          return [tfOps.tan(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        case 'ClipByValue':\n          return [tfOps.clipByValue(\n              getParamValue('x', node, tensorMap, context) as Tensor,\n              getParamValue('clipValueMin', node, tensorMap, context) as number,\n              getParamValue('clipValueMax', node, tensorMap, context) as\n                  number)];\n        case 'Relu6':\n          return [tfOps.relu6(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        case 'Rsqrt':\n          return [tfOps.rsqrt(\n              getTensor(node.inputNames[0], tensorMap, context))];\n        case 'Prod':\n          return [tfOps.prod(\n              getParamValue('x', node, tensorMap, context) as Tensor,\n              getParamValue('axes', node, tensorMap, context) as number[])];\n        case 'LeakyRelu':\n          return [tfOps.leakyRelu(\n              getParamValue('x', node, tensorMap, context) as Tensor,\n              getParamValue('alpha', node, tensorMap, context) as number)];\n        case 'Prelu':\n          return [tfOps.prelu(\n              getParamValue('x', node, tensorMap, context) as Tensor,\n              getParamValue('alpha', node, tensorMap, context) as Tensor)];\n        case 'IsNan':\n          return [tfOps.isNaN(\n              getTensor(node.inputNames[0], tensorMap, context))];\n        default:\n          throw TypeError(`Node type ${node.op} is not implemented`);\n      }\n    };\n\nexport const CATEGORY = 'basic_math';\n"]}","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * This differs from util.assertShapesMatch in that it allows values of\n * negative one, an undefined size of a dimensinon, in a shape to match\n * anything.\n */\nimport { util } from '@tensorflow/tfjs-core';\n/**\n * Used by TensorList and TensorArray to verify if elementShape matches, support\n * negative value as the dim shape.\n * @param shapeA\n * @param shapeB\n * @param errorMessagePrefix\n */\nexport function assertShapesMatchAllowUndefinedSize(shapeA, shapeB, errorMessagePrefix = '') {\n // constant shape means unknown rank\n if (typeof shapeA === 'number' || typeof shapeB === 'number') {\n return;\n }\n util.assert(shapeA.length === shapeB.length, () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);\n for (let i = 0; i < shapeA.length; i++) {\n const dim0 = shapeA[i];\n const dim1 = shapeB[i];\n util.assert(dim0 < 0 || dim1 < 0 || dim0 === dim1, () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);\n }\n}\nexport function fullDefinedShape(elementShape) {\n if (typeof elementShape === 'number' || elementShape.some(dim => dim < 0)) {\n return false;\n }\n return true;\n}\n/**\n * Generate the output element shape from the list elementShape, list tensors\n * and input param.\n * @param listElementShape\n * @param tensors\n * @param elementShape\n */\nexport function inferElementShape(listElementShape, tensors, elementShape) {\n let partialShape = mergeElementShape(listElementShape, elementShape);\n const notfullDefinedShape = !fullDefinedShape(partialShape);\n if (notfullDefinedShape && tensors.length === 0) {\n throw new Error(`Tried to calculate elements of an empty list` +\n ` with non-fully-defined elementShape: ${partialShape}`);\n }\n if (notfullDefinedShape) {\n tensors.forEach(tensor => {\n partialShape = mergeElementShape(tensor.shape, partialShape);\n });\n }\n if (!fullDefinedShape(partialShape)) {\n throw new Error(`Non-fully-defined elementShape: ${partialShape}`);\n }\n return partialShape;\n}\nexport function mergeElementShape(elementShapeA, elementShapeB) {\n if (typeof elementShapeA === 'number') {\n return elementShapeB;\n }\n if (typeof elementShapeB === 'number') {\n return elementShapeA;\n }\n if (elementShapeA.length !== elementShapeB.length) {\n throw new Error(`Incompatible ranks during merge: ${elementShapeA} vs. ${elementShapeB}`);\n }\n const result = [];\n for (let i = 0; i < elementShapeA.length; ++i) {\n const dim0 = elementShapeA[i];\n const dim1 = elementShapeB[i];\n if (dim0 >= 0 && dim1 >= 0 && dim0 !== dim1) {\n throw new Error(`Incompatible shape during merge: ${elementShapeA} vs. ${elementShapeB}`);\n }\n result[i] = dim0 >= 0 ? 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All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * This differs from util.assertShapesMatch in that it allows values of\n * negative one, an undefined size of a dimensinon, in a shape to match\n * anything.\n */\n\nimport {Tensor, util} from '@tensorflow/tfjs-core';\n\n/**\n * Used by TensorList and TensorArray to verify if elementShape matches, support\n * negative value as the dim shape.\n * @param shapeA\n * @param shapeB\n * @param errorMessagePrefix\n */\nexport function assertShapesMatchAllowUndefinedSize(\n    shapeA: number|number[], shapeB: number|number[],\n    errorMessagePrefix = ''): void {\n  // constant shape means unknown rank\n  if (typeof shapeA === 'number' || typeof shapeB === 'number') {\n    return;\n  }\n  util.assert(\n      shapeA.length === shapeB.length,\n      () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);\n  for (let i = 0; i < shapeA.length; i++) {\n    const dim0 = shapeA[i];\n    const dim1 = shapeB[i];\n    util.assert(\n        dim0 < 0 || dim1 < 0 || dim0 === dim1,\n        () =>\n            errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);\n  }\n}\n\nexport function fullDefinedShape(elementShape: number|number[]): boolean {\n  if (typeof elementShape === 'number' || elementShape.some(dim => dim < 0)) {\n    return false;\n  }\n  return true;\n}\n/**\n * Generate the output element shape from the list elementShape, list tensors\n * and input param.\n * @param listElementShape\n * @param tensors\n * @param elementShape\n */\nexport function inferElementShape(\n    listElementShape: number|number[], tensors: Tensor[],\n    elementShape: number|number[]): number[] {\n  let partialShape = mergeElementShape(listElementShape, elementShape);\n  const notfullDefinedShape = !fullDefinedShape(partialShape);\n  if (notfullDefinedShape && tensors.length === 0) {\n    throw new Error(\n        `Tried to calculate elements of an empty list` +\n        ` with non-fully-defined elementShape: ${partialShape}`);\n  }\n  if (notfullDefinedShape) {\n    tensors.forEach(tensor => {\n      partialShape = mergeElementShape(tensor.shape, partialShape);\n    });\n  }\n  if (!fullDefinedShape(partialShape)) {\n    throw new Error(`Non-fully-defined elementShape: ${partialShape}`);\n  }\n  return partialShape as number[];\n}\n\nexport function mergeElementShape(\n    elementShapeA: number|number[], elementShapeB: number|number[]): number|\n    number[] {\n  if (typeof elementShapeA === 'number') {\n    return elementShapeB;\n  }\n  if (typeof elementShapeB === 'number') {\n    return elementShapeA;\n  }\n\n  if (elementShapeA.length !== elementShapeB.length) {\n    throw new Error(`Incompatible ranks during merge: ${elementShapeA} vs. ${\n        elementShapeB}`);\n  }\n\n  const result: number[] = [];\n  for (let i = 0; i < elementShapeA.length; ++i) {\n    const dim0 = elementShapeA[i];\n    const dim1 = elementShapeB[i];\n    if (dim0 >= 0 && dim1 >= 0 && dim0 !== dim1) {\n      throw new Error(`Incompatible shape during merge: ${elementShapeA} vs. ${\n          elementShapeB}`);\n    }\n    result[i] = dim0 >= 0 ? dim0 : dim1;\n  }\n  return result;\n}\n"]}","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { concat, keep, reshape, scalar, slice, stack, tensor, tidy, unstack } from '@tensorflow/tfjs-core';\nimport { assertShapesMatchAllowUndefinedSize } from './tensor_utils';\n/**\n * The TensorArray object keeps an array of Tensors. It\n * allows reading from the array and writing to the array.\n */\nexport class TensorArray {\n constructor(name, dtype, maxSize, elementShape, identicalElementShapes, dynamicSize, clearAfterRead) {\n this.name = name;\n this.dtype = dtype;\n this.maxSize = maxSize;\n this.elementShape = elementShape;\n this.identicalElementShapes = identicalElementShapes;\n this.dynamicSize = dynamicSize;\n this.clearAfterRead = clearAfterRead;\n this.tensors = [];\n this.closed_ = false;\n this.idTensor = scalar(0);\n keep(this.idTensor);\n }\n get id() {\n return this.idTensor.id;\n }\n get closed() {\n return this.closed_;\n }\n /**\n * Dispose the tensors and idTensor and mark the TensoryArray as closed.\n */\n clearAndClose(keepIds) {\n this.tensors.forEach(tensor => {\n if (keepIds == null || !keepIds.has(tensor.tensor.id)) {\n tensor.tensor.dispose();\n }\n });\n this.tensors = [];\n this.closed_ = true;\n this.idTensor.dispose();\n }\n size() {\n return this.tensors.length;\n }\n /**\n * Read the value at location index in the TensorArray.\n * @param index Number the index to read from.\n */\n read(index) {\n if (this.closed_) {\n throw new Error(`TensorArray ${this.name} has already been closed.`);\n }\n if (index < 0 || index >= this.size()) {\n throw new Error(`Tried to read from index ${index}, but array size is: ${this.size()}`);\n }\n const tensorWithState = this.tensors[index];\n if (tensorWithState.cleared) {\n throw new Error(`TensorArray ${this.name}: Could not read index ${index} twice because it was cleared after a previous read ` +\n `(perhaps try setting clear_after_read = false?).`);\n }\n if (this.clearAfterRead) {\n tensorWithState.cleared = true;\n }\n tensorWithState.read = true;\n return tensorWithState.tensor;\n }\n /**\n * Helper method to read multiple tensors from the specified indices.\n */\n readMany(indices) {\n return indices.map(index => this.read(index));\n }\n /**\n * Write value into the index of the TensorArray.\n * @param index number the index to write to.\n * @param tensor\n */\n write(index, tensor) {\n if (this.closed_) {\n throw new Error(`TensorArray ${this.name} has already been closed.`);\n }\n if (index < 0 || !this.dynamicSize && index >= this.maxSize) {\n throw new Error(`Tried to write to index ${index}, but array is not resizeable and size is: ${this.maxSize}`);\n }\n const t = this.tensors[index] || {};\n if (tensor.dtype !== this.dtype) {\n throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index},\n because the value dtype is ${tensor.dtype}, but TensorArray dtype is ${this.dtype}.`);\n }\n // Set the shape for the first time write to unknow shape tensor array\n if (this.size() === 0 &&\n (this.elementShape == null || this.elementShape.length === 0)) {\n this.elementShape = tensor.shape;\n }\n assertShapesMatchAllowUndefinedSize(this.elementShape, tensor.shape, `TensorArray ${this.name}: Could not write to TensorArray index ${index}.`);\n if (t.read) {\n throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been read.`);\n }\n if (t.written) {\n throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been written.`);\n }\n t.tensor = tensor;\n keep(tensor);\n t.written = true;\n this.tensors[index] = t;\n }\n /**\n * Helper method to write multiple tensors to the specified indices.\n */\n writeMany(indices, tensors) {\n if (indices.length !== tensors.length) {\n throw new Error(`TensorArray ${this.name}: could not write multiple tensors,` +\n `because the index size: ${indices.length} is not the same as tensors size: ${tensors.length}.`);\n }\n indices.forEach((i, index) => this.write(i, tensors[index]));\n }\n /**\n * Return selected values in the TensorArray as a packed Tensor. All of\n * selected values must have been written and their shapes must all match.\n * @param [indices] number[] Optional. Taking values in [0, max_value). If the\n * TensorArray is not dynamic, max_value=size(). If not specified returns\n * all tensors in the original order.\n * @param [dtype]\n */\n gather(indices, dtype) {\n if (!!dtype && dtype !== this.dtype) {\n throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${dtype}`);\n }\n if (!indices) {\n indices = [];\n for (let i = 0; i < this.size(); i++) {\n indices.push(i);\n }\n }\n else {\n indices = indices.slice(0, this.size());\n }\n if (indices.length === 0) {\n return tensor([], [0].concat(this.elementShape));\n }\n // Read all the PersistentTensors into a vector to keep track of\n // their memory.\n const tensors = this.readMany(indices);\n assertShapesMatchAllowUndefinedSize(this.elementShape, tensors[0].shape, 'TensorArray shape mismatch: ');\n return stack(tensors, 0);\n }\n /**\n * Return the values in the TensorArray as a concatenated Tensor.\n */\n concat(dtype) {\n if (!!dtype && dtype !== this.dtype) {\n throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${dtype}`);\n }\n if (this.size() === 0) {\n return tensor([], [0].concat(this.elementShape));\n }\n const indices = [];\n for (let i = 0; i < this.size(); i++) {\n indices.push(i);\n }\n // Collect all the tensors from the tensors array.\n const tensors = this.readMany(indices);\n assertShapesMatchAllowUndefinedSize(this.elementShape, tensors[0].shape, `TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${tensors[0].shape})`);\n return concat(tensors, 0);\n }\n /**\n * Scatter the values of a Tensor in specific indices of a TensorArray.\n * @param indices nummber[] values in [0, max_value). If the\n * TensorArray is not dynamic, max_value=size().\n * @param tensor Tensor input tensor.\n */\n scatter(indices, tensor) {\n if (tensor.dtype !== this.dtype) {\n throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor.dtype}`);\n }\n if (indices.length !== tensor.shape[0]) {\n throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor.shape[0]}`);\n }\n const maxIndex = Math.max(...indices);\n if (!this.dynamicSize && maxIndex >= this.maxSize) {\n throw new Error(`Max index must be < array size (${maxIndex} vs. ${this.maxSize})`);\n }\n this.writeMany(indices, unstack(tensor, 0));\n }\n /**\n * Split the values of a Tensor into the TensorArray.\n * @param length number[] with the lengths to use when splitting value along\n * its first dimension.\n * @param tensor Tensor, the tensor to split.\n */\n split(length, tensor) {\n if (tensor.dtype !== this.dtype) {\n throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor.dtype}`);\n }\n let totalLength = 0;\n const cumulativeLengths = length.map(len => {\n totalLength += len;\n return totalLength;\n });\n if (totalLength !== tensor.shape[0]) {\n throw new Error(`Expected sum of lengths to be equal to\n tensor.shape[0], but sum of lengths is\n ${totalLength}, and tensor's shape is: ${tensor.shape}`);\n }\n if (!this.dynamicSize && length.length !== this.maxSize) {\n throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${length.length}), ` +\n 'and the TensorArray is not marked as dynamically resizeable');\n }\n const elementPerRow = totalLength === 0 ? 0 : tensor.size / totalLength;\n const tensors = [];\n tidy(() => {\n tensor = reshape(tensor, [1, totalLength, elementPerRow]);\n for (let i = 0; i < length.length; ++i) {\n const previousLength = (i === 0) ? 0 : cumulativeLengths[i - 1];\n const indices = [0, previousLength, 0];\n const sizes = [1, length[i], elementPerRow];\n tensors[i] = reshape(slice(tensor, indices, sizes), this.elementShape);\n }\n return tensors;\n });\n const indices = [];\n for (let i = 0; i < length.length; i++) {\n indices[i] = i;\n }\n this.writeMany(indices, tensors);\n }\n}\n//# 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* @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {concat, DataType, keep, reshape, scalar, slice, stack, Tensor, tensor, tidy, unstack} from '@tensorflow/tfjs-core';\n\nimport {assertShapesMatchAllowUndefinedSize} from './tensor_utils';\n\nexport interface TensorWithState {\n  tensor?: Tensor;\n  written?: boolean;\n  read?: boolean;\n  cleared?: boolean;\n}\n/**\n * The TensorArray object keeps an array of Tensors.  It\n * allows reading from the array and writing to the array.\n */\nexport class TensorArray {\n  private tensors: TensorWithState[] = [];\n  private closed_ = false;\n  readonly idTensor: Tensor;\n  constructor(\n      readonly name: string, readonly dtype: DataType, private maxSize: number,\n      private elementShape: number[], readonly identicalElementShapes: boolean,\n      readonly dynamicSize: boolean, readonly clearAfterRead: boolean) {\n    this.idTensor = scalar(0);\n    keep(this.idTensor);\n  }\n\n  get id() {\n    return this.idTensor.id;\n  }\n\n  get closed() {\n    return this.closed_;\n  }\n\n  /**\n   * Dispose the tensors and idTensor and mark the TensoryArray as closed.\n   */\n  clearAndClose(keepIds?: Set<number>) {\n    this.tensors.forEach(tensor => {\n      if (keepIds == null || !keepIds.has(tensor.tensor.id)) {\n        tensor.tensor.dispose();\n      }\n    });\n    this.tensors = [];\n    this.closed_ = true;\n    this.idTensor.dispose();\n  }\n\n  size(): number {\n    return this.tensors.length;\n  }\n\n  /**\n   * Read the value at location index in the TensorArray.\n   * @param index Number the index to read from.\n   */\n  read(index: number): Tensor {\n    if (this.closed_) {\n      throw new Error(`TensorArray ${this.name} has already been closed.`);\n    }\n\n    if (index < 0 || index >= this.size()) {\n      throw new Error(`Tried to read from index ${index}, but array size is: ${\n          this.size()}`);\n    }\n\n    const tensorWithState = this.tensors[index];\n    if (tensorWithState.cleared) {\n      throw new Error(\n          `TensorArray ${this.name}: Could not read index ${\n              index} twice because it was cleared after a previous read ` +\n          `(perhaps try setting clear_after_read = false?).`);\n    }\n\n    if (this.clearAfterRead) {\n      tensorWithState.cleared = true;\n    }\n\n    tensorWithState.read = true;\n    return tensorWithState.tensor;\n  }\n\n  /**\n   * Helper method to read multiple tensors from the specified indices.\n   */\n  readMany(indices: number[]): Tensor[] {\n    return indices.map(index => this.read(index));\n  }\n\n  /**\n   * Write value into the index of the TensorArray.\n   * @param index number the index to write to.\n   * @param tensor\n   */\n  write(index: number, tensor: Tensor) {\n    if (this.closed_) {\n      throw new Error(`TensorArray ${this.name} has already been closed.`);\n    }\n\n    if (index < 0 || !this.dynamicSize && index >= this.maxSize) {\n      throw new Error(`Tried to write to index ${\n          index}, but array is not resizeable and size is: ${this.maxSize}`);\n    }\n\n    const t = this.tensors[index] || {};\n\n    if (tensor.dtype !== this.dtype) {\n      throw new Error(`TensorArray ${\n          this.name}: Could not write to TensorArray index ${index},\n          because the value dtype is ${\n          tensor.dtype}, but TensorArray dtype is ${this.dtype}.`);\n    }\n\n    // Set the shape for the first time write to unknow shape tensor array\n    if (this.size() === 0 &&\n        (this.elementShape == null || this.elementShape.length === 0)) {\n      this.elementShape = tensor.shape;\n    }\n\n    assertShapesMatchAllowUndefinedSize(\n        this.elementShape, tensor.shape,\n        `TensorArray ${this.name}: Could not write to TensorArray index ${\n            index}.`);\n\n    if (t.read) {\n      throw new Error(\n          `TensorArray ${this.name}: Could not write to TensorArray index ${\n              index}, because it has already been read.`);\n    }\n\n    if (t.written) {\n      throw new Error(\n          `TensorArray ${this.name}: Could not write to TensorArray index ${\n              index}, because it has already been written.`);\n    }\n\n    t.tensor = tensor;\n    keep(tensor);\n    t.written = true;\n\n    this.tensors[index] = t;\n  }\n\n  /**\n   * Helper method to write multiple tensors to the specified indices.\n   */\n  writeMany(indices: number[], tensors: Tensor[]) {\n    if (indices.length !== tensors.length) {\n      throw new Error(\n          `TensorArray ${this.name}: could not write multiple tensors,` +\n          `because the index size: ${\n              indices.length} is not the same as tensors size: ${\n              tensors.length}.`);\n    }\n\n    indices.forEach((i, index) => this.write(i, tensors[index]));\n  }\n\n  /**\n   * Return selected values in the TensorArray as a packed Tensor. All of\n   * selected values must have been written and their shapes must all match.\n   * @param [indices] number[] Optional. Taking values in [0, max_value). If the\n   *    TensorArray is not dynamic, max_value=size(). If not specified returns\n   *    all tensors in the original order.\n   * @param [dtype]\n   */\n  gather(indices?: number[], dtype?: DataType): Tensor {\n    if (!!dtype && dtype !== this.dtype) {\n      throw new Error(`TensorArray dtype is ${\n          this.dtype} but gather requested dtype ${dtype}`);\n    }\n\n    if (!indices) {\n      indices = [];\n      for (let i = 0; i < this.size(); i++) {\n        indices.push(i);\n      }\n    } else {\n      indices = indices.slice(0, this.size());\n    }\n\n    if (indices.length === 0) {\n      return tensor([], [0].concat(this.elementShape));\n    }\n\n    // Read all the PersistentTensors into a vector to keep track of\n    // their memory.\n    const tensors = this.readMany(indices);\n\n    assertShapesMatchAllowUndefinedSize(\n        this.elementShape, tensors[0].shape, 'TensorArray shape mismatch: ');\n\n    return stack(tensors, 0);\n  }\n\n  /**\n   * Return the values in the TensorArray as a concatenated Tensor.\n   */\n  concat(dtype?: DataType): Tensor {\n    if (!!dtype && dtype !== this.dtype) {\n      throw new Error(`TensorArray dtype is ${\n          this.dtype} but concat requested dtype ${dtype}`);\n    }\n\n    if (this.size() === 0) {\n      return tensor([], [0].concat(this.elementShape));\n    }\n\n    const indices = [];\n    for (let i = 0; i < this.size(); i++) {\n      indices.push(i);\n    }\n    // Collect all the tensors from the tensors array.\n    const tensors = this.readMany(indices);\n\n    assertShapesMatchAllowUndefinedSize(\n        this.elementShape, tensors[0].shape,\n        `TensorArray shape mismatch: tensor array shape (${\n            this.elementShape}) vs first tensor shape (${tensors[0].shape})`);\n\n    return concat(tensors, 0);\n  }\n\n  /**\n   * Scatter the values of a Tensor in specific indices of a TensorArray.\n   * @param indices nummber[] values in [0, max_value). If the\n   *    TensorArray is not dynamic, max_value=size().\n   * @param tensor Tensor input tensor.\n   */\n  scatter(indices: number[], tensor: Tensor) {\n    if (tensor.dtype !== this.dtype) {\n      throw new Error(`TensorArray dtype is ${\n          this.dtype} but tensor has dtype ${tensor.dtype}`);\n    }\n\n    if (indices.length !== tensor.shape[0]) {\n      throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${\n          indices.length} vs. ${tensor.shape[0]}`);\n    }\n\n    const maxIndex = Math.max(...indices);\n\n    if (!this.dynamicSize && maxIndex >= this.maxSize) {\n      throw new Error(\n          `Max index must be < array size (${maxIndex}  vs. ${this.maxSize})`);\n    }\n\n    this.writeMany(indices, unstack(tensor, 0));\n  }\n\n  /**\n   * Split the values of a Tensor into the TensorArray.\n   * @param length number[] with the lengths to use when splitting value along\n   *    its first dimension.\n   * @param tensor Tensor, the tensor to split.\n   */\n  split(length: number[], tensor: Tensor) {\n    if (tensor.dtype !== this.dtype) {\n      throw new Error(`TensorArray dtype is ${\n          this.dtype} but tensor has dtype ${tensor.dtype}`);\n    }\n    let totalLength = 0;\n    const cumulativeLengths = length.map(len => {\n      totalLength += len;\n      return totalLength;\n    });\n\n    if (totalLength !== tensor.shape[0]) {\n      throw new Error(`Expected sum of lengths to be equal to\n          tensor.shape[0], but sum of lengths is\n        ${totalLength}, and tensor's shape is: ${tensor.shape}`);\n    }\n\n    if (!this.dynamicSize && length.length !== this.maxSize) {\n      throw new Error(\n          `TensorArray's size is not equal to the size of lengths (${\n              this.maxSize} vs. ${length.length}), ` +\n          'and the TensorArray is not marked as dynamically resizeable');\n    }\n\n    const elementPerRow = totalLength === 0 ? 0 : tensor.size / totalLength;\n    const tensors: Tensor[] = [];\n    tidy(() => {\n      tensor = reshape(tensor, [1, totalLength, elementPerRow]);\n      for (let i = 0; i < length.length; ++i) {\n        const previousLength = (i === 0) ? 0 : cumulativeLengths[i - 1];\n        const indices = [0, previousLength, 0];\n        const sizes = [1, length[i], elementPerRow];\n        tensors[i] = reshape(slice(tensor, indices, sizes), this.elementShape);\n      }\n      return tensors;\n    });\n    const indices = [];\n    for (let i = 0; i < length.length; i++) {\n      indices[i] = i;\n    }\n    this.writeMany(indices, tensors);\n  }\n}\n"]}","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { concat, keep, reshape, scalar, slice, stack, tensor, tidy, unstack } from '@tensorflow/tfjs-core';\nimport { assertShapesMatchAllowUndefinedSize, inferElementShape, mergeElementShape } from './tensor_utils';\n/**\n * TensorList stores a container of `tf.Tensor` objects, which are accessible\n * via tensors field.\n *\n * In order to get a copy of the underlying list, use the copy method:\n * ```\n * TensorList b = a.copy();\n * b.tensors().pushBack(t); // This does not modify a.tensors().\n * ```\n *\n * Note that this is not a deep copy: the memory locations of the underlying\n * tensors will still point to the same locations of the corresponding tensors\n * in the original.\n */\nexport class TensorList {\n /**\n *\n * @param tensors list of tensors\n * @param elementShape shape of each tensor, this can be a single number (any\n * shape is allowed) or partial shape (dim = -1).\n * @param elementDtype data type of each tensor\n * @param maxNumElements The maximum allowed size of `tensors`. Defaults to -1\n * meaning that the size of `tensors` is unbounded.\n */\n constructor(tensors, elementShape, elementDtype, maxNumElements = -1) {\n this.tensors = tensors;\n this.elementShape = elementShape;\n this.elementDtype = elementDtype;\n if (tensors != null) {\n tensors.forEach(tensor => {\n if (elementDtype !== tensor.dtype) {\n throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${tensor.dtype}`);\n }\n assertShapesMatchAllowUndefinedSize(elementShape, tensor.shape, 'TensorList shape mismatch: ');\n keep(tensor);\n });\n }\n this.idTensor = scalar(0);\n this.maxNumElements = maxNumElements;\n keep(this.idTensor);\n }\n get id() {\n return this.idTensor.id;\n }\n /**\n * Get a new TensorList containing a copy of the underlying tensor container.\n */\n copy() {\n return new TensorList([...this.tensors], this.elementShape, this.elementDtype);\n }\n /**\n * Dispose the tensors and idTensor and clear the tensor list.\n */\n clearAndClose(keepIds) {\n this.tensors.forEach(tensor => {\n if (keepIds == null || !keepIds.has(tensor.id)) {\n tensor.dispose();\n }\n });\n this.tensors.length = 0;\n this.idTensor.dispose();\n }\n /**\n * The size of the tensors in the tensor list.\n */\n size() {\n return this.tensors.length;\n }\n /**\n * Return a tensor that stacks a list of rank-R tf.Tensors into one rank-(R+1)\n * tf.Tensor.\n * @param elementShape shape of each tensor\n * @param elementDtype data type of each tensor\n * @param numElements the number of elements to stack\n */\n stack(elementShape, elementDtype, numElements = -1) {\n if (elementDtype !== this.elementDtype) {\n throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n }\n if (numElements !== -1 && this.tensors.length !== numElements) {\n throw new Error(`Operation expected a list with ${numElements} elements but got a list with ${this.tensors.length} elements.`);\n }\n assertShapesMatchAllowUndefinedSize(elementShape, this.elementShape, 'TensorList shape mismatch: ');\n const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape);\n return tidy(() => {\n const reshapedTensors = this.tensors.map(tensor => reshape(tensor, outputElementShape));\n return stack(reshapedTensors, 0);\n });\n }\n /**\n * Pop a tensor from the end of the list.\n * @param elementShape shape of the tensor\n * @param elementDtype data type of the tensor\n */\n popBack(elementShape, elementDtype) {\n if (elementDtype !== this.elementDtype) {\n throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n }\n if (this.size() === 0) {\n throw new Error('Trying to pop from an empty list.');\n }\n const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape);\n const tensor = this.tensors.pop();\n assertShapesMatchAllowUndefinedSize(tensor.shape, elementShape, 'TensorList shape mismatch: ');\n return reshape(tensor, outputElementShape);\n }\n /**\n * Push a tensor to the end of the list.\n * @param tensor Tensor to be pushed.\n */\n pushBack(tensor) {\n if (tensor.dtype !== this.elementDtype) {\n throw new Error(`Invalid data types; op elements ${tensor.dtype}, but list elements ${this.elementDtype}`);\n }\n assertShapesMatchAllowUndefinedSize(tensor.shape, this.elementShape, 'TensorList shape mismatch: ');\n if (this.maxNumElements === this.size()) {\n throw new Error(`Trying to push element into a full list.`);\n }\n keep(tensor);\n this.tensors.push(tensor);\n }\n /**\n * Update the size of the list.\n * @param size the new size of the list.\n */\n resize(size) {\n if (size < 0) {\n throw new Error(`TensorListResize expects size to be non-negative. Got: ${size}`);\n }\n if (this.maxNumElements !== -1 && size > this.maxNumElements) {\n throw new Error(`TensorListResize input size ${size} is greater maxNumElement ${this.maxNumElements}.`);\n }\n this.tensors.length = size;\n }\n /**\n * Retrieve the element at the provided index\n * @param elementShape shape of the tensor\n * @param elementDtype dtype of the tensor\n * @param elementIndex index of the tensor\n */\n getItem(elementIndex, elementShape, elementDtype) {\n if (elementDtype !== this.elementDtype) {\n throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n }\n if (elementIndex < 0 || elementIndex > this.tensors.length) {\n throw new Error(`Trying to access element ${elementIndex} in a list with ${this.tensors.length} elements.`);\n }\n if (this.tensors[elementIndex] == null) {\n throw new Error(`element at index ${elementIndex} is null.`);\n }\n assertShapesMatchAllowUndefinedSize(this.tensors[elementIndex].shape, elementShape, 'TensorList shape mismatch: ');\n const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape);\n return reshape(this.tensors[elementIndex], outputElementShape);\n }\n /**\n * Set the tensor at the index\n * @param elementIndex index of the tensor\n * @param tensor the tensor to be inserted into the list\n */\n setItem(elementIndex, tensor) {\n if (tensor.dtype !== this.elementDtype) {\n throw new Error(`Invalid data types; op elements ${tensor.dtype}, but list elements ${this.elementDtype}`);\n }\n if (elementIndex < 0 ||\n this.maxNumElements !== -1 && elementIndex >= this.maxNumElements) {\n throw new Error(`Trying to set element ${elementIndex} in a list with max ${this.maxNumElements} elements.`);\n }\n assertShapesMatchAllowUndefinedSize(this.elementShape, tensor.shape, 'TensorList shape mismatch: ');\n keep(tensor);\n this.tensors[elementIndex] = tensor;\n }\n /**\n * Return selected values in the TensorList as a stacked Tensor. All of\n * selected values must have been written and their shapes must all match.\n * @param indices indices of tensors to gather\n * @param elementDtype output tensor dtype\n * @param elementShape output tensor element shape\n */\n gather(indices, elementDtype, elementShape) {\n if (elementDtype !== this.elementDtype) {\n throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n }\n assertShapesMatchAllowUndefinedSize(this.elementShape, elementShape, 'TensorList shape mismatch: ');\n // When indices is greater than the size of the list, indices beyond the\n // size of the list are ignored.\n indices = indices.slice(0, this.size());\n const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape);\n if (indices.length === 0) {\n return tensor([], [0].concat(outputElementShape));\n }\n return tidy(() => {\n const tensors = indices.map(i => reshape(this.tensors[i], outputElementShape));\n return stack(tensors, 0);\n });\n }\n /**\n * Return the values in the TensorList as a concatenated Tensor.\n * @param elementDtype output tensor dtype\n * @param elementShape output tensor element shape\n */\n concat(elementDtype, elementShape) {\n if (!!elementDtype && elementDtype !== this.elementDtype) {\n throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${elementDtype}`);\n }\n assertShapesMatchAllowUndefinedSize(this.elementShape, elementShape, 'TensorList shape mismatch: ');\n const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape);\n if (this.size() === 0) {\n return tensor([], [0].concat(outputElementShape));\n }\n return tidy(() => {\n const tensors = this.tensors.map(t => reshape(t, outputElementShape));\n return concat(tensors, 0);\n });\n }\n}\n/**\n * Creates a TensorList which, when stacked, has the value of tensor.\n * @param tensor from tensor\n * @param elementShape output tensor element shape\n */\nexport function fromTensor(tensor, elementShape, elementDtype) {\n const dtype = tensor.dtype;\n if (tensor.shape.length < 1) {\n throw new Error(`Tensor must be at least a vector, but saw shape: ${tensor.shape}`);\n }\n if (tensor.dtype !== elementDtype) {\n throw new Error(`Invalid data types; op elements ${tensor.dtype}, but list elements ${elementDtype}`);\n }\n const tensorElementShape = tensor.shape.slice(1);\n assertShapesMatchAllowUndefinedSize(tensorElementShape, elementShape, 'TensorList shape mismatch: ');\n const tensorList = unstack(tensor);\n return new TensorList(tensorList, elementShape, dtype);\n}\n/**\n * Return a TensorList of the given size with empty elements.\n * @param elementShape the shape of the future elements of the list\n * @param elementDtype the desired type of elements in the list\n * @param numElements the number of elements to reserve\n */\nexport function reserve(elementShape, elementDtype, numElements) {\n return new TensorList([], elementShape, elementDtype, numElements);\n}\n/**\n * Put tensors at specific indices of a stacked tensor into a TensorList.\n * @param indices list of indices on how to scatter the tensor.\n * @param tensor input tensor.\n * @param elementShape the shape of the future elements of the list\n * @param numElements the number of elements to scatter\n */\nexport function scatter(tensor, indices, elementShape, numElements) {\n if (indices.length !== tensor.shape[0]) {\n throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor.shape[0]}`);\n }\n const maxIndex = Math.max(...indices);\n if (numElements != null && numElements !== -1 && maxIndex >= numElements) {\n throw new Error(`Max index must be < array size (${maxIndex} vs. ${numElements})`);\n }\n const list = new TensorList([], elementShape, tensor.dtype, numElements);\n const tensors = unstack(tensor, 0);\n indices.forEach((value, index) => {\n list.setItem(value, tensors[index]);\n });\n return list;\n}\n/**\n * Split the values of a Tensor into a TensorList.\n * @param length the lengths to use when splitting value along\n * its first dimension.\n * @param tensor the tensor to split.\n * @param elementShape the shape of the future elements of the list\n */\nexport function split(tensor, length, elementShape) {\n let totalLength = 0;\n const cumulativeLengths = length.map(len => {\n totalLength += len;\n return totalLength;\n });\n if (totalLength !== tensor.shape[0]) {\n throw new Error(`Expected sum of lengths to be equal to\n tensor.shape[0], but sum of lengths is\n ${totalLength}, and tensor's shape is: ${tensor.shape}`);\n }\n const shapeWithoutFirstDim = tensor.shape.slice(1);\n const outputElementShape = mergeElementShape(shapeWithoutFirstDim, elementShape);\n const elementPerRow = totalLength === 0 ? 0 : tensor.size / totalLength;\n const tensors = tidy(() => {\n const tensors = [];\n tensor = reshape(tensor, [1, totalLength, elementPerRow]);\n for (let i = 0; i < length.length; ++i) {\n const previousLength = (i === 0) ? 0 : cumulativeLengths[i - 1];\n const indices = [0, previousLength, 0];\n const sizes = [1, length[i], elementPerRow];\n tensors[i] = reshape(slice(tensor, indices, sizes), outputElementShape);\n }\n tensor.dispose();\n return tensors;\n });\n const list = new TensorList([], elementShape, tensor.dtype, length.length);\n for (let i = 0; i < tensors.length; i++) {\n list.setItem(i, tensors[i]);\n }\n return list;\n}\n//# 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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {concat, DataType, keep, reshape, scalar, slice, stack, Tensor, tensor, tidy, unstack} from '@tensorflow/tfjs-core';\n\nimport {assertShapesMatchAllowUndefinedSize, inferElementShape, mergeElementShape} from './tensor_utils';\n\n/**\n * TensorList stores a container of `tf.Tensor` objects, which are accessible\n * via tensors field.\n *\n * In order to get a copy of the underlying list, use the copy method:\n * ```\n *    TensorList b = a.copy();\n *    b.tensors().pushBack(t);  // This does not modify a.tensors().\n * ```\n *\n * Note that this is not a deep copy: the memory locations of the underlying\n * tensors will still point to the same locations of the corresponding tensors\n * in the original.\n */\n\nexport class TensorList {\n  readonly idTensor: Tensor;\n  maxNumElements: number;\n\n  get id() {\n    return this.idTensor.id;\n  }\n  /**\n   *\n   * @param tensors list of tensors\n   * @param elementShape shape of each tensor, this can be a single number (any\n   * shape is allowed) or partial shape (dim = -1).\n   * @param elementDtype data type of each tensor\n   * @param maxNumElements The maximum allowed size of `tensors`. Defaults to -1\n   *   meaning that the size of `tensors` is unbounded.\n   */\n  constructor(\n      readonly tensors: Tensor[], readonly elementShape: number|number[],\n      readonly elementDtype: DataType, maxNumElements = -1) {\n    if (tensors != null) {\n      tensors.forEach(tensor => {\n        if (elementDtype !== tensor.dtype) {\n          throw new Error(`Invalid data types; op elements ${\n              elementDtype}, but list elements ${tensor.dtype}`);\n        }\n        assertShapesMatchAllowUndefinedSize(\n            elementShape, tensor.shape, 'TensorList shape mismatch: ');\n\n        keep(tensor);\n      });\n    }\n    this.idTensor = scalar(0);\n    this.maxNumElements = maxNumElements;\n    keep(this.idTensor);\n  }\n\n  /**\n   * Get a new TensorList containing a copy of the underlying tensor container.\n   */\n  copy(): TensorList {\n    return new TensorList(\n        [...this.tensors], this.elementShape, this.elementDtype);\n  }\n\n  /**\n   * Dispose the tensors and idTensor and clear the tensor list.\n   */\n  clearAndClose(keepIds?: Set<number>) {\n    this.tensors.forEach(tensor => {\n      if (keepIds == null || !keepIds.has(tensor.id)) {\n        tensor.dispose();\n      }\n    });\n    this.tensors.length = 0;\n    this.idTensor.dispose();\n  }\n  /**\n   * The size of the tensors in the tensor list.\n   */\n  size() {\n    return this.tensors.length;\n  }\n\n  /**\n   * Return a tensor that stacks a list of rank-R tf.Tensors into one rank-(R+1)\n   * tf.Tensor.\n   * @param elementShape shape of each tensor\n   * @param elementDtype data type of each tensor\n   * @param numElements the number of elements to stack\n   */\n  stack(elementShape: number[], elementDtype: DataType, numElements = -1):\n      Tensor {\n    if (elementDtype !== this.elementDtype) {\n      throw new Error(`Invalid data types; op elements ${\n          elementDtype}, but list elements ${this.elementDtype}`);\n    }\n    if (numElements !== -1 && this.tensors.length !== numElements) {\n      throw new Error(`Operation expected a list with ${\n          numElements} elements but got a list with ${\n          this.tensors.length} elements.`);\n    }\n    assertShapesMatchAllowUndefinedSize(\n        elementShape, this.elementShape, 'TensorList shape mismatch: ');\n    const outputElementShape =\n        inferElementShape(this.elementShape, this.tensors, elementShape);\n    return tidy(() => {\n      const reshapedTensors =\n          this.tensors.map(tensor => reshape(tensor, outputElementShape));\n      return stack(reshapedTensors, 0);\n    });\n  }\n\n  /**\n   * Pop a tensor from the end of the list.\n   * @param elementShape shape of the tensor\n   * @param elementDtype data type of the tensor\n   */\n  popBack(elementShape: number[], elementDtype: DataType): Tensor {\n    if (elementDtype !== this.elementDtype) {\n      throw new Error(`Invalid data types; op elements ${\n          elementDtype}, but list elements ${this.elementDtype}`);\n    }\n\n    if (this.size() === 0) {\n      throw new Error('Trying to pop from an empty list.');\n    }\n    const outputElementShape =\n        inferElementShape(this.elementShape, this.tensors, elementShape);\n    const tensor = this.tensors.pop();\n\n    assertShapesMatchAllowUndefinedSize(\n        tensor.shape, elementShape, 'TensorList shape mismatch: ');\n\n    return reshape(tensor, outputElementShape);\n  }\n\n  /**\n   * Push a tensor to the end of the list.\n   * @param tensor Tensor to be pushed.\n   */\n  pushBack(tensor: Tensor) {\n    if (tensor.dtype !== this.elementDtype) {\n      throw new Error(`Invalid data types; op elements ${\n          tensor.dtype}, but list elements ${this.elementDtype}`);\n    }\n\n    assertShapesMatchAllowUndefinedSize(\n        tensor.shape, this.elementShape, 'TensorList shape mismatch: ');\n\n    if (this.maxNumElements === this.size()) {\n      throw new Error(`Trying to push element into a full list.`);\n    }\n    keep(tensor);\n    this.tensors.push(tensor);\n  }\n\n  /**\n   * Update the size of the list.\n   * @param size the new size of the list.\n   */\n  resize(size: number) {\n    if (size < 0) {\n      throw new Error(\n          `TensorListResize expects size to be non-negative. Got: ${size}`);\n    }\n\n    if (this.maxNumElements !== -1 && size > this.maxNumElements) {\n      throw new Error(`TensorListResize input size ${\n          size} is greater maxNumElement ${this.maxNumElements}.`);\n    }\n    this.tensors.length = size;\n  }\n\n  /**\n   * Retrieve the element at the provided index\n   * @param elementShape shape of the tensor\n   * @param elementDtype dtype of the tensor\n   * @param elementIndex index of the tensor\n   */\n  getItem(elementIndex: number, elementShape: number[], elementDtype: DataType):\n      Tensor {\n    if (elementDtype !== this.elementDtype) {\n      throw new Error(`Invalid data types; op elements ${\n          elementDtype}, but list elements ${this.elementDtype}`);\n    }\n    if (elementIndex < 0 || elementIndex > this.tensors.length) {\n      throw new Error(`Trying to access element ${\n          elementIndex} in a list with ${this.tensors.length} elements.`);\n    }\n\n    if (this.tensors[elementIndex] == null) {\n      throw new Error(`element at index ${elementIndex} is null.`);\n    }\n\n    assertShapesMatchAllowUndefinedSize(\n        this.tensors[elementIndex].shape, elementShape,\n        'TensorList shape mismatch: ');\n    const outputElementShape =\n        inferElementShape(this.elementShape, this.tensors, elementShape);\n    return reshape(this.tensors[elementIndex], outputElementShape);\n  }\n\n  /**\n   * Set the tensor at the index\n   * @param elementIndex index of the tensor\n   * @param tensor the tensor to be inserted into the list\n   */\n  setItem(elementIndex: number, tensor: Tensor) {\n    if (tensor.dtype !== this.elementDtype) {\n      throw new Error(`Invalid data types; op elements ${\n          tensor.dtype}, but list elements ${this.elementDtype}`);\n    }\n\n    if (elementIndex < 0 ||\n        this.maxNumElements !== -1 && elementIndex >= this.maxNumElements) {\n      throw new Error(`Trying to set element ${\n          elementIndex} in a list with max ${this.maxNumElements} elements.`);\n    }\n\n    assertShapesMatchAllowUndefinedSize(\n        this.elementShape, tensor.shape, 'TensorList shape mismatch: ');\n    keep(tensor);\n    this.tensors[elementIndex] = tensor;\n  }\n\n  /**\n   * Return selected values in the TensorList as a stacked Tensor. All of\n   * selected values must have been written and their shapes must all match.\n   * @param indices indices of tensors to gather\n   * @param elementDtype output tensor dtype\n   * @param elementShape output tensor element shape\n   */\n  gather(indices: number[], elementDtype: DataType, elementShape: number[]):\n      Tensor {\n    if (elementDtype !== this.elementDtype) {\n      throw new Error(`Invalid data types; op elements ${\n          elementDtype}, but list elements ${this.elementDtype}`);\n    }\n\n    assertShapesMatchAllowUndefinedSize(\n        this.elementShape, elementShape, 'TensorList shape mismatch: ');\n\n    // When indices is greater than the size of the list, indices beyond the\n    // size of the list are ignored.\n    indices = indices.slice(0, this.size());\n    const outputElementShape =\n        inferElementShape(this.elementShape, this.tensors, elementShape);\n    if (indices.length === 0) {\n      return tensor([], [0].concat(outputElementShape));\n    }\n\n    return tidy(() => {\n      const tensors =\n          indices.map(i => reshape(this.tensors[i], outputElementShape));\n      return stack(tensors, 0);\n    });\n  }\n\n  /**\n   * Return the values in the TensorList as a concatenated Tensor.\n   * @param elementDtype output tensor dtype\n   * @param elementShape output tensor element shape\n   */\n  concat(elementDtype: DataType, elementShape: number[]): Tensor {\n    if (!!elementDtype && elementDtype !== this.elementDtype) {\n      throw new Error(`TensorList dtype is ${\n          this.elementDtype} but concat requested dtype ${elementDtype}`);\n    }\n\n    assertShapesMatchAllowUndefinedSize(\n        this.elementShape, elementShape, 'TensorList shape mismatch: ');\n    const outputElementShape =\n        inferElementShape(this.elementShape, this.tensors, elementShape);\n\n    if (this.size() === 0) {\n      return tensor([], [0].concat(outputElementShape));\n    }\n    return tidy(() => {\n      const tensors = this.tensors.map(t => reshape(t, outputElementShape));\n      return concat(tensors, 0);\n    });\n  }\n}\n\n/**\n * Creates a TensorList which, when stacked, has the value of tensor.\n * @param tensor from tensor\n * @param elementShape output tensor element shape\n */\nexport function fromTensor(\n    tensor: Tensor, elementShape: number[], elementDtype: DataType) {\n  const dtype = tensor.dtype;\n  if (tensor.shape.length < 1) {\n    throw new Error(\n        `Tensor must be at least a vector, but saw shape: ${tensor.shape}`);\n  }\n  if (tensor.dtype !== elementDtype) {\n    throw new Error(`Invalid data types; op elements ${\n        tensor.dtype}, but list elements ${elementDtype}`);\n  }\n  const tensorElementShape = tensor.shape.slice(1);\n  assertShapesMatchAllowUndefinedSize(\n      tensorElementShape, elementShape, 'TensorList shape mismatch: ');\n  const tensorList: Tensor[] = unstack(tensor);\n  return new TensorList(tensorList, elementShape, dtype);\n}\n\n/**\n * Return a TensorList of the given size with empty elements.\n * @param elementShape the shape of the future elements of the list\n * @param elementDtype the desired type of elements in the list\n * @param numElements the number of elements to reserve\n */\nexport function reserve(\n    elementShape: number[], elementDtype: DataType, numElements: number) {\n  return new TensorList([], elementShape, elementDtype, numElements);\n}\n\n/**\n * Put tensors at specific indices of a stacked tensor into a TensorList.\n * @param indices list of indices on how to scatter the tensor.\n * @param tensor input tensor.\n * @param elementShape the shape of the future elements of the list\n * @param numElements the number of elements to scatter\n */\nexport function scatter(\n    tensor: Tensor, indices: number[], elementShape: number[],\n    numElements?: number): TensorList {\n  if (indices.length !== tensor.shape[0]) {\n    throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${\n        indices.length} vs. ${tensor.shape[0]}`);\n  }\n\n  const maxIndex = Math.max(...indices);\n\n  if (numElements != null && numElements !== -1 && maxIndex >= numElements) {\n    throw new Error(\n        `Max index must be < array size (${maxIndex}  vs. ${numElements})`);\n  }\n\n  const list = new TensorList([], elementShape, tensor.dtype, numElements);\n  const tensors = unstack(tensor, 0);\n  indices.forEach((value, index) => {\n    list.setItem(value, tensors[index]);\n  });\n  return list;\n}\n\n/**\n * Split the values of a Tensor into a TensorList.\n * @param length the lengths to use when splitting value along\n *    its first dimension.\n * @param tensor the tensor to split.\n * @param elementShape the shape of the future elements of the list\n */\nexport function split(\n    tensor: Tensor, length: number[], elementShape: number[]) {\n  let totalLength = 0;\n  const cumulativeLengths = length.map(len => {\n    totalLength += len;\n    return totalLength;\n  });\n\n  if (totalLength !== tensor.shape[0]) {\n    throw new Error(`Expected sum of lengths to be equal to\n          tensor.shape[0], but sum of lengths is\n        ${totalLength}, and tensor's shape is: ${tensor.shape}`);\n  }\n\n  const shapeWithoutFirstDim = tensor.shape.slice(1);\n  const outputElementShape =\n      mergeElementShape(shapeWithoutFirstDim, elementShape);\n  const elementPerRow = totalLength === 0 ? 0 : tensor.size / totalLength;\n  const tensors: Tensor[] = tidy(() => {\n    const tensors = [];\n    tensor = reshape(tensor, [1, totalLength, elementPerRow]);\n    for (let i = 0; i < length.length; ++i) {\n      const previousLength = (i === 0) ? 0 : cumulativeLengths[i - 1];\n      const indices = [0, previousLength, 0];\n      const sizes = [1, length[i], elementPerRow];\n      tensors[i] = reshape(\n          slice(tensor, indices, sizes), outputElementShape as number[]);\n    }\n    tensor.dispose();\n    return tensors;\n  });\n\n  const list = new TensorList([], elementShape, tensor.dtype, length.length);\n\n  for (let i = 0; i < tensors.length; i++) {\n    list.setItem(i, tensors[i]);\n  }\n  return list;\n}\n"]}","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { scalar } from '@tensorflow/tfjs-core';\nimport { TensorArray } from '../../executor/tensor_array';\nimport { fromTensor, reserve, scatter, split } from '../../executor/tensor_list';\nimport { cloneTensor, getParamValue, getTensor } from './utils';\nexport const executeOp = async (node, tensorMap, context) => {\n switch (node.op) {\n case 'If':\n case 'StatelessIf': {\n const thenFunc = getParamValue('thenBranch', node, tensorMap, context);\n const elseFunc = getParamValue('elseBranch', node, tensorMap, context);\n const cond = getParamValue('cond', node, tensorMap, context);\n const args = getParamValue('args', node, tensorMap, context);\n const condValue = await cond.data();\n if (condValue[0]) {\n return context.functionMap[thenFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap);\n }\n else {\n return context.functionMap[elseFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap);\n }\n }\n case 'While':\n case 'StatelessWhile': {\n const bodyFunc = getParamValue('body', node, tensorMap, context);\n const condFunc = getParamValue('cond', node, tensorMap, context);\n const args = getParamValue('args', node, tensorMap, context);\n // Calculate the condition of the loop\n const condResult = (await context.functionMap[condFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap));\n const argIds = args.map(tensor => tensor.id);\n let condValue = await condResult[0].data();\n // Dispose the intermediate tensors for condition function\n condResult.forEach(tensor => {\n if (!tensor.kept && argIds.indexOf(tensor.id) === -1) {\n tensor.dispose();\n }\n });\n let result = args;\n while (condValue[0]) {\n // Record the previous result for intermediate tensor tracking\n const origResult = result;\n // Execution the body of the loop\n result = await context.functionMap[bodyFunc].executeFunctionAsync(result, context.tensorArrayMap, context.tensorListMap);\n const resultIds = result.map(tensor => tensor.id);\n // Dispose the intermediate tensor for body function that is not global\n // kept, not input/output of the body function\n origResult.forEach(tensor => {\n if (!tensor.kept && argIds.indexOf(tensor.id) === -1 &&\n resultIds.indexOf(tensor.id) === -1) {\n tensor.dispose();\n }\n });\n // Recalcuate the condition of the loop using the latest results.\n const condResult = (await context.functionMap[condFunc].executeFunctionAsync(result, context.tensorArrayMap, context.tensorListMap));\n condValue = await condResult[0].data();\n // Dispose the intermediate tensors for condition function\n condResult.forEach(tensor => {\n if (!tensor.kept && argIds.indexOf(tensor.id) === -1 &&\n resultIds.indexOf(tensor.id) === -1) {\n tensor.dispose();\n }\n });\n }\n return result;\n }\n case 'LoopCond': {\n const pred = getParamValue('pred', node, tensorMap, context);\n return [cloneTensor(pred)];\n }\n case 'Switch': {\n const pred = getParamValue('pred', node, tensorMap, context);\n let data = getParamValue('data', node, tensorMap, context);\n if (!data.kept) {\n data = cloneTensor(data);\n }\n // Outputs nodes :0 => false, :1 => true\n return (await pred.data())[0] ? [undefined, data] : [data, undefined];\n }\n case 'Merge': {\n const inputName = node.inputNames.find(name => getTensor(name, tensorMap, context) !== undefined);\n if (inputName) {\n const data = getTensor(inputName, tensorMap, context);\n return [cloneTensor(data)];\n }\n return undefined;\n }\n case 'Enter': {\n const frameId = getParamValue('frameName', node, tensorMap, context);\n const data = getParamValue('tensor', node, tensorMap, context);\n context.enterFrame(frameId);\n return [cloneTensor(data)];\n }\n case 'Exit': {\n const data = getParamValue('tensor', node, tensorMap, context);\n context.exitFrame();\n return [cloneTensor(data)];\n }\n case 'NextIteration': {\n const data = getParamValue('tensor', node, tensorMap, context);\n context.nextIteration();\n return [cloneTensor(data)];\n }\n case 'TensorArrayV3': {\n const size = getParamValue('size', node, tensorMap, context);\n const dtype = getParamValue('dtype', node, tensorMap, context);\n const elementShape = getParamValue('elementShape', node, tensorMap, context);\n const dynamicSize = getParamValue('dynamicSize', node, tensorMap, context);\n const clearAfterRead = getParamValue('clearAfterRead', node, tensorMap, context);\n const identicalElementShapes = getParamValue('identicalElementShapes', node, tensorMap, context);\n const name = getParamValue('name', node, tensorMap, context);\n const tensorArray = new TensorArray(name, dtype, size, elementShape, identicalElementShapes, dynamicSize, clearAfterRead);\n context.addTensorArray(tensorArray);\n return [tensorArray.idTensor, scalar(1.0)];\n }\n case 'TensorArrayWriteV3': {\n const id = getParamValue('tensorArrayId', node, tensorMap, context);\n const index = getParamValue('index', node, tensorMap, context);\n const writeTensor = getParamValue('tensor', node, tensorMap, context);\n const writeTensorArray = context.getTensorArray(id.id);\n writeTensorArray.write(index, writeTensor);\n return [writeTensorArray.idTensor];\n }\n case 'TensorArrayReadV3': {\n const readId = getParamValue('tensorArrayId', node, tensorMap, context);\n const readIndex = getParamValue('index', node, tensorMap, context);\n const readTensorArray = context.getTensorArray(readId.id);\n return [readTensorArray.read(readIndex)];\n }\n case 'TensorArrayGatherV3': {\n const gatherId = getParamValue('tensorArrayId', node, tensorMap, context);\n const gatherIndices = getParamValue('indices', node, tensorMap, context);\n const gatherDtype = getParamValue('dtype', node, tensorMap, context);\n const gatherTensorArray = context.getTensorArray(gatherId.id);\n return [gatherTensorArray.gather(gatherIndices, gatherDtype)];\n }\n case 'TensorArrayScatterV3': {\n const scatterId = getParamValue('tensorArrayId', node, tensorMap, context);\n const scatterIndices = getParamValue('indices', node, tensorMap, context);\n const scatterTensor = getParamValue('tensor', node, tensorMap, context);\n const scatterTensorArray = context.getTensorArray(scatterId.id);\n scatterTensorArray.scatter(scatterIndices, scatterTensor);\n return [scatterTensorArray.idTensor];\n }\n case 'TensorArrayConcatV3': {\n const concatId = getParamValue('tensorArrayId', node, tensorMap, context);\n const concatTensorArray = context.getTensorArray(concatId.id);\n const concatDtype = getParamValue('dtype', node, tensorMap, context);\n return [concatTensorArray.concat(concatDtype)];\n }\n case 'TensorArraySplitV3': {\n const splitId = getParamValue('tensorArrayId', node, tensorMap, context);\n const splitTensor = getParamValue('tensor', node, tensorMap, context);\n const lengths = getParamValue('lengths', node, tensorMap, context);\n const splitTensorArray = context.getTensorArray(splitId.id);\n splitTensorArray.split(lengths, splitTensor);\n return [splitTensorArray.idTensor];\n }\n case 'TensorArraySizeV3': {\n const sizeId = getParamValue('tensorArrayId', node, tensorMap, context);\n const sizeTensorArray = context.getTensorArray(sizeId.id);\n return [scalar(sizeTensorArray.size(), 'int32')];\n }\n case 'TensorArrayCloseV3': {\n const closeId = getParamValue('tensorArrayId', node, tensorMap, context);\n const closeTensorArray = context.getTensorArray(closeId.id);\n closeTensorArray.clearAndClose();\n return [closeTensorArray.idTensor];\n }\n case 'TensorListSetItem': {\n const idTensor = getParamValue('tensorListId', node, tensorMap, context);\n const index = getParamValue('index', node, tensorMap, context);\n const writeTensor = getParamValue('tensor', node, tensorMap, context);\n const tensorList = context.getTensorList(idTensor.id);\n tensorList.setItem(index, writeTensor);\n return [tensorList.idTensor];\n }\n case 'TensorListGetItem': {\n const idTensor = getParamValue('tensorListId', node, tensorMap, context);\n const readIndex = getParamValue('index', node, tensorMap, context);\n const elementShape = getParamValue('elementShape', node, tensorMap, context);\n const elementDType = getParamValue('elementDType', node, tensorMap, context);\n const tensorList = context.getTensorList(idTensor.id);\n return [tensorList.getItem(readIndex, elementShape, elementDType)];\n }\n case 'TensorListScatterV2':\n case 'TensorListScatter': {\n const scatterIndices = getParamValue('indices', node, tensorMap, context);\n const scatterTensor = getParamValue('tensor', node, tensorMap, context);\n const elementShape = getParamValue('elementShape', node, tensorMap, context);\n const numElements = getParamValue('numElements', node, tensorMap, context);\n const tensorList = scatter(scatterTensor, scatterIndices, elementShape, numElements);\n context.addTensorList(tensorList);\n return [tensorList.idTensor];\n }\n case 'TensorListReserve':\n case 'EmptyTensorList': {\n const elementShape = getParamValue('elementShape', node, tensorMap, context);\n const elementDtype = getParamValue('elementDType', node, tensorMap, context);\n let numElementsParam;\n if (node.op === 'TensorListReserve') {\n numElementsParam = 'numElements';\n }\n else {\n numElementsParam = 'maxNumElements';\n }\n const numElements = getParamValue(numElementsParam, node, tensorMap, context);\n const tensorList = reserve(elementShape, elementDtype, numElements);\n context.addTensorList(tensorList);\n return [tensorList.idTensor];\n }\n case 'TensorListGather': {\n const gatherId = getParamValue('tensorListId', node, tensorMap, context);\n const gatherIndices = getParamValue('indices', node, tensorMap, context);\n const elementShape = getParamValue('elementShape', node, tensorMap, context);\n const elementDtype = getParamValue('elementDType', node, tensorMap, context);\n const tensorList = context.getTensorList(gatherId.id);\n return [tensorList.gather(gatherIndices, elementDtype, elementShape)];\n }\n case 'TensorListStack': {\n const idTensor = getParamValue('tensorListId', node, tensorMap, context);\n const elementShape = getParamValue('elementShape', node, tensorMap, context);\n const elementDtype = getParamValue('elementDType', node, tensorMap, context);\n const numElements = getParamValue('numElements', node, tensorMap, context);\n const tensorList = context.getTensorList(idTensor.id);\n return [tensorList.stack(elementShape, elementDtype, numElements)];\n }\n case 'TensorListFromTensor': {\n const tensor = getParamValue('tensor', node, tensorMap, context);\n const elementShape = getParamValue('elementShape', node, tensorMap, context);\n const elementDtype = getParamValue('elementDType', node, tensorMap, context);\n const tensorList = fromTensor(tensor, elementShape, elementDtype);\n context.addTensorList(tensorList);\n return [tensorList.idTensor];\n }\n case 'TensorListConcat': {\n const concatId = getParamValue('tensorListId', node, tensorMap, context);\n const tensorList = context.getTensorList(concatId.id);\n const concatDtype = getParamValue('dtype', node, tensorMap, context);\n const elementShape = getParamValue('elementShape', node, tensorMap, context);\n return [tensorList.concat(concatDtype, elementShape)];\n }\n case 'TensorListPushBack': {\n const idTensor = getParamValue('tensorListId', node, tensorMap, context);\n const writeTensor = getParamValue('tensor', node, tensorMap, context);\n const tensorList = context.getTensorList(idTensor.id);\n tensorList.pushBack(writeTensor);\n return [tensorList.idTensor];\n }\n case 'TensorListPopBack': {\n const idTensor = getParamValue('tensorListId', node, tensorMap, context);\n const elementShape = getParamValue('elementShape', node, tensorMap, context);\n const elementDType = getParamValue('elementDType', node, tensorMap, context);\n const tensorList = context.getTensorList(idTensor.id);\n return [tensorList.popBack(elementShape, elementDType)];\n }\n case 'TensorListSplit': {\n const splitTensor = getParamValue('tensor', node, tensorMap, context);\n const elementShape = getParamValue('elementShape', node, tensorMap, context);\n const lengths = getParamValue('lengths', node, tensorMap, context);\n const tensorList = split(splitTensor, lengths, elementShape);\n context.addTensorList(tensorList);\n return [tensorList.idTensor];\n }\n default:\n throw TypeError(`Node type ${node.op} is not implemented`);\n }\n};\nexport const CATEGORY = 'control';\n//# 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* @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {DataType, scalar, Tensor} from '@tensorflow/tfjs-core';\n\nimport {NamedTensorsMap} from '../../data/types';\nimport {ExecutionContext} from '../../executor/execution_context';\nimport {TensorArray} from '../../executor/tensor_array';\nimport {fromTensor, reserve, scatter, split} from '../../executor/tensor_list';\nimport {InternalOpAsyncExecutor, Node} from '../types';\n\nimport {cloneTensor, getParamValue, getTensor} from './utils';\n\nexport const executeOp: InternalOpAsyncExecutor = async(\n    node: Node, tensorMap: NamedTensorsMap,\n    context: ExecutionContext): Promise<Tensor[]> => {\n  switch (node.op) {\n    case 'If':\n    case 'StatelessIf': {\n      const thenFunc =\n          getParamValue('thenBranch', node, tensorMap, context) as string;\n      const elseFunc =\n          getParamValue('elseBranch', node, tensorMap, context) as string;\n      const cond = getParamValue('cond', node, tensorMap, context) as Tensor;\n      const args = getParamValue('args', node, tensorMap, context) as Tensor[];\n      const condValue = await cond.data();\n      if (condValue[0]) {\n        return context.functionMap[thenFunc].executeFunctionAsync(\n            args, context.tensorArrayMap, context.tensorListMap);\n      } else {\n        return context.functionMap[elseFunc].executeFunctionAsync(\n            args, context.tensorArrayMap, context.tensorListMap);\n      }\n    }\n    case 'While':\n    case 'StatelessWhile': {\n      const bodyFunc =\n          getParamValue('body', node, tensorMap, context) as string;\n      const condFunc =\n          getParamValue('cond', node, tensorMap, context) as string;\n      const args = getParamValue('args', node, tensorMap, context) as Tensor[];\n\n      // Calculate the condition of the loop\n      const condResult =\n          (await context.functionMap[condFunc].executeFunctionAsync(\n              args, context.tensorArrayMap, context.tensorListMap));\n      const argIds = args.map(tensor => tensor.id);\n      let condValue = await condResult[0].data();\n      // Dispose the intermediate tensors for condition function\n      condResult.forEach(tensor => {\n        if (!tensor.kept && argIds.indexOf(tensor.id) === -1) {\n          tensor.dispose();\n        }\n      });\n\n      let result: Tensor[] = args;\n\n      while (condValue[0]) {\n        // Record the previous result for intermediate tensor tracking\n        const origResult = result;\n        // Execution the body of the loop\n        result = await context.functionMap[bodyFunc].executeFunctionAsync(\n            result, context.tensorArrayMap, context.tensorListMap);\n        const resultIds = result.map(tensor => tensor.id);\n\n        // Dispose the intermediate tensor for body function that is not global\n        // kept, not input/output of the body function\n        origResult.forEach(tensor => {\n          if (!tensor.kept && argIds.indexOf(tensor.id) === -1 &&\n              resultIds.indexOf(tensor.id) === -1) {\n            tensor.dispose();\n          }\n        });\n\n        // Recalcuate the condition of the loop using the latest results.\n        const condResult =\n            (await context.functionMap[condFunc].executeFunctionAsync(\n                result, context.tensorArrayMap, context.tensorListMap));\n        condValue = await condResult[0].data();\n        // Dispose the intermediate tensors for condition function\n        condResult.forEach(tensor => {\n          if (!tensor.kept && argIds.indexOf(tensor.id) === -1 &&\n              resultIds.indexOf(tensor.id) === -1) {\n            tensor.dispose();\n          }\n        });\n      }\n      return result;\n    }\n    case 'LoopCond': {\n      const pred = getParamValue('pred', node, tensorMap, context) as Tensor;\n      return [cloneTensor(pred)];\n    }\n    case 'Switch': {\n      const pred = getParamValue('pred', node, tensorMap, context) as Tensor;\n      let data = getParamValue('data', node, tensorMap, context) as Tensor;\n      if (!data.kept) {\n        data = cloneTensor(data);\n      }\n      // Outputs nodes :0 => false, :1 => true\n      return (await pred.data())[0] ? [undefined, data] : [data, undefined];\n    }\n    case 'Merge': {\n      const inputName = node.inputNames.find(\n          name => getTensor(name, tensorMap, context) !== undefined);\n      if (inputName) {\n        const data = getTensor(inputName, tensorMap, context);\n        return [cloneTensor(data)];\n      }\n      return undefined;\n    }\n    case 'Enter': {\n      const frameId =\n          getParamValue('frameName', node, tensorMap, context) as string;\n      const data = getParamValue('tensor', node, tensorMap, context) as Tensor;\n      context.enterFrame(frameId);\n      return [cloneTensor(data)];\n    }\n    case 'Exit': {\n      const data = getParamValue('tensor', node, tensorMap, context) as Tensor;\n      context.exitFrame();\n      return [cloneTensor(data)];\n    }\n    case 'NextIteration': {\n      const data = getParamValue('tensor', node, tensorMap, context) as Tensor;\n      context.nextIteration();\n      return [cloneTensor(data)];\n    }\n    case 'TensorArrayV3': {\n      const size = getParamValue('size', node, tensorMap, context) as number;\n      const dtype =\n          getParamValue('dtype', node, tensorMap, context) as DataType;\n      const elementShape =\n          getParamValue('elementShape', node, tensorMap, context) as number[];\n      const dynamicSize =\n          getParamValue('dynamicSize', node, tensorMap, context) as boolean;\n      const clearAfterRead =\n          getParamValue('clearAfterRead', node, tensorMap, context) as boolean;\n      const identicalElementShapes =\n          getParamValue('identicalElementShapes', node, tensorMap, context) as\n          boolean;\n      const name = getParamValue('name', node, tensorMap, context) as string;\n      const tensorArray = new TensorArray(\n          name, dtype, size, elementShape, identicalElementShapes, dynamicSize,\n          clearAfterRead);\n      context.addTensorArray(tensorArray);\n      return [tensorArray.idTensor, scalar(1.0)];\n    }\n    case 'TensorArrayWriteV3': {\n      const id =\n          getParamValue('tensorArrayId', node, tensorMap, context) as Tensor;\n      const index = getParamValue('index', node, tensorMap, context) as number;\n      const writeTensor =\n          getParamValue('tensor', node, tensorMap, context) as Tensor;\n      const writeTensorArray = context.getTensorArray(id.id);\n      writeTensorArray.write(index, writeTensor);\n      return [writeTensorArray.idTensor];\n    }\n    case 'TensorArrayReadV3': {\n      const readId =\n          getParamValue('tensorArrayId', node, tensorMap, context) as Tensor;\n      const readIndex =\n          getParamValue('index', node, tensorMap, context) as number;\n      const readTensorArray = context.getTensorArray(readId.id);\n      return [readTensorArray.read(readIndex)];\n    }\n    case 'TensorArrayGatherV3': {\n      const gatherId =\n          getParamValue('tensorArrayId', node, tensorMap, context) as Tensor;\n      const gatherIndices =\n          getParamValue('indices', node, tensorMap, context) as number[];\n      const gatherDtype =\n          getParamValue('dtype', node, tensorMap, context) as DataType;\n      const gatherTensorArray = context.getTensorArray(gatherId.id);\n      return [gatherTensorArray.gather(gatherIndices, gatherDtype)];\n    }\n    case 'TensorArrayScatterV3': {\n      const scatterId =\n          getParamValue('tensorArrayId', node, tensorMap, context) as Tensor;\n      const scatterIndices =\n          getParamValue('indices', node, tensorMap, context) as number[];\n      const scatterTensor =\n          getParamValue('tensor', node, tensorMap, context) as Tensor;\n      const scatterTensorArray = context.getTensorArray(scatterId.id);\n      scatterTensorArray.scatter(scatterIndices, scatterTensor);\n      return [scatterTensorArray.idTensor];\n    }\n    case 'TensorArrayConcatV3': {\n      const concatId =\n          getParamValue('tensorArrayId', node, tensorMap, context) as Tensor;\n      const concatTensorArray = context.getTensorArray(concatId.id);\n      const concatDtype =\n          getParamValue('dtype', node, tensorMap, context) as DataType;\n      return [concatTensorArray.concat(concatDtype)];\n    }\n    case 'TensorArraySplitV3': {\n      const splitId =\n          getParamValue('tensorArrayId', node, tensorMap, context) as Tensor;\n      const splitTensor =\n          getParamValue('tensor', node, tensorMap, context) as Tensor;\n      const lengths =\n          getParamValue('lengths', node, tensorMap, context) as number[];\n      const splitTensorArray = context.getTensorArray(splitId.id);\n      splitTensorArray.split(lengths, splitTensor);\n      return [splitTensorArray.idTensor];\n    }\n    case 'TensorArraySizeV3': {\n      const sizeId =\n          getParamValue('tensorArrayId', node, tensorMap, context) as Tensor;\n      const sizeTensorArray = context.getTensorArray(sizeId.id);\n      return [scalar(sizeTensorArray.size(), 'int32')];\n    }\n    case 'TensorArrayCloseV3': {\n      const closeId =\n          getParamValue('tensorArrayId', node, tensorMap, context) as Tensor;\n      const closeTensorArray = context.getTensorArray(closeId.id);\n      closeTensorArray.clearAndClose();\n      return [closeTensorArray.idTensor];\n    }\n    case 'TensorListSetItem': {\n      const idTensor =\n          getParamValue('tensorListId', node, tensorMap, context) as Tensor;\n      const index = getParamValue('index', node, tensorMap, context) as number;\n      const writeTensor =\n          getParamValue('tensor', node, tensorMap, context) as Tensor;\n      const tensorList = context.getTensorList(idTensor.id);\n      tensorList.setItem(index, writeTensor);\n      return [tensorList.idTensor];\n    }\n    case 'TensorListGetItem': {\n      const idTensor =\n          getParamValue('tensorListId', node, tensorMap, context) as Tensor;\n      const readIndex =\n          getParamValue('index', node, tensorMap, context) as number;\n      const elementShape =\n          getParamValue('elementShape', node, tensorMap, context) as number[];\n\n      const elementDType =\n          getParamValue('elementDType', node, tensorMap, context) as DataType;\n      const tensorList = context.getTensorList(idTensor.id);\n      return [tensorList.getItem(readIndex, elementShape, elementDType)];\n    }\n    case 'TensorListScatterV2':\n    case 'TensorListScatter': {\n      const scatterIndices =\n          getParamValue('indices', node, tensorMap, context) as number[];\n      const scatterTensor =\n          getParamValue('tensor', node, tensorMap, context) as Tensor;\n      const elementShape =\n          getParamValue('elementShape', node, tensorMap, context) as number[];\n      const numElements =\n          getParamValue('numElements', node, tensorMap, context) as number;\n      const tensorList =\n          scatter(scatterTensor, scatterIndices, elementShape, numElements);\n      context.addTensorList(tensorList);\n      return [tensorList.idTensor];\n    }\n    case 'TensorListReserve':\n    case 'EmptyTensorList': {\n      const elementShape =\n          getParamValue('elementShape', node, tensorMap, context) as number[];\n      const elementDtype =\n          getParamValue('elementDType', node, tensorMap, context) as DataType;\n      let numElementsParam;\n\n      if (node.op === 'TensorListReserve') {\n        numElementsParam = 'numElements';\n      } else {\n        numElementsParam = 'maxNumElements';\n      }\n\n      const numElements =\n          getParamValue(numElementsParam, node, tensorMap, context) as number;\n\n      const tensorList = reserve(elementShape, elementDtype, numElements);\n      context.addTensorList(tensorList);\n      return [tensorList.idTensor];\n    }\n    case 'TensorListGather': {\n      const gatherId =\n          getParamValue('tensorListId', node, tensorMap, context) as Tensor;\n      const gatherIndices =\n          getParamValue('indices', node, tensorMap, context) as number[];\n      const elementShape =\n          getParamValue('elementShape', node, tensorMap, context) as number[];\n      const elementDtype =\n          getParamValue('elementDType', node, tensorMap, context) as DataType;\n      const tensorList = context.getTensorList(gatherId.id);\n      return [tensorList.gather(gatherIndices, elementDtype, elementShape)];\n    }\n    case 'TensorListStack': {\n      const idTensor =\n          getParamValue('tensorListId', node, tensorMap, context) as Tensor;\n      const elementShape =\n          getParamValue('elementShape', node, tensorMap, context) as number[];\n      const elementDtype =\n          getParamValue('elementDType', node, tensorMap, context) as DataType;\n      const numElements =\n          getParamValue('numElements', node, tensorMap, context) as number;\n      const tensorList = context.getTensorList(idTensor.id);\n      return [tensorList.stack(elementShape, elementDtype, numElements)];\n    }\n    case 'TensorListFromTensor': {\n      const tensor =\n          getParamValue('tensor', node, tensorMap, context) as Tensor;\n      const elementShape =\n          getParamValue('elementShape', node, tensorMap, context) as number[];\n      const elementDtype =\n          getParamValue('elementDType', node, tensorMap, context) as DataType;\n      const tensorList = fromTensor(tensor, elementShape, elementDtype);\n      context.addTensorList(tensorList);\n      return [tensorList.idTensor];\n    }\n    case 'TensorListConcat': {\n      const concatId =\n          getParamValue('tensorListId', node, tensorMap, context) as Tensor;\n      const tensorList = context.getTensorList(concatId.id);\n      const concatDtype =\n          getParamValue('dtype', node, tensorMap, context) as DataType;\n      const elementShape =\n          getParamValue('elementShape', node, tensorMap, context) as number[];\n      return [tensorList.concat(concatDtype, elementShape)];\n    }\n    case 'TensorListPushBack': {\n      const idTensor =\n          getParamValue('tensorListId', node, tensorMap, context) as Tensor;\n      const writeTensor =\n          getParamValue('tensor', node, tensorMap, context) as Tensor;\n      const tensorList = context.getTensorList(idTensor.id);\n      tensorList.pushBack(writeTensor);\n      return [tensorList.idTensor];\n    }\n    case 'TensorListPopBack': {\n      const idTensor =\n          getParamValue('tensorListId', node, tensorMap, context) as Tensor;\n      const elementShape =\n          getParamValue('elementShape', node, tensorMap, context) as number[];\n      const elementDType =\n          getParamValue('elementDType', node, tensorMap, context) as DataType;\n      const tensorList = context.getTensorList(idTensor.id);\n      return [tensorList.popBack(elementShape, elementDType)];\n    }\n    case 'TensorListSplit': {\n      const splitTensor =\n          getParamValue('tensor', node, tensorMap, context) as Tensor;\n      const elementShape =\n          getParamValue('elementShape', node, tensorMap, context) as number[];\n      const lengths =\n          getParamValue('lengths', node, tensorMap, context) as number[];\n\n      const tensorList = split(splitTensor, lengths, elementShape);\n      context.addTensorList(tensorList);\n      return [tensorList.idTensor];\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\nexport const CATEGORY = 'control';\n"]}","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getPadding, getParamValue } from './utils';\nfunction fusedConvAndDepthWiseParams(node, tensorMap, context) {\n const [extraOp, activationFunc] = getParamValue('fusedOps', node, tensorMap, context);\n const isBiasAdd = extraOp === 'biasadd';\n const noBiasAdd = !isBiasAdd;\n const isPrelu = activationFunc === 'prelu';\n const isBatchNorm = extraOp === 'fusedbatchnorm';\n const numArgs = getParamValue('numArgs', node, tensorMap, context);\n if (isBiasAdd) {\n if (isPrelu && numArgs !== 2) {\n throw new Error('FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu ' +\n 'must have two extra arguments: bias and alpha.');\n }\n if (!isPrelu && isBiasAdd && numArgs !== 1) {\n throw new Error('FusedConv2d and DepthwiseConv2d with BiasAdd must have ' +\n 'one extra argument: bias.');\n }\n }\n if (isBatchNorm) {\n throw new Error('FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported');\n }\n const stride = getParamValue('strides', node, tensorMap, context);\n const pad = getPadding(node, tensorMap, context);\n const dataFormat = getParamValue('dataFormat', node, tensorMap, context)\n .toUpperCase();\n const dilations = getParamValue('dilations', node, tensorMap, context);\n let [biasArg, preluArg] = getParamValue('args', node, tensorMap, context);\n if (noBiasAdd) {\n preluArg = biasArg;\n biasArg = undefined;\n }\n const leakyreluAlpha = getParamValue('leakyreluAlpha', node, tensorMap, context);\n return {\n stride,\n pad,\n dataFormat,\n dilations,\n biasArg,\n preluArg,\n activationFunc,\n leakyreluAlpha\n };\n}\nexport const executeOp = (node, tensorMap, context) => {\n switch (node.op) {\n case 'Conv1D': {\n const stride = getParamValue('stride', node, tensorMap, context);\n const pad = getParamValue('pad', node, tensorMap, context);\n const dataFormat = getParamValue('dataFormat', node, tensorMap, context)\n .toUpperCase();\n const dilation = getParamValue('dilation', node, tensorMap, context);\n return [tfOps.conv1d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), stride, pad, dataFormat, dilation)];\n }\n case 'Conv2D': {\n const stride = getParamValue('strides', node, tensorMap, context);\n const pad = getPadding(node, tensorMap, context);\n const dataFormat = getParamValue('dataFormat', node, tensorMap, context)\n .toUpperCase();\n const dilations = getParamValue('dilations', node, tensorMap, context);\n return [tfOps.conv2d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [stride[1], stride[2]], pad, dataFormat, [dilations[1], dilations[2]])];\n }\n case '_FusedConv2D': {\n const { stride, pad, dataFormat, dilations, biasArg, preluArg, activationFunc, leakyreluAlpha } = fusedConvAndDepthWiseParams(node, tensorMap, context);\n return [tfOps.fused.conv2d({\n x: getParamValue('x', node, tensorMap, context),\n filter: getParamValue('filter', node, tensorMap, context),\n strides: [stride[1], stride[2]],\n pad: pad,\n dataFormat: dataFormat,\n dilations: [dilations[1], dilations[2]],\n bias: biasArg,\n activation: activationFunc,\n preluActivationWeights: preluArg,\n leakyreluAlpha\n })];\n }\n case 'FusedDepthwiseConv2dNative': {\n const { stride, pad, dataFormat, dilations, biasArg, preluArg, activationFunc, leakyreluAlpha, } = fusedConvAndDepthWiseParams(node, tensorMap, context);\n return [tfOps.fused.depthwiseConv2d({\n x: getParamValue('x', node, tensorMap, context),\n filter: getParamValue('filter', node, tensorMap, context),\n strides: [stride[1], stride[2]],\n pad: pad,\n dataFormat: dataFormat,\n dilations: [dilations[1], dilations[2]],\n bias: biasArg,\n activation: activationFunc,\n preluActivationWeights: preluArg,\n leakyreluAlpha\n })];\n }\n case 'Conv2DBackpropInput':\n case 'Conv2dTranspose': {\n const shape = getParamValue('outputShape', node, tensorMap, context);\n const stride = getParamValue('strides', node, tensorMap, context);\n const pad = getPadding(node, tensorMap, context);\n return [tfOps.conv2dTranspose(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), shape, [stride[1], stride[2]], pad)];\n }\n case 'DepthwiseConv2dNative':\n case 'DepthwiseConv2d': {\n const stride = getParamValue('strides', node, tensorMap, context);\n const pad = getPadding(node, tensorMap, context);\n const dilations = getParamValue('dilations', node, tensorMap, context);\n const dataFormat = getParamValue('dataFormat', node, tensorMap, context)\n .toUpperCase();\n return [tfOps.depthwiseConv2d(getParamValue('input', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [stride[1], stride[2]], pad, dataFormat, [dilations[1], dilations[2]])];\n }\n case 'Conv3D': {\n const stride = getParamValue('strides', node, tensorMap, context);\n const pad = getParamValue('pad', node, tensorMap, context);\n const dataFormat = getParamValue('dataFormat', node, tensorMap, context)\n .toUpperCase();\n const dilations = getParamValue('dilations', node, tensorMap, context);\n return [tfOps.conv3d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [stride[1], stride[2], stride[3]], pad, dataFormat, [dilations[1], dilations[2], dilations[3]])];\n }\n case 'AvgPool': {\n const stride = getParamValue('strides', node, tensorMap, context);\n const pad = getParamValue('pad', node, tensorMap, context);\n const kernelSize = getParamValue('kernelSize', node, tensorMap, context);\n return [tfOps.avgPool(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad)];\n }\n case 'MaxPool': {\n const stride = getParamValue('strides', node, tensorMap, context);\n const pad = getParamValue('pad', node, tensorMap, context);\n const kernelSize = getParamValue('kernelSize', node, tensorMap, context);\n return [tfOps.maxPool(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad)];\n }\n case 'MaxPoolWithArgmax': {\n const stride = getParamValue('strides', node, tensorMap, context);\n const pad = getParamValue('pad', node, tensorMap, context);\n const kernelSize = getParamValue('kernelSize', node, tensorMap, context);\n const includeBatchInIndex = getParamValue('includeBatchInIndex', node, tensorMap, context);\n const { result, indexes } = tfOps.maxPoolWithArgmax(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad, includeBatchInIndex);\n return [result, indexes];\n }\n case 'AvgPool3D': {\n const stride = getParamValue('strides', node, tensorMap, context);\n const pad = getParamValue('pad', node, tensorMap, context);\n const kernelSize = getParamValue('kernelSize', node, tensorMap, context);\n return [tfOps.avgPool3d(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2], kernelSize[3]], [stride[1], stride[2], stride[3]], pad)];\n }\n case 'MaxPool3D': {\n const stride = getParamValue('strides', node, tensorMap, context);\n const pad = getParamValue('pad', node, tensorMap, context);\n const kernelSize = getParamValue('kernelSize', node, tensorMap, context);\n return [tfOps.maxPool3d(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2], kernelSize[3]], [stride[1], stride[2], stride[3]], pad)];\n }\n case 'Dilation2D': {\n const strides = getParamValue('strides', node, tensorMap, context);\n const pad = getParamValue('pad', node, tensorMap, context);\n const dilations = getParamValue('dilations', node, tensorMap, context);\n // strides: [1, stride_height, stride_width, 1].\n const strideHeight = strides[1];\n const strideWidth = strides[2];\n // dilations: [1, dilation_height, dilation_width, 1].\n const dilationHeight = dilations[1];\n const dilationWidth = dilations[2];\n return [tfOps.dilation2d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [strideHeight, strideWidth], pad, [dilationHeight, dilationWidth], 'NHWC' /* dataFormat */)];\n }\n default:\n throw TypeError(`Node type ${node.op} is not implemented`);\n }\n};\nexport const CATEGORY = 'convolution';\n//# 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* @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Rank, Tensor, Tensor3D, Tensor4D, Tensor5D} from '@tensorflow/tfjs-core';\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\n\nimport {NamedTensorsMap} from '../../data/types';\nimport {ExecutionContext} from '../../executor/execution_context';\nimport {InternalOpExecutor, Node} from '../types';\n\nimport {getPadding, getParamValue} from './utils';\n\nfunction fusedConvAndDepthWiseParams(\n    node: Node, tensorMap: NamedTensorsMap, context: ExecutionContext) {\n  const [extraOp, activationFunc] =\n      (getParamValue('fusedOps', node, tensorMap, context) as string[]);\n\n  const isBiasAdd = extraOp === 'biasadd';\n  const noBiasAdd = !isBiasAdd;\n  const isPrelu = activationFunc === 'prelu';\n  const isBatchNorm = extraOp === 'fusedbatchnorm';\n\n  const numArgs =\n      (getParamValue('numArgs', node, tensorMap, context) as number);\n  if (isBiasAdd) {\n    if (isPrelu && numArgs !== 2) {\n      throw new Error(\n          'FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu ' +\n          'must have two extra arguments: bias and alpha.');\n    }\n    if (!isPrelu && isBiasAdd && numArgs !== 1) {\n      throw new Error(\n          'FusedConv2d and DepthwiseConv2d with BiasAdd must have ' +\n          'one extra argument: bias.');\n    }\n  }\n  if (isBatchNorm) {\n    throw new Error(\n        'FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported');\n  }\n  const stride = getParamValue('strides', node, tensorMap, context) as number[];\n  const pad = getPadding(node, tensorMap, context);\n  const dataFormat =\n      (getParamValue('dataFormat', node, tensorMap, context) as string)\n          .toUpperCase();\n  const dilations =\n      getParamValue('dilations', node, tensorMap, context) as number[];\n  let [biasArg, preluArg] =\n      getParamValue('args', node, tensorMap, context) as Tensor[];\n  if (noBiasAdd) {\n    preluArg = biasArg;\n    biasArg = undefined;\n  }\n  const leakyreluAlpha =\n      getParamValue('leakyreluAlpha', node, tensorMap, context) as number;\n\n  return {\n    stride,\n    pad,\n    dataFormat,\n    dilations,\n    biasArg,\n    preluArg,\n    activationFunc,\n    leakyreluAlpha\n  };\n}\n\nexport const executeOp: InternalOpExecutor =\n    (node: Node, tensorMap: NamedTensorsMap,\n     context: ExecutionContext): Tensor[] => {\n      switch (node.op) {\n        case 'Conv1D': {\n          const stride =\n              getParamValue('stride', node, tensorMap, context) as number;\n          const pad = getParamValue('pad', node, tensorMap, context);\n          const dataFormat =\n              (getParamValue('dataFormat', node, tensorMap, context) as string)\n                  .toUpperCase();\n          const dilation =\n              getParamValue('dilation', node, tensorMap, context) as number;\n          return [tfOps.conv1d(\n              getParamValue('x', node, tensorMap, context) as Tensor3D,\n              getParamValue('filter', node, tensorMap, context) as Tensor3D,\n              stride, pad as 'valid' | 'same', dataFormat as 'NWC' | 'NCW',\n              dilation)];\n        }\n        case 'Conv2D': {\n          const stride =\n              getParamValue('strides', node, tensorMap, context) as number[];\n          const pad = getPadding(node, tensorMap, context);\n          const dataFormat =\n              (getParamValue('dataFormat', node, tensorMap, context) as string)\n                  .toUpperCase();\n          const dilations =\n              getParamValue('dilations', node, tensorMap, context) as number[];\n          return [tfOps.conv2d(\n              getParamValue('x', node, tensorMap, context) as Tensor3D |\n                  Tensor4D,\n              getParamValue('filter', node, tensorMap, context) as Tensor4D,\n              [stride[1], stride[2]], pad as 'valid' | 'same',\n              dataFormat as 'NHWC' | 'NCHW', [dilations[1], dilations[2]])];\n        }\n        case '_FusedConv2D': {\n          const {\n            stride,\n            pad,\n            dataFormat,\n            dilations,\n            biasArg,\n            preluArg,\n            activationFunc,\n            leakyreluAlpha\n          } = fusedConvAndDepthWiseParams(node, tensorMap, context);\n\n          return [tfOps.fused.conv2d({\n            x: getParamValue('x', node, tensorMap, context) as Tensor3D |\n                Tensor4D,\n            filter: getParamValue('filter', node, tensorMap, context) as\n                Tensor4D,\n            strides: [stride[1], stride[2]],\n            pad: pad as 'valid' | 'same',\n            dataFormat: dataFormat as 'NHWC' | 'NCHW',\n            dilations: [dilations[1], dilations[2]],\n            bias: biasArg,\n            activation: activationFunc as tfOps.fused.Activation,\n            preluActivationWeights: preluArg,\n            leakyreluAlpha\n          })];\n        }\n\n        case 'FusedDepthwiseConv2dNative': {\n          const {\n            stride,\n            pad,\n            dataFormat,\n            dilations,\n            biasArg,\n            preluArg,\n            activationFunc,\n            leakyreluAlpha,\n          } = fusedConvAndDepthWiseParams(node, tensorMap, context);\n\n          return [tfOps.fused.depthwiseConv2d({\n            x: getParamValue('x', node, tensorMap, context) as Tensor3D |\n                Tensor4D,\n            filter: getParamValue('filter', node, tensorMap, context) as\n                Tensor4D,\n            strides: [stride[1], stride[2]],\n            pad: pad as 'valid' | 'same',\n            dataFormat: dataFormat as 'NHWC' | 'NCHW',\n            dilations: [dilations[1], dilations[2]],\n            bias: biasArg,\n            activation: activationFunc as tfOps.fused.Activation,\n            preluActivationWeights: preluArg,\n            leakyreluAlpha\n          })];\n        }\n        case 'Conv2DBackpropInput':\n        case 'Conv2dTranspose': {\n          const shape = getParamValue(\n                            'outputShape', node, tensorMap,\n                            context) as [number, number, number] |\n              [number, number, number, number];\n          const stride =\n              getParamValue('strides', node, tensorMap, context) as number[];\n          const pad = getPadding(node, tensorMap, context);\n          return [tfOps.conv2dTranspose(\n              getParamValue('x', node, tensorMap, context) as Tensor3D |\n                  Tensor4D,\n              getParamValue('filter', node, tensorMap, context) as Tensor4D,\n              shape, [stride[1], stride[2]], pad as 'valid' | 'same')];\n        }\n        case 'DepthwiseConv2dNative':\n        case 'DepthwiseConv2d': {\n          const stride =\n              getParamValue('strides', node, tensorMap, context) as number[];\n          const pad = getPadding(node, tensorMap, context);\n          const dilations =\n              getParamValue('dilations', node, tensorMap, context) as number[];\n          const dataFormat =\n              (getParamValue('dataFormat', node, tensorMap, context) as string)\n                  .toUpperCase();\n\n          return [tfOps.depthwiseConv2d(\n              getParamValue('input', node, tensorMap, context) as Tensor3D |\n                  Tensor4D,\n              getParamValue('filter', node, tensorMap, context) as Tensor4D,\n              [stride[1], stride[2]], pad as 'valid' | 'same',\n              dataFormat as 'NHWC' | 'NCHW', [dilations[1], dilations[2]])];\n        }\n        case 'Conv3D': {\n          const stride =\n              getParamValue('strides', node, tensorMap, context) as number[];\n          const pad = getParamValue('pad', node, tensorMap, context);\n          const dataFormat =\n              (getParamValue('dataFormat', node, tensorMap, context) as string)\n                  .toUpperCase();\n          const dilations =\n              getParamValue('dilations', node, tensorMap, context) as number[];\n          return [tfOps.conv3d(\n              getParamValue('x', node, tensorMap, context) as Tensor4D |\n                  Tensor<Rank.R5>,\n              getParamValue('filter', node, tensorMap, context) as\n                  Tensor<Rank.R5>,\n              [stride[1], stride[2], stride[3]], pad as 'valid' | 'same',\n              dataFormat as 'NDHWC' | 'NCDHW',\n              [dilations[1], dilations[2], dilations[3]])];\n        }\n        case 'AvgPool': {\n          const stride =\n              getParamValue('strides', node, tensorMap, context) as number[];\n          const pad = getParamValue('pad', node, tensorMap, context);\n          const kernelSize =\n              getParamValue('kernelSize', node, tensorMap, context) as number[];\n\n          return [tfOps.avgPool(\n              getParamValue('x', node, tensorMap, context) as Tensor3D |\n                  Tensor4D,\n              [kernelSize[1], kernelSize[2]], [stride[1], stride[2]],\n              pad as 'valid' | 'same')];\n        }\n        case 'MaxPool': {\n          const stride =\n              getParamValue('strides', node, tensorMap, context) as number[];\n          const pad = getParamValue('pad', node, tensorMap, context);\n          const kernelSize =\n              getParamValue('kernelSize', node, tensorMap, context) as number[];\n\n          return [tfOps.maxPool(\n              getParamValue('x', node, tensorMap, context) as Tensor3D |\n                  Tensor4D,\n              [kernelSize[1], kernelSize[2]], [stride[1], stride[2]],\n              pad as 'valid' | 'same')];\n        }\n        case 'MaxPoolWithArgmax': {\n          const stride =\n              getParamValue('strides', node, tensorMap, context) as number[];\n          const pad = getParamValue('pad', node, tensorMap, context);\n          const kernelSize =\n              getParamValue('kernelSize', node, tensorMap, context) as number[];\n          const includeBatchInIndex =\n              getParamValue('includeBatchInIndex', node, tensorMap, context) as\n              boolean;\n          const {result, indexes} = tfOps.maxPoolWithArgmax(\n              getParamValue('x', node, tensorMap, context) as Tensor4D,\n              [kernelSize[1], kernelSize[2]], [stride[1], stride[2]],\n              pad as 'valid' | 'same', includeBatchInIndex);\n          return [result, indexes];\n        }\n        case 'AvgPool3D': {\n          const stride =\n              getParamValue('strides', node, tensorMap, context) as number[];\n          const pad = getParamValue('pad', node, tensorMap, context);\n          const kernelSize =\n              getParamValue('kernelSize', node, tensorMap, context) as number[];\n\n          return [tfOps.avgPool3d(\n              getParamValue('x', node, tensorMap, context) as Tensor5D,\n              [kernelSize[1], kernelSize[2], kernelSize[3]],\n              [stride[1], stride[2], stride[3]], pad as 'valid' | 'same')];\n        }\n\n        case 'MaxPool3D': {\n          const stride =\n              getParamValue('strides', node, tensorMap, context) as number[];\n          const pad = getParamValue('pad', node, tensorMap, context);\n          const kernelSize =\n              getParamValue('kernelSize', node, tensorMap, context) as number[];\n\n          return [tfOps.maxPool3d(\n              getParamValue('x', node, tensorMap, context) as Tensor5D,\n              [kernelSize[1], kernelSize[2], kernelSize[3]],\n              [stride[1], stride[2], stride[3]], pad as 'valid' | 'same')];\n        }\n\n        case 'Dilation2D': {\n          const strides =\n              getParamValue('strides', node, tensorMap, context) as number[];\n          const pad = getParamValue('pad', node, tensorMap, context);\n          const dilations =\n              getParamValue('dilations', node, tensorMap, context) as number[];\n\n          // strides: [1, stride_height, stride_width, 1].\n          const strideHeight = strides[1];\n          const strideWidth = strides[2];\n\n          // dilations: [1, dilation_height, dilation_width, 1].\n          const dilationHeight = dilations[1];\n          const dilationWidth = dilations[2];\n\n          return [tfOps.dilation2d(\n              getParamValue('x', node, tensorMap, context) as Tensor3D |\n                  Tensor4D,\n              getParamValue('filter', node, tensorMap, context) as Tensor3D,\n              [strideHeight, strideWidth], pad as 'valid' | 'same',\n              [dilationHeight, dilationWidth], 'NHWC' /* dataFormat */)];\n        }\n\n        default:\n          throw TypeError(`Node type ${node.op} is not implemented`);\n      }\n    };\n\nexport const CATEGORY = 'convolution';\n"]}","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n switch (node.op) {\n case 'Fill': {\n const shape = getParamValue('shape', node, tensorMap, context);\n const dtype = getParamValue('dtype', node, tensorMap, context);\n const value = getParamValue('value', node, tensorMap, context);\n return [tfOps.fill(shape, value, dtype)];\n }\n case 'LinSpace': {\n const start = getParamValue('start', node, tensorMap, context);\n const stop = getParamValue('stop', node, tensorMap, context);\n const num = getParamValue('num', node, tensorMap, context);\n return [tfOps.linspace(start, stop, num)];\n }\n case 'Multinomial': {\n const logits = getParamValue('logits', node, tensorMap, context);\n const numSamples = getParamValue('numSamples', node, tensorMap, context);\n const seed = getParamValue('seed', node, tensorMap, context);\n return [tfOps.multinomial(logits, numSamples, seed)];\n }\n case 'OneHot': {\n const indices = getParamValue('indices', node, tensorMap, context);\n const depth = getParamValue('depth', node, tensorMap, context);\n const onValue = getParamValue('onValue', node, tensorMap, context);\n const offValue = getParamValue('offValue', node, tensorMap, context);\n return [tfOps.oneHot(indices, depth, onValue, offValue)];\n }\n case 'Ones': {\n return [tfOps.ones(getParamValue('shape', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))];\n }\n case 'OnesLike': {\n return [tfOps.onesLike(getParamValue('x', node, tensorMap, context))];\n }\n case 'RandomUniform': {\n return [tfOps.randomUniform(\n // tslint:disable-next-line:no-any\n getParamValue('shape', node, tensorMap, context), getParamValue('minval', node, tensorMap, context), getParamValue('maxval', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))];\n }\n case 'Range': {\n const start = getParamValue('start', node, tensorMap, context);\n const stop = getParamValue('stop', node, tensorMap, context);\n const step = getParamValue('step', node, tensorMap, context);\n return [tfOps.range(start, stop, step, getParamValue('dtype', node, tensorMap, context))];\n }\n case 'TruncatedNormal': {\n const shape = getParamValue('shape', node, tensorMap, context);\n const mean = getParamValue('mean', node, tensorMap, context);\n const stdDev = getParamValue('stdDev', node, tensorMap, context);\n const seed = getParamValue('seed', node, tensorMap, context);\n return [tfOps.truncatedNormal(shape, mean, stdDev, getParamValue('dtype', node, tensorMap, context), seed)];\n }\n case 'Zeros': {\n return [tfOps.zeros(getParamValue('shape', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))];\n }\n case 'ZerosLike': {\n return [tfOps.zerosLike(getParamValue('x', node, tensorMap, context))];\n }\n default:\n throw TypeError(`Node type ${node.op} is not implemented`);\n }\n};\nexport const CATEGORY = 'creation';\n//# 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* @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {DataType, Tensor, Tensor1D} from '@tensorflow/tfjs-core';\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\n\nimport {NamedTensorsMap} from '../../data/types';\nimport {ExecutionContext} from '../../executor/execution_context';\nimport {InternalOpExecutor, Node} from '../types';\n\nimport {getParamValue} from './utils';\n\nexport const executeOp: InternalOpExecutor =\n    (node: Node, tensorMap: NamedTensorsMap,\n     context: ExecutionContext): Tensor[] => {\n      switch (node.op) {\n        case 'Fill': {\n          const shape =\n              getParamValue('shape', node, tensorMap, context) as number[];\n          const dtype =\n              getParamValue('dtype', node, tensorMap, context) as DataType;\n          const value =\n              getParamValue('value', node, tensorMap, context) as number;\n          return [tfOps.fill(shape, value, dtype)];\n        }\n        case 'LinSpace': {\n          const start =\n              getParamValue('start', node, tensorMap, context) as number;\n          const stop =\n              getParamValue('stop', node, tensorMap, context) as number;\n          const num = getParamValue('num', node, tensorMap, context) as number;\n          return [tfOps.linspace(start, stop, num)];\n        }\n        case 'Multinomial': {\n          const logits =\n              getParamValue('logits', node, tensorMap, context) as Tensor1D;\n          const numSamples =\n              getParamValue('numSamples', node, tensorMap, context) as number;\n          const seed =\n              getParamValue('seed', node, tensorMap, context) as number;\n          return [tfOps.multinomial(logits, numSamples, seed)];\n        }\n        case 'OneHot': {\n          const indices =\n              getParamValue('indices', node, tensorMap, context) as Tensor1D;\n          const depth =\n              getParamValue('depth', node, tensorMap, context) as number;\n          const onValue =\n              getParamValue('onValue', node, tensorMap, context) as number;\n          const offValue =\n              getParamValue('offValue', node, tensorMap, context) as number;\n          return [tfOps.oneHot(indices, depth, onValue, offValue)];\n        }\n        case 'Ones': {\n          return [tfOps.ones(\n              getParamValue('shape', node, tensorMap, context) as number[],\n              getParamValue('dtype', node, tensorMap, context) as DataType)];\n        }\n        case 'OnesLike': {\n          return [tfOps.onesLike(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        }\n        case 'RandomUniform': {\n          return [tfOps.randomUniform(\n              // tslint:disable-next-line:no-any\n              getParamValue('shape', node, tensorMap, context) as any,\n              getParamValue('minval', node, tensorMap, context) as number,\n              getParamValue('maxval', node, tensorMap, context) as number,\n              getParamValue('dtype', node, tensorMap, context) as DataType)];\n        }\n        case 'Range': {\n          const start =\n              getParamValue('start', node, tensorMap, context) as number;\n          const stop =\n              getParamValue('stop', node, tensorMap, context) as number;\n          const step =\n              getParamValue('step', node, tensorMap, context) as number;\n          return [tfOps.range(\n              start, stop, step,\n              getParamValue('dtype', node, tensorMap, context) as 'float32' |\n                  'int32')];\n        }\n        case 'TruncatedNormal': {\n          const shape =\n              getParamValue('shape', node, tensorMap, context) as number[];\n          const mean =\n              getParamValue('mean', node, tensorMap, context) as number;\n          const stdDev =\n              getParamValue('stdDev', node, tensorMap, context) as number;\n          const seed =\n              getParamValue('seed', node, tensorMap, context) as number;\n          return [tfOps.truncatedNormal(\n              shape, mean, stdDev,\n              getParamValue('dtype', node, tensorMap, context) as 'float32' |\n                  'int32',\n              seed)];\n        }\n        case 'Zeros': {\n          return [tfOps.zeros(\n              getParamValue('shape', node, tensorMap, context) as number[],\n              getParamValue('dtype', node, tensorMap, context) as DataType)];\n        }\n        case 'ZerosLike': {\n          return [tfOps.zerosLike(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        }\n        default:\n          throw TypeError(`Node type ${node.op} is not implemented`);\n      }\n    };\n\nexport const CATEGORY = 'creation';\n"]}","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nfunction nmsParams(node, tensorMap, context) {\n const boxes = getParamValue('boxes', node, tensorMap, context);\n const scores = getParamValue('scores', node, tensorMap, context);\n const maxOutputSize = getParamValue('maxOutputSize', node, tensorMap, context);\n const iouThreshold = getParamValue('iouThreshold', node, tensorMap, context);\n const scoreThreshold = getParamValue('scoreThreshold', node, tensorMap, context);\n const softNmsSigma = getParamValue('softNmsSigma', node, tensorMap, context);\n return {\n boxes,\n scores,\n maxOutputSize,\n iouThreshold,\n scoreThreshold,\n softNmsSigma\n };\n}\nexport const executeOp = async (node, tensorMap, context) => {\n switch (node.op) {\n case 'NonMaxSuppressionV5': {\n const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = nmsParams(node, tensorMap, context);\n const result = await tfOps.image.nonMaxSuppressionWithScoreAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n return [result.selectedIndices, result.selectedScores];\n }\n case 'NonMaxSuppressionV4': {\n const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold } = nmsParams(node, tensorMap, context);\n const padToMaxOutputSize = getParamValue('padToMaxOutputSize', node, tensorMap, context);\n const result = await tfOps.image.nonMaxSuppressionPaddedAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);\n return [result.selectedIndices, result.validOutputs];\n }\n case 'NonMaxSuppressionV3':\n case 'NonMaxSuppressionV2': {\n const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold } = nmsParams(node, tensorMap, context);\n return [await tfOps.image.nonMaxSuppressionAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold)];\n }\n case 'Where': {\n const condition = tfOps.cast(getParamValue('condition', node, tensorMap, context), 'bool');\n const result = [await tfOps.whereAsync(condition)];\n condition.dispose();\n return result;\n }\n case 'ListDiff': {\n return tfOps.setdiff1dAsync(getParamValue('x', node, tensorMap, context), getParamValue('y', node, tensorMap, context));\n }\n default:\n throw TypeError(`Node type ${node.op} is not implemented`);\n }\n};\nexport const CATEGORY = 'dynamic';\n//# 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* @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor, Tensor1D, Tensor2D} from '@tensorflow/tfjs-core';\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\n\nimport {NamedTensorsMap} from '../../data/types';\nimport {ExecutionContext} from '../../executor/execution_context';\nimport {InternalOpAsyncExecutor, Node} from '../types';\n\nimport {getParamValue} from './utils';\n\nfunction nmsParams(\n    node: Node, tensorMap: NamedTensorsMap, context: ExecutionContext) {\n  const boxes = getParamValue('boxes', node, tensorMap, context) as Tensor;\n  const scores = getParamValue('scores', node, tensorMap, context) as Tensor;\n  const maxOutputSize =\n      getParamValue('maxOutputSize', node, tensorMap, context) as number;\n  const iouThreshold =\n      getParamValue('iouThreshold', node, tensorMap, context) as number;\n  const scoreThreshold =\n      getParamValue('scoreThreshold', node, tensorMap, context) as number;\n  const softNmsSigma =\n      getParamValue('softNmsSigma', node, tensorMap, context) as number;\n\n  return {\n    boxes,\n    scores,\n    maxOutputSize,\n    iouThreshold,\n    scoreThreshold,\n    softNmsSigma\n  };\n}\n\nexport const executeOp: InternalOpAsyncExecutor = async(\n    node: Node, tensorMap: NamedTensorsMap,\n    context: ExecutionContext): Promise<Tensor[]> => {\n  switch (node.op) {\n    case 'NonMaxSuppressionV5': {\n      const {\n        boxes,\n        scores,\n        maxOutputSize,\n        iouThreshold,\n        scoreThreshold,\n        softNmsSigma\n      } = nmsParams(node, tensorMap, context);\n\n      const result = await tfOps.image.nonMaxSuppressionWithScoreAsync(\n          boxes as Tensor2D, scores as Tensor1D, maxOutputSize, iouThreshold,\n          scoreThreshold, softNmsSigma);\n\n      return [result.selectedIndices, result.selectedScores];\n    }\n    case 'NonMaxSuppressionV4': {\n      const {boxes, scores, maxOutputSize, iouThreshold, scoreThreshold} =\n          nmsParams(node, tensorMap, context);\n\n      const padToMaxOutputSize =\n          getParamValue('padToMaxOutputSize', node, tensorMap, context) as\n          boolean;\n\n      const result = await tfOps.image.nonMaxSuppressionPaddedAsync(\n          boxes as Tensor2D, scores as Tensor1D, maxOutputSize, iouThreshold,\n          scoreThreshold, padToMaxOutputSize);\n\n      return [result.selectedIndices, result.validOutputs];\n    }\n    case 'NonMaxSuppressionV3':\n    case 'NonMaxSuppressionV2': {\n      const {boxes, scores, maxOutputSize, iouThreshold, scoreThreshold} =\n          nmsParams(node, tensorMap, context);\n\n      return [await tfOps.image.nonMaxSuppressionAsync(\n          boxes as Tensor2D, scores as Tensor1D, maxOutputSize, iouThreshold,\n          scoreThreshold)];\n    }\n    case 'Where': {\n      const condition = tfOps.cast(\n          (getParamValue('condition', node, tensorMap, context) as Tensor),\n          'bool');\n      const result = [await tfOps.whereAsync(condition)];\n      condition.dispose();\n      return result;\n    }\n    case 'ListDiff': {\n      return tfOps.setdiff1dAsync(\n          getParamValue('x', node, tensorMap, context) as Tensor,\n          getParamValue('y', node, tensorMap, context) as Tensor);\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\nexport const CATEGORY = 'dynamic';\n"]}","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n switch (node.op) {\n case 'TopKV2': {\n const x = getParamValue('x', node, tensorMap, context);\n const k = getParamValue('k', node, tensorMap, context);\n const sorted = getParamValue('sorted', node, tensorMap, context);\n const result = tfOps.topk(x, k, sorted);\n return [result.values, result.indices];\n }\n case 'Unique': {\n const x = getParamValue('x', node, tensorMap, context);\n const result = tfOps.unique(x);\n return [result.values, result.indices];\n }\n case 'UniqueV2': {\n const x = getParamValue('x', node, tensorMap, context);\n const axis = getParamValue('axis', node, tensorMap, context);\n const result = tfOps.unique(x, axis);\n return [result.values, result.indices];\n }\n default:\n throw TypeError(`Node type ${node.op} is not implemented`);\n }\n};\nexport const CATEGORY = 'evaluation';\n//# 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* @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { cloneTensor, getParamValue, getTensor } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n switch (node.op) {\n case 'Const': {\n return tensorMap[node.name];\n }\n case 'PlaceholderWithDefault':\n const def = getParamValue('default', node, tensorMap, context);\n return [getTensor(node.name, tensorMap, context) || def];\n case 'Placeholder':\n return [getTensor(node.name, tensorMap, context)];\n case 'Identity':\n case 'StopGradient':\n case 'FakeQuantWithMinMaxVars': { // This op is currently ignored.\n const data = getParamValue('x', node, tensorMap, context);\n return [cloneTensor(data)];\n }\n case 'IdentityN':\n return getParamValue('x', node, tensorMap, context)\n .map((t) => cloneTensor(t));\n case 'Snapshot':\n const snapshot = getParamValue('x', node, tensorMap, context);\n return [cloneTensor(snapshot)];\n case 'Shape':\n return [tfOps.tensor1d(getParamValue('x', node, tensorMap, context).shape, 'int32')];\n case 'ShapeN':\n return getParamValue('x', node, tensorMap, context)\n .map((t) => tfOps.tensor1d(t.shape));\n case 'Size':\n return [tfOps.scalar(getParamValue('x', node, tensorMap, context).size, 'int32')];\n case 'Rank':\n return [tfOps.scalar(getParamValue('x', node, tensorMap, context).rank, 'int32')];\n case 'NoOp':\n return [tfOps.scalar(1)];\n case 'Print':\n const input = getParamValue('x', node, tensorMap, context);\n const data = getParamValue('data', node, tensorMap, context);\n const message = getParamValue('message', node, tensorMap, context);\n const summarize = getParamValue('summarize', node, tensorMap, context);\n console.warn('The graph has a tf.print() operation,' +\n 'usually used for debugging, which slows down performance.');\n console.log(message);\n for (let i = 0; i < data.length; i++) {\n console.log(Array.prototype.slice.call(data[i].dataSync())\n .slice(0, summarize));\n }\n return [input];\n default:\n throw TypeError(`Node type ${node.op} is not implemented`);\n }\n};\nexport const CATEGORY = 'graph';\n//# sourceMappingURL=data:application/json;base64,{"version":3,"file":"graph_executor.js","sourceRoot":"","sources":["../../../../../../../tfjs-converter/src/operations/executors/graph_executor.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAGH,iDAAiD;AACjD,OAAO,KAAK,KAAK,MAAM,kDAAkD,CAAC;AAM1E,OAAO,EAAC,WAAW,EAAE,aAAa,EAAE,SAAS,EAAC,MAAM,SAAS,CAAC;AAE9D,MAAM,CAAC,MAAM,SAAS,GAClB,CAAC,IAAU,EAAE,SAA0B,EACtC,OAAyB,EAAY,EAAE;IACtC,QAAQ,IAAI,CAAC,EAAE,EAAE;QACf,KAAK,OAAO,CAAC,CAAC;YACZ,OAAO,SAAS,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC;SAC7B;QACD,KAAK,wBAAwB;YAC3B,MAAM,GAAG,GACL,aAAa,CAAC,SAAS,EAAE,IAAI,EAAE,SAAS,EAAE,OAAO,CAAW,CAAC;YACjE,OAAO,CAAC,SAAS,CAAC,IAAI,CAAC,IAAI,EAAE,SAAS,EAAE,OAAO,CAAC,IAAI,GAAG,CAAC,CAAC;QAC3D,KAAK,aAAa;YAChB,OAAO,CAAC,SAAS,CAAC,IAAI,CAAC,IAAI,EAAE,SAAS,EAAE,OAAO,CAAC,CAAC,CAAC;QACpD,KAAK,UAAU,CAAC;QAChB,KAAK,cAAc,CAAC;QACpB,KAAK,yBAAyB,CAAC,CAAC,EAAG,gCAAgC;YACjE,MAAM,IAAI,GAAG,aAAa,CAAC,GAAG,EAAE,IAAI,EAAE,SAAS,EAAE,OAAO,CAAW,CAAC;YACpE,OAAO,CAAC,WAAW,CAAC,IAAI,CAAC,CAAC,CAAC;SAC5B;QACD,KAAK,WAAW;YACd,OAAQ,aAAa,CAAC,GAAG,EAAE,IAAI,EAAE,SAAS,EAAE,OAAO,CAAc;iBAC5D,GAAG,CAAC,CAAC,CAAS,EAAE,EAAE,CAAC,WAAW,CAAC,CAAC,CAAC,CAAC,CAAC;QAC1C,KAAK,UAAU;YACb,MAAM,QAAQ,GACT,aAAa,CAAC,GAAG,EAAE,IAAI,EAAE,SAAS,EAAE,OAAO,CAAY,CAAC;YAC7D,OAAO,CAAC,WAAW,CAAC,QAAQ,CAAC,CAAC,CAAC;QACjC,KAAK,OAAO;YACV,OAAO,CAAC,KAAK,CAAC,QAAQ,CACjB,aAAa,CAAC,GAAG,EAAE,IAAI,EAAE,SAAS,EAAE,OAAO,CAAY,CAAC,KAAK,EAC9D,OAAO,CAAC,CAAC,CAAC;QAChB,KAAK,QAAQ;YACX,OAAQ,aAAa,CAAC,GAAG,EAAE,IAAI,EAAE,SAAS,EAAE,OAAO,CAAc;iBAC5D,GAAG,CAAC,CAAC,CAAS,EAAE,EAAE,CAAC,KAAK,CAAC,QAAQ,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC;QACnD,KAAK,MAAM;YACT,OAAO,CAAC,KAAK,CAAC,MAAM,CACf,aAAa,CAAC,GAAG,EAAE,IAAI,EAAE,SAAS,EAAE,OAAO,CAAY,CAAC,IAAI,EAC7D,OAAO,CAAC,CAAC,CAAC;QAChB,KAAK,MAAM;YACT,OAAO,CAAC,KAAK,CAAC,MAAM,CACf,aAAa,CAAC,GAAG,EAAE,IAAI,EAAE,SAAS,EAAE,OAAO,CAAY,CAAC,IAAI,EAC7D,OAAO,CAAC,CAAC,CAAC;QAChB,KAAK,MAAM;YACT,OAAO,CAAC,KAAK,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC;QAC3B,KAAK,OAAO;YACV,MAAM,KAAK,GAAG,aAAa,CAAC,GAAG,EAAE,IAAI,EAAE,SAAS,EAAE,OAAO,CAAW,CAAC;YACrE,MAAM,IAAI,GACN,aAAa,CAAC,MAAM,EAAE,IAAI,EAAE,SAAS,EAAE,OAAO,CAAa,CAAC;YAChE,MAAM,OAAO,GACT,aAAa,CAAC,SAAS,EAAE,IAAI,EAAE,SAAS,EAAE,OAAO,CAAW,CAAC;YACjE,MAAM,SAAS,GACX,aAAa,CAAC,WAAW,EAAE,IAAI,EAAE,SAAS,EAAE,OAAO,CAAW,CAAC;YACnE,OAAO,CAAC,IAAI,CACR,uCAAuC;gBACvC,2DAA2D,CAAC,CAAC;YACjE,OAAO,CAAC,GAAG,CAAC,OAAO,CAAC,CAAC;YACrB,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,IAAI,CAAC,MAAM,EAAE,CAAC,EAAE,EAAE;gBACpC,OAAO,CAAC,GAAG,CAAC,KAAK,CAAC,SAAS,CAAC,KAAK,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,QAAQ,EAAE,CAAC;qBACzC,KAAK,CAAC,CAAC,EAAE,SAAS,CAAC,CAAC,CAAC;aACvC;YACD,OAAO,CAAC,KAAK,CAAC,CAAC;QAEjB;YACE,MAAM,SAAS,CAAC,aAAa,IAAI,CAAC,EAAE,qBAAqB,CAAC,CAAC;KAC9D;AACH,CAAC,CAAC;AAEN,MAAM,CAAC,MAAM,QAAQ,GAAG,OAAO,CAAC","sourcesContent":["/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '@tensorflow/tfjs-core';\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\n\nimport {NamedTensorsMap} from '../../data/types';\nimport {ExecutionContext} from '../../executor/execution_context';\nimport {InternalOpExecutor, Node} from '../types';\n\nimport {cloneTensor, getParamValue, getTensor} from './utils';\n\nexport const executeOp: InternalOpExecutor =\n    (node: Node, tensorMap: NamedTensorsMap,\n     context: ExecutionContext): Tensor[] => {\n      switch (node.op) {\n        case 'Const': {\n          return tensorMap[node.name];\n        }\n        case 'PlaceholderWithDefault':\n          const def =\n              getParamValue('default', node, tensorMap, context) as Tensor;\n          return [getTensor(node.name, tensorMap, context) || def];\n        case 'Placeholder':\n          return [getTensor(node.name, tensorMap, context)];\n        case 'Identity':\n        case 'StopGradient':\n        case 'FakeQuantWithMinMaxVars': {  // This op is currently ignored.\n          const data = getParamValue('x', node, tensorMap, context) as Tensor;\n          return [cloneTensor(data)];\n        }\n        case 'IdentityN':\n          return (getParamValue('x', node, tensorMap, context) as Tensor[])\n              .map((t: Tensor) => cloneTensor(t));\n        case 'Snapshot':\n          const snapshot =\n              (getParamValue('x', node, tensorMap, context) as Tensor);\n          return [cloneTensor(snapshot)];\n        case 'Shape':\n          return [tfOps.tensor1d(\n              (getParamValue('x', node, tensorMap, context) as Tensor).shape,\n              'int32')];\n        case 'ShapeN':\n          return (getParamValue('x', node, tensorMap, context) as Tensor[])\n              .map((t: Tensor) => tfOps.tensor1d(t.shape));\n        case 'Size':\n          return [tfOps.scalar(\n              (getParamValue('x', node, tensorMap, context) as Tensor).size,\n              'int32')];\n        case 'Rank':\n          return [tfOps.scalar(\n              (getParamValue('x', node, tensorMap, context) as Tensor).rank,\n              'int32')];\n        case 'NoOp':\n          return [tfOps.scalar(1)];\n        case 'Print':\n          const input = getParamValue('x', node, tensorMap, context) as Tensor;\n          const data =\n              getParamValue('data', node, tensorMap, context) as Tensor[];\n          const message =\n              getParamValue('message', node, tensorMap, context) as string;\n          const summarize =\n              getParamValue('summarize', node, tensorMap, context) as number;\n          console.warn(\n              'The graph has a tf.print() operation,' +\n              'usually used for debugging, which slows down performance.');\n          console.log(message);\n          for (let i = 0; i < data.length; i++) {\n            console.log(Array.prototype.slice.call(data[i].dataSync())\n                            .slice(0, summarize));\n          }\n          return [input];\n\n        default:\n          throw TypeError(`Node type ${node.op} is not implemented`);\n      }\n    };\n\nexport const CATEGORY = 'graph';\n"]}","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { keep, scalar, stack, tidy, unstack, util } from '@tensorflow/tfjs-core';\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\n/**\n * Hashtable contains a set of tensors, which can be accessed by key.\n */\nexport class HashTable {\n /**\n * Constructor of HashTable. Creates a hash table.\n *\n * @param keyDType `dtype` of the table keys.\n * @param valueDType `dtype` of the table values.\n */\n constructor(keyDType, valueDType) {\n this.keyDType = keyDType;\n this.valueDType = valueDType;\n this.handle = scalar(0);\n // tslint:disable-next-line: no-any\n this.tensorMap = new Map();\n keep(this.handle);\n }\n get id() {\n return this.handle.id;\n }\n /**\n * Dispose the tensors and handle and clear the hashtable.\n */\n clearAndClose() {\n this.tensorMap.forEach(value => value.dispose());\n this.tensorMap.clear();\n this.handle.dispose();\n }\n /**\n * The number of items in the hash table.\n */\n size() {\n return this.tensorMap.size;\n }\n /**\n * The number of items in the hash table as a rank-0 tensor.\n */\n tensorSize() {\n return tfOps.scalar(this.size(), 'int32');\n }\n /**\n * Replaces the contents of the table with the specified keys and values.\n * @param keys Keys to store in the hashtable.\n * @param values Values to store in the hashtable.\n */\n async import(keys, values) {\n this.checkKeyAndValueTensor(keys, values);\n // We only store the primitive values of the keys, this allows lookup\n // to be O(1).\n const $keys = await keys.data();\n // Clear the hashTable before inserting new values.\n this.tensorMap.forEach(value => value.dispose());\n this.tensorMap.clear();\n return tidy(() => {\n const $values = unstack(values);\n const keysLength = $keys.length;\n const valuesLength = $values.length;\n util.assert(keysLength === valuesLength, () => `The number of elements doesn't match, keys has ` +\n `${keysLength} elements, the values has ${valuesLength} ` +\n `elements.`);\n for (let i = 0; i < keysLength; i++) {\n const key = $keys[i];\n const value = $values[i];\n keep(value);\n this.tensorMap.set(key, value);\n }\n return this.handle;\n });\n }\n /**\n * Looks up keys in a hash table, outputs the corresponding values.\n *\n * Performs batch lookups, for every element in the key tensor, `find`\n * stacks the corresponding value into the return tensor.\n *\n * If an element is not present in the table, the given `defaultValue` is\n * used.\n *\n * @param keys Keys to look up. Must have the same type as the keys of the\n * table.\n * @param defaultValue The scalar `defaultValue` is the value output for keys\n * not present in the table. It must also be of the same type as the\n * table values.\n */\n async find(keys, defaultValue) {\n this.checkKeyAndValueTensor(keys, defaultValue);\n const $keys = await keys.data();\n return tidy(() => {\n const result = [];\n for (let i = 0; i < $keys.length; i++) {\n const key = $keys[i];\n const value = this.findWithDefault(key, defaultValue);\n result.push(value);\n }\n return stack(result);\n });\n }\n // tslint:disable-next-line: no-any\n findWithDefault(key, defaultValue) {\n const result = this.tensorMap.get(key);\n return result != null ? result : defaultValue;\n }\n checkKeyAndValueTensor(key, value) {\n if (key.dtype !== this.keyDType) {\n throw new Error(`Expect key dtype ${this.keyDType}, but got ` +\n `${key.dtype}`);\n }\n if (value.dtype !== this.valueDType) {\n throw new Error(`Expect value dtype ${this.valueDType}, but got ` +\n `${value.dtype}`);\n }\n }\n}\n//# sourceMappingURL=data:application/json;base64,{"version":3,"file":"hash_table.js","sourceRoot":"","sources":["../../../../../../tfjs-converter/src/executor/hash_table.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AACH,OAAO,EAAW,IAAI,EAAE,MAAM,EAAE,KAAK,EAAU,IAAI,EAAE,OAAO,EAAE,IAAI,EAAC,MAAM,uBAAuB,CAAC;AACjG,iDAAiD;AACjD,OAAO,KAAK,KAAK,MAAM,kDAAkD,CAAC;AAE1E;;GAEG;AACH,MAAM,OAAO,SAAS;IAUpB;;;;;OAKG;IACH,YAAqB,QAAkB,EAAW,UAAoB;QAAjD,aAAQ,GAAR,QAAQ,CAAU;QAAW,eAAU,GAAV,UAAU,CAAU;QACpE,IAAI,CAAC,MAAM,GAAG,MAAM,CAAC,CAAC,CAAC,CAAC;QACxB,mCAAmC;QACnC,IAAI,CAAC,SAAS,GAAG,IAAI,GAAG,EAAe,CAAC;QAExC,IAAI,CAAC,IAAI,CAAC,MAAM,CAAC,CAAC;IACpB,CAAC;IAhBD,IAAI,EAAE;QACJ,OAAO,IAAI,CAAC,MAAM,CAAC,EAAE,CAAC;IACxB,CAAC;IAgBD;;OAEG;IACH,aAAa;QACX,IAAI,CAAC,SAAS,CAAC,OAAO,CAAC,KAAK,CAAC,EAAE,CAAC,KAAK,CAAC,OAAO,EAAE,CAAC,CAAC;QACjD,IAAI,CAAC,SAAS,CAAC,KAAK,EAAE,CAAC;QACvB,IAAI,CAAC,MAAM,CAAC,OAAO,EAAE,CAAC;IACxB,CAAC;IAED;;OAEG;IACH,IAAI;QACF,OAAO,IAAI,CAAC,SAAS,CAAC,IAAI,CAAC;IAC7B,CAAC;IAED;;OAEG;IACH,UAAU;QACR,OAAO,KAAK,CAAC,MAAM,CAAC,IAAI,CAAC,IAAI,EAAE,EAAE,OAAO,CAAC,CAAC;IAC5C,CAAC;IAED;;;;OAIG;IACH,KAAK,CAAC,MAAM,CAAC,IAAY,EAAE,MAAc;QACvC,IAAI,CAAC,sBAAsB,CAAC,IAAI,EAAE,MAAM,CAAC,CAAC;QAE1C,qEAAqE;QACrE,cAAc;QACd,MAAM,KAAK,GAAG,MAAM,IAAI,CAAC,IAAI,EAAE,CAAC;QAEhC,mDAAmD;QACnD,IAAI,CAAC,SAAS,CAAC,OAAO,CAAC,KAAK,CAAC,EAAE,CAAC,KAAK,CAAC,OAAO,EAAE,CAAC,CAAC;QACjD,IAAI,CAAC,SAAS,CAAC,KAAK,EAAE,CAAC;QAEvB,OAAO,IAAI,CAAC,GAAG,EAAE;YACf,MAAM,OAAO,GAAG,OAAO,CAAC,MAAM,CAAC,CAAC;YAEhC,MAAM,UAAU,GAAG,KAAK,CAAC,MAAM,CAAC;YAChC,MAAM,YAAY,GAAG,OAAO,CAAC,MAAM,CAAC;YAEpC,IAAI,CAAC,MAAM,CACP,UAAU,KAAK,YAAY,EAC3B,GAAG,EAAE,CAAC,iDAAiD;gBACnD,GAAG,UAAU,6BAA6B,YAAY,GAAG;gBACzD,WAAW,CAAC,CAAC;YAErB,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,UAAU,EAAE,CAAC,EAAE,EAAE;gBACnC,MAAM,GAAG,GAAG,KAAK,CAAC,CAAC,CAAC,CAAC;gBACrB,MAAM,KAAK,GAAG,OAAO,CAAC,CAAC,CAAC,CAAC;gBAEzB,IAAI,CAAC,KAAK,CAAC,CAAC;gBACZ,IAAI,CAAC,SAAS,CAAC,GAAG,CAAC,GAAG,EAAE,KAAK,CAAC,CAAC;aAChC;YAED,OAAO,IAAI,CAAC,MAAM,CAAC;QACrB,CAAC,CAAC,CAAC;IACL,CAAC;IAED;;;;;;;;;;;;;;OAcG;IACH,KAAK,CAAC,IAAI,CAAC,IAAY,EAAE,YAAoB;QAC3C,IAAI,CAAC,sBAAsB,CAAC,IAAI,EAAE,YAAY,CAAC,CAAC;QAEhD,MAAM,KAAK,GAAG,MAAM,IAAI,CAAC,IAAI,EAAE,CAAC;QAEhC,OAAO,IAAI,CAAC,GAAG,EAAE;YACf,MAAM,MAAM,GAAa,EAAE,CAAC;YAE5B,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,KAAK,CAAC,MAAM,EAAE,CAAC,EAAE,EAAE;gBACrC,MAAM,GAAG,GAAG,KAAK,CAAC,CAAC,CAAC,CAAC;gBAErB,MAAM,KAAK,GAAG,IAAI,CAAC,eAAe,CAAC,GAAG,EAAE,YAAY,CAAC,CAAC;gBACtD,MAAM,CAAC,IAAI,CAAC,KAAK,CAAC,CAAC;aACpB;YAED,OAAO,KAAK,CAAC,MAAM,CAAC,CAAC;QACvB,CAAC,CAAC,CAAC;IACL,CAAC;IAED,mCAAmC;IAC3B,eAAe,CAAC,GAAQ,EAAE,YAAoB;QACpD,MAAM,MAAM,GAAG,IAAI,CAAC,SAAS,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC;QAEvC,OAAO,MAAM,IAAI,IAAI,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,YAAY,CAAC;IAChD,CAAC;IAEO,sBAAsB,CAAC,GAAW,EAAE,KAAa;QACvD,IAAI,GAAG,CAAC,KAAK,KAAK,IAAI,CAAC,QAAQ,EAAE;YAC/B,MAAM,IAAI,KAAK,CACX,oBAAoB,IAAI,CAAC,QAAQ,YAAY;gBAC7C,GAAG,GAAG,CAAC,KAAK,EAAE,CAAC,CAAC;SACrB;QAED,IAAI,KAAK,CAAC,KAAK,KAAK,IAAI,CAAC,UAAU,EAAE;YACnC,MAAM,IAAI,KAAK,CACX,sBAAsB,IAAI,CAAC,UAAU,YAAY;gBACjD,GAAG,KAAK,CAAC,KAAK,EAAE,CAAC,CAAC;SACvB;IACH,CAAC;CACF","sourcesContent":["/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {DataType, keep, scalar, stack, Tensor, tidy, unstack, util} from '@tensorflow/tfjs-core';\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\n\n/**\n * Hashtable contains a set of tensors, which can be accessed by key.\n */\nexport class HashTable {\n  readonly handle: Tensor;\n\n  // tslint:disable-next-line: no-any\n  private tensorMap: Map<any, Tensor>;\n\n  get id() {\n    return this.handle.id;\n  }\n\n  /**\n   * Constructor of HashTable. Creates a hash table.\n   *\n   * @param keyDType `dtype` of the table keys.\n   * @param valueDType `dtype` of the table values.\n   */\n  constructor(readonly keyDType: DataType, readonly valueDType: DataType) {\n    this.handle = scalar(0);\n    // tslint:disable-next-line: no-any\n    this.tensorMap = new Map<any, Tensor>();\n\n    keep(this.handle);\n  }\n\n  /**\n   * Dispose the tensors and handle and clear the hashtable.\n   */\n  clearAndClose() {\n    this.tensorMap.forEach(value => value.dispose());\n    this.tensorMap.clear();\n    this.handle.dispose();\n  }\n\n  /**\n   * The number of items in the hash table.\n   */\n  size(): number {\n    return this.tensorMap.size;\n  }\n\n  /**\n   * The number of items in the hash table as a rank-0 tensor.\n   */\n  tensorSize(): Tensor {\n    return tfOps.scalar(this.size(), 'int32');\n  }\n\n  /**\n   * Replaces the contents of the table with the specified keys and values.\n   * @param keys Keys to store in the hashtable.\n   * @param values Values to store in the hashtable.\n   */\n  async import(keys: Tensor, values: Tensor): Promise<Tensor> {\n    this.checkKeyAndValueTensor(keys, values);\n\n    // We only store the primitive values of the keys, this allows lookup\n    // to be O(1).\n    const $keys = await keys.data();\n\n    // Clear the hashTable before inserting new values.\n    this.tensorMap.forEach(value => value.dispose());\n    this.tensorMap.clear();\n\n    return tidy(() => {\n      const $values = unstack(values);\n\n      const keysLength = $keys.length;\n      const valuesLength = $values.length;\n\n      util.assert(\n          keysLength === valuesLength,\n          () => `The number of elements doesn't match, keys has ` +\n              `${keysLength} elements, the values has ${valuesLength} ` +\n              `elements.`);\n\n      for (let i = 0; i < keysLength; i++) {\n        const key = $keys[i];\n        const value = $values[i];\n\n        keep(value);\n        this.tensorMap.set(key, value);\n      }\n\n      return this.handle;\n    });\n  }\n\n  /**\n   * Looks up keys in a hash table, outputs the corresponding values.\n   *\n   * Performs batch lookups, for every element in the key tensor, `find`\n   * stacks the corresponding value into the return tensor.\n   *\n   * If an element is not present in the table, the given `defaultValue` is\n   * used.\n   *\n   * @param keys Keys to look up. Must have the same type as the keys of the\n   *     table.\n   * @param defaultValue The scalar `defaultValue` is the value output for keys\n   *     not present in the table. It must also be of the same type as the\n   *     table values.\n   */\n  async find(keys: Tensor, defaultValue: Tensor): Promise<Tensor> {\n    this.checkKeyAndValueTensor(keys, defaultValue);\n\n    const $keys = await keys.data();\n\n    return tidy(() => {\n      const result: Tensor[] = [];\n\n      for (let i = 0; i < $keys.length; i++) {\n        const key = $keys[i];\n\n        const value = this.findWithDefault(key, defaultValue);\n        result.push(value);\n      }\n\n      return stack(result);\n    });\n  }\n\n  // tslint:disable-next-line: no-any\n  private findWithDefault(key: any, defaultValue: Tensor): Tensor {\n    const result = this.tensorMap.get(key);\n\n    return result != null ? result : defaultValue;\n  }\n\n  private checkKeyAndValueTensor(key: Tensor, value: Tensor) {\n    if (key.dtype !== this.keyDType) {\n      throw new Error(\n          `Expect key dtype ${this.keyDType}, but got ` +\n          `${key.dtype}`);\n    }\n\n    if (value.dtype !== this.valueDType) {\n      throw new Error(\n          `Expect value dtype ${this.valueDType}, but got ` +\n          `${value.dtype}`);\n    }\n  }\n}\n"]}","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { HashTable } from '../../executor/hash_table';\nimport { getParamValue } from './utils';\nexport const executeOp = async (node, tensorMap, context, resourceManager) => {\n switch (node.op) {\n case 'HashTable':\n case 'HashTableV2': {\n const keyDType = getParamValue('keyDType', node, tensorMap, context);\n const valueDType = getParamValue('valueDType', node, tensorMap, context);\n const hashTable = new HashTable(keyDType, valueDType);\n resourceManager.addHashTable(node.name, hashTable);\n return [hashTable.handle];\n }\n case 'LookupTableImport':\n case 'LookupTableImportV2': {\n const handle = getParamValue('tableHandle', node, tensorMap, context, resourceManager);\n const keys = getParamValue('keys', node, tensorMap, context);\n const values = getParamValue('values', node, tensorMap, context);\n const hashTable = resourceManager.getHashTableById(handle.id);\n return [await hashTable.import(keys, values)];\n }\n case 'LookupTableFind':\n case 'LookupTableFindV2': {\n const handle = getParamValue('tableHandle', node, tensorMap, context, resourceManager);\n const keys = getParamValue('keys', node, tensorMap, context);\n const defaultValue = getParamValue('defaultValue', node, tensorMap, context);\n const hashTable = resourceManager.getHashTableById(handle.id);\n return [await hashTable.find(keys, defaultValue)];\n }\n case 'LookupTableSize':\n case 'LookupTableSizeV2': {\n const handle = getParamValue('tableHandle', node, tensorMap, context, resourceManager);\n const hashTable = resourceManager.getHashTableById(handle.id);\n return [hashTable.tensorSize()];\n }\n default:\n throw TypeError(`Node type ${node.op} is not implemented`);\n }\n};\nexport const CATEGORY = 'hash_table';\n//# 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* @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n switch (node.op) {\n case 'ResizeBilinear': {\n const images = getParamValue('images', node, tensorMap, context);\n const size = getParamValue('size', node, tensorMap, context);\n const alignCorners = getParamValue('alignCorners', node, tensorMap, context);\n const halfPixelCenters = getParamValue('halfPixelCenters', node, tensorMap, context);\n return [tfOps.image.resizeBilinear(images, [size[0], size[1]], alignCorners, halfPixelCenters)];\n }\n case 'ResizeNearestNeighbor': {\n const images = getParamValue('images', node, tensorMap, context);\n const size = getParamValue('size', node, tensorMap, context);\n const alignCorners = getParamValue('alignCorners', node, tensorMap, context);\n const halfPixelCenters = getParamValue('halfPixelCenters', node, tensorMap, context);\n return [tfOps.image.resizeNearestNeighbor(images, [size[0], size[1]], alignCorners, halfPixelCenters)];\n }\n case 'CropAndResize': {\n const image = getParamValue('image', node, tensorMap, context);\n const boxes = getParamValue('boxes', node, tensorMap, context);\n const boxInd = getParamValue('boxInd', node, tensorMap, context);\n const cropSize = getParamValue('cropSize', node, tensorMap, context);\n const method = getParamValue('method', node, tensorMap, context);\n const extrapolationValue = getParamValue('extrapolationValue', node, tensorMap, context);\n return [tfOps.image.cropAndResize(image, boxes, boxInd, cropSize, method, extrapolationValue)];\n }\n case 'ImageProjectiveTransformV3': {\n const images = getParamValue('images', node, tensorMap, context);\n const transforms = getParamValue('transforms', node, tensorMap, context);\n const outputShape = getParamValue('outputShape', node, tensorMap, context);\n const fillValue = getParamValue('fillValue', node, tensorMap, context);\n const interpolation = getParamValue('interpolation', node, tensorMap, context);\n const fillMode = getParamValue('fillMode', node, tensorMap, context);\n return [tfOps.image.transform(images, transforms, interpolation.toLowerCase(), fillMode.toLowerCase(), fillValue, outputShape)];\n }\n default:\n throw TypeError(`Node type ${node.op} is not implemented`);\n }\n};\nexport const CATEGORY = 'image';\n//# 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* @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D} from '@tensorflow/tfjs-core';\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\n\nimport {NamedTensorsMap} from '../../data/types';\nimport {ExecutionContext} from '../../executor/execution_context';\nimport {InternalOpExecutor, Node} from '../types';\n\nimport {getParamValue} from './utils';\n\nexport const executeOp: InternalOpExecutor =\n    (node: Node, tensorMap: NamedTensorsMap,\n     context: ExecutionContext): Tensor[] => {\n      switch (node.op) {\n        case 'ResizeBilinear': {\n          const images =\n              getParamValue('images', node, tensorMap, context) as Tensor;\n          const size =\n              getParamValue('size', node, tensorMap, context) as number[];\n          const alignCorners =\n              getParamValue('alignCorners', node, tensorMap, context) as\n              boolean;\n          const halfPixelCenters =\n              getParamValue('halfPixelCenters', node, tensorMap, context) as\n              boolean;\n          return [tfOps.image.resizeBilinear(\n              images as Tensor3D | Tensor4D, [size[0], size[1]], alignCorners,\n              halfPixelCenters)];\n        }\n        case 'ResizeNearestNeighbor': {\n          const images =\n              getParamValue('images', node, tensorMap, context) as Tensor;\n          const size =\n              getParamValue('size', node, tensorMap, context) as number[];\n          const alignCorners =\n              getParamValue('alignCorners', node, tensorMap, context) as\n              boolean;\n          const halfPixelCenters =\n              getParamValue('halfPixelCenters', node, tensorMap, context) as\n              boolean;\n          return [tfOps.image.resizeNearestNeighbor(\n              images as Tensor3D | Tensor4D, [size[0], size[1]], alignCorners,\n              halfPixelCenters)];\n        }\n        case 'CropAndResize': {\n          const image =\n              getParamValue('image', node, tensorMap, context) as Tensor;\n          const boxes =\n              getParamValue('boxes', node, tensorMap, context) as Tensor;\n          const boxInd =\n              getParamValue('boxInd', node, tensorMap, context) as Tensor;\n          const cropSize =\n              getParamValue('cropSize', node, tensorMap, context) as number[];\n          const method =\n              getParamValue('method', node, tensorMap, context) as string;\n          const extrapolationValue =\n              getParamValue('extrapolationValue', node, tensorMap, context) as\n              number;\n          return [tfOps.image.cropAndResize(\n              image as Tensor4D, boxes as Tensor2D, boxInd as Tensor1D,\n              cropSize as [number, number], method as 'bilinear' | 'nearest',\n              extrapolationValue)];\n        }\n        case 'ImageProjectiveTransformV3': {\n          const images =\n              getParamValue('images', node, tensorMap, context) as Tensor;\n          const transforms =\n              getParamValue('transforms', node, tensorMap, context) as Tensor;\n          const outputShape =\n              getParamValue('outputShape', node, tensorMap, context) as\n              number[];\n          const fillValue =\n              getParamValue('fillValue', node, tensorMap, context) as number;\n          const interpolation =\n              getParamValue('interpolation', node, tensorMap, context) as\n              string;\n          const fillMode =\n              getParamValue('fillMode', node, tensorMap, context) as string;\n          return [tfOps.image.transform(\n              images as Tensor4D,\n              transforms as Tensor2D,\n              interpolation.toLowerCase() as 'bilinear' | 'nearest',\n              fillMode.toLowerCase() as 'constant' | 'reflect' | 'wrap' | 'nearest',\n              fillValue,\n              outputShape as [number, number])];\n        }\n        default:\n          throw TypeError(`Node type ${node.op} is not implemented`);\n      }\n    };\n\nexport const CATEGORY = 'image';\n"]}","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n switch (node.op) {\n case 'Equal': {\n return [tfOps.equal(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n }\n case 'NotEqual': {\n return [tfOps.notEqual(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n }\n case 'Greater': {\n return [tfOps.greater(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n }\n case 'GreaterEqual': {\n return [tfOps.greaterEqual(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n }\n case 'Less': {\n return [tfOps.less(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n }\n case 'LessEqual': {\n return [tfOps.lessEqual(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n }\n case 'LogicalAnd': {\n return [tfOps.logicalAnd(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n }\n case 'LogicalNot': {\n return [tfOps.logicalNot(getParamValue('a', node, tensorMap, context))];\n }\n case 'LogicalOr': {\n return [tfOps.logicalOr(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n }\n case 'Select':\n case 'SelectV2': {\n return [tfOps.where(getParamValue('condition', node, tensorMap, context), getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n }\n default:\n throw TypeError(`Node type ${node.op} is not implemented`);\n }\n};\nexport const CATEGORY = 'logical';\n//# 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* @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '@tensorflow/tfjs-core';\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\n\nimport {NamedTensorsMap} from '../../data/types';\nimport {ExecutionContext} from '../../executor/execution_context';\nimport {InternalOpExecutor, Node} from '../types';\n\nimport {getParamValue} from './utils';\n\nexport const executeOp: InternalOpExecutor =\n    (node: Node, tensorMap: NamedTensorsMap,\n     context: ExecutionContext): Tensor[] => {\n      switch (node.op) {\n        case 'Equal': {\n          return [tfOps.equal(\n              getParamValue('a', node, tensorMap, context) as Tensor,\n              getParamValue('b', node, tensorMap, context) as Tensor)];\n        }\n        case 'NotEqual': {\n          return [tfOps.notEqual(\n              getParamValue('a', node, tensorMap, context) as Tensor,\n              getParamValue('b', node, tensorMap, context) as Tensor)];\n        }\n        case 'Greater': {\n          return [tfOps.greater(\n              getParamValue('a', node, tensorMap, context) as Tensor,\n              getParamValue('b', node, tensorMap, context) as Tensor)];\n        }\n        case 'GreaterEqual': {\n          return [tfOps.greaterEqual(\n              getParamValue('a', node, tensorMap, context) as Tensor,\n              getParamValue('b', node, tensorMap, context) as Tensor)];\n        }\n        case 'Less': {\n          return [tfOps.less(\n              getParamValue('a', node, tensorMap, context) as Tensor,\n              getParamValue('b', node, tensorMap, context) as Tensor)];\n        }\n        case 'LessEqual': {\n          return [tfOps.lessEqual(\n              getParamValue('a', node, tensorMap, context) as Tensor,\n              getParamValue('b', node, tensorMap, context) as Tensor)];\n        }\n        case 'LogicalAnd': {\n          return [tfOps.logicalAnd(\n              getParamValue('a', node, tensorMap, context) as Tensor,\n              getParamValue('b', node, tensorMap, context) as Tensor)];\n        }\n        case 'LogicalNot': {\n          return [tfOps.logicalNot(\n              getParamValue('a', node, tensorMap, context) as Tensor)];\n        }\n        case 'LogicalOr': {\n          return [tfOps.logicalOr(\n              getParamValue('a', node, tensorMap, context) as Tensor,\n              getParamValue('b', node, tensorMap, context) as Tensor)];\n        }\n        case 'Select':\n        case 'SelectV2': {\n          return [tfOps.where(\n              getParamValue('condition', node, tensorMap, context) as Tensor,\n              getParamValue('a', node, tensorMap, context) as Tensor,\n              getParamValue('b', node, tensorMap, context) as Tensor)];\n        }\n        default:\n          throw TypeError(`Node type ${node.op} is not implemented`);\n      }\n    };\n\nexport const CATEGORY = 'logical';\n"]}","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n switch (node.op) {\n case 'BatchMatMul':\n case 'BatchMatMulV2':\n case 'MatMul':\n return [tfOps.matMul(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context), getParamValue('transposeA', node, tensorMap, context), getParamValue('transposeB', node, tensorMap, context))];\n case 'Einsum':\n return [tfOps.einsum(getParamValue('equation', node, tensorMap, context), ...getParamValue('tensors', node, tensorMap, context))];\n case 'Transpose':\n return [tfOps.transpose(getParamValue('x', node, tensorMap, context), getParamValue('perm', node, tensorMap, context))];\n case '_FusedMatMul':\n const [extraOp, activationFunc] = getParamValue('fusedOps', node, tensorMap, context);\n const isBiasAdd = extraOp === 'biasadd';\n const isPrelu = activationFunc === 'prelu';\n const numArgs = getParamValue('numArgs', node, tensorMap, context);\n const leakyreluAlpha = getParamValue('leakyreluAlpha', node, tensorMap, context);\n if (isBiasAdd) {\n if (isPrelu && numArgs !== 2) {\n throw new Error('Fused MatMul with BiasAdd and Prelu must have two ' +\n 'extra arguments: bias and alpha.');\n }\n if (!isPrelu && numArgs !== 1) {\n throw new Error('Fused MatMul with BiasAdd must have one extra argument: bias.');\n }\n }\n const [biasArg, preluArg] = getParamValue('args', node, tensorMap, context);\n return [tfOps.fused.matMul({\n a: getParamValue('a', node, tensorMap, context),\n b: getParamValue('b', node, tensorMap, context),\n transposeA: getParamValue('transposeA', node, tensorMap, context),\n transposeB: getParamValue('transposeB', node, tensorMap, context),\n bias: biasArg,\n activation: activationFunc,\n preluActivationWeights: preluArg,\n leakyreluAlpha\n })];\n default:\n throw TypeError(`Node type ${node.op} is not implemented`);\n }\n};\nexport const CATEGORY = 'matrices';\n//# 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* @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor, Tensor2D} from '@tensorflow/tfjs-core';\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\n\nimport {NamedTensorsMap} from '../../data/types';\nimport {ExecutionContext} from '../../executor/execution_context';\nimport {InternalOpExecutor, Node} from '../types';\n\nimport {getParamValue} from './utils';\n\nexport const executeOp: InternalOpExecutor =\n    (node: Node, tensorMap: NamedTensorsMap,\n     context: ExecutionContext): Tensor[] => {\n      switch (node.op) {\n        case 'BatchMatMul':\n        case 'BatchMatMulV2':\n        case 'MatMul':\n          return [tfOps.matMul(\n              getParamValue('a', node, tensorMap, context) as Tensor2D,\n              getParamValue('b', node, tensorMap, context) as Tensor2D,\n              getParamValue('transposeA', node, tensorMap, context) as boolean,\n              getParamValue('transposeB', node, tensorMap, context) as\n                  boolean)];\n\n        case 'Einsum':\n          return [tfOps.einsum(\n              getParamValue('equation', node, tensorMap, context) as string,\n              ...getParamValue('tensors', node, tensorMap, context) as\n                  Tensor[])];\n\n        case 'Transpose':\n          return [tfOps.transpose(\n              getParamValue('x', node, tensorMap, context) as Tensor,\n              getParamValue('perm', node, tensorMap, context) as number[])];\n\n        case '_FusedMatMul':\n          const [extraOp, activationFunc] =\n              (getParamValue('fusedOps', node, tensorMap, context) as string[]);\n\n          const isBiasAdd = extraOp === 'biasadd';\n          const isPrelu = activationFunc === 'prelu';\n\n          const numArgs =\n              (getParamValue('numArgs', node, tensorMap, context) as number);\n          const leakyreluAlpha =\n              getParamValue('leakyreluAlpha', node, tensorMap, context) as\n              number;\n\n          if (isBiasAdd) {\n            if (isPrelu && numArgs !== 2) {\n              throw new Error(\n                  'Fused MatMul with BiasAdd and Prelu must have two ' +\n                  'extra arguments: bias and alpha.');\n            }\n            if (!isPrelu && numArgs !== 1) {\n              throw new Error(\n                  'Fused MatMul with BiasAdd must have one extra argument: bias.');\n            }\n          }\n          const [biasArg, preluArg] =\n              getParamValue('args', node, tensorMap, context) as Tensor[];\n          return [tfOps.fused.matMul({\n            a: getParamValue('a', node, tensorMap, context) as Tensor2D,\n            b: getParamValue('b', node, tensorMap, context) as Tensor2D,\n            transposeA: getParamValue('transposeA', node, tensorMap, context) as\n                boolean,\n            transposeB: getParamValue('transposeB', node, tensorMap, context) as\n                boolean,\n            bias: biasArg,\n            activation: activationFunc as tfOps.fused.Activation,\n            preluActivationWeights: preluArg,\n            leakyreluAlpha\n          })];\n\n        default:\n          throw TypeError(`Node type ${node.op} is not implemented`);\n      }\n    };\n\nexport const CATEGORY = 'matrices';\n"]}","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n switch (node.op) {\n case 'FusedBatchNorm':\n case 'FusedBatchNormV2': {\n return [tfOps.batchNorm(getParamValue('x', node, tensorMap, context), getParamValue('mean', node, tensorMap, context), getParamValue('variance', node, tensorMap, context), getParamValue('offset', node, tensorMap, context), getParamValue('scale', node, tensorMap, context), getParamValue('epsilon', node, tensorMap, context))];\n }\n case 'FusedBatchNormV3': {\n return [tfOps.batchNorm(getParamValue('x', node, tensorMap, context), getParamValue('mean', node, tensorMap, context), getParamValue('variance', node, tensorMap, context), getParamValue('offset', node, tensorMap, context), getParamValue('scale', node, tensorMap, context), getParamValue('epsilon', node, tensorMap, context))];\n }\n case 'LRN': {\n return [tfOps.localResponseNormalization(getParamValue('x', node, tensorMap, context), getParamValue('radius', node, tensorMap, context), getParamValue('bias', node, tensorMap, context), getParamValue('alpha', node, tensorMap, context), getParamValue('beta', node, tensorMap, context))];\n }\n case 'Softmax': {\n return [tfOps.softmax(getParamValue('x', node, tensorMap, context))];\n }\n case 'LogSoftmax': {\n return [tfOps.logSoftmax(getParamValue('x', node, tensorMap, context))];\n }\n case 'SparseToDense': {\n return [tfOps.sparseToDense(getParamValue('sparseIndices', node, tensorMap, context), getParamValue('outputShape', node, tensorMap, context), getParamValue('sparseValues', node, tensorMap, context), getParamValue('defaultValue', node, tensorMap, context))];\n }\n default:\n throw TypeError(`Node type ${node.op} is not implemented`);\n }\n};\nexport const CATEGORY = 'normalization';\n//# 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* @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Scalar, Tensor, Tensor3D, Tensor4D} from '@tensorflow/tfjs-core';\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\n\nimport {NamedTensorsMap} from '../../data/types';\nimport {ExecutionContext} from '../../executor/execution_context';\nimport {InternalOpExecutor, Node} from '../types';\n\nimport {getParamValue} from './utils';\n\nexport const executeOp: InternalOpExecutor =\n    (node: Node, tensorMap: NamedTensorsMap,\n     context: ExecutionContext): Tensor[] => {\n      switch (node.op) {\n        case 'FusedBatchNorm':\n        case 'FusedBatchNormV2': {\n          return [tfOps.batchNorm(\n              getParamValue('x', node, tensorMap, context) as Tensor,\n              getParamValue('mean', node, tensorMap, context) as Tensor,\n              getParamValue('variance', node, tensorMap, context) as Tensor,\n              getParamValue('offset', node, tensorMap, context) as Tensor,\n              getParamValue('scale', node, tensorMap, context) as Tensor,\n              getParamValue('epsilon', node, tensorMap, context) as number)];\n        }\n        case 'FusedBatchNormV3': {\n          return [tfOps.batchNorm(\n              getParamValue('x', node, tensorMap, context) as Tensor,\n              getParamValue('mean', node, tensorMap, context) as Tensor,\n              getParamValue('variance', node, tensorMap, context) as Tensor,\n              getParamValue('offset', node, tensorMap, context) as Tensor,\n              getParamValue('scale', node, tensorMap, context) as Tensor,\n              getParamValue('epsilon', node, tensorMap, context) as number)];\n        }\n        case 'LRN': {\n          return [tfOps.localResponseNormalization(\n              getParamValue('x', node, tensorMap, context) as Tensor3D |\n                  Tensor4D,\n              getParamValue('radius', node, tensorMap, context) as number,\n              getParamValue('bias', node, tensorMap, context) as number,\n              getParamValue('alpha', node, tensorMap, context) as number,\n              getParamValue('beta', node, tensorMap, context) as number)];\n        }\n        case 'Softmax': {\n          return [tfOps.softmax(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        }\n        case 'LogSoftmax': {\n          return [tfOps.logSoftmax(\n              getParamValue('x', node, tensorMap, context) as Tensor)];\n        }\n        case 'SparseToDense': {\n          return [tfOps.sparseToDense(\n              getParamValue('sparseIndices', node, tensorMap, context) as\n                  Tensor,\n              getParamValue('outputShape', node, tensorMap, context) as Tensor,\n              getParamValue('sparseValues', node, tensorMap, context) as\n                  number[],\n              getParamValue('defaultValue', node, tensorMap, context) as\n                  Scalar)];\n        }\n        default:\n          throw TypeError(`Node type ${node.op} is not implemented`);\n      }\n    };\n\nexport const CATEGORY = 'normalization';\n"]}","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n switch (node.op) {\n case 'Max': {\n const axis = getParamValue('axis', node, tensorMap, context);\n const keepDims = getParamValue('keepDims', node, tensorMap, context);\n return [tfOps.max(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n }\n case 'Mean': {\n const axis = getParamValue('axis', node, tensorMap, context);\n const keepDims = getParamValue('keepDims', node, tensorMap, context);\n return [tfOps.mean(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n }\n case 'Min': {\n const axis = getParamValue('axis', node, tensorMap, context);\n const keepDims = getParamValue('keepDims', node, tensorMap, context);\n return [tfOps.min(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n }\n case 'Sum': {\n const axis = getParamValue('axis', node, tensorMap, context);\n const keepDims = getParamValue('keepDims', node, tensorMap, context);\n return [tfOps.sum(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n }\n case 'All': {\n const axis = getParamValue('axis', node, tensorMap, context);\n const keepDims = getParamValue('keepDims', node, tensorMap, context);\n return [tfOps.all(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n }\n case 'Any': {\n const axis = getParamValue('axis', node, tensorMap, context);\n const keepDims = getParamValue('keepDims', node, tensorMap, context);\n return [tfOps.any(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n }\n case 'ArgMax': {\n const axis = getParamValue('axis', node, tensorMap, context);\n return [tfOps.argMax(getParamValue('x', node, tensorMap, context), axis)];\n }\n case 'ArgMin': {\n const axis = getParamValue('axis', node, tensorMap, context);\n return [tfOps.argMin(getParamValue('x', node, tensorMap, context), axis)];\n }\n case 'Prod': {\n const axis = getParamValue('axis', node, tensorMap, context);\n const keepDims = getParamValue('keepDims', node, tensorMap, context);\n return [tfOps.prod(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n }\n case 'Cumprod': {\n const axis = getParamValue('axis', node, tensorMap, context);\n const exclusive = getParamValue('exclusive', node, tensorMap, context);\n const reverse = getParamValue('reverse', node, tensorMap, context);\n return [tfOps.cumprod(getParamValue('x', node, tensorMap, context), axis, exclusive, reverse)];\n }\n case 'Cumsum': {\n const axis = getParamValue('axis', node, tensorMap, context);\n const exclusive = getParamValue('exclusive', node, tensorMap, context);\n const reverse = getParamValue('reverse', node, tensorMap, context);\n return [tfOps.cumsum(getParamValue('x', node, tensorMap, context), axis, exclusive, reverse)];\n }\n case 'Bincount':\n const x = getParamValue('x', node, tensorMap, context);\n const weights = getParamValue('weights', node, tensorMap, context);\n const size = getParamValue('size', node, tensorMap, context);\n return [tfOps.bincount(x, weights, size)];\n case 'DenseBincount': {\n const x = getParamValue('x', node, tensorMap, context);\n const weights = getParamValue('weights', node, tensorMap, context);\n const size = getParamValue('size', node, tensorMap, context);\n const binaryOutput = getParamValue('binaryOutput', node, tensorMap, context);\n return [tfOps.denseBincount(x, weights, size, binaryOutput)];\n }\n default:\n throw TypeError(`Node type ${node.op} is not implemented`);\n }\n};\nexport const CATEGORY = 'reduction';\n//# 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* @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor, Tensor1D, Tensor2D} from '@tensorflow/tfjs-core';\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\n\nimport {NamedTensorsMap} from '../../data/types';\nimport {ExecutionContext} from '../../executor/execution_context';\nimport {InternalOpExecutor, Node} from '../types';\n\nimport {getParamValue} from './utils';\n\nexport const executeOp: InternalOpExecutor =\n    (node: Node, tensorMap: NamedTensorsMap,\n     context: ExecutionContext): Tensor[] => {\n      switch (node.op) {\n        case 'Max': {\n          const axis =\n              getParamValue('axis', node, tensorMap, context) as number[];\n          const keepDims =\n              getParamValue('keepDims', node, tensorMap, context) as boolean;\n          return [tfOps.max(\n              getParamValue('x', node, tensorMap, context) as Tensor, axis,\n              keepDims)];\n        }\n        case 'Mean': {\n          const axis =\n              getParamValue('axis', node, tensorMap, context) as number[];\n          const keepDims =\n              getParamValue('keepDims', node, tensorMap, context) as boolean;\n          return [tfOps.mean(\n              getParamValue('x', node, tensorMap, context) as Tensor, axis,\n              keepDims)];\n        }\n        case 'Min': {\n          const axis =\n              getParamValue('axis', node, tensorMap, context) as number[];\n          const keepDims =\n              getParamValue('keepDims', node, tensorMap, context) as boolean;\n          return [tfOps.min(\n              getParamValue('x', node, tensorMap, context) as Tensor, axis,\n              keepDims)];\n        }\n        case 'Sum': {\n          const axis =\n              getParamValue('axis', node, tensorMap, context) as number[];\n          const keepDims =\n              getParamValue('keepDims', node, tensorMap, context) as boolean;\n          return [tfOps.sum(\n              getParamValue('x', node, tensorMap, context) as Tensor, axis,\n              keepDims)];\n        }\n        case 'All': {\n          const axis =\n              getParamValue('axis', node, tensorMap, context) as number[];\n          const keepDims =\n              getParamValue('keepDims', node, tensorMap, context) as boolean;\n          return [tfOps.all(\n              getParamValue('x', node, tensorMap, context) as Tensor, axis,\n              keepDims)];\n        }\n        case 'Any': {\n          const axis =\n              getParamValue('axis', node, tensorMap, context) as number[];\n          const keepDims =\n              getParamValue('keepDims', node, tensorMap, context) as boolean;\n          return [tfOps.any(\n              getParamValue('x', node, tensorMap, context) as Tensor, axis,\n              keepDims)];\n        }\n        case 'ArgMax': {\n          const axis =\n              getParamValue('axis', node, tensorMap, context) as number;\n          return [tfOps.argMax(\n              getParamValue('x', node, tensorMap, context) as Tensor, axis)];\n        }\n        case 'ArgMin': {\n          const axis =\n              getParamValue('axis', node, tensorMap, context) as number;\n          return [tfOps.argMin(\n              getParamValue('x', node, tensorMap, context) as Tensor, axis)];\n        }\n        case 'Prod': {\n          const axis =\n              getParamValue('axis', node, tensorMap, context) as number[];\n          const keepDims =\n              getParamValue('keepDims', node, tensorMap, context) as boolean;\n          return [tfOps.prod(\n              getParamValue('x', node, tensorMap, context) as Tensor, axis,\n              keepDims)];\n        }\n        case 'Cumprod': {\n          const axis =\n              getParamValue('axis', node, tensorMap, context) as number;\n          const exclusive =\n              getParamValue('exclusive', node, tensorMap, context) as boolean;\n          const reverse =\n              getParamValue('reverse', node, tensorMap, context) as boolean;\n          return [tfOps.cumprod(\n              getParamValue('x', node, tensorMap, context) as Tensor, axis,\n              exclusive, reverse)];\n        }\n        case 'Cumsum': {\n          const axis =\n              getParamValue('axis', node, tensorMap, context) as number;\n          const exclusive =\n              getParamValue('exclusive', node, tensorMap, context) as boolean;\n          const reverse =\n              getParamValue('reverse', node, tensorMap, context) as boolean;\n          return [tfOps.cumsum(\n              getParamValue('x', node, tensorMap, context) as Tensor, axis,\n              exclusive, reverse)];\n        }\n        case 'Bincount':\n          const x = getParamValue('x', node, tensorMap, context) as Tensor1D;\n          const weights =\n              getParamValue('weights', node, tensorMap, context) as Tensor1D;\n          const size =\n              getParamValue('size', node, tensorMap, context) as number;\n\n          return [tfOps.bincount(x, weights, size)];\n        case 'DenseBincount': {\n          const x = getParamValue('x', node, tensorMap, context) as Tensor1D |\n              Tensor2D;\n          const weights =\n              getParamValue('weights', node, tensorMap, context) as Tensor1D |\n              Tensor2D;\n          const size =\n              getParamValue('size', node, tensorMap, context) as number;\n\n          const binaryOutput =\n              getParamValue('binaryOutput', node, tensorMap, context) as\n              boolean;\n\n          return [tfOps.denseBincount(x, weights, size, binaryOutput)];\n        }\n        default:\n          throw TypeError(`Node type ${node.op} is not implemented`);\n      }\n    };\n\nexport const CATEGORY = 'reduction';\n"]}","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { tidy, util } from '@tensorflow/tfjs-core';\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n switch (node.op) {\n case 'ConcatV2':\n case 'Concat': {\n const n = getParamValue('n', node, tensorMap, context);\n const axis = getParamValue('axis', node, tensorMap, context);\n let inputs = getParamValue('tensors', node, tensorMap, context);\n inputs = inputs.slice(0, n);\n return [tfOps.concat(inputs, axis)];\n }\n case 'Gather': {\n const input = getParamValue('x', node, tensorMap, context);\n const indices = getParamValue('indices', node, tensorMap, context);\n return [tfOps.gather(input, tfOps.cast(indices, 'int32'), 0)];\n }\n case 'GatherV2': {\n const axis = getParamValue('axis', node, tensorMap, context);\n const batchDims = getParamValue('batchDims', node, tensorMap, context);\n const input = getParamValue('x', node, tensorMap, context);\n const indices = getParamValue('indices', node, tensorMap, context);\n return [tfOps.gather(input, tfOps.cast(indices, 'int32'), axis, batchDims)];\n }\n case 'Reverse': {\n const dims = getParamValue('dims', node, tensorMap, context);\n const axis = [];\n for (let i = 0; i < dims.length; i++) {\n if (dims[i]) {\n axis.push(i);\n }\n }\n const input = getParamValue('x', node, tensorMap, context);\n return [tfOps.reverse(input, axis)];\n }\n case 'ReverseV2': {\n const axis = getParamValue('axis', node, tensorMap, context);\n const input = getParamValue('x', node, tensorMap, context);\n return [tfOps.reverse(input, axis)];\n }\n case 'Slice': {\n // tslint:disable-next-line:no-any\n const begin = getParamValue('begin', node, tensorMap, context);\n // tslint:disable-next-line:no-any\n const size = getParamValue('size', node, tensorMap, context);\n return [tfOps.slice(getParamValue('x', node, tensorMap, context), begin, size)];\n }\n case 'StridedSlice': {\n const begin = getParamValue('begin', node, tensorMap, context);\n const end = getParamValue('end', node, tensorMap, context);\n const strides = getParamValue('strides', node, tensorMap, context);\n const beginMask = getParamValue('beginMask', node, tensorMap, context);\n const endMask = getParamValue('endMask', node, tensorMap, context);\n const ellipsisMask = getParamValue('ellipsisMask', node, tensorMap, context);\n const newAxisMask = getParamValue('newAxisMask', node, tensorMap, context);\n const shrinkAxisMask = getParamValue('shrinkAxisMask', node, tensorMap, context);\n const tensor = getParamValue('x', node, tensorMap, context);\n return [tfOps.stridedSlice(tensor, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask)];\n }\n case 'Pack': {\n return tidy(() => {\n const axis = getParamValue('axis', node, tensorMap, context);\n const tensors = getParamValue('tensors', node, tensorMap, context);\n // Reshape the tensors to the first tensor's shape if they don't\n // match.\n const shape = tensors[0].shape;\n const squeezedShape = tfOps.squeeze(tensors[0]).shape;\n const mapped = tensors.map(tensor => {\n const sameShape = util.arraysEqual(tensor.shape, shape);\n if (!sameShape &&\n !util.arraysEqual(tfOps.squeeze(tensor).shape, squeezedShape)) {\n throw new Error('the input tensors shape does not match');\n }\n return sameShape ? tensor : tfOps.reshape(tensor, shape);\n });\n return [tfOps.stack(mapped, axis)];\n });\n }\n case 'Unpack': {\n const axis = getParamValue('axis', node, tensorMap, context);\n const tensor = getParamValue('tensor', node, tensorMap, context);\n return tfOps.unstack(tensor, axis);\n }\n case 'Tile': {\n const reps = getParamValue('reps', node, tensorMap, context);\n return [tfOps.tile(getParamValue('x', node, tensorMap, context), reps)];\n }\n case 'Split':\n case 'SplitV': {\n const axis = getParamValue('axis', node, tensorMap, context);\n const numOrSizeSplits = getParamValue('numOrSizeSplits', node, tensorMap, context);\n const tensor = getParamValue('x', node, tensorMap, context);\n return tfOps.split(tensor, numOrSizeSplits, axis);\n }\n case 'ScatterNd': {\n const indices = getParamValue('indices', node, tensorMap, context);\n const values = getParamValue('values', node, tensorMap, context);\n const shape = getParamValue('shape', node, tensorMap, context);\n return [tfOps.scatterND(indices, values, shape)];\n }\n case 'GatherNd': {\n const x = getParamValue('x', node, tensorMap, context);\n const indices = getParamValue('indices', node, tensorMap, context);\n return [tfOps.gatherND(x, indices)];\n }\n case 'SparseToDense': {\n const indices = getParamValue('sparseIndices', node, tensorMap, context);\n const shape = getParamValue('outputShape', node, tensorMap, context);\n const sparseValues = getParamValue('sparseValues', node, tensorMap, context);\n const defaultValue = getParamValue('defaultValue', node, tensorMap, context);\n return [tfOps.sparseToDense(indices, sparseValues, shape, sparseValues.dtype === defaultValue.dtype ?\n defaultValue :\n tfOps.cast(defaultValue, sparseValues.dtype))];\n }\n default:\n throw TypeError(`Node type ${node.op} is not implemented`);\n }\n};\nexport const CATEGORY = 'slice_join';\n//# 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* @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Scalar, Tensor, Tensor1D, tidy, util} from '@tensorflow/tfjs-core';\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\n\nimport {NamedTensorsMap} from '../../data/types';\nimport {ExecutionContext} from '../../executor/execution_context';\nimport {InternalOpExecutor, Node} from '../types';\n\nimport {getParamValue} from './utils';\n\nexport const executeOp: InternalOpExecutor =\n    (node: Node, tensorMap: NamedTensorsMap,\n     context: ExecutionContext): Tensor[] => {\n      switch (node.op) {\n        case 'ConcatV2':\n        case 'Concat': {\n          const n = getParamValue('n', node, tensorMap, context) as number;\n          const axis =\n              getParamValue('axis', node, tensorMap, context) as number;\n          let inputs =\n              getParamValue('tensors', node, tensorMap, context) as Tensor[];\n          inputs = inputs.slice(0, n);\n          return [tfOps.concat(inputs, axis)];\n        }\n        case 'Gather': {\n          const input = getParamValue('x', node, tensorMap, context) as Tensor;\n          const indices =\n              getParamValue('indices', node, tensorMap, context) as Tensor1D;\n          return [tfOps.gather(input, tfOps.cast(indices, 'int32'), 0)];\n        }\n        case 'GatherV2': {\n          const axis =\n              getParamValue('axis', node, tensorMap, context) as number;\n          const batchDims =\n              getParamValue('batchDims', node, tensorMap, context) as number;\n          const input = getParamValue('x', node, tensorMap, context) as Tensor;\n          const indices =\n              getParamValue('indices', node, tensorMap, context) as Tensor1D;\n          return [tfOps.gather(\n              input, tfOps.cast(indices, 'int32'), axis, batchDims)];\n        }\n        case 'Reverse': {\n          const dims =\n              getParamValue('dims', node, tensorMap, context) as boolean[];\n          const axis = [];\n          for (let i = 0; i < dims.length; i++) {\n            if (dims[i]) {\n              axis.push(i);\n            }\n          }\n          const input = getParamValue('x', node, tensorMap, context) as Tensor;\n          return [tfOps.reverse(input, axis)];\n        }\n        case 'ReverseV2': {\n          const axis =\n              getParamValue('axis', node, tensorMap, context) as number[];\n          const input = getParamValue('x', node, tensorMap, context) as Tensor;\n          return [tfOps.reverse(input, axis)];\n        }\n        case 'Slice': {\n          // tslint:disable-next-line:no-any\n          const begin = getParamValue('begin', node, tensorMap, context) as any;\n          // tslint:disable-next-line:no-any\n          const size = getParamValue('size', node, tensorMap, context) as any;\n          return [tfOps.slice(\n              getParamValue('x', node, tensorMap, context) as Tensor, begin,\n              size)];\n        }\n        case 'StridedSlice': {\n          const begin =\n              getParamValue('begin', node, tensorMap, context) as number[];\n          const end =\n              getParamValue('end', node, tensorMap, context) as number[];\n          const strides =\n              getParamValue('strides', node, tensorMap, context) as number[];\n          const beginMask =\n              getParamValue('beginMask', node, tensorMap, context) as number;\n          const endMask =\n              getParamValue('endMask', node, tensorMap, context) as number;\n          const ellipsisMask =\n              getParamValue('ellipsisMask', node, tensorMap, context) as number;\n          const newAxisMask =\n              getParamValue('newAxisMask', node, tensorMap, context) as number;\n          const shrinkAxisMask =\n              getParamValue('shrinkAxisMask', node, tensorMap, context) as\n              number;\n          const tensor = getParamValue('x', node, tensorMap, context) as Tensor;\n\n          return [tfOps.stridedSlice(\n              tensor, begin, end, strides, beginMask, endMask, ellipsisMask,\n              newAxisMask, shrinkAxisMask)];\n        }\n        case 'Pack': {\n          return tidy(() => {\n            const axis =\n                getParamValue('axis', node, tensorMap, context) as number;\n            const tensors =\n                getParamValue('tensors', node, tensorMap, context) as Tensor[];\n            // Reshape the tensors to the first tensor's shape if they don't\n            // match.\n            const shape = tensors[0].shape;\n            const squeezedShape = tfOps.squeeze(tensors[0]).shape;\n            const mapped = tensors.map(tensor => {\n              const sameShape = util.arraysEqual(tensor.shape, shape);\n              if (!sameShape &&\n                  !util.arraysEqual(\n                      tfOps.squeeze(tensor).shape, squeezedShape)) {\n                throw new Error('the input tensors shape does not match');\n              }\n              return sameShape ? tensor : tfOps.reshape(tensor, shape);\n            });\n            return [tfOps.stack(mapped, axis)];\n          });\n        }\n        case 'Unpack': {\n          const axis =\n              getParamValue('axis', node, tensorMap, context) as number;\n          const tensor =\n              getParamValue('tensor', node, tensorMap, context) as Tensor;\n          return tfOps.unstack(tensor, axis);\n        }\n        case 'Tile': {\n          const reps =\n              getParamValue('reps', node, tensorMap, context) as number[];\n          return [tfOps.tile(\n              getParamValue('x', node, tensorMap, context) as Tensor, reps)];\n        }\n        case 'Split':\n        case 'SplitV': {\n          const axis =\n              getParamValue('axis', node, tensorMap, context) as number;\n          const numOrSizeSplits =\n              getParamValue('numOrSizeSplits', node, tensorMap, context) as\n                  number |\n              number[];\n          const tensor = getParamValue('x', node, tensorMap, context) as Tensor;\n\n          return tfOps.split(tensor, numOrSizeSplits, axis);\n        }\n        case 'ScatterNd': {\n          const indices =\n              getParamValue('indices', node, tensorMap, context) as Tensor;\n          const values =\n              getParamValue('values', node, tensorMap, context) as Tensor;\n          const shape =\n              getParamValue('shape', node, tensorMap, context) as number[];\n          return [tfOps.scatterND(indices, values, shape)];\n        }\n        case 'GatherNd': {\n          const x = getParamValue('x', node, tensorMap, context) as Tensor;\n          const indices =\n              getParamValue('indices', node, tensorMap, context) as Tensor;\n          return [tfOps.gatherND(x, indices)];\n        }\n        case 'SparseToDense': {\n          const indices =\n              getParamValue('sparseIndices', node, tensorMap, context) as\n              Tensor;\n          const shape =\n              getParamValue('outputShape', node, tensorMap, context) as\n              number[];\n          const sparseValues =\n              getParamValue('sparseValues', node, tensorMap, context) as Tensor;\n          const defaultValue =\n              getParamValue('defaultValue', node, tensorMap, context) as Scalar;\n          return [tfOps.sparseToDense(\n              indices, sparseValues, shape,\n              sparseValues.dtype === defaultValue.dtype ?\n                  defaultValue :\n                  tfOps.cast(defaultValue, sparseValues.dtype))];\n        }\n        default:\n          throw TypeError(`Node type ${node.op} is not implemented`);\n      }\n    };\n\nexport const CATEGORY = 'slice_join';\n"]}","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n switch (node.op) {\n case 'SparseFillEmptyRows': {\n const { outputIndices, outputValues, emptyRowIndicator, reverseIndexMap } = tfOps.sparse.sparseFillEmptyRows(getParamValue('indices', node, tensorMap, context), getParamValue('values', node, tensorMap, context), getParamValue('denseShape', node, tensorMap, context), getParamValue('defaultValue', node, tensorMap, context));\n return [\n outputIndices, outputValues, emptyRowIndicator, reverseIndexMap\n ];\n }\n case 'SparseReshape': {\n const { outputIndices, outputShape } = tfOps.sparse.sparseReshape(getParamValue('inputIndices', node, tensorMap, context), getParamValue('inputShape', node, tensorMap, context), getParamValue('newShape', node, tensorMap, context));\n return [outputIndices, outputShape];\n }\n case 'SparseSegmentMean': {\n const outputData = tfOps.sparse.sparseSegmentMean(getParamValue('data', node, tensorMap, context), getParamValue('indices', node, tensorMap, context), getParamValue('segmentIds', node, tensorMap, context));\n return [outputData];\n }\n case 'SparseSegmentSum': {\n const outputData = tfOps.sparse.sparseSegmentSum(getParamValue('data', node, tensorMap, context), getParamValue('indices', node, tensorMap, context), getParamValue('segmentIds', node, tensorMap, context));\n return [outputData];\n }\n default:\n throw TypeError(`Node type ${node.op} is not implemented`);\n }\n};\nexport const CATEGORY = 'sparse';\n//# 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* @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n switch (node.op) {\n case 'FFT': {\n return [tfOps.fft(getParamValue('x', node, tensorMap, context))];\n }\n case 'IFFT': {\n return [tfOps.ifft(getParamValue('x', node, tensorMap, context))];\n }\n case 'RFFT': {\n return [tfOps.rfft(getParamValue('x', node, tensorMap, context))];\n }\n case 'IRFFT': {\n return [tfOps.irfft(getParamValue('x', node, tensorMap, context))];\n }\n default:\n throw TypeError(`Node type ${node.op} is not implemented`);\n }\n};\nexport const CATEGORY = 'spectral';\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n switch (node.op) {\n case 'StringNGrams': {\n const { nGrams, nGramsSplits } = tfOps.string.stringNGrams(getParamValue('data', node, tensorMap, context), getParamValue('dataSplits', node, tensorMap, context), getParamValue('separator', node, tensorMap, context), getParamValue('nGramWidths', node, tensorMap, context), getParamValue('leftPad', node, tensorMap, context), getParamValue('rightPad', node, tensorMap, context), getParamValue('padWidth', node, tensorMap, context), getParamValue('preserveShortSequences', node, tensorMap, context));\n return [nGrams, nGramsSplits];\n }\n case 'StringSplit': {\n const { indices, values, shape } = tfOps.string.stringSplit(getParamValue('input', node, tensorMap, context), getParamValue('delimiter', node, tensorMap, context), getParamValue('skipEmpty', node, tensorMap, context));\n return [indices, values, shape];\n }\n case 'StringToHashBucketFast': {\n const output = tfOps.string.stringToHashBucketFast(getParamValue('input', node, tensorMap, context), getParamValue('numBuckets', node, tensorMap, context));\n return [output];\n }\n default:\n throw TypeError(`Node type ${node.op} is not implemented`);\n }\n};\nexport const CATEGORY = 'string';\n//# sourceMappingURL=data:application/json;base64,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* @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n switch (node.op) {\n case 'Cast': {\n return [tfOps.cast(getParamValue('x', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))];\n }\n case 'ExpandDims': {\n const axis = getParamValue('axis', node, tensorMap, context);\n return [tfOps.expandDims(getParamValue('x', node, tensorMap, context), axis)];\n }\n case 'Squeeze': {\n const axis = getParamValue('axis', node, tensorMap, context);\n return [tfOps.squeeze(getParamValue('x', node, tensorMap, context), axis)];\n }\n case 'Reshape': {\n return [tfOps.reshape(getParamValue('x', node, tensorMap, context), getParamValue('shape', node, tensorMap, context))];\n }\n case 'MirrorPad': {\n return [tfOps.mirrorPad(getParamValue('x', node, tensorMap, context), getParamValue('padding', node, tensorMap, context), getParamValue('mode', node, tensorMap, context))];\n }\n case 'PadV2':\n case 'Pad': {\n return [tfOps.pad(getParamValue('x', node, tensorMap, context), getParamValue('padding', node, tensorMap, context), getParamValue('constantValue', node, tensorMap, context))];\n }\n case 'SpaceToBatchND': {\n const blockShape = getParamValue('blockShape', node, tensorMap, context);\n const paddings = getParamValue('paddings', node, tensorMap, context);\n return [tfOps.spaceToBatchND(getParamValue('x', node, tensorMap, context), blockShape, paddings)];\n }\n case 'BatchToSpaceND': {\n const blockShape = getParamValue('blockShape', node, tensorMap, context);\n const crops = getParamValue('crops', node, tensorMap, context);\n return [tfOps.batchToSpaceND(getParamValue('x', node, tensorMap, context), blockShape, crops)];\n }\n case 'DepthToSpace': {\n const blockSize = getParamValue('blockSize', node, tensorMap, context);\n const dataFormat = getParamValue('dataFormat', node, tensorMap, context).toUpperCase();\n return [tfOps.depthToSpace(getParamValue('x', node, tensorMap, context), blockSize, dataFormat)];\n }\n case 'BroadcastTo': {\n return [tfOps.broadcastTo(getParamValue('x', node, tensorMap, context), getParamValue('shape', node, tensorMap, context))];\n }\n case 'BroadcastArgs': {\n return [tfOps.broadcastArgs(getParamValue('s0', node, tensorMap, context), getParamValue('s1', node, tensorMap, context))];\n }\n default:\n throw TypeError(`Node type ${node.op} is not implemented`);\n }\n};\nexport const CATEGORY = 'transformation';\n//# 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* @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor, Tensor4D} from '@tensorflow/tfjs-core';\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\n\nimport {NamedTensorsMap} from '../../data/types';\nimport {ExecutionContext} from '../../executor/execution_context';\nimport {InternalOpExecutor, Node} from '../types';\n\nimport {getParamValue} from './utils';\n\nexport const executeOp: InternalOpExecutor =\n    (node: Node, tensorMap: NamedTensorsMap,\n     context: ExecutionContext): Tensor[] => {\n      switch (node.op) {\n        case 'Cast': {\n          return [tfOps.cast(\n              getParamValue('x', node, tensorMap, context) as Tensor,\n              getParamValue('dtype', node, tensorMap, context) as 'int32' |\n                  'float32' | 'bool')];\n        }\n        case 'ExpandDims': {\n          const axis =\n              getParamValue('axis', node, tensorMap, context) as number;\n          return [tfOps.expandDims(\n              getParamValue('x', node, tensorMap, context) as Tensor, axis)];\n        }\n        case 'Squeeze': {\n          const axis =\n              getParamValue('axis', node, tensorMap, context) as number[];\n          return [tfOps.squeeze(\n              getParamValue('x', node, tensorMap, context) as Tensor, axis)];\n        }\n\n        case 'Reshape': {\n          return [tfOps.reshape(\n              getParamValue('x', node, tensorMap, context) as Tensor,\n              getParamValue('shape', node, tensorMap, context) as number[])];\n        }\n        case 'MirrorPad': {\n          return [tfOps.mirrorPad(\n              getParamValue('x', node, tensorMap, context) as Tensor,\n              getParamValue('padding', node, tensorMap, context) as\n                  Array<[number, number]>,\n              getParamValue('mode', node, tensorMap, context) as 'reflect' |\n                  'symmetric')];\n        }\n        case 'PadV2':\n        case 'Pad': {\n          return [tfOps.pad(\n              getParamValue('x', node, tensorMap, context) as Tensor,\n              getParamValue('padding', node, tensorMap, context) as\n                  Array<[number, number]>,\n              getParamValue('constantValue', node, tensorMap, context) as\n                  number)];\n        }\n        case 'SpaceToBatchND': {\n          const blockShape =\n              getParamValue('blockShape', node, tensorMap, context) as number[];\n          const paddings =\n              getParamValue('paddings', node, tensorMap, context) as number[][];\n          return [tfOps.spaceToBatchND(\n              getParamValue('x', node, tensorMap, context) as Tensor,\n              blockShape, paddings)];\n        }\n        case 'BatchToSpaceND': {\n          const blockShape =\n              getParamValue('blockShape', node, tensorMap, context) as number[];\n          const crops =\n              getParamValue('crops', node, tensorMap, context) as number[][];\n          return [tfOps.batchToSpaceND(\n              getParamValue('x', node, tensorMap, context) as Tensor,\n              blockShape, crops)];\n        }\n        case 'DepthToSpace': {\n          const blockSize =\n              getParamValue('blockSize', node, tensorMap, context) as number;\n          const dataFormat =\n              (getParamValue('dataFormat', node, tensorMap, context) as\n               string).toUpperCase() as 'NHWC' |\n              'NCHW';\n          return [tfOps.depthToSpace(\n              getParamValue('x', node, tensorMap, context) as Tensor4D,\n              blockSize, dataFormat)];\n        }\n        case 'BroadcastTo': {\n          return [tfOps.broadcastTo(\n              getParamValue('x', node, tensorMap, context) as Tensor,\n              getParamValue('shape', node, tensorMap, context) as number[])];\n        }\n        case 'BroadcastArgs': {\n          return [tfOps.broadcastArgs(\n              getParamValue('s0', node, tensorMap, context) as Tensor,\n              getParamValue('s1', node, tensorMap, context) as Tensor)];\n        }\n        default:\n          throw TypeError(`Node type ${node.op} is not implemented`);\n      }\n    };\n\nexport const CATEGORY = 'transformation';\n"]}","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { NodeValueImpl } from './custom_op/node_value_impl';\nimport { getRegisteredOp } from './custom_op/register';\nimport * as arithmetic from './executors/arithmetic_executor';\nimport * as basicMath from './executors/basic_math_executor';\nimport * as control from './executors/control_executor';\nimport * as convolution from './executors/convolution_executor';\nimport * as creation from './executors/creation_executor';\nimport * as dynamic from './executors/dynamic_executor';\nimport * as evaluation from './executors/evaluation_executor';\nimport * as graph from './executors/graph_executor';\nimport * as hashTable from './executors/hash_table_executor';\nimport * as image from './executors/image_executor';\nimport * as logical from './executors/logical_executor';\nimport * as matrices from './executors/matrices_executor';\nimport * as normalization from './executors/normalization_executor';\nimport * as reduction from './executors/reduction_executor';\nimport * as sliceJoin from './executors/slice_join_executor';\nimport * as sparse from './executors/sparse_executor';\nimport * as spectral from './executors/spectral_executor';\nimport * as string from './executors/string_executor';\nimport * as transformation from './executors/transformation_executor';\n/**\n * Executes the op defined by the node object.\n * @param node\n * @param tensorMap contains tensors for executed nodes and weights\n * @param context contains tensors and information for running the current node.\n * @param resourceManager Optional. Contains global resources of the model.\n */\nexport function executeOp(node, tensorMap, context, resourceManager) {\n const value = ((node, tensorMap, context) => {\n switch (node.category) {\n case 'arithmetic':\n return tfc.tidy(() => arithmetic.executeOp(node, tensorMap, context));\n case 'basic_math':\n return tfc.tidy(() => basicMath.executeOp(node, tensorMap, context));\n case 'control':\n return control.executeOp(node, tensorMap, context);\n case 'convolution':\n return tfc.tidy(() => convolution.executeOp(node, tensorMap, context));\n case 'creation':\n return tfc.tidy(() => creation.executeOp(node, tensorMap, context));\n case 'dynamic':\n return dynamic.executeOp(node, tensorMap, context);\n case 'evaluation':\n return tfc.tidy(() => evaluation.executeOp(node, tensorMap, context));\n case 'image':\n return tfc.tidy(() => image.executeOp(node, tensorMap, context));\n case 'graph':\n return tfc.tidy(() => graph.executeOp(node, tensorMap, context));\n case 'logical':\n return tfc.tidy(() => logical.executeOp(node, tensorMap, context));\n case 'matrices':\n return tfc.tidy(() => matrices.executeOp(node, tensorMap, context));\n case 'normalization':\n return tfc.tidy(() => normalization.executeOp(node, tensorMap, context));\n case 'reduction':\n return tfc.tidy(() => reduction.executeOp(node, tensorMap, context));\n case 'slice_join':\n return tfc.tidy(() => sliceJoin.executeOp(node, tensorMap, context));\n case 'sparse':\n return tfc.tidy(() => sparse.executeOp(node, tensorMap, context));\n case 'spectral':\n return tfc.tidy(() => spectral.executeOp(node, tensorMap, context));\n case 'string':\n return tfc.tidy(() => string.executeOp(node, tensorMap, context));\n case 'transformation':\n return tfc.tidy(() => transformation.executeOp(node, tensorMap, context));\n case 'hash_table':\n return hashTable.executeOp(node, tensorMap, context, resourceManager);\n case 'custom':\n const opMapper = getRegisteredOp(node.op);\n if (opMapper && opMapper.customExecutor) {\n return opMapper.customExecutor(new NodeValueImpl(node, tensorMap, context));\n }\n else {\n throw TypeError(`Custom op ${node.op} is not registered.`);\n }\n default:\n throw TypeError(`Unknown op '${node.op}'. File an issue at ` +\n `https://github.com/tensorflow/tfjs/issues so we can add it` +\n `, or register a custom execution with tf.registerOp()`);\n }\n })(node, tensorMap, context);\n if (tfc.util.isPromise(value)) {\n return value.then((data) => [].concat(data));\n }\n return [].concat(value);\n}\n//# sourceMappingURL=data:application/json;base64,{"version":3,"file":"operation_executor.js","sourceRoot":"","sources":["../../../../../../tfjs-converter/src/operations/operation_executor.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAEH,OAAO,KAAK,GAAG,MAAM,uBAAuB,CAAC;AAM7C,OAAO,EAAC,aAAa,EAAC,MAAM,6BAA6B,CAAC;AAC1D,OAAO,EAAC,eAAe,EAAC,MAAM,sBAAsB,CAAC;AACrD,OAAO,KAAK,UAAU,MAAM,iCAAiC,CAAC;AAC9D,OAAO,KAAK,SAAS,MAAM,iCAAiC,CAAC;AAC7D,OAAO,KAAK,OAAO,MAAM,8BAA8B,CAAC;AACxD,OAAO,KAAK,WAAW,MAAM,kCAAkC,CAAC;AAChE,OAAO,KAAK,QAAQ,MAAM,+BAA+B,CAAC;AAC1D,OAAO,KAAK,OAAO,MAAM,8BAA8B,CAAC;AACxD,OAAO,KAAK,UAAU,MAAM,iCAAiC,CAAC;AAC9D,OAAO,KAAK,KAAK,MAAM,4BAA4B,CAAC;AACpD,OAAO,KAAK,SAAS,MAAM,iCAAiC,CAAC;AAC7D,OAAO,KAAK,KAAK,MAAM,4BAA4B,CAAC;AACpD,OAAO,KAAK,OAAO,MAAM,8BAA8B,CAAC;AACxD,OAAO,KAAK,QAAQ,MAAM,+BAA+B,CAAC;AAC1D,OAAO,KAAK,aAAa,MAAM,oCAAoC,CAAC;AACpE,OAAO,KAAK,SAAS,MAAM,gCAAgC,CAAC;AAC5D,OAAO,KAAK,SAAS,MAAM,iCAAiC,CAAC;AAC7D,OAAO,KAAK,MAAM,MAAM,6BAA6B,CAAC;AACtD,OAAO,KAAK,QAAQ,MAAM,+BAA+B,CAAC;AAC1D,OAAO,KAAK,MAAM,MAAM,6BAA6B,CAAC;AACtD,OAAO,KAAK,cAAc,MAAM,qCAAqC,CAAC;AAGtE;;;;;;GAMG;AACH,MAAM,UAAU,SAAS,CACrB,IAAU,EAAE,SAA0B,EAAE,OAAyB,EACjE,eAAiC;IACnC,MAAM,KAAK,GACP,CAAC,CAAC,IAAU,EAAE,SAA0B,EAAE,OAAyB,EAAE,EAAE;QACrE,QAAQ,IAAI,CAAC,QAAQ,EAAE;YACrB,KAAK,YAAY;gBACf,OAAO,GAAG,CAAC,IAAI,CACX,GAAG,EAAE,CAAC,UAAU,CAAC,SAAS,CAAC,IAAI,EAAE,SAAS,EAAE,OAAO,CAAC,CAAC,CAAC;YAC5D,KAAK,YAAY;gBACf,OAAO,GAAG,CAAC,IAAI,CACX,GAAG,EAAE,CAAC,SAAS,CAAC,SAAS,CAAC,IAAI,EAAE,SAAS,EAAE,OAAO,CAAC,CAAC,CAAC;YAC3D,KAAK,SAAS;gBACZ,OAAO,OAAO,CAAC,SAAS,CAAC,IAAI,EAAE,SAAS,EAAE,OAAO,CAAC,CAAC;YACrD,KAAK,aAAa;gBAChB,OAAO,GAAG,CAAC,IAAI,CACX,GAAG,EAAE,CAAC,WAAW,CAAC,SAAS,CAAC,IAAI,EAAE,SAAS,EAAE,OAAO,CAAC,CAAC,CAAC;YAC7D,KAAK,UAAU;gBACb,OAAO,GAAG,CAAC,IAAI,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,SAAS,CAAC,IAAI,EAAE,SAAS,EAAE,OAAO,CAAC,CAAC,CAAC;YACtE,KAAK,SAAS;gBACZ,OAAO,OAAO,CAAC,SAAS,CAAC,IAAI,EAAE,SAAS,EAAE,OAAO,CAAC,CAAC;YACrD,KAAK,YAAY;gBACf,OAAO,GAAG,CAAC,IAAI,CACX,GAAG,EAAE,CAAC,UAAU,CAAC,SAAS,CAAC,IAAI,EAAE,SAAS,EAAE,OAAO,CAAC,CAAC,CAAC;YAC5D,KAAK,OAAO;gBACV,OAAO,GAAG,CAAC,IAAI,CAAC,GAAG,EAAE,CAAC,KAAK,CAAC,SAAS,CAAC,IAAI,EAAE,SAAS,EAAE,OAAO,CAAC,CAAC,CAAC;YACnE,KAAK,OAAO;gBACV,OAAO,GAAG,CAAC,IAAI,CAAC,GAAG,EAAE,CAAC,KAAK,CAAC,SAAS,CAAC,IAAI,EAAE,SAAS,EAAE,OAAO,CAAC,CAAC,CAAC;YACnE,KAAK,SAAS;gBACZ,OAAO,GAAG,CAAC,IAAI,CAAC,GAAG,EAAE,CAAC,OAAO,CAAC,SAAS,CAAC,IAAI,EAAE,SAAS,EAAE,OAAO,CAAC,CAAC,CAAC;YACrE,KAAK,UAAU;gBACb,OAAO,GAAG,CAAC,IAAI,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,SAAS,CAAC,IAAI,EAAE,SAAS,EAAE,OAAO,CAAC,CAAC,CAAC;YACtE,KAAK,eAAe;gBAClB,OAAO,GAAG,CAAC,IAAI,CACX,GAAG,EAAE,CAAC,aAAa,CAAC,SAAS,CAAC,IAAI,EAAE,SAAS,EAAE,OAAO,CAAC,CAAC,CAAC;YAC/D,KAAK,WAAW;gBACd,OAAO,GAAG,CAAC,IAAI,CACX,GAAG,EAAE,CAAC,SAAS,CAAC,SAAS,CAAC,IAAI,EAAE,SAAS,EAAE,OAAO,CAAC,CAAC,CAAC;YAC3D,KAAK,YAAY;gBACf,OAAO,GAAG,CAAC,IAAI,CACX,GAAG,EAAE,CAAC,SAAS,CAAC,SAAS,CAAC,IAAI,EAAE,SAAS,EAAE,OAAO,CAAC,CAAC,CAAC;YAC3D,KAAK,QAAQ;gBACX,OAAO,GAAG,CAAC,IAAI,CAAC,GAAG,EAAE,CAAC,MAAM,CAAC,SAAS,CAAC,IAAI,EAAE,SAAS,EAAE,OAAO,CAAC,CAAC,CAAC;YACpE,KAAK,UAAU;gBACb,OAAO,GAAG,CAAC,IAAI,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,SAAS,CAAC,IAAI,EAAE,SAAS,EAAE,OAAO,CAAC,CAAC,CAAC;YACtE,KAAK,QAAQ;gBACX,OAAO,GAAG,CAAC,IAAI,CAAC,GAAG,EAAE,CAAC,MAAM,CAAC,SAAS,CAAC,IAAI,EAAE,SAAS,EAAE,OAAO,CAAC,CAAC,CAAC;YACpE,KAAK,gBAAgB;gBACnB,OAAO,GAAG,CAAC,IAAI,CACX,GAAG,EAAE,CAAC,cAAc,CAAC,SAAS,CAAC,IAAI,EAAE,SAAS,EAAE,OAAO,CAAC,CAAC,CAAC;YAChE,KAAK,YAAY;gBACf,OAAO,SAAS,CAAC,SAAS,CACtB,IAAI,EAAE,SAAS,EAAE,OAAO,EAAE,eAAe,CAAC,CAAC;YACjD,KAAK,QAAQ;gBACX,MAAM,QAAQ,GAAG,eAAe,CAAC,IAAI,CAAC,EAAE,CAAC,CAAC;gBAC1C,IAAI,QAAQ,IAAI,QAAQ,CAAC,cAAc,EAAE;oBACvC,OAAO,QAAQ,CAAC,cAAc,CAC1B,IAAI,aAAa,CAAC,IAAI,EAAE,SAAS,EAAE,OAAO,CAAC,CAAC,CAAC;iBAClD;qBAAM;oBACL,MAAM,SAAS,CAAC,aAAa,IAAI,CAAC,EAAE,qBAAqB,CAAC,CAAC;iBAC5D;YACH;gBACE,MAAM,SAAS,CACX,eAAe,IAAI,CAAC,EAAE,sBAAsB;oBAC5C,4DAA4D;oBAC5D,uDAAuD,CAAC,CAAC;SAChE;IACH,CAAC,CAAC,CAAC,IAAI,EAAE,SAAS,EAAE,OAAO,CAAC,CAAC;IACjC,IAAI,GAAG,CAAC,IAAI,CAAC,SAAS,CAAC,KAAK,CAAC,EAAE;QAC7B,OAAQ,KAA6B,CAAC,IAAI,CAAC,CAAC,IAAI,EAAE,EAAE,CAAC,EAAE,CAAC,MAAM,CAAC,IAAI,CAAC,CAAC,CAAC;KACvE;IACD,OAAO,EAAE,CAAC,MAAM,CAAC,KAAK,CAAC,CAAC;AAC1B,CAAC","sourcesContent":["/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as tfc from '@tensorflow/tfjs-core';\n\nimport {NamedTensorsMap} from '../data/types';\nimport {ExecutionContext} from '../executor/execution_context';\nimport {ResourceManager} from '../executor/resource_manager';\n\nimport {NodeValueImpl} from './custom_op/node_value_impl';\nimport {getRegisteredOp} from './custom_op/register';\nimport * as arithmetic from './executors/arithmetic_executor';\nimport * as basicMath from './executors/basic_math_executor';\nimport * as control from './executors/control_executor';\nimport * as convolution from './executors/convolution_executor';\nimport * as creation from './executors/creation_executor';\nimport * as dynamic from './executors/dynamic_executor';\nimport * as evaluation from './executors/evaluation_executor';\nimport * as graph from './executors/graph_executor';\nimport * as hashTable from './executors/hash_table_executor';\nimport * as image from './executors/image_executor';\nimport * as logical from './executors/logical_executor';\nimport * as matrices from './executors/matrices_executor';\nimport * as normalization from './executors/normalization_executor';\nimport * as reduction from './executors/reduction_executor';\nimport * as sliceJoin from './executors/slice_join_executor';\nimport * as sparse from './executors/sparse_executor';\nimport * as spectral from './executors/spectral_executor';\nimport * as string from './executors/string_executor';\nimport * as transformation from './executors/transformation_executor';\nimport {Node} from './types';\n\n/**\n * Executes the op defined by the node object.\n * @param node\n * @param tensorMap contains tensors for executed nodes and weights\n * @param context contains tensors and information for running the current node.\n * @param resourceManager Optional. Contains global resources of the model.\n */\nexport function executeOp(\n    node: Node, tensorMap: NamedTensorsMap, context: ExecutionContext,\n    resourceManager?: ResourceManager): tfc.Tensor[]|Promise<tfc.Tensor[]> {\n  const value =\n      ((node: Node, tensorMap: NamedTensorsMap, context: ExecutionContext) => {\n        switch (node.category) {\n          case 'arithmetic':\n            return tfc.tidy(\n                () => arithmetic.executeOp(node, tensorMap, context));\n          case 'basic_math':\n            return tfc.tidy(\n                () => basicMath.executeOp(node, tensorMap, context));\n          case 'control':\n            return control.executeOp(node, tensorMap, context);\n          case 'convolution':\n            return tfc.tidy(\n                () => convolution.executeOp(node, tensorMap, context));\n          case 'creation':\n            return tfc.tidy(() => creation.executeOp(node, tensorMap, context));\n          case 'dynamic':\n            return dynamic.executeOp(node, tensorMap, context);\n          case 'evaluation':\n            return tfc.tidy(\n                () => evaluation.executeOp(node, tensorMap, context));\n          case 'image':\n            return tfc.tidy(() => image.executeOp(node, tensorMap, context));\n          case 'graph':\n            return tfc.tidy(() => graph.executeOp(node, tensorMap, context));\n          case 'logical':\n            return tfc.tidy(() => logical.executeOp(node, tensorMap, context));\n          case 'matrices':\n            return tfc.tidy(() => matrices.executeOp(node, tensorMap, context));\n          case 'normalization':\n            return tfc.tidy(\n                () => normalization.executeOp(node, tensorMap, context));\n          case 'reduction':\n            return tfc.tidy(\n                () => reduction.executeOp(node, tensorMap, context));\n          case 'slice_join':\n            return tfc.tidy(\n                () => sliceJoin.executeOp(node, tensorMap, context));\n          case 'sparse':\n            return tfc.tidy(() => sparse.executeOp(node, tensorMap, context));\n          case 'spectral':\n            return tfc.tidy(() => spectral.executeOp(node, tensorMap, context));\n          case 'string':\n            return tfc.tidy(() => string.executeOp(node, tensorMap, context));\n          case 'transformation':\n            return tfc.tidy(\n                () => transformation.executeOp(node, tensorMap, context));\n          case 'hash_table':\n            return hashTable.executeOp(\n                node, tensorMap, context, resourceManager);\n          case 'custom':\n            const opMapper = getRegisteredOp(node.op);\n            if (opMapper && opMapper.customExecutor) {\n              return opMapper.customExecutor(\n                  new NodeValueImpl(node, tensorMap, context));\n            } else {\n              throw TypeError(`Custom op ${node.op} is not registered.`);\n            }\n          default:\n            throw TypeError(\n                `Unknown op '${node.op}'. File an issue at ` +\n                `https://github.com/tensorflow/tfjs/issues so we can add it` +\n                `, or register a custom execution with tf.registerOp()`);\n        }\n      })(node, tensorMap, context);\n  if (tfc.util.isPromise(value)) {\n    return (value as Promise<tfc.Tensor>).then((data) => [].concat(data));\n  }\n  return [].concat(value);\n}\n"]}","/**\n * ExecutionContext captures the runtime environment of the node. It keeps\n * track of the current frame and iteration for the control flow ops.\n *\n * For example, typical Dynamic RNN model may contain loops, for which\n * TensorFlow will generate graphs with Enter/Exit nodes to control the\n * current execution frame, and NextIteration Nodes for iteration id increment.\n * For model with branch logic, TensorFLow will generate Switch/Merge ops.\n */\nexport class ExecutionContext {\n constructor(weightMap = {}, tensorArrayMap = {}, tensorListMap = {}, functionMap = {}) {\n this.weightMap = weightMap;\n this.tensorArrayMap = tensorArrayMap;\n this.tensorListMap = tensorListMap;\n this.functionMap = functionMap;\n this.rootContext = { id: 0, frameName: '', iterationId: 0 };\n this.contexts = [this.rootContext];\n this.lastId = 0;\n this.generateCurrentContextIds();\n }\n newFrame(id, frameName) {\n return { id, frameName, iterationId: 0 };\n }\n /**\n * Set the current context\n * @param contexts: ExecutionContextInfo[] the current path of execution\n * frames\n */\n set currentContext(contexts) {\n if (this.contexts !== contexts) {\n this.contexts = contexts;\n this.generateCurrentContextIds();\n }\n }\n get currentContext() {\n return this.contexts;\n }\n /**\n * Returns the current context in string format.\n */\n get currentContextId() {\n return this._currentContextIds[0];\n }\n /**\n * Returns the current context and all parent contexts in string format.\n * This allow access to the nodes in the current and parent frames.\n */\n get currentContextIds() {\n return this._currentContextIds;\n }\n generateCurrentContextIds() {\n const names = [];\n for (let i = 0; i < this.contexts.length - 1; i++) {\n const contexts = this.contexts.slice(0, this.contexts.length - i);\n names.push(this.contextIdforContexts(contexts));\n }\n names.push('');\n this._currentContextIds = names;\n }\n contextIdforContexts(contexts) {\n return contexts ?\n contexts\n .map(context => (context.id === 0 && context.iterationId === 0) ?\n '' :\n `${context.frameName}-${context.iterationId}`)\n .join('/') :\n '';\n }\n /**\n * Enter a new frame, a new context is pushed on the current context list.\n * @param frameId new frame id\n */\n enterFrame(frameId) {\n if (this.contexts) {\n this.lastId++;\n this.contexts = this.contexts.slice();\n this.contexts.push(this.newFrame(this.lastId, frameId));\n this._currentContextIds.unshift(this.contextIdforContexts(this.contexts));\n }\n }\n /**\n * Exit the current frame, the last context is removed from the current\n * context list.\n */\n exitFrame() {\n if (this.contexts && this.contexts.length > 1) {\n this.contexts = this.contexts.slice();\n this.contexts.splice(-1);\n this.currentContextIds.shift();\n }\n else {\n throw new Error('Cannot exit frame, the context is empty');\n }\n }\n /**\n * Enter the next iteration of a loop, the iteration id of last context is\n * increased.\n */\n nextIteration() {\n if (this.contexts && this.contexts.length > 0) {\n this.contexts = this.contexts.slice();\n this.lastId++;\n const context = Object.assign({}, this.contexts[this.contexts.length - 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All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor} from '@tensorflow/tfjs-core';\n\nimport {NamedTensorsMap, TensorArrayMap, TensorListMap} from '../data/types';\n\nimport {TensorArray} from './tensor_array';\nimport {TensorList} from './tensor_list';\nimport {FunctionExecutor} from './types';\n\nexport interface ExecutionContextInfo {\n  id: number;           // the unique id of the context info\n  frameName: string;    // The frame name of the loop, this comes from\n                        // the TensorFlow NodeDef.\n  iterationId: number;  // The iteration id of the loop\n}\n\n/**\n * ExecutionContext captures the runtime environment of the node. It keeps\n * track of the current frame and iteration for the control flow ops.\n *\n * For example, typical Dynamic RNN model may contain loops, for which\n * TensorFlow will generate graphs with Enter/Exit nodes to control the\n * current execution frame, and NextIteration Nodes for iteration id increment.\n * For model with branch logic, TensorFLow will generate Switch/Merge ops.\n */\nexport class ExecutionContext {\n  private rootContext = {id: 0, frameName: '', iterationId: 0};\n  private contexts: ExecutionContextInfo[] = [this.rootContext];\n  private lastId = 0;\n  private _currentContextIds: string[];\n\n  constructor(\n      readonly weightMap: NamedTensorsMap = {},\n      readonly tensorArrayMap: TensorArrayMap = {},\n      readonly tensorListMap: TensorListMap = {},\n      readonly functionMap: {[key: string]: FunctionExecutor} = {}) {\n    this.generateCurrentContextIds();\n  }\n\n  private newFrame(id: number, frameName: string) {\n    return {id, frameName, iterationId: 0};\n  }\n\n  /**\n   * Set the current context\n   * @param contexts: ExecutionContextInfo[] the current path of execution\n   * frames\n   */\n  set currentContext(contexts: ExecutionContextInfo[]) {\n    if (this.contexts !== contexts) {\n      this.contexts = contexts;\n      this.generateCurrentContextIds();\n    }\n  }\n\n  get currentContext(): ExecutionContextInfo[] {\n    return this.contexts;\n  }\n\n  /**\n   * Returns the current context in string format.\n   */\n  get currentContextId(): string {\n    return this._currentContextIds[0];\n  }\n\n  /**\n   * Returns the current context and all parent contexts in string format.\n   * This allow access to the nodes in the current and parent frames.\n   */\n  get currentContextIds(): string[] {\n    return this._currentContextIds;\n  }\n\n  private generateCurrentContextIds() {\n    const names = [];\n    for (let i = 0; i < this.contexts.length - 1; i++) {\n      const contexts = this.contexts.slice(0, this.contexts.length - i);\n      names.push(this.contextIdforContexts(contexts));\n    }\n    names.push('');\n    this._currentContextIds = names;\n  }\n\n  private contextIdforContexts(contexts: ExecutionContextInfo[]) {\n    return contexts ?\n        contexts\n            .map(\n                context => (context.id === 0 && context.iterationId === 0) ?\n                    '' :\n                    `${context.frameName}-${context.iterationId}`)\n            .join('/') :\n        '';\n  }\n\n  /**\n   * Enter a new frame, a new context is pushed on the current context list.\n   * @param frameId new frame id\n   */\n  enterFrame(frameId: string) {\n    if (this.contexts) {\n      this.lastId++;\n      this.contexts = this.contexts.slice();\n      this.contexts.push(this.newFrame(this.lastId, frameId));\n      this._currentContextIds.unshift(this.contextIdforContexts(this.contexts));\n    }\n  }\n\n  /**\n   * Exit the current frame, the last context is removed from the current\n   * context list.\n   */\n  exitFrame() {\n    if (this.contexts && this.contexts.length > 1) {\n      this.contexts = this.contexts.slice();\n      this.contexts.splice(-1);\n      this.currentContextIds.shift();\n    } else {\n      throw new Error('Cannot exit frame, the context is empty');\n    }\n  }\n\n  /**\n   * Enter the next iteration of a loop, the iteration id of last context is\n   * increased.\n   */\n  nextIteration() {\n    if (this.contexts && this.contexts.length > 0) {\n      this.contexts = this.contexts.slice();\n      this.lastId++;\n      const context =\n          Object.assign({}, this.contexts[this.contexts.length - 1]);\n      context.iterationId += 1;\n      context.id = this.lastId;\n      this.contexts.splice(-1, 1, context);\n      this._currentContextIds.splice(\n          0, 1, this.contextIdforContexts(this.contexts));\n    } else {\n      throw new Error('Cannot increase frame iteration, the context is empty');\n    }\n  }\n\n  getWeight(name: string): Tensor[] {\n    return this.weightMap[name];\n  }\n\n  addTensorArray(tensorArray: TensorArray) {\n    this.tensorArrayMap[tensorArray.id] = tensorArray;\n  }\n\n  getTensorArray(id: number): TensorArray {\n    return this.tensorArrayMap[id];\n  }\n\n  addTensorList(tensorList: TensorList) {\n    this.tensorListMap[tensorList.id] = tensorList;\n  }\n\n  getTensorList(id: number): TensorList {\n    return this.tensorListMap[id];\n  }\n\n  dispose(keepIds: Set<number>) {\n    for (const key in this.tensorArrayMap) {\n      this.tensorArrayMap[key].clearAndClose(keepIds);\n    }\n\n    for (const key in this.tensorListMap) {\n      this.tensorListMap[key].clearAndClose(keepIds);\n    }\n  }\n}\n"]}","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { parseNodeName } from '../operations/executors/utils';\n/**\n * Given graph inputs and desired outputs, find the minimal set of nodes\n * to execute in order to compute the outputs. In addition return other useful\n * info such:\n * - Missing inputs needed to compute the output.\n * - Whether the subgraph contains dynamic ops (control flow, dynamic shape).\n * - Alternative inputs in order to avoid async (dynamic op) execution.\n */\nexport function getExecutionSubgraph(inputs, outputs, weightMap, initNodes) {\n const usedNodes = new Set();\n const missingInputs = [];\n let dynamicNode = null;\n let syncInputs = null;\n // Start with the outputs, going backwards and find all the nodes that are\n // needed to compute those outputs.\n const seen = new Set();\n const inputNodeNames = Object.keys(inputs).map(name => parseNodeName(name)[0]);\n let initNodeNames = [];\n if (initNodes != null) {\n initNodeNames = initNodes.map(node => parseNodeName(node.name)[0]);\n }\n const frontier = [...outputs];\n while (frontier.length > 0) {\n const node = frontier.pop();\n if (isControlFlow(node) || isDynamicShape(node) || isHashTable(node)) {\n if (dynamicNode == null) {\n dynamicNode = node;\n syncInputs = dynamicNode.children.map(child => child.name)\n .filter(name => usedNodes.has(name));\n }\n }\n usedNodes.add(node.name);\n // Weights are dead end since we already have their values.\n if (weightMap[node.name] != null) {\n continue;\n }\n // This node is a dead end since it's one of the user-provided inputs.\n if (inputNodeNames.indexOf(node.name) !== -1) {\n continue;\n }\n // This node is a dead end since it doesn't have any inputs.\n if (initNodeNames.indexOf(node.name) !== -1) {\n continue;\n }\n if (node.inputs.length === 0) {\n missingInputs.push(node.name);\n continue;\n }\n node.inputs.forEach(input => {\n // Don't add to the frontier if it is already there.\n if (seen.has(input.name)) {\n return;\n }\n seen.add(input.name);\n frontier.push(input);\n });\n }\n return { inputs, outputs, usedNodes, missingInputs, dynamicNode, syncInputs };\n}\n/**\n * Given the execution info, return a list of nodes in topological order that\n * need to be executed to compute the output.\n */\nexport function getNodesInTopologicalOrder(graph, weightMap, executionInfo) {\n const { usedNodes, inputs } = executionInfo;\n const frontier = [];\n const inputNodes = Object.keys(inputs)\n .map(name => parseNodeName(name)[0])\n .map(name => graph.nodes[name]);\n const initNodes = graph.initNodes;\n inputNodes.forEach(input => {\n if (usedNodes.has(input.name)) {\n frontier.push(input);\n }\n });\n graph.weights.forEach(weight => {\n if (usedNodes.has(weight.name)) {\n frontier.push(weight);\n }\n });\n if (initNodes != null) {\n initNodes.forEach(node => {\n if (usedNodes.has(node.name)) {\n frontier.push(node);\n }\n });\n }\n const seen = new Set();\n const orderedNodes = [];\n while (frontier.length > 0) {\n const node = frontier.pop();\n seen.add(node.name);\n if (!weightMap[node.name]) {\n orderedNodes.push(node);\n }\n node.children.forEach(child => {\n if (!seen.has(child.name) && usedNodes.has(child.name) &&\n child.inputs.every(input => seen.has(input.name))) {\n frontier.push(child);\n }\n });\n }\n return orderedNodes;\n}\nconst CONTROL_FLOW_OPS = [\n 'Switch', 'Merge', 'Enter', 'Exit', 'NextIteration', 'StatelessIf',\n 'StatelessWhile', 'if', 'While'\n];\nconst DYNAMIC_SHAPE_OPS = [\n 'NonMaxSuppressionV2', 'NonMaxSuppressionV3', 'NonMaxSuppressionV5', 'Where'\n];\nconst HASH_TABLE_OPS = [\n 'HashTable', 'HashTableV2', 'LookupTableImport', 'LookupTableImportV2',\n 'LookupTableFind', 'LookupTableFindV2', 'LookupTableSize', 'LookupTableSizeV2'\n];\nexport function isControlFlow(node) {\n return CONTROL_FLOW_OPS.indexOf(node.op) >= 0;\n}\nexport function isDynamicShape(node) {\n return DYNAMIC_SHAPE_OPS.indexOf(node.op) >= 0;\n}\nexport function isHashTable(node) {\n return HASH_TABLE_OPS.indexOf(node.op) >= 0;\n}\n//# 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* @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {NamedTensorMap} from '@tensorflow/tfjs-core';\n\nimport {NamedTensorsMap} from '../data/types';\nimport {parseNodeName} from '../operations/executors/utils';\nimport {Graph, Node} from '../operations/types';\n\nexport interface ExecutionInfo {\n  inputs: NamedTensorMap;\n  outputs: Node[];\n  usedNodes: Set<string>;\n  missingInputs: string[];\n  dynamicNode: Node;\n  syncInputs: string[];\n}\n\n/**\n * Given graph inputs and desired outputs, find the minimal set of nodes\n * to execute in order to compute the outputs. In addition return other useful\n * info such:\n * - Missing inputs needed to compute the output.\n * - Whether the subgraph contains dynamic ops (control flow, dynamic shape).\n * - Alternative inputs in order to avoid async (dynamic op) execution.\n */\nexport function getExecutionSubgraph(\n    inputs: NamedTensorMap, outputs: Node[], weightMap: NamedTensorsMap,\n    initNodes?: Node[]): ExecutionInfo {\n  const usedNodes = new Set<string>();\n  const missingInputs: string[] = [];\n  let dynamicNode: Node = null;\n  let syncInputs: string[] = null;\n\n  // Start with the outputs, going backwards and find all the nodes that are\n  // needed to compute those outputs.\n  const seen = new Set<string>();\n  const inputNodeNames =\n      Object.keys(inputs).map(name => parseNodeName(name)[0]);\n\n  let initNodeNames: string[] = [];\n  if (initNodes != null) {\n    initNodeNames = initNodes.map(node => parseNodeName(node.name)[0]);\n  }\n\n  const frontier = [...outputs];\n  while (frontier.length > 0) {\n    const node = frontier.pop();\n    if (isControlFlow(node) || isDynamicShape(node) || isHashTable(node)) {\n      if (dynamicNode == null) {\n        dynamicNode = node;\n        syncInputs = dynamicNode.children.map(child => child.name)\n                         .filter(name => usedNodes.has(name));\n      }\n    }\n    usedNodes.add(node.name);\n\n    // Weights are dead end since we already have their values.\n    if (weightMap[node.name] != null) {\n      continue;\n    }\n    // This node is a dead end since it's one of the user-provided inputs.\n    if (inputNodeNames.indexOf(node.name) !== -1) {\n      continue;\n    }\n    // This node is a dead end since it doesn't have any inputs.\n    if (initNodeNames.indexOf(node.name) !== -1) {\n      continue;\n    }\n    if (node.inputs.length === 0) {\n      missingInputs.push(node.name);\n      continue;\n    }\n    node.inputs.forEach(input => {\n      // Don't add to the frontier if it is already there.\n      if (seen.has(input.name)) {\n        return;\n      }\n      seen.add(input.name);\n      frontier.push(input);\n    });\n  }\n  return {inputs, outputs, usedNodes, missingInputs, dynamicNode, syncInputs};\n}\n\n/**\n * Given the execution info, return a list of nodes in topological order that\n * need to be executed to compute the output.\n */\nexport function getNodesInTopologicalOrder(\n    graph: Graph, weightMap: NamedTensorsMap,\n    executionInfo: ExecutionInfo): Node[] {\n  const {usedNodes, inputs} = executionInfo;\n  const frontier: Node[] = [];\n  const inputNodes = Object.keys(inputs)\n                         .map(name => parseNodeName(name)[0])\n                         .map(name => graph.nodes[name]);\n  const initNodes = graph.initNodes;\n\n  inputNodes.forEach(input => {\n    if (usedNodes.has(input.name)) {\n      frontier.push(input);\n    }\n  });\n  graph.weights.forEach(weight => {\n    if (usedNodes.has(weight.name)) {\n      frontier.push(weight);\n    }\n  });\n  if (initNodes != null) {\n    initNodes.forEach(node => {\n      if (usedNodes.has(node.name)) {\n        frontier.push(node);\n      }\n    });\n  }\n  const seen = new Set<string>();\n  const orderedNodes: Node[] = [];\n  while (frontier.length > 0) {\n    const node = frontier.pop();\n    seen.add(node.name);\n    if (!weightMap[node.name]) {\n      orderedNodes.push(node);\n    }\n    node.children.forEach(child => {\n      if (!seen.has(child.name) && usedNodes.has(child.name) &&\n          child.inputs.every(input => seen.has(input.name))) {\n        frontier.push(child);\n      }\n    });\n  }\n  return orderedNodes;\n}\n\nconst CONTROL_FLOW_OPS = [\n  'Switch', 'Merge', 'Enter', 'Exit', 'NextIteration', 'StatelessIf',\n  'StatelessWhile', 'if', 'While'\n];\nconst DYNAMIC_SHAPE_OPS = [\n  'NonMaxSuppressionV2', 'NonMaxSuppressionV3', 'NonMaxSuppressionV5', 'Where'\n];\nconst HASH_TABLE_OPS = [\n  'HashTable', 'HashTableV2', 'LookupTableImport', 'LookupTableImportV2',\n  'LookupTableFind', 'LookupTableFindV2', 'LookupTableSize', 'LookupTableSizeV2'\n];\n\nexport function isControlFlow(node: Node) {\n  return CONTROL_FLOW_OPS.indexOf(node.op) >= 0;\n}\n\nexport function isDynamicShape(node: Node) {\n  return DYNAMIC_SHAPE_OPS.indexOf(node.op) >= 0;\n}\n\nexport function isHashTable(node: Node) {\n  return HASH_TABLE_OPS.indexOf(node.op) >= 0;\n}\n"]}","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env, tidy, util } from '@tensorflow/tfjs-core';\nimport { getNodeNameAndIndex, getParamValue, getTensor, getTensorsForCurrentContenxt, parseNodeName } from '../operations/executors/utils';\nimport { executeOp } from '../operations/operation_executor';\nimport { ExecutionContext } from './execution_context';\nimport { getExecutionSubgraph, getNodesInTopologicalOrder, isControlFlow } from './model_analysis';\nexport class GraphExecutor {\n /**\n *\n * @param graph Graph the model or function graph to be executed.\n * @param parent When building function exector you need to set the parent\n * executor. Since the weights and function executor maps are set at parant\n * level, that function executor can access the function maps and weight maps\n * through the parent.\n */\n constructor(graph, parent) {\n this.graph = graph;\n this.parent = parent;\n this.compiledMap = new Map();\n this._weightMap = {};\n this.SEPERATOR = ',';\n this._functions = {};\n this._functionExecutorMap = {};\n this.intermediateTensors = {};\n this.keepTensorForDebug = false;\n this._outputs = graph.outputs;\n this._inputs = graph.inputs;\n this._initNodes = graph.initNodes;\n this._signature = graph.signature;\n this._functions = graph.functions;\n // create sub-graph executors\n if (graph.functions != null) {\n Object.keys(graph.functions).forEach(name => {\n this._functionExecutorMap[name] =\n new GraphExecutor(graph.functions[name], this);\n });\n }\n }\n get weightIds() {\n return this.parent ? this.parent.weightIds : this._weightIds;\n }\n get functionExecutorMap() {\n return this.parent ? this.parent.functionExecutorMap :\n this._functionExecutorMap;\n }\n get weightMap() {\n return this.parent ? this.parent.weightMap : this._weightMap;\n }\n set weightMap(weightMap) {\n const weightIds = Object.keys(weightMap).map(key => weightMap[key].map(tensor => tensor.id));\n this._weightIds = [].concat(...weightIds);\n this._weightMap = weightMap;\n }\n /**\n * Set `ResourceManager` shared by executors of a model.\n * @param resourceManager: `ResourceManager` of the `GraphModel`.\n */\n set resourceManager(resourceManager) {\n this._resourceManager = resourceManager;\n }\n get inputs() {\n return this._inputs.map(node => {\n return {\n name: node.name,\n shape: node.attrParams['shape'] ?\n node.attrParams['shape'].value :\n undefined,\n dtype: node.attrParams['dtype'] ?\n node.attrParams['dtype'].value :\n undefined\n };\n });\n }\n get outputs() {\n return this._outputs.map(node => {\n return {\n name: node.name,\n shape: node.attrParams['shape'] ?\n node.attrParams['shape'].value :\n undefined,\n dtype: node.attrParams['dtype'] ?\n node.attrParams['dtype'].value :\n undefined\n };\n });\n }\n get inputNodes() {\n return this._inputs.map(node => node.signatureKey || node.name);\n }\n get outputNodes() {\n return this._outputs.map((node) => {\n const name = node.signatureKey || node.name;\n return node.defaultOutput ? (`${name}:${node.defaultOutput}`) : name;\n });\n }\n get functions() {\n return Object.keys(this._functions).reduce((map, key) => {\n map[key] = this._functions[key].signature;\n return map;\n }, {});\n }\n getCompilationKey(inputs, outputs) {\n const sortedInputs = inputs.map(node => node.name).sort();\n const sortedOutputs = outputs.map(node => node.name).sort();\n return sortedInputs.join(this.SEPERATOR) + '--' +\n sortedOutputs.join(this.SEPERATOR);\n }\n /**\n * Compiles the inference graph and returns the minimal set of nodes that are\n * required for execution, in the correct execution order.\n */\n compile(inputs, outputs) {\n const executionInfo = getExecutionSubgraph(inputs, outputs, this.weightMap, this._initNodes);\n const { missingInputs, dynamicNode, syncInputs } = executionInfo;\n if (dynamicNode != null) {\n throw new Error(`This execution contains the node '${dynamicNode.name}', which has ` +\n `the dynamic op '${dynamicNode.op}'. Please use ` +\n `model.executeAsync() instead. Alternatively, to avoid the ` +\n `dynamic ops, specify the inputs [${syncInputs}]`);\n }\n if (missingInputs.length > 0) {\n const outNames = outputs.map(n => n.name);\n const inNames = Object.keys(inputs);\n throw new Error(`Cannot compute the outputs [${outNames}] from the provided inputs ` +\n `[${inNames}]. Missing the following inputs: [${missingInputs}]`);\n }\n return getNodesInTopologicalOrder(this.graph, this.weightMap, executionInfo);\n }\n /**\n * Executes the inference for given input tensors.\n * @param inputs Tensor map for the model inputs, keyed by the input node\n * names.\n * @param outputs Optional. output node name from the Tensorflow model, if\n * no outputs are specified, the default outputs of the model would be used.\n * You can inspect intermediate nodes of the model by adding them to the\n * outputs array.\n */\n execute(inputs, outputs) {\n inputs = this.mapInputs(inputs);\n const names = Object.keys(inputs).sort();\n this.checkInputs(inputs);\n this.checkInputShapeAndType(inputs);\n outputs = this.mapOutputs(outputs);\n this.checkOutputs(outputs);\n const inputNodes = names.map(name => this.graph.nodes[parseNodeName(name)[0]]);\n const outputNodeNames = outputs.map(name => parseNodeName(name)[0]);\n let outputNodes = outputNodeNames.map(name => this.graph.nodes[name]);\n this.resetIntermediateTensors();\n // If no outputs are specified, then use the default outputs of the model.\n if (outputNodes.length === 0) {\n outputNodes = this._outputs;\n }\n const compilationKey = this.getCompilationKey(inputNodes, outputNodes);\n // Do nothing if the compiled graph cache contains the input.\n let orderedNodes = this.compiledMap.get(compilationKey);\n if (orderedNodes == null) {\n orderedNodes = this.compile(inputs, outputNodes);\n this.compiledMap.set(compilationKey, orderedNodes);\n }\n const tensorArrayMap = {};\n const tensorListMap = {};\n return tidy(() => {\n const context = new ExecutionContext(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap);\n const tensorsMap = Object.assign({}, this.weightMap);\n Object.keys(inputs).forEach(name => {\n const [nodeName, index] = parseNodeName(name);\n const tensors = [];\n tensors[index] = inputs[name];\n tensorsMap[nodeName] = tensors;\n });\n const tensorsToKeep = this.getFrozenTensorIds(tensorsMap);\n const intermediateTensorConsumerCount = {};\n for (let i = 0; i < orderedNodes.length; i++) {\n const node = orderedNodes[i];\n if (!tensorsMap[node.name]) {\n const tensors = executeOp(node, tensorsMap, context, this._resourceManager);\n if (util.isPromise(tensors)) {\n throw new Error(`The execution of the op '${node.op}' returned a promise. ` +\n `Please use model.executeAsync() instead.`);\n }\n tensorsMap[node.name] = tensors;\n this.checkTensorForDisposal(node.name, node, tensorsMap, context, tensorsToKeep, outputNodeNames, intermediateTensorConsumerCount);\n }\n }\n // dispose the context for the root executor\n if (this.parent == null) {\n context.dispose(tensorsToKeep);\n }\n return outputs.map(name => getTensor(name, tensorsMap, context));\n });\n }\n getFrozenTensorIds(tensorMap) {\n const ids = [].concat.apply([], Object.keys(tensorMap)\n .map(key => tensorMap[key])\n .map(tensors => tensors.map(tensor => tensor.id)));\n return new Set(ids);\n }\n checkTensorForDisposal(nodeName, node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount) {\n // Skip output nodes and any control flow nodes, since its dependency is\n // tricky to track correctly.\n if (node.category === 'control' || outputNames.indexOf(nodeName) !== -1) {\n return;\n }\n tensorMap[nodeName].forEach(tensor => {\n if (tensor != null) {\n intermediateTensorConsumerCount[tensor.id] =\n (intermediateTensorConsumerCount[tensor.id] || 0) +\n node.children.length;\n }\n });\n node.inputs.forEach(input => {\n // Skip any control flow nodes, since its dependency is tricky to track\n // correctly.\n if (input.category !== 'control') {\n const tensors = getTensorsForCurrentContenxt(input.name, tensorMap, context);\n if (tensors != null) {\n tensors.forEach(tensor => {\n if (tensor && !tensor.kept && !tensorsToKeep.has(tensor.id)) {\n const count = intermediateTensorConsumerCount[tensor.id];\n if (count === 1) {\n if (!this.keepTensorForDebug) {\n tensor.dispose();\n }\n else {\n const [nodeName, index] = getNodeNameAndIndex(node.name, context);\n if (this.intermediateTensors[nodeName]) {\n this.intermediateTensors[nodeName][index] = tensor;\n }\n else {\n this.intermediateTensors[nodeName] = [];\n this.intermediateTensors[nodeName][index] = tensor;\n }\n }\n delete intermediateTensorConsumerCount[tensor.id];\n }\n else if (count != null) {\n // only intermediate nodes has count set, inputs and weights are\n // not.\n intermediateTensorConsumerCount[tensor.id]--;\n }\n }\n });\n }\n }\n });\n }\n /**\n * Executes the inference for given input tensors in Async fashion.\n * @param inputs Tensor map for the model inputs, keyed by the input node\n * names.\n * @param outputs output node name from the Tensorflow model, if no outputs\n * are specified, the default outputs of the model would be used. You can\n * inspect intermediate nodes of the model by adding them to the outputs\n * array.\n */\n async executeAsync(inputs, outputs) {\n return this._executeAsync(inputs, outputs);\n }\n disposeIntermediateTensors() {\n if (!this.intermediateTensors) {\n return;\n }\n Object.keys(this.intermediateTensors)\n .forEach(key => this.intermediateTensors[key].forEach(tensor => tensor.dispose()));\n this.disposeTensorsMap();\n }\n disposeTensorsMap() {\n if (!this.tensorsMap) {\n return;\n }\n Object.keys(this.tensorsMap).forEach(key => {\n const tensorArray = this.tensorsMap[key];\n tensorArray.forEach(tensor => {\n if (tensor && !tensor.kept && !tensor.isDisposed &&\n !this.keepIds.has(tensor.id)) {\n tensor.dispose();\n }\n });\n });\n }\n getIntermediateTensors() {\n return this.tensorsMap;\n }\n resetIntermediateTensors() {\n for (const key in this.intermediateTensors) {\n this.intermediateTensors[key].forEach(tensor => tensor.dispose());\n delete this.intermediateTensors[key];\n }\n }\n /**\n * Executes the inference for given input tensors in Async fashion.\n * @param inputs Tensor map for the model inputs, keyed by the input node\n * names.\n * @param outputs Optional. output node name from the Tensorflow model,\n * if no outputs are specified, the default outputs of the model would be\n * used. You can inspect intermediate nodes of the model by adding them to the\n * outputs array.\n * @param isFunctionExecution Optional. Flag for executing a function.\n * @param tensorArrayMap Optional, global TensorArray map by id. Used for\n * function execution.\n * @param tensorArrayMap Optinal global TensorList map by id. Used for\n * function execution.\n */\n async _executeAsync(inputs, outputs, isFunctionExecution = false, tensorArrayMap = {}, tensorListMap = {}) {\n if (!isFunctionExecution) {\n inputs = this.mapInputs(inputs);\n this.checkInputs(inputs);\n this.checkInputShapeAndType(inputs);\n outputs = this.mapOutputs(outputs);\n this.checkOutputs(outputs);\n }\n // For model debug.\n try {\n this.keepTensorForDebug = env().getBool('KEEP_INTERMEDIATE_TENSORS');\n }\n catch (e) {\n console.warn(e.message);\n }\n this.resetIntermediateTensors();\n const context = new ExecutionContext(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap);\n // Graph with control flow op requires runtime evaluation of the execution\n // order, while without control flow the execution order is pre-determined\n // in the compile method.\n this.tensorsMap = await this.executeWithControlFlow(inputs, context, outputs, isFunctionExecution);\n const results = outputs.map(name => getTensor(name, this.tensorsMap, context));\n // dispose all the intermediate tensors\n const outputIds = results.map(t => t.id);\n const inputIds = Object.keys(inputs).map(name => inputs[name].id);\n this.keepIds =\n new Set([...outputIds, ...inputIds, ...this.weightIds]);\n if (!this.keepTensorForDebug) {\n this.disposeTensorsMap();\n }\n // dispose the context for the root executor\n if (this.parent == null) {\n context.dispose(this.keepIds);\n }\n return results;\n }\n async executeFunctionAsync(inputs, tensorArrayMap, tensorListMap) {\n const mappedInputs = inputs.reduce((map, tensor, index) => {\n map[this.inputs[index].name] = tensor;\n return map;\n }, {});\n return this._executeAsync(mappedInputs, this.outputNodes, true, tensorArrayMap, tensorListMap);\n }\n /**\n * When there are control flow nodes in the graph, the graph execution use\n * ExecutionContext to keep track of the frames and loop iterators.\n * @param inputs placeholder tensors for the graph.\n * @param context the execution context object for current execution.\n * @param outputNames Optional. output node name from the Tensorflow model,\n * if no outputs are specified, the default outputs of the model would be\n * used. You can inspect intermediate nodes of the model by adding them to the\n * outputs array.\n * @param isFunctionExecution Flag for executing a function.\n */\n async executeWithControlFlow(inputs, context, outputNames, isFunctionExecution) {\n const names = Object.keys(inputs);\n const inputNodes = names.map(name => this.graph.nodes[parseNodeName(name)[0]]);\n const outputNodeNames = outputNames.map(name => parseNodeName(name)[0]);\n let outputNodes = outputNodeNames.map(name => this.graph.nodes[name]);\n // If no outputs are specified, then use the default outputs of the model.\n if (outputNodes.length === 0) {\n outputNodes = this._outputs;\n }\n const { usedNodes, missingInputs, dynamicNode, syncInputs } = getExecutionSubgraph(inputs, outputNodes, this.weightMap, this._initNodes);\n // First nodes to execute include inputNodes, weights, and initNodes.\n const stack = [\n ...inputNodes, ...this.graph.weights, ...(this._initNodes || [])\n ].map(node => {\n return { node, contexts: context.currentContext };\n });\n const tensorsMap = Object.assign({}, this.weightMap);\n Object.keys(inputs).forEach(name => {\n const [nodeName, index] = parseNodeName(name);\n const tensors = [];\n tensors[index] = inputs[name];\n tensorsMap[nodeName] = tensors;\n });\n const intermediateTensorConsumerCount = {};\n const tensorsToKeep = this.getFrozenTensorIds(tensorsMap);\n const added = {};\n while (stack.length > 0) {\n const promises = this.processStack(inputNodes, stack, context, tensorsMap, added, tensorsToKeep, outputNodeNames, intermediateTensorConsumerCount, usedNodes);\n await Promise.all(promises);\n }\n if (dynamicNode == null && !isFunctionExecution) {\n console.warn(`This model execution did not contain any nodes with control flow ` +\n `or dynamic output shapes. You can use model.execute() instead.`);\n }\n const missingOutputs = outputNodes\n .filter(node => !isControlFlow(node) &&\n !getTensor(node.name, tensorsMap, context))\n .map(node => node.name);\n if (missingOutputs.length > 0) {\n let alternativeMsg = '';\n if (dynamicNode != null) {\n alternativeMsg =\n `Alternatively, to avoid the dynamic ops, use model.execute() ` +\n `and specify the inputs [${syncInputs}]`;\n }\n throw new Error(`Cannot compute the outputs [${missingOutputs}] from the provided ` +\n `inputs [${names}]. Consider providing the following inputs: ` +\n `[${missingInputs}]. ${alternativeMsg}`);\n }\n return tensorsMap;\n }\n processStack(inputNodes, stack, context, tensorMap, added, tensorsToKeep, outputNames, intermediateTensorConsumerCount, usedNodes) {\n const promises = [];\n while (stack.length > 0) {\n const item = stack.pop();\n context.currentContext = item.contexts;\n let nodeName = '';\n // The tensor of the Enter op with isConstant set should be set\n // in the parent scope, so it will be available as constant for the\n // whole loop.\n if (item.node.op === 'Enter' &&\n getParamValue('isConstant', item.node, tensorMap, context)) {\n [nodeName] = getNodeNameAndIndex(item.node.name, context);\n }\n // only process nodes that are not in the tensorMap yet, this include\n // inputNodes and internal initNodes.\n if (tensorMap[item.node.name] == null) {\n const tensors = executeOp(item.node, tensorMap, context, this._resourceManager);\n if (!nodeName) {\n [nodeName] = getNodeNameAndIndex(item.node.name, context);\n }\n const currentContext = context.currentContext;\n if (util.isPromise(tensors)) {\n promises.push(tensors.then(t => {\n tensorMap[nodeName] = t;\n context.currentContext = currentContext;\n this.checkTensorForDisposal(nodeName, item.node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount);\n this.processChildNodes(item.node, stack, context, tensorMap, added, usedNodes);\n return t;\n }));\n }\n else {\n tensorMap[nodeName] = tensors;\n this.checkTensorForDisposal(nodeName, item.node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount);\n this.processChildNodes(item.node, stack, context, tensorMap, added, usedNodes);\n }\n }\n else {\n this.processChildNodes(item.node, stack, context, tensorMap, added, usedNodes);\n }\n }\n return promises;\n }\n processChildNodes(node, stack, context, tensorMap, added, usedNodes) {\n node.children.forEach((childNode) => {\n const [nodeName,] = getNodeNameAndIndex(childNode.name, context);\n if (added[nodeName] || !usedNodes.has(childNode.name)) {\n return;\n }\n // Merge op can be pushed if any of its inputs has value.\n if (childNode.op === 'Merge') {\n if (childNode.inputNames.some(name => {\n return !!getTensor(name, tensorMap, context);\n })) {\n added[nodeName] = true;\n stack.push({ contexts: context.currentContext, node: childNode });\n }\n }\n else // Otherwise all inputs must to have value.\n if (childNode.inputNames.every(name => {\n return !!getTensor(name, tensorMap, context);\n })) {\n added[nodeName] = true;\n stack.push({ contexts: context.currentContext, node: childNode });\n }\n });\n }\n /**\n * Releases the memory used by the weight tensors.\n */\n dispose() {\n Object.keys(this.weightMap)\n .forEach(key => this.weightMap[key].forEach(tensor => tensor.dispose()));\n }\n checkInputShapeAndType(inputs) {\n Object.keys(inputs).forEach(name => {\n const input = inputs[name];\n const [nodeName,] = 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* @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {DataType, env, NamedTensorMap, Tensor, tidy, util} from '@tensorflow/tfjs-core';\n\nimport {ISignatureDef} from '../data/compiled_api';\nimport {NamedTensorsMap, TensorArrayMap, TensorInfo, TensorListMap} from '../data/types';\nimport {getNodeNameAndIndex, getParamValue, getTensor, getTensorsForCurrentContenxt, parseNodeName} from '../operations/executors/utils';\nimport {executeOp} from '../operations/operation_executor';\nimport {Graph, Node} from '../operations/types';\n\nimport {ExecutionContext, ExecutionContextInfo} from './execution_context';\nimport {getExecutionSubgraph, getNodesInTopologicalOrder, isControlFlow} from './model_analysis';\nimport {ResourceManager} from './resource_manager';\nimport {FunctionExecutor} from './types';\n\ninterface NodeWithContexts {\n  contexts: ExecutionContextInfo[];\n  node: Node;\n}\n\nexport class GraphExecutor implements FunctionExecutor {\n  private compiledMap: Map<string, Node[]> = new Map();\n  private _weightMap: NamedTensorsMap = {};\n  private _weightIds: number[];\n  private _signature: ISignatureDef;\n  private _inputs: Node[];\n  private _outputs: Node[];\n  private _initNodes: Node[];  // Internal init nodes to start initialization.\n  private SEPERATOR = ',';\n  private _functions: {[key: string]: Graph} = {};\n  private _functionExecutorMap: {[key: string]: FunctionExecutor} = {};\n  private _resourceManager: ResourceManager;\n  private intermediateTensors: NamedTensorsMap = {};\n  private keepIds: Set<number>;\n  private tensorsMap: NamedTensorsMap;\n  private keepTensorForDebug = false;\n\n  get weightIds(): number[] {\n    return this.parent ? this.parent.weightIds : this._weightIds;\n  }\n\n  get functionExecutorMap(): {[key: string]: FunctionExecutor} {\n    return this.parent ? this.parent.functionExecutorMap :\n                         this._functionExecutorMap;\n  }\n\n  get weightMap(): NamedTensorsMap {\n    return this.parent ? this.parent.weightMap : this._weightMap;\n  }\n\n  set weightMap(weightMap: NamedTensorsMap) {\n    const weightIds = Object.keys(weightMap).map(\n        key => weightMap[key].map(tensor => tensor.id));\n    this._weightIds = [].concat(...weightIds);\n    this._weightMap = weightMap;\n  }\n\n  /**\n   * Set `ResourceManager` shared by executors of a model.\n   * @param resourceManager: `ResourceManager` of the `GraphModel`.\n   */\n  set resourceManager(resourceManager: ResourceManager) {\n    this._resourceManager = resourceManager;\n  }\n\n  get inputs(): TensorInfo[] {\n    return this._inputs.map(node => {\n      return {\n        name: node.name,\n        shape: node.attrParams['shape'] ?\n            node.attrParams['shape'].value as number[] :\n            undefined,\n        dtype: node.attrParams['dtype'] ?\n            node.attrParams['dtype'].value as DataType :\n            undefined\n      };\n    });\n  }\n\n  get outputs(): TensorInfo[] {\n    return this._outputs.map(node => {\n      return {\n        name: node.name,\n        shape: node.attrParams['shape'] ?\n            node.attrParams['shape'].value as number[] :\n            undefined,\n        dtype: node.attrParams['dtype'] ?\n            node.attrParams['dtype'].value as DataType :\n            undefined\n      };\n    });\n  }\n\n  get inputNodes(): string[] {\n    return this._inputs.map(node => node.signatureKey || node.name);\n  }\n\n  get outputNodes(): string[] {\n    return this._outputs.map((node) => {\n      const name = node.signatureKey || node.name;\n      return node.defaultOutput ? (`${name}:${node.defaultOutput}`) : name;\n    });\n  }\n\n  get functions(): {[key: string]: ISignatureDef} {\n    return Object.keys(this._functions).reduce((map, key) => {\n      map[key] = this._functions[key].signature;\n      return map;\n    }, {} as {[key: string]: ISignatureDef});\n  }\n\n  /**\n   *\n   * @param graph Graph the model or function graph to be executed.\n   * @param parent When building function exector you need to set the parent\n   * executor. Since the weights and function executor maps are set at parant\n   * level, that function executor can access the function maps and weight maps\n   * through the parent.\n   */\n  constructor(private graph: Graph, private parent?: GraphExecutor) {\n    this._outputs = graph.outputs;\n    this._inputs = graph.inputs;\n    this._initNodes = graph.initNodes;\n    this._signature = graph.signature;\n    this._functions = graph.functions;\n    // create sub-graph executors\n    if (graph.functions != null) {\n      Object.keys(graph.functions).forEach(name => {\n        this._functionExecutorMap[name] =\n            new GraphExecutor(graph.functions[name], this);\n      });\n    }\n  }\n\n  private getCompilationKey(inputs: Node[], outputs: Node[]): string {\n    const sortedInputs = inputs.map(node => node.name).sort();\n    const sortedOutputs = outputs.map(node => node.name).sort();\n    return sortedInputs.join(this.SEPERATOR) + '--' +\n        sortedOutputs.join(this.SEPERATOR);\n  }\n\n  /**\n   * Compiles the inference graph and returns the minimal set of nodes that are\n   * required for execution, in the correct execution order.\n   */\n  private compile(inputs: NamedTensorMap, outputs: Node[]): Node[] {\n    const executionInfo =\n        getExecutionSubgraph(inputs, outputs, this.weightMap, this._initNodes);\n    const {missingInputs, dynamicNode, syncInputs} = executionInfo;\n    if (dynamicNode != null) {\n      throw new Error(\n          `This execution contains the node '${dynamicNode.name}', which has ` +\n          `the dynamic op '${dynamicNode.op}'. Please use ` +\n          `model.executeAsync() instead. Alternatively, to avoid the ` +\n          `dynamic ops, specify the inputs [${syncInputs}]`);\n    }\n\n    if (missingInputs.length > 0) {\n      const outNames = outputs.map(n => n.name);\n      const inNames = Object.keys(inputs);\n      throw new Error(\n          `Cannot compute the outputs [${outNames}] from the provided inputs ` +\n          `[${inNames}]. Missing the following inputs: [${missingInputs}]`);\n    }\n\n    return getNodesInTopologicalOrder(\n        this.graph, this.weightMap, executionInfo);\n  }\n\n  /**\n   * Executes the inference for given input tensors.\n   * @param inputs Tensor map for the model inputs, keyed by the input node\n   * names.\n   * @param outputs Optional. output node name from the Tensorflow model, if\n   * no outputs are specified, the default outputs of the model would be used.\n   * You can inspect intermediate nodes of the model by adding them to the\n   * outputs array.\n   */\n  execute(inputs: NamedTensorMap, outputs?: string[]): Tensor[] {\n    inputs = this.mapInputs(inputs);\n    const names = Object.keys(inputs).sort();\n    this.checkInputs(inputs);\n    this.checkInputShapeAndType(inputs);\n    outputs = this.mapOutputs(outputs);\n    this.checkOutputs(outputs);\n    const inputNodes =\n        names.map(name => this.graph.nodes[parseNodeName(name)[0]]);\n    const outputNodeNames = outputs.map(name => parseNodeName(name)[0]);\n    let outputNodes = outputNodeNames.map(name => this.graph.nodes[name]);\n    this.resetIntermediateTensors();\n    // If no outputs are specified, then use the default outputs of the model.\n    if (outputNodes.length === 0) {\n      outputNodes = this._outputs;\n    }\n\n    const compilationKey = this.getCompilationKey(inputNodes, outputNodes);\n\n    // Do nothing if the compiled graph cache contains the input.\n    let orderedNodes = this.compiledMap.get(compilationKey);\n    if (orderedNodes == null) {\n      orderedNodes = this.compile(inputs, outputNodes);\n      this.compiledMap.set(compilationKey, orderedNodes);\n    }\n\n    const tensorArrayMap: TensorArrayMap = {};\n    const tensorListMap: TensorListMap = {};\n\n    return tidy(() => {\n      const context = new ExecutionContext(\n          this.weightMap, tensorArrayMap, tensorListMap,\n          this.functionExecutorMap);\n      const tensorsMap: NamedTensorsMap = {...this.weightMap};\n\n      Object.keys(inputs).forEach(name => {\n        const [nodeName, index] = parseNodeName(name);\n        const tensors: Tensor[] = [];\n        tensors[index] = inputs[name];\n        tensorsMap[nodeName] = tensors;\n      });\n\n      const tensorsToKeep = this.getFrozenTensorIds(tensorsMap);\n      const intermediateTensorConsumerCount: {[key: number]: number} = {};\n      for (let i = 0; i < orderedNodes.length; i++) {\n        const node = orderedNodes[i];\n        if (!tensorsMap[node.name]) {\n          const tensors =\n              executeOp(node, tensorsMap, context, this._resourceManager) as\n              Tensor[];\n          if (util.isPromise(tensors)) {\n            throw new Error(\n                `The execution of the op '${node.op}' returned a promise. ` +\n                `Please use model.executeAsync() instead.`);\n          }\n          tensorsMap[node.name] = tensors;\n          this.checkTensorForDisposal(\n              node.name, node, tensorsMap, context, tensorsToKeep,\n              outputNodeNames, intermediateTensorConsumerCount);\n        }\n      }\n      // dispose the context for the root executor\n      if (this.parent == null) {\n        context.dispose(tensorsToKeep);\n      }\n      return outputs.map(name => getTensor(name, tensorsMap, context));\n    });\n  }\n\n  private getFrozenTensorIds(tensorMap: NamedTensorsMap): Set<number> {\n    const ids = [].concat.apply(\n        [],\n        Object.keys(tensorMap)\n            .map(key => tensorMap[key])\n            .map(tensors => tensors.map(tensor => tensor.id)));\n    return new Set(ids);\n  }\n  private checkTensorForDisposal(\n      nodeName: string, node: Node, tensorMap: NamedTensorsMap,\n      context: ExecutionContext, tensorsToKeep: Set<number>,\n      outputNames: string[],\n      intermediateTensorConsumerCount: {[key: string]: number}) {\n    // Skip output nodes and any control flow nodes, since its dependency is\n    // tricky to track correctly.\n    if (node.category === 'control' || outputNames.indexOf(nodeName) !== -1) {\n      return;\n    }\n\n    tensorMap[nodeName].forEach(tensor => {\n      if (tensor != null) {\n        intermediateTensorConsumerCount[tensor.id] =\n            (intermediateTensorConsumerCount[tensor.id] || 0) +\n            node.children.length;\n      }\n    });\n    node.inputs.forEach(input => {\n      // Skip any control flow nodes, since its dependency is tricky to track\n      // correctly.\n      if (input.category !== 'control') {\n        const tensors =\n            getTensorsForCurrentContenxt(input.name, tensorMap, context);\n        if (tensors != null) {\n          tensors.forEach(tensor => {\n            if (tensor && !tensor.kept && !tensorsToKeep.has(tensor.id)) {\n              const count = intermediateTensorConsumerCount[tensor.id];\n              if (count === 1) {\n                if (!this.keepTensorForDebug) {\n                  tensor.dispose();\n                } else {\n                  const [nodeName, index] =\n                      getNodeNameAndIndex(node.name, context);\n                  if (this.intermediateTensors[nodeName]) {\n                    this.intermediateTensors[nodeName][index] = tensor;\n                  } else {\n                    this.intermediateTensors[nodeName] = [];\n                    this.intermediateTensors[nodeName][index] = tensor;\n                  }\n                }\n                delete intermediateTensorConsumerCount[tensor.id];\n              } else if (count != null) {\n                // only intermediate nodes has count set, inputs and weights are\n                // not.\n                intermediateTensorConsumerCount[tensor.id]--;\n              }\n            }\n          });\n        }\n      }\n    });\n  }\n\n  /**\n   * Executes the inference for given input tensors in Async fashion.\n   * @param inputs Tensor map for the model inputs, keyed by the input node\n   * names.\n   * @param outputs output node name from the Tensorflow model, if no outputs\n   * are specified, the default outputs of the model would be used. You can\n   * inspect intermediate nodes of the model by adding them to the outputs\n   * array.\n   */\n  async executeAsync(inputs: NamedTensorMap, outputs?: string[]):\n      Promise<Tensor[]> {\n    return this._executeAsync(inputs, outputs);\n  }\n\n  disposeIntermediateTensors() {\n    if (!this.intermediateTensors) {\n      return;\n    }\n    Object.keys(this.intermediateTensors)\n        .forEach(\n            key => this.intermediateTensors[key].forEach(\n                tensor => tensor.dispose()));\n    this.disposeTensorsMap();\n  }\n\n  private disposeTensorsMap() {\n    if (!this.tensorsMap) {\n      return;\n    }\n    Object.keys(this.tensorsMap).forEach(key => {\n      const tensorArray = this.tensorsMap[key];\n      tensorArray.forEach(tensor => {\n        if (tensor && !tensor.kept && !tensor.isDisposed &&\n            !this.keepIds.has(tensor.id)) {\n          tensor.dispose();\n        }\n      });\n    });\n  }\n\n  getIntermediateTensors(): NamedTensorsMap {\n    return this.tensorsMap;\n  }\n\n  private resetIntermediateTensors() {\n    for (const key in this.intermediateTensors) {\n      this.intermediateTensors[key].forEach(tensor => tensor.dispose());\n      delete this.intermediateTensors[key];\n    }\n  }\n\n  /**\n   * Executes the inference for given input tensors in Async fashion.\n   * @param inputs Tensor map for the model inputs, keyed by the input node\n   * names.\n   * @param outputs Optional. output node name from the Tensorflow model,\n   * if no outputs are specified, the default outputs of the model would be\n   * used. You can inspect intermediate nodes of the model by adding them to the\n   * outputs array.\n   * @param isFunctionExecution Optional. Flag for executing a function.\n   * @param tensorArrayMap Optional, global TensorArray map by id. Used for\n   * function execution.\n   * @param tensorArrayMap Optinal global TensorList map by id. Used for\n   * function execution.\n   */\n  private async _executeAsync(\n      inputs: NamedTensorMap, outputs?: string[], isFunctionExecution = false,\n      tensorArrayMap: TensorArrayMap = {},\n      tensorListMap: TensorListMap = {}): Promise<Tensor[]> {\n    if (!isFunctionExecution) {\n      inputs = this.mapInputs(inputs);\n      this.checkInputs(inputs);\n      this.checkInputShapeAndType(inputs);\n      outputs = this.mapOutputs(outputs);\n      this.checkOutputs(outputs);\n    }\n\n    // For model debug.\n    try {\n      this.keepTensorForDebug = env().getBool('KEEP_INTERMEDIATE_TENSORS');\n    } catch (e) {\n      console.warn(e.message);\n    }\n    this.resetIntermediateTensors();\n\n    const context = new ExecutionContext(\n        this.weightMap, tensorArrayMap, tensorListMap,\n        this.functionExecutorMap);\n\n    // Graph with control flow op requires runtime evaluation of the execution\n    // order, while without control flow the execution order is pre-determined\n    // in the compile method.\n    this.tensorsMap = await this.executeWithControlFlow(\n        inputs, context, outputs, isFunctionExecution);\n    const results =\n        outputs.map(name => getTensor(name, this.tensorsMap, context));\n\n    // dispose all the intermediate tensors\n    const outputIds = results.map(t => t.id);\n    const inputIds = Object.keys(inputs).map(name => inputs[name].id);\n    this.keepIds =\n        new Set<number>([...outputIds, ...inputIds, ...this.weightIds]);\n    if (!this.keepTensorForDebug) {\n      this.disposeTensorsMap();\n    }\n\n    // dispose the context for the root executor\n    if (this.parent == null) {\n      context.dispose(this.keepIds);\n    }\n\n    return results;\n  }\n\n  async executeFunctionAsync(\n      inputs: Tensor[], tensorArrayMap: TensorArrayMap,\n      tensorListMap: TensorListMap): Promise<Tensor[]> {\n    const mappedInputs = inputs.reduce((map, tensor, index) => {\n      map[this.inputs[index].name] = tensor;\n      return map;\n    }, {} as NamedTensorMap);\n\n    return this._executeAsync(\n        mappedInputs, this.outputNodes, true, tensorArrayMap, tensorListMap);\n  }\n\n  /**\n   * When there are control flow nodes in the graph, the graph execution use\n   * ExecutionContext to keep track of the frames and loop iterators.\n   * @param inputs placeholder tensors for the graph.\n   * @param context the execution context object for current execution.\n   * @param outputNames Optional. output node name from the Tensorflow model,\n   * if no outputs are specified, the default outputs of the model would be\n   * used. You can inspect intermediate nodes of the model by adding them to the\n   * outputs array.\n   * @param isFunctionExecution Flag for executing a function.\n   */\n  private async executeWithControlFlow(\n      inputs: NamedTensorMap, context: ExecutionContext, outputNames?: string[],\n      isFunctionExecution?: boolean): Promise<NamedTensorsMap> {\n    const names = Object.keys(inputs);\n    const inputNodes =\n        names.map(name => this.graph.nodes[parseNodeName(name)[0]]);\n    const outputNodeNames = outputNames.map(name => parseNodeName(name)[0]);\n    let outputNodes = outputNodeNames.map(name => this.graph.nodes[name]);\n\n    // If no outputs are specified, then use the default outputs of the model.\n    if (outputNodes.length === 0) {\n      outputNodes = this._outputs;\n    }\n\n    const {usedNodes, missingInputs, dynamicNode, syncInputs} =\n        getExecutionSubgraph(\n            inputs, outputNodes, this.weightMap, this._initNodes);\n\n    // First nodes to execute include inputNodes, weights, and initNodes.\n    const stack: NodeWithContexts[] = [\n      ...inputNodes, ...this.graph.weights, ...(this._initNodes || [])\n    ].map(node => {\n      return {node, contexts: context.currentContext};\n    });\n    const tensorsMap: NamedTensorsMap = {...this.weightMap};\n    Object.keys(inputs).forEach(name => {\n      const [nodeName, index] = parseNodeName(name);\n      const tensors: Tensor[] = [];\n      tensors[index] = inputs[name];\n      tensorsMap[nodeName] = tensors;\n    });\n    const intermediateTensorConsumerCount: {[key: number]: number} = {};\n    const tensorsToKeep = this.getFrozenTensorIds(tensorsMap);\n    const added: {[key: string]: boolean} = {};\n    while (stack.length > 0) {\n      const promises = this.processStack(\n          inputNodes, stack, context, tensorsMap, added, tensorsToKeep,\n          outputNodeNames, intermediateTensorConsumerCount, usedNodes);\n      await Promise.all(promises);\n    }\n    if (dynamicNode == null && !isFunctionExecution) {\n      console.warn(\n          `This model execution did not contain any nodes with control flow ` +\n          `or dynamic output shapes. You can use model.execute() instead.`);\n    }\n    const missingOutputs =\n        outputNodes\n            .filter(\n                node => !isControlFlow(node) &&\n                    !getTensor(node.name, tensorsMap, context))\n            .map(node => node.name);\n    if (missingOutputs.length > 0) {\n      let alternativeMsg = '';\n      if (dynamicNode != null) {\n        alternativeMsg =\n            `Alternatively, to avoid the dynamic ops, use model.execute() ` +\n            `and specify the inputs [${syncInputs}]`;\n      }\n      throw new Error(\n          `Cannot compute the outputs [${missingOutputs}] from the provided ` +\n          `inputs [${names}]. Consider providing the following inputs: ` +\n          `[${missingInputs}]. ${alternativeMsg}`);\n    }\n    return tensorsMap;\n  }\n\n  private processStack(\n      inputNodes: Node[], stack: NodeWithContexts[], context: ExecutionContext,\n      tensorMap: NamedTensorsMap, added: {[key: string]: boolean},\n      tensorsToKeep: Set<number>, outputNames: string[],\n      intermediateTensorConsumerCount: {[key: number]: number},\n      usedNodes: Set<string>) {\n    const promises: Array<Promise<Tensor[]>> = [];\n    while (stack.length > 0) {\n      const item = stack.pop();\n      context.currentContext = item.contexts;\n      let nodeName = '';\n      // The tensor of the Enter op with isConstant set should be set\n      // in the parent scope, so it will be available as constant for the\n      // whole loop.\n      if (item.node.op === 'Enter' &&\n          getParamValue('isConstant', item.node, tensorMap, context)) {\n        [nodeName] = getNodeNameAndIndex(item.node.name, context);\n      }\n\n      // only process nodes that are not in the tensorMap yet, this include\n      // inputNodes and internal initNodes.\n      if (tensorMap[item.node.name] == null) {\n        const tensors =\n            executeOp(item.node, tensorMap, context, this._resourceManager);\n        if (!nodeName) {\n          [nodeName] = getNodeNameAndIndex(item.node.name, context);\n        }\n        const currentContext = context.currentContext;\n        if (util.isPromise(tensors)) {\n          promises.push(tensors.then(t => {\n            tensorMap[nodeName] = t;\n            context.currentContext = currentContext;\n            this.checkTensorForDisposal(\n                nodeName, item.node, tensorMap, context, tensorsToKeep,\n                outputNames, intermediateTensorConsumerCount);\n            this.processChildNodes(\n                item.node, stack, context, tensorMap, added, usedNodes);\n            return t;\n          }));\n        } else {\n          tensorMap[nodeName] = tensors;\n          this.checkTensorForDisposal(\n              nodeName, item.node, tensorMap, context, tensorsToKeep,\n              outputNames, intermediateTensorConsumerCount);\n          this.processChildNodes(\n              item.node, stack, context, tensorMap, added, usedNodes);\n        }\n      } else {\n        this.processChildNodes(\n            item.node, stack, context, tensorMap, added, usedNodes);\n      }\n    }\n    return promises;\n  }\n\n  private processChildNodes(\n      node: Node, stack: NodeWithContexts[], context: ExecutionContext,\n      tensorMap: NamedTensorsMap, added: {[key: string]: boolean},\n      usedNodes: Set<string>) {\n    node.children.forEach((childNode) => {\n      const [nodeName, ] = getNodeNameAndIndex(childNode.name, context);\n      if (added[nodeName] || !usedNodes.has(childNode.name)) {\n        return;\n      }\n      // Merge op can be pushed if any of its inputs has value.\n      if (childNode.op === 'Merge') {\n        if (childNode.inputNames.some(name => {\n              return !!getTensor(name, tensorMap, context);\n            })) {\n          added[nodeName] = true;\n          stack.push({contexts: context.currentContext, node: childNode});\n        }\n      } else  // Otherwise all inputs must to have value.\n          if (childNode.inputNames.every(name => {\n                return !!getTensor(name, tensorMap, context);\n              })) {\n        added[nodeName] = true;\n        stack.push({contexts: context.currentContext, node: childNode});\n      }\n    });\n  }\n\n  /**\n   * Releases the memory used by the weight tensors.\n   */\n  dispose() {\n    Object.keys(this.weightMap)\n        .forEach(\n            key => this.weightMap[key].forEach(tensor => tensor.dispose()));\n  }\n\n  private checkInputShapeAndType(inputs: NamedTensorMap) {\n    Object.keys(inputs).forEach(name => {\n      const input = inputs[name];\n      const [nodeName, ] = parseNodeName(name);\n      const node = this.graph.nodes[nodeName];\n      if (node.attrParams['shape'] && node.attrParams['shape'].value) {\n        const shape = node.attrParams['shape'].value as number[];\n        const match = shape.length === input.shape.length &&\n            input.shape.every(\n                (dim, index) => shape[index] === -1 || shape[index] === dim);\n        util.assert(\n            match,\n            () => `The shape of dict['${node.name}'] provided in ` +\n                `model.execute(dict) must be [${shape}], but was ` +\n                `[${input.shape}]`);\n      }\n      if (node.attrParams['dtype'] && node.attrParams['dtype'].value) {\n        util.assert(\n            input.dtype === node.attrParams['dtype'].value as string,\n            () => `The dtype of dict['${node.name}'] provided in ` +\n                `model.execute(dict) must be ` +\n                `${node.attrParams['dtype'].value}, but was ${input.dtype}`);\n      }\n    });\n  }\n\n  private mapInputs(inputs: NamedTensorMap) {\n    const result: NamedTensorMap = {};\n    for (const inputName in inputs) {\n      if (this._signature != null && this._signature.inputs != null &&\n          this._signature.inputs[inputName] != null) {\n        const tensor = this._signature.inputs[inputName];\n        result[tensor.name] = inputs[inputName];\n      } else {\n        result[inputName] = inputs[inputName];\n      }\n    }\n    return result;\n  }\n\n  private checkInputs(inputs: NamedTensorMap) {\n    const notInGraph = Object.keys(inputs).filter(name => {\n      const [nodeName] = parseNodeName(name);\n      return this.graph.nodes[nodeName] == null;\n    });\n    if (notInGraph.length > 0) {\n      throw new Error(\n          `The dict provided in model.execute(dict) has ` +\n          `keys: [${notInGraph}] that are not part of graph`);\n    }\n  }\n\n  private mapOutputs(outputs: string[]) {\n    return outputs.map(name => {\n      if (this._signature != null && this._signature.outputs != null &&\n          this._signature.outputs[name] != null) {\n        const tensor = this._signature.outputs[name];\n        return tensor.name;\n      }\n      return name;\n    }, {});\n  }\n\n  private checkOutputs(outputs: string[]): void {\n    outputs.forEach(name => {\n      const [normalizedName] = parseNodeName(name);\n      if (!this.graph.nodes[normalizedName]) {\n        throw new Error(`The output '${name}' is not found in the graph`);\n      }\n    });\n  }\n}\n"]}","/**\n * Contains global resources of a model.\n */\nexport class ResourceManager {\n constructor(hashTableNameToHandle = {}, hashTableMap = {}) {\n this.hashTableNameToHandle = hashTableNameToHandle;\n this.hashTableMap = hashTableMap;\n }\n /**\n * Register a `HashTable` in the resource manager.\n *\n * The `HashTable` can be retrieved by `resourceManager.getHashTableById`,\n * where id is the table handle tensor's id.\n *\n * @param name Op node name that creates the `HashTable`.\n * @param hashTable The `HashTable` to be added to resource manager.\n */\n addHashTable(name, hashTable) {\n this.hashTableNameToHandle[name] = hashTable.handle;\n this.hashTableMap[hashTable.id] = hashTable;\n }\n /**\n * Get the table handle by node name.\n * @param name Op node name that creates the `HashTable`. This name is also\n * used in the inputs list of lookup and import `HashTable` ops.\n */\n getHashTableHandleByName(name) {\n return this.hashTableNameToHandle[name];\n }\n /**\n * Get the actual `HashTable` by its handle tensor's id.\n * @param id The id of the handle tensor.\n */\n getHashTableById(id) {\n return this.hashTableMap[id];\n }\n /**\n * Dispose `ResourceManager`, including its hashTables and tensors in them.\n */\n dispose() {\n for (const key in this.hashTableMap) {\n this.hashTableMap[key].clearAndClose();\n delete this.hashTableMap[key];\n }\n for (const name in this.hashTableNameToHandle) {\n this.hashTableNameToHandle[name].dispose();\n delete this.hashTableNameToHandle[name];\n }\n }\n}\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { io, Tensor } from '@tensorflow/tfjs-core';\nimport { OperationMapper } from '../operations/operation_mapper';\nimport { GraphExecutor } from './graph_executor';\nimport { ResourceManager } from './resource_manager';\nexport const TFHUB_SEARCH_PARAM = '?tfjs-format=file';\nexport const DEFAULT_MODEL_NAME = 'model.json';\n/**\n * A `tf.GraphModel` is a directed, acyclic graph built from a\n * SavedModel GraphDef and allows inference execution.\n *\n * A `tf.GraphModel` can only be created by loading from a model converted from\n * a [TensorFlow SavedModel](https://www.tensorflow.org/guide/saved_model) using\n * the command line converter tool and loaded via `tf.loadGraphModel`.\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\nexport class GraphModel {\n /**\n * @param modelUrl url for the model, or an `io.IOHandler`.\n * @param weightManifestUrl url for the weight file generated by\n * scripts/convert.py script.\n * @param requestOption options for Request, which allows to send credentials\n * and custom headers.\n * @param onProgress Optional, progress callback function, fired periodically\n * before the load is completed.\n */\n constructor(modelUrl, loadOptions = {}) {\n this.modelUrl = modelUrl;\n this.loadOptions = loadOptions;\n this.version = 'n/a';\n if (loadOptions == null) {\n this.loadOptions = {};\n }\n this.resourceManager = new ResourceManager();\n }\n // Returns the version information for the tensorflow model GraphDef.\n get modelVersion() {\n return this.version;\n }\n get inputNodes() {\n return this.executor.inputNodes;\n }\n get outputNodes() {\n return this.executor.outputNodes;\n }\n get inputs() {\n return this.executor.inputs;\n }\n get outputs() {\n return this.executor.outputs;\n }\n get weights() {\n return this.executor.weightMap;\n }\n get metadata() {\n return this.artifacts.userDefinedMetadata;\n }\n get modelSignature() {\n return this.signature;\n }\n findIOHandler() {\n const path = this.modelUrl;\n if (path.load != null) {\n // Path is an IO Handler.\n this.handler = path;\n }\n else if (this.loadOptions.requestInit != null) {\n this.handler = io.browserHTTPRequest(path, this.loadOptions);\n }\n else {\n const handlers = io.getLoadHandlers(path, this.loadOptions);\n if (handlers.length === 0) {\n // For backward compatibility: if no load handler can be found,\n // assume it is a relative http path.\n handlers.push(io.browserHTTPRequest(path, this.loadOptions));\n }\n else if (handlers.length > 1) {\n throw new Error(`Found more than one (${handlers.length}) load handlers for ` +\n `URL '${[path]}'`);\n }\n this.handler = handlers[0];\n }\n }\n /**\n * Loads the model and weight files, construct the in memory weight map and\n * compile the inference graph.\n */\n async load() {\n this.findIOHandler();\n if (this.handler.load == null) {\n throw new Error('Cannot proceed with model loading because the IOHandler provided ' +\n 'does not have the `load` method implemented.');\n }\n const artifacts = await this.handler.load();\n return this.loadSync(artifacts);\n }\n /**\n * Synchronously construct the in memory weight map and\n * compile the inference graph. Also initialize hashtable if any.\n *\n * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}\n */\n loadSync(artifacts) {\n this.artifacts = artifacts;\n const graph = this.artifacts.modelTopology;\n let signature;\n if (this.artifacts.userDefinedMetadata != null &&\n this.artifacts.userDefinedMetadata.signature != null) {\n signature = // tslint:disable-next-line:no-any\n this.artifacts.userDefinedMetadata.signature;\n }\n else {\n signature = this.artifacts.signature;\n }\n this.signature = signature;\n this.version = `${graph.versions.producer}.${graph.versions.minConsumer}`;\n const weightMap = io.decodeWeights(this.artifacts.weightData, this.artifacts.weightSpecs);\n this.executor = new GraphExecutor(OperationMapper.Instance.transformGraph(graph, this.signature));\n this.executor.weightMap = this.convertTensorMapToTensorsMap(weightMap);\n // Attach a model-level resourceManager to each executor to share resources,\n // such as `HashTable`.\n this.executor.resourceManager = this.resourceManager;\n if (artifacts.modelInitializer != null &&\n artifacts.modelInitializer.node != null) {\n const initializer = OperationMapper.Instance.transformGraph(artifacts.modelInitializer);\n this.initializer = new GraphExecutor(initializer);\n this.initializer.weightMap = this.executor.weightMap;\n // Attach a model-level resourceManager to the initializer, the\n // hashTables created from when executing the initializer will be stored\n // in the resourceManager.\n this.initializer.resourceManager = this.resourceManager;\n this.initializer.executeAsync({}, []);\n }\n return true;\n }\n /**\n * Save the configuration and/or weights of the GraphModel.\n *\n * An `IOHandler` is an object that has a `save` method of the proper\n * signature defined. The `save` method manages the storing or\n * transmission of serialized data (\"artifacts\") that represent the\n * model's topology and weights onto or via a specific medium, such as\n * file downloads, local storage, IndexedDB in the web browser and HTTP\n * requests to a server. TensorFlow.js provides `IOHandler`\n * implementations for a number of frequently used saving mediums, such as\n * `tf.io.browserDownloads` and `tf.io.browserLocalStorage`. See `tf.io`\n * for more details.\n *\n * This method also allows you to refer to certain types of `IOHandler`s\n * as URL-like string shortcuts, such as 'localstorage://' and\n * 'indexeddb://'.\n *\n * Example 1: Save `model`'s topology and weights to browser [local\n * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);\n * then load it back.\n *\n * ```js\n * const modelUrl =\n * 'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json';\n * const model = await tf.loadGraphModel(modelUrl);\n * const zeros = tf.zeros([1, 224, 224, 3]);\n * model.predict(zeros).print();\n *\n * const saveResults = await model.save('localstorage://my-model-1');\n *\n * const loadedModel = await tf.loadGraphModel('localstorage://my-model-1');\n * console.log('Prediction from loaded model:');\n * model.predict(zeros).print();\n * ```\n *\n * @param handlerOrURL An instance of `IOHandler` or a URL-like,\n * scheme-based string shortcut for `IOHandler`.\n * @param config Options for saving the model.\n * @returns A `Promise` of `SaveResult`, which summarizes the result of\n * the saving, such as byte sizes of the saved artifacts for the model's\n * topology and weight values.\n *\n * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}\n */\n async save(handlerOrURL, config) {\n if (typeof handlerOrURL === 'string') {\n const handlers = io.getSaveHandlers(handlerOrURL);\n if (handlers.length === 0) {\n throw new Error(`Cannot find any save handlers for URL '${handlerOrURL}'`);\n }\n else if (handlers.length > 1) {\n throw new Error(`Found more than one (${handlers.length}) save handlers for ` +\n `URL '${handlerOrURL}'`);\n }\n handlerOrURL = handlers[0];\n }\n if (handlerOrURL.save == null) {\n throw new Error('GraphModel.save() cannot proceed because the IOHandler ' +\n 'provided does not have the `save` attribute defined.');\n }\n return handlerOrURL.save(this.artifacts);\n }\n /**\n * Execute the inference for the input tensors.\n *\n * @param input The input tensors, when there is single input for the model,\n * inputs param should be a `tf.Tensor`. For models with mutliple inputs,\n * inputs params should be in either `tf.Tensor`[] if the input order is\n * fixed, or otherwise NamedTensorMap format.\n *\n * For model with multiple inputs, we recommend you use NamedTensorMap as the\n * input type, if you use `tf.Tensor`[], the order of the array needs to\n * follow the\n * order of inputNodes array. @see {@link GraphModel.inputNodes}\n *\n * You can also feed any intermediate nodes using the NamedTensorMap as the\n * input type. For example, given the graph\n * InputNode => Intermediate => OutputNode,\n * you can execute the subgraph Intermediate => OutputNode by calling\n * model.execute('IntermediateNode' : tf.tensor(...));\n *\n * This is useful for models that uses tf.dynamic_rnn, where the intermediate\n * state needs to be fed manually.\n *\n * For batch inference execution, the tensors for each input need to be\n * concatenated together. For example with mobilenet, the required input shape\n * is [1, 244, 244, 3], which represents the [batch, height, width, channel].\n * If we are provide a batched data of 100 images, the input tensor should be\n * in the shape of [100, 244, 244, 3].\n *\n * @param config Prediction configuration for specifying the batch size and\n * output node names. Currently the batch size option is ignored for graph\n * model.\n *\n * @returns Inference result tensors. The output would be single `tf.Tensor`\n * if model has single output node, otherwise Tensor[] or NamedTensorMap[]\n * will be returned for model with multiple outputs.\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\n predict(inputs, config) {\n return this.execute(inputs, this.outputNodes);\n }\n normalizeInputs(inputs) {\n if (!(inputs instanceof Tensor) && !Array.isArray(inputs)) {\n // The input is already a NamedTensorMap.\n return inputs;\n }\n inputs = Array.isArray(inputs) ? inputs : [inputs];\n if (inputs.length !== this.inputNodes.length) {\n throw new Error('Input tensor count mismatch,' +\n `the graph model has ${this.inputNodes.length} placeholders, ` +\n `while there are ${inputs.length} input tensors.`);\n }\n return this.inputNodes.reduce((map, inputName, i) => {\n map[inputName] = inputs[i];\n return map;\n }, {});\n }\n normalizeOutputs(outputs) {\n outputs = outputs || this.outputNodes;\n return !Array.isArray(outputs) ? [outputs] : outputs;\n }\n /**\n * Executes inference for the model for given input tensors.\n * @param inputs tensor, tensor array or tensor map of the inputs for the\n * model, keyed by the input node names.\n * @param outputs output node name from the Tensorflow model, if no\n * outputs are specified, the default outputs of the model would be used.\n * You can inspect intermediate nodes of the model by adding them to the\n * outputs array.\n *\n * @returns A single tensor if provided with a single output or no outputs\n * are provided and there is only one default output, otherwise return a\n * tensor array. The order of the tensor array is the same as the outputs\n * if provided, otherwise the order of outputNodes attribute of the model.\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\n execute(inputs, outputs) {\n inputs = this.normalizeInputs(inputs);\n outputs = this.normalizeOutputs(outputs);\n const result = this.executor.execute(inputs, outputs);\n return result.length > 1 ? result : result[0];\n }\n /**\n * Executes inference for the model for given input tensors in async\n * fashion, use this method when your model contains control flow ops.\n * @param inputs tensor, tensor array or tensor map of the inputs for the\n * model, keyed by the input node names.\n * @param outputs output node name from the Tensorflow model, if no outputs\n * are specified, the default outputs of the model would be used. You can\n * inspect intermediate nodes of the model by adding them to the outputs\n * array.\n *\n * @returns A Promise of single tensor if provided with a single output or\n * no outputs are provided and there is only one default output, otherwise\n * return a tensor map.\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\n async executeAsync(inputs, outputs) {\n inputs = this.normalizeInputs(inputs);\n outputs = this.normalizeOutputs(outputs);\n const result = await this.executor.executeAsync(inputs, outputs);\n return result.length > 1 ? result : result[0];\n }\n /**\n * Get intermediate tensors for model debugging mode (flag\n * KEEP_INTERMEDIATE_TENSORS is true).\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\n getIntermediateTensors() {\n return this.executor.getIntermediateTensors();\n }\n /**\n * Dispose intermediate tensors for model debugging mode (flag\n * KEEP_INTERMEDIATE_TENSORS is true).\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\n disposeIntermediateTensors() {\n this.executor.disposeIntermediateTensors();\n }\n convertTensorMapToTensorsMap(map) {\n return Object.keys(map).reduce((newMap, key) => {\n newMap[key] = [map[key]];\n return newMap;\n }, {});\n }\n /**\n * Releases the memory used by the weight tensors and resourceManager.\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\n dispose() {\n this.executor.dispose();\n if (this.initializer) {\n this.initializer.dispose();\n }\n this.resourceManager.dispose();\n }\n}\n/**\n * Load a graph model given a URL to the model definition.\n *\n * Example of loading MobileNetV2 from a URL and making a prediction with a\n * zeros input:\n *\n * ```js\n * const modelUrl =\n * 'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json';\n * const model = await tf.loadGraphModel(modelUrl);\n * const zeros = tf.zeros([1, 224, 224, 3]);\n * model.predict(zeros).print();\n * ```\n *\n * Example of loading MobileNetV2 from a TF Hub URL and making a prediction with\n * a zeros input:\n *\n * ```js\n * const modelUrl =\n * 'https://tfhub.dev/google/imagenet/mobilenet_v2_140_224/classification/2';\n * const model = await tf.loadGraphModel(modelUrl, {fromTFHub: true});\n * const zeros = tf.zeros([1, 224, 224, 3]);\n * model.predict(zeros).print();\n * ```\n * @param modelUrl The url or an `io.IOHandler` that loads the model.\n * @param options Options for the HTTP request, which allows to send credentials\n * and custom headers.\n *\n * @doc {heading: 'Models', subheading: 'Loading'}\n */\nexport async function loadGraphModel(modelUrl, options = {}) {\n if (modelUrl == null) {\n throw new Error('modelUrl in loadGraphModel() cannot be null. Please provide a url ' +\n 'or an IOHandler that loads the model');\n }\n if (options == null) {\n options = {};\n }\n if (options.fromTFHub) {\n if (modelUrl.load == null) {\n if (!modelUrl.endsWith('/')) {\n modelUrl = modelUrl + '/';\n }\n modelUrl = `${modelUrl}${DEFAULT_MODEL_NAME}${TFHUB_SEARCH_PARAM}`;\n }\n }\n const model = new GraphModel(modelUrl, options);\n await model.load();\n return model;\n}\n//# 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* @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {InferenceModel, io, ModelPredictConfig, NamedTensorMap, Tensor} from '@tensorflow/tfjs-core';\n\nimport * as tensorflow from '../data/compiled_api';\nimport {NamedTensorsMap, TensorInfo} from '../data/types';\nimport {OperationMapper} from '../operations/operation_mapper';\n\nimport {GraphExecutor} from './graph_executor';\nimport {ResourceManager} from './resource_manager';\n\nexport const TFHUB_SEARCH_PARAM = '?tfjs-format=file';\nexport const DEFAULT_MODEL_NAME = 'model.json';\n/**\n * A `tf.GraphModel` is a directed, acyclic graph built from a\n * SavedModel GraphDef and allows inference execution.\n *\n * A `tf.GraphModel` can only be created by loading from a model converted from\n * a [TensorFlow SavedModel](https://www.tensorflow.org/guide/saved_model) using\n * the command line converter tool and loaded via `tf.loadGraphModel`.\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\nexport class GraphModel implements InferenceModel {\n  private executor: GraphExecutor;\n  private version = 'n/a';\n  private handler: io.IOHandler;\n  private artifacts: io.ModelArtifacts;\n  private initializer: GraphExecutor;\n  private resourceManager: ResourceManager;\n  private signature: tensorflow.ISignatureDef;\n\n  // Returns the version information for the tensorflow model GraphDef.\n  get modelVersion(): string {\n    return this.version;\n  }\n\n  get inputNodes(): string[] {\n    return this.executor.inputNodes;\n  }\n\n  get outputNodes(): string[] {\n    return this.executor.outputNodes;\n  }\n\n  get inputs(): TensorInfo[] {\n    return this.executor.inputs;\n  }\n\n  get outputs(): TensorInfo[] {\n    return this.executor.outputs;\n  }\n\n  get weights(): NamedTensorsMap {\n    return this.executor.weightMap;\n  }\n\n  get metadata(): {} {\n    return this.artifacts.userDefinedMetadata;\n  }\n\n  get modelSignature(): {} {\n    return this.signature;\n  }\n\n  /**\n   * @param modelUrl url for the model, or an `io.IOHandler`.\n   * @param weightManifestUrl url for the weight file generated by\n   * scripts/convert.py script.\n   * @param requestOption options for Request, which allows to send credentials\n   * and custom headers.\n   * @param onProgress Optional, progress callback function, fired periodically\n   * before the load is completed.\n   */\n  constructor(\n      private modelUrl: string|io.IOHandler,\n      private loadOptions: io.LoadOptions = {}) {\n    if (loadOptions == null) {\n      this.loadOptions = {};\n    }\n    this.resourceManager = new ResourceManager();\n  }\n\n  private findIOHandler() {\n    const path = this.modelUrl;\n    if ((path as io.IOHandler).load != null) {\n      // Path is an IO Handler.\n      this.handler = path as io.IOHandler;\n    } else if (this.loadOptions.requestInit != null) {\n      this.handler = io.browserHTTPRequest(path as string, this.loadOptions);\n    } else {\n      const handlers = io.getLoadHandlers(path as string, this.loadOptions);\n      if (handlers.length === 0) {\n        // For backward compatibility: if no load handler can be found,\n        // assume it is a relative http path.\n        handlers.push(io.browserHTTPRequest(path as string, this.loadOptions));\n      } else if (handlers.length > 1) {\n        throw new Error(\n            `Found more than one (${handlers.length}) load handlers for ` +\n            `URL '${[path]}'`);\n      }\n      this.handler = handlers[0];\n    }\n  }\n\n  /**\n   * Loads the model and weight files, construct the in memory weight map and\n   * compile the inference graph.\n   */\n  async load(): Promise<boolean> {\n    this.findIOHandler();\n    if (this.handler.load == null) {\n      throw new Error(\n          'Cannot proceed with model loading because the IOHandler provided ' +\n          'does not have the `load` method implemented.');\n    }\n    const artifacts = await this.handler.load();\n\n    return this.loadSync(artifacts);\n  }\n\n  /**\n   * Synchronously construct the in memory weight map and\n   * compile the inference graph. Also initialize hashtable if any.\n   *\n   * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}\n   */\n  loadSync(artifacts: io.ModelArtifacts) {\n    this.artifacts = artifacts;\n    const graph = this.artifacts.modelTopology as tensorflow.IGraphDef;\n\n    let signature;\n    if (this.artifacts.userDefinedMetadata != null &&\n        this.artifacts.userDefinedMetadata.signature != null) {\n      signature =  // tslint:disable-next-line:no-any\n          (this.artifacts.userDefinedMetadata as any).signature as\n          tensorflow.ISignatureDef;\n    } else {\n      signature = this.artifacts.signature;\n    }\n    this.signature = signature;\n\n    this.version = `${graph.versions.producer}.${graph.versions.minConsumer}`;\n    const weightMap =\n        io.decodeWeights(this.artifacts.weightData, this.artifacts.weightSpecs);\n    this.executor = new GraphExecutor(\n        OperationMapper.Instance.transformGraph(graph, this.signature));\n    this.executor.weightMap = this.convertTensorMapToTensorsMap(weightMap);\n    // Attach a model-level resourceManager to each executor to share resources,\n    // such as `HashTable`.\n    this.executor.resourceManager = this.resourceManager;\n\n    if (artifacts.modelInitializer != null &&\n        (artifacts.modelInitializer as tensorflow.IGraphDef).node != null) {\n      const initializer =\n          OperationMapper.Instance.transformGraph(artifacts.modelInitializer);\n      this.initializer = new GraphExecutor(initializer);\n      this.initializer.weightMap = this.executor.weightMap;\n      // Attach a model-level resourceManager to the initializer, the\n      // hashTables created from when executing the initializer will be stored\n      // in the resourceManager.\n      this.initializer.resourceManager = this.resourceManager;\n      this.initializer.executeAsync({}, []);\n    }\n\n    return true;\n  }\n\n  /**\n   * Save the configuration and/or weights of the GraphModel.\n   *\n   * An `IOHandler` is an object that has a `save` method of the proper\n   * signature defined. The `save` method manages the storing or\n   * transmission of serialized data (\"artifacts\") that represent the\n   * model's topology and weights onto or via a specific medium, such as\n   * file downloads, local storage, IndexedDB in the web browser and HTTP\n   * requests to a server. TensorFlow.js provides `IOHandler`\n   * implementations for a number of frequently used saving mediums, such as\n   * `tf.io.browserDownloads` and `tf.io.browserLocalStorage`. See `tf.io`\n   * for more details.\n   *\n   * This method also allows you to refer to certain types of `IOHandler`s\n   * as URL-like string shortcuts, such as 'localstorage://' and\n   * 'indexeddb://'.\n   *\n   * Example 1: Save `model`'s topology and weights to browser [local\n   * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);\n   * then load it back.\n   *\n   * ```js\n   * const modelUrl =\n   *    'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json';\n   * const model = await tf.loadGraphModel(modelUrl);\n   * const zeros = tf.zeros([1, 224, 224, 3]);\n   * model.predict(zeros).print();\n   *\n   * const saveResults = await model.save('localstorage://my-model-1');\n   *\n   * const loadedModel = await tf.loadGraphModel('localstorage://my-model-1');\n   * console.log('Prediction from loaded model:');\n   * model.predict(zeros).print();\n   * ```\n   *\n   * @param handlerOrURL An instance of `IOHandler` or a URL-like,\n   * scheme-based string shortcut for `IOHandler`.\n   * @param config Options for saving the model.\n   * @returns A `Promise` of `SaveResult`, which summarizes the result of\n   * the saving, such as byte sizes of the saved artifacts for the model's\n   *   topology and weight values.\n   *\n   * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}\n   */\n  async save(handlerOrURL: io.IOHandler|string, config?: io.SaveConfig):\n      Promise<io.SaveResult> {\n    if (typeof handlerOrURL === 'string') {\n      const handlers = io.getSaveHandlers(handlerOrURL);\n      if (handlers.length === 0) {\n        throw new Error(\n            `Cannot find any save handlers for URL '${handlerOrURL}'`);\n      } else if (handlers.length > 1) {\n        throw new Error(\n            `Found more than one (${handlers.length}) save handlers for ` +\n            `URL '${handlerOrURL}'`);\n      }\n      handlerOrURL = handlers[0];\n    }\n    if (handlerOrURL.save == null) {\n      throw new Error(\n          'GraphModel.save() cannot proceed because the IOHandler ' +\n          'provided does not have the `save` attribute defined.');\n    }\n\n    return handlerOrURL.save(this.artifacts);\n  }\n\n  /**\n   * Execute the inference for the input tensors.\n   *\n   * @param input The input tensors, when there is single input for the model,\n   * inputs param should be a `tf.Tensor`. For models with mutliple inputs,\n   * inputs params should be in either `tf.Tensor`[] if the input order is\n   * fixed, or otherwise NamedTensorMap format.\n   *\n   * For model with multiple inputs, we recommend you use NamedTensorMap as the\n   * input type, if you use `tf.Tensor`[], the order of the array needs to\n   * follow the\n   * order of inputNodes array. @see {@link GraphModel.inputNodes}\n   *\n   * You can also feed any intermediate nodes using the NamedTensorMap as the\n   * input type. For example, given the graph\n   *    InputNode => Intermediate => OutputNode,\n   * you can execute the subgraph Intermediate => OutputNode by calling\n   *    model.execute('IntermediateNode' : tf.tensor(...));\n   *\n   * This is useful for models that uses tf.dynamic_rnn, where the intermediate\n   * state needs to be fed manually.\n   *\n   * For batch inference execution, the tensors for each input need to be\n   * concatenated together. For example with mobilenet, the required input shape\n   * is [1, 244, 244, 3], which represents the [batch, height, width, channel].\n   * If we are provide a batched data of 100 images, the input tensor should be\n   * in the shape of [100, 244, 244, 3].\n   *\n   * @param config Prediction configuration for specifying the batch size and\n   * output node names. Currently the batch size option is ignored for graph\n   * model.\n   *\n   * @returns Inference result tensors. The output would be single `tf.Tensor`\n   * if model has single output node, otherwise Tensor[] or NamedTensorMap[]\n   * will be returned for model with multiple outputs.\n   *\n   * @doc {heading: 'Models', subheading: 'Classes'}\n   */\n  predict(inputs: Tensor|Tensor[]|NamedTensorMap, config?: ModelPredictConfig):\n      Tensor|Tensor[]|NamedTensorMap {\n    return this.execute(inputs, this.outputNodes);\n  }\n\n  private normalizeInputs(inputs: Tensor|Tensor[]|\n                          NamedTensorMap): NamedTensorMap {\n    if (!(inputs instanceof Tensor) && !Array.isArray(inputs)) {\n      // The input is already a NamedTensorMap.\n      return inputs;\n    }\n    inputs = Array.isArray(inputs) ? inputs : [inputs];\n    if (inputs.length !== this.inputNodes.length) {\n      throw new Error(\n          'Input tensor count mismatch,' +\n          `the graph model has ${this.inputNodes.length} placeholders, ` +\n          `while there are ${inputs.length} input tensors.`);\n    }\n    return this.inputNodes.reduce((map, inputName, i) => {\n      map[inputName] = (inputs as Tensor[])[i];\n      return map;\n    }, {} as NamedTensorMap);\n  }\n\n  private normalizeOutputs(outputs: string|string[]): string[] {\n    outputs = outputs || this.outputNodes;\n    return !Array.isArray(outputs) ? [outputs] : outputs;\n  }\n\n  /**\n   * Executes inference for the model for given input tensors.\n   * @param inputs tensor, tensor array or tensor map of the inputs for the\n   * model, keyed by the input node names.\n   * @param outputs output node name from the Tensorflow model, if no\n   * outputs are specified, the default outputs of the model would be used.\n   * You can inspect intermediate nodes of the model by adding them to the\n   * outputs array.\n   *\n   * @returns A single tensor if provided with a single output or no outputs\n   * are provided and there is only one default output, otherwise return a\n   * tensor array. The order of the tensor array is the same as the outputs\n   * if provided, otherwise the order of outputNodes attribute of the model.\n   *\n   * @doc {heading: 'Models', subheading: 'Classes'}\n   */\n  execute(inputs: Tensor|Tensor[]|NamedTensorMap, outputs?: string|string[]):\n      Tensor|Tensor[] {\n    inputs = this.normalizeInputs(inputs);\n    outputs = this.normalizeOutputs(outputs);\n    const result = this.executor.execute(inputs, outputs);\n    return result.length > 1 ? result : result[0];\n  }\n  /**\n   * Executes inference for the model for given input tensors in async\n   * fashion, use this method when your model contains control flow ops.\n   * @param inputs tensor, tensor array or tensor map of the inputs for the\n   * model, keyed by the input node names.\n   * @param outputs output node name from the Tensorflow model, if no outputs\n   * are specified, the default outputs of the model would be used. You can\n   * inspect intermediate nodes of the model by adding them to the outputs\n   * array.\n   *\n   * @returns A Promise of single tensor if provided with a single output or\n   * no outputs are provided and there is only one default output, otherwise\n   * return a tensor map.\n   *\n   * @doc {heading: 'Models', subheading: 'Classes'}\n   */\n  async executeAsync(\n      inputs: Tensor|Tensor[]|NamedTensorMap,\n      outputs?: string|string[]): Promise<Tensor|Tensor[]> {\n    inputs = this.normalizeInputs(inputs);\n    outputs = this.normalizeOutputs(outputs);\n    const result = await this.executor.executeAsync(inputs, outputs);\n    return result.length > 1 ? result : result[0];\n  }\n\n  /**\n   * Get intermediate tensors for model debugging mode (flag\n   * KEEP_INTERMEDIATE_TENSORS is true).\n   *\n   * @doc {heading: 'Models', subheading: 'Classes'}\n   */\n  getIntermediateTensors(): NamedTensorsMap {\n    return this.executor.getIntermediateTensors();\n  }\n\n  /**\n   * Dispose intermediate tensors for model debugging mode (flag\n   * KEEP_INTERMEDIATE_TENSORS is true).\n   *\n   * @doc {heading: 'Models', subheading: 'Classes'}\n   */\n  disposeIntermediateTensors() {\n    this.executor.disposeIntermediateTensors();\n  }\n\n  private convertTensorMapToTensorsMap(map: NamedTensorMap): NamedTensorsMap {\n    return Object.keys(map).reduce((newMap: NamedTensorsMap, key) => {\n      newMap[key] = [map[key]];\n      return newMap;\n    }, {});\n  }\n\n  /**\n   * Releases the memory used by the weight tensors and resourceManager.\n   *\n   * @doc {heading: 'Models', subheading: 'Classes'}\n   */\n  dispose() {\n    this.executor.dispose();\n\n    if (this.initializer) {\n      this.initializer.dispose();\n    }\n\n    this.resourceManager.dispose();\n  }\n}\n\n/**\n * Load a graph model given a URL to the model definition.\n *\n * Example of loading MobileNetV2 from a URL and making a prediction with a\n * zeros input:\n *\n * ```js\n * const modelUrl =\n *    'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json';\n * const model = await tf.loadGraphModel(modelUrl);\n * const zeros = tf.zeros([1, 224, 224, 3]);\n * model.predict(zeros).print();\n * ```\n *\n * Example of loading MobileNetV2 from a TF Hub URL and making a prediction with\n * a zeros input:\n *\n * ```js\n * const modelUrl =\n *    'https://tfhub.dev/google/imagenet/mobilenet_v2_140_224/classification/2';\n * const model = await tf.loadGraphModel(modelUrl, {fromTFHub: true});\n * const zeros = tf.zeros([1, 224, 224, 3]);\n * model.predict(zeros).print();\n * ```\n * @param modelUrl The url or an `io.IOHandler` that loads the model.\n * @param options Options for the HTTP request, which allows to send credentials\n *    and custom headers.\n *\n * @doc {heading: 'Models', subheading: 'Loading'}\n */\nexport async function loadGraphModel(\n    modelUrl: string|io.IOHandler,\n    options: io.LoadOptions = {}): Promise<GraphModel> {\n  if (modelUrl == null) {\n    throw new Error(\n        'modelUrl in loadGraphModel() cannot be null. Please provide a url ' +\n        'or an IOHandler that loads the model');\n  }\n  if (options == null) {\n    options = {};\n  }\n\n  if (options.fromTFHub) {\n    if ((modelUrl as io.IOHandler).load == null) {\n      if (!(modelUrl as string).endsWith('/')) {\n        modelUrl = (modelUrl as string) + '/';\n      }\n      modelUrl = `${modelUrl}${DEFAULT_MODEL_NAME}${TFHUB_SEARCH_PARAM}`;\n    }\n  }\n  const model = new GraphModel(modelUrl, options);\n  await model.load();\n  return model;\n}\n"]}","/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '3.15.0';\nexport { version };\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport './flags';\nexport { GraphModel, loadGraphModel } from './executor/graph_model';\nexport { deregisterOp, registerOp } from './operations/custom_op/register';\nexport { version as version_converter } from './version';\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/* Type definitions for exporting and importing of models. */\n/**\n * A map from Tensor dtype to number of bytes per element of the Tensor.\n */\nexport const DTYPE_VALUE_SIZE_MAP = {\n 'float32': 4,\n 'float16': 2,\n 'int32': 4,\n 'uint16': 2,\n 'uint8': 1,\n 'bool': 1,\n 'complex64': 8\n};\n//# sourceMappingURL=data:application/json;base64,{"version":3,"file":"types.js","sourceRoot":"","sources":["../../../../../../tfjs-core/src/io/types.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAEH,6DAA6D;AAE7D;;GAEG;AACH,MAAM,CAAC,MAAM,oBAAoB,GAA8B;IAC7D,SAAS,EAAE,CAAC;IACZ,SAAS,EAAE,CAAC;IACZ,OAAO,EAAE,CAAC;IACV,QAAQ,EAAE,CAAC;IACX,OAAO,EAAE,CAAC;IACV,MAAM,EAAE,CAAC;IACT,WAAW,EAAE,CAAC;CACf,CAAC","sourcesContent":["/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/* Type definitions for exporting and importing of models. */\n\n/**\n * A map from Tensor dtype to number of bytes per element of the Tensor.\n */\nexport const DTYPE_VALUE_SIZE_MAP: {[dtype: string]: number} = {\n  'float32': 4,\n  'float16': 2,\n  'int32': 4,\n  'uint16': 2,\n  'uint8': 1,\n  'bool': 1,\n  'complex64': 8\n};\n\n/**\n * A weight manifest.\n *\n * The weight manifest consists of an ordered list of weight-manifest groups.\n * Each weight-manifest group (\"group\" for short hereafter) consists of a\n * number of weight values stored in a number of paths.\n * See the documentation of `WeightManifestGroupConfig` below for more details.\n */\nexport declare type WeightsManifestConfig = WeightsManifestGroupConfig[];\n\n/**\n * A weight-manifest group.\n *\n * Consists of an ordered list of weight values encoded in binary format,\n * stored in an ordered list of paths.\n */\nexport declare interface WeightsManifestGroupConfig {\n  /**\n   * An ordered list of paths.\n   *\n   * Paths are intentionally abstract in order to be general. For example, they\n   * can be relative URL paths or relative paths on the file system.\n   */\n  paths: string[];\n\n  /**\n   * Specifications of the weights stored in the paths.\n   */\n  weights: WeightsManifestEntry[];\n}\n\n/**\n * Group to which the weight belongs.\n *\n * - 'optimizer': Weight from a stateful optimizer.\n */\nexport type WeightGroup = 'model'|'optimizer';\n\n/**\n * An entry in the weight manifest.\n *\n * The entry contains specification of a weight.\n */\nexport declare interface WeightsManifestEntry {\n  /**\n   * Name of the weight, e.g., 'Dense_1/bias'\n   */\n  name: string;\n\n  /**\n   * Shape of the weight.\n   */\n  shape: number[];\n\n  /**\n   * Data type of the weight.\n   */\n  dtype: 'float32'|'int32'|'bool'|'string'|'complex64';\n\n  /**\n   * Type of the weight.\n   *\n   * Optional.\n   *\n   * The value 'optimizer' indicates the weight belongs to an optimizer\n   * (i.e., used only during model training and not during inference).\n   */\n  group?: WeightGroup;\n\n  /**\n   * Information for dequantization of the weight.\n   */\n  quantization?: {\n    scale?: number,  // The scaling constant to multiply by.\n    min?: number,    // The (possibly nudged) minimum weight to add.\n       dtype: 'uint16'|'uint8'|'float16'  // The dtype of the quantized weights.\n  };\n}\n\n/**\n * Options for saving a model.\n * @innamespace io\n */\nexport interface SaveConfig {\n  /**\n   * Whether to save only the trainable weights of the model, ignoring the\n   * non-trainable ones.\n   */\n  trainableOnly?: boolean;\n\n  /**\n   * Whether the optimizer will be saved (if exists).\n   *\n   * Default: `false`.\n   */\n  includeOptimizer?: boolean;\n}\n\n/**\n * Result of a saving operation.\n */\nexport interface SaveResult {\n  /**\n   * Information about the model artifacts saved.\n   */\n  modelArtifactsInfo: ModelArtifactsInfo;\n\n  /**\n   * HTTP responses from the server that handled the model-saving request (if\n   * any). This is applicable only to server-based saving routes.\n   */\n  responses?: Response[];\n\n  /**\n   * Error messages and related data (if any).\n   */\n  errors?: Array<{}|string>;\n}\n\nexport declare interface ModelArtifactsInfo {\n  /**\n   * Timestamp for when the model is saved.\n   */\n  dateSaved: Date;\n\n  /**\n   * TODO (cais,yassogba) consider removing GraphDef as GraphDefs now\n   * come in a JSON format and none of our IOHandlers support a non json\n   * format. We could conder replacing this with 'Binary' if we want to\n   * allow future handlers to save to non json formats (though they will\n   * probably want more information than 'Binary').\n   * Type of the model topology\n   *\n   * Type of the model topology\n   *\n   * Possible values:\n   *   - JSON: JSON config (human-readable, e.g., Keras JSON).\n   *   - GraphDef: TensorFlow\n   *     [GraphDef](https://www.tensorflow.org/extend/tool_developers/#graphdef)\n   *     protocol buffer (binary).\n   */\n  modelTopologyType: 'JSON'|'GraphDef';\n\n  /**\n   * Size of model topology (Keras JSON or GraphDef), in bytes.\n   */\n  modelTopologyBytes?: number;\n\n  /**\n   * Size of weight specification or manifest, in bytes.\n   */\n  weightSpecsBytes?: number;\n\n  /**\n   * Size of weight value data, in bytes.\n   */\n  weightDataBytes?: number;\n}\n\n/** Model training configuration. */\nexport declare interface TrainingConfig {\n  // TODO(cais): Tighten the typing once keras spec is available to tfjs-core.\n  // See\n  // tslint:disable-next-line:max-line-length\n  // https://github.com/tensorflow/tfjs-layers/blob/master/src/keras_format/training_config.ts\n  /** Optimizer used for the model training. */\n  optimizer_config: {};\n\n  // TODO(cais): Tighten the typing once keras spec is available to tfjs-core.\n  /** Loss function(s) for the model's output(s). */\n  loss: string|string[]|{[key: string]: string};\n\n  // TODO(cais): Tighten the typing once keras spec is available to tfjs-core.\n  /** Metric function(s) for the model's output(s). */\n  metrics?: string[]|{[key: string]: string};\n\n  // TODO(cais): Tighten the typing once keras spec is available to tfjs-core.\n  weighted_metrics?: string[];\n\n  // TODO(cais): Tighten the typing once keras spec is available to tfjs-core.\n  sample_weight_mode?: string;\n\n  loss_weights?: number[]|{[key: string]: number};\n}\n\n/**\n * The serialized artifacts of a model, including topology and weights.\n *\n * The `modelTopology`, `trainingConfig`, `weightSpecs` and `weightData` fields\n * of this interface are optional, in order to support topology- or weights-only\n * saving and loading.\n *\n * Note this interface is used internally in IOHandlers.  For the file format\n * written to disk as `model.json`, see `ModelJSON`.\n */\nexport declare interface ModelArtifacts {\n  /**\n   * Model topology.\n   *\n   * For Keras-style `tf.Model`s, this is a JSON object.\n   * For TensorFlow-style models (e.g., `SavedModel`), this is the JSON\n   * encoding of the `GraphDef` protocol buffer.\n   */\n  modelTopology?: {}|ArrayBuffer;\n\n  /**\n   * Serialized configuration for the model's training.\n   */\n  trainingConfig?: TrainingConfig;\n\n  /**\n   * Weight specifications.\n   *\n   * This corresponds to the weightsData below.\n   */\n  weightSpecs?: WeightsManifestEntry[];\n\n  /**\n   * Binary buffer for all weight values concatenated in the order specified\n   * by `weightSpecs`.\n   */\n  weightData?: ArrayBuffer;\n\n  /**\n   * Hard-coded format name for models saved from TensorFlow.js or converted\n   * by TensorFlow.js Converter.\n   */\n  format?: string;\n\n  /**\n   * What library is responsible for originally generating this artifact.\n   *\n   * Used for debugging purposes. E.g., 'TensorFlow.js v1.0.0'.\n   */\n  generatedBy?: string;\n\n  /**\n   * What library or tool is responsible for converting the original model\n   * to this format, applicable only if the model is output by a converter.\n   *\n   * Used for debugging purposes.  E.g., 'TensorFlow.js Converter v1.0.0'.\n   *\n   * A value of `null` means the model artifacts are generated without any\n   * conversion process (e.g., saved directly from a TensorFlow.js\n   * `tf.LayersModel` instance.)\n   */\n  convertedBy?: string|null;\n\n  /**\n   * Inputs and outputs signature for saved model.\n   */\n  signature?: {};\n\n  /**\n   * User-defined metadata about the model.\n   */\n  userDefinedMetadata?: {[key: string]: {}};\n\n  /**\n   * Initializer for the model.\n   */\n  modelInitializer?: {};\n}\n\n/**\n * The on-disk format of the `model.json` file.\n *\n * TF.js 1.0 always populates the optional fields when writing model.json.\n * Prior versions did not provide those fields.\n */\nexport declare interface ModelJSON {\n  /**\n   * Model topology.\n   *\n   * For Keras-style `tf.Model`s, this is a JSON object.\n   * For TensorFlow-style models (e.g., `SavedModel`), this is the JSON\n   * encoding of the `GraphDef` protocol buffer.\n   */\n  modelTopology: {};\n\n  /** Model training configuration. */\n  trainingConfig?: TrainingConfig;\n\n  /**\n   * Weights manifest.\n   *\n   * The weights manifest consists of an ordered list of weight-manifest\n   * groups. Each weight-manifest group consists of a number of weight values\n   * stored in a number of paths. See the documentation of\n   * `WeightsManifestConfig` for more details.\n   */\n  weightsManifest: WeightsManifestConfig;\n\n  /**\n   * Hard-coded format name for models saved from TensorFlow.js or converted\n   * by TensorFlow.js Converter.\n   */\n  format?: string;\n\n  /**\n   * What library is responsible for originally generating this artifact.\n   *\n   * Used for debugging purposes. E.g., 'TensorFlow.js v1.0.0'.\n   */\n  generatedBy?: string;\n\n  /**\n   * What library or tool is responsible for converting the original model\n   * to this format, applicable only if the model is output by a converter.\n   *\n   * Used for debugging purposes.  E.g., 'TensorFlow.js Converter v1.0.0'.\n   *\n   * A value of `null` means the model artifacts are generated without any\n   * conversion process (e.g., saved directly from a TensorFlow.js\n   * `tf.LayersModel` instance.)\n   */\n  convertedBy?: string|null;\n\n  /**\n   * Inputs and outputs signature for saved model.\n   */\n  signature?: {};\n\n  /**\n   * User-defined metadata about the model.\n   */\n  userDefinedMetadata?: {[key: string]: {}};\n\n  /**\n   * Initializer for the model.\n   */\n  modelInitializer?: {};\n}\n\n/**\n * Type definition for handlers of loading operations.\n */\nexport type LoadHandler = () => Promise<ModelArtifacts>;\n\n/**\n * Type definition for handlers of saving operations.\n */\nexport type SaveHandler = (modelArtifact: ModelArtifacts) =>\n    Promise<SaveResult>;\n\n/**\n * Interface for a model import/export handler.\n *\n * The `save` and `load` handlers are both optional, in order to allow handlers\n * that support only saving or loading.\n */\n// tslint:disable-next-line:interface-name\nexport interface IOHandler {\n  save?: SaveHandler;\n  load?: LoadHandler;\n}\n\n/**\n * An interface for the manager of a model store.\n *\n * A model store is defined as a storage medium on which multiple models can\n * be stored. Each stored model has a unique `path` as its identifier.\n * A `ModelStoreManager` for the store allows actions including\n *\n * - Listing the models stored in the store.\n * - Deleting a model from the store.\n */\nexport interface ModelStoreManager {\n  /**\n   * List all models in the model store.\n   *\n   * @returns A dictionary mapping paths of existing models to their\n   *   model artifacts info. Model artifacts info include type of the model's\n   *   topology, byte sizes of the topology, weights, etc.\n   */\n  listModels(): Promise<{[path: string]: ModelArtifactsInfo}>;\n\n  /**\n   * Remove a model specified by `path`.\n   *\n   * @param path\n   * @returns ModelArtifactsInfo of the deleted model (if and only if deletion\n   *   is successful).\n   * @throws Error if deletion fails, e.g., if no model exists at `path`.\n   */\n  removeModel(path: string): Promise<ModelArtifactsInfo>;\n}\n\n/**\n * Callback for the progress of a long-running action such as an HTTP\n * request for a large binary object.\n *\n * `fraction` should be a number in the [0, 1] interval, indicating how\n * much of the action has completed.\n */\nexport type OnProgressCallback = (fraction: number) => void;\n\n/** @innamespace io */\nexport interface LoadOptions {\n  /**\n   * RequestInit (options) for HTTP requests.\n   *\n   * For detailed information on the supported fields, see\n   * [https://developer.mozilla.org/en-US/docs/Web/API/Request/Request](\n   *     https://developer.mozilla.org/en-US/docs/Web/API/Request/Request)\n   */\n  requestInit?: RequestInit;\n\n  /**\n   * Progress callback.\n   */\n  onProgress?: OnProgressCallback;\n\n  /**\n   * A function used to override the `window.fetch` function.\n   */\n  fetchFunc?: Function;\n\n  /**\n   * Strict loading model: whether extraneous weights or missing\n   * weights should trigger an `Error`.\n   *\n   * If `true`, require that the provided weights exactly match those\n   * required by the layers. `false` means that both extra weights\n   * and missing weights will be silently ignored.\n   *\n   * Default: `true`.\n   */\n  strict?: boolean;\n\n  /**\n   * Path prefix for weight files, by default this is calculated from the\n   * path of the model JSON file.\n   *\n   * For instance, if the path to the model JSON file is\n   * `http://localhost/foo/model.json`, then the default path prefix will be\n   * `http://localhost/foo/`. If a weight file has the path value\n   * `group1-shard1of2` in the weight manifest, then the weight file will be\n   * loaded from `http://localhost/foo/group1-shard1of2` by default. However,\n   * if you provide a `weightPathPrefix` value of\n   * `http://localhost/foo/alt-weights`, then the weight file will be loaded\n   * from the path `http://localhost/foo/alt-weights/group1-shard1of2` instead.\n   */\n  weightPathPrefix?: string;\n\n  /**\n   * Whether the module or model is to be loaded from TF Hub.\n   *\n   * Setting this to `true` allows passing a TF-Hub module URL, omitting the\n   * standard model file name and the query parameters.\n   *\n   * Default: `false`.\n   */\n  fromTFHub?: boolean;\n\n  /**\n   * An async function to convert weight file name to URL. The weight file\n   * names are stored in model.json's weightsManifest.paths field. By default we\n   * consider weight files are colocated with the model.json file. For example:\n   *     model.json URL: https://www.google.com/models/1/model.json\n   *     group1-shard1of1.bin url:\n   *        https://www.google.com/models/1/group1-shard1of1.bin\n   *\n   * With this func you can convert the weight file name to any URL.\n   */\n  weightUrlConverter?: (weightFileName: string) => Promise<string>;\n}\n\n/**\n * Additional options for Platform.fetch\n */\nexport interface RequestDetails {\n  /**\n   * Is this request for a binary file (as opposed to a json file)\n   */\n  isBinary?: boolean;\n}\n"]}","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { add } from './add';\nimport { concat } from './concat';\nimport { matMul } from './mat_mul';\nimport { mul } from './mul';\nimport { op } from './operation';\nimport { sigmoid } from './sigmoid';\nimport { slice } from './slice';\nimport { tanh } from './tanh';\n/**\n * Computes the next state and output of a BasicLSTMCell.\n *\n * Returns `[newC, newH]`.\n *\n * Derived from tf.contrib.rnn.BasicLSTMCell.\n *\n * @param forgetBias Forget bias for the cell.\n * @param lstmKernel The weights for the cell.\n * @param lstmBias The bias for the cell.\n * @param data The input to the cell.\n * @param c Previous cell state.\n * @param h Previous cell output.\n *\n * @doc {heading: 'Operations', subheading: 'RNN'}\n */\nfunction basicLSTMCell_(forgetBias, lstmKernel, lstmBias, data, c, h) {\n const $forgetBias = convertToTensor(forgetBias, 'forgetBias', 'basicLSTMCell');\n const $lstmKernel = convertToTensor(lstmKernel, 'lstmKernel', 'basicLSTMCell');\n const $lstmBias = convertToTensor(lstmBias, 'lstmBias', 'basicLSTMCell');\n const $data = convertToTensor(data, 'data', 'basicLSTMCell');\n const $c = convertToTensor(c, 'c', 'basicLSTMCell');\n const $h = convertToTensor(h, 'h', 'basicLSTMCell');\n const combined = concat([$data, $h], 1);\n const weighted = matMul(combined, $lstmKernel);\n const res = add(weighted, $lstmBias);\n // i = input_gate, j = new_input, f = forget_gate, o = output_gate\n const batchSize = res.shape[0];\n const sliceCols = res.shape[1] / 4;\n const sliceSize = [batchSize, sliceCols];\n const i = slice(res, [0, 0], sliceSize);\n const j = slice(res, [0, sliceCols], sliceSize);\n const f = slice(res, [0, sliceCols * 2], sliceSize);\n const o = slice(res, [0, sliceCols * 3], sliceSize);\n const newC = add(mul(sigmoid(i), tanh(j)), mul($c, sigmoid(add($forgetBias, f))));\n const newH = mul(tanh(newC), sigmoid(o));\n return [newC, newH];\n}\nexport const basicLSTMCell = op({ basicLSTMCell_ });\n//# sourceMappingURL=data:application/json;base64,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","import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { batchNorm } from './batchnorm';\nimport { op } from './operation';\n/**\n * Batch normalization, strictly for 2D. For the more relaxed version, see\n * `tf.batchNorm`.\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n */\nfunction batchNorm2d_(x, mean, variance, offset, scale, varianceEpsilon) {\n const $x = convertToTensor(x, 'x', 'batchNorm');\n const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n let $scale;\n if (scale != null) {\n $scale = convertToTensor(scale, 'scale', 'batchNorm');\n }\n let $offset;\n if (offset != null) {\n $offset = convertToTensor(offset, 'offset', 'batchNorm');\n }\n util.assert($x.rank === 2, () => `Error in batchNorm2D: x must be rank 2 but got rank ` +\n `${$x.rank}.`);\n util.assert($mean.rank === 2 || $mean.rank === 1, () => `Error in batchNorm2D: mean must be rank 2 or rank 1 but ` +\n `got rank ${$mean.rank}.`);\n util.assert($variance.rank === 2 || $variance.rank === 1, () => `Error in batchNorm2D: variance must be rank 2 or rank 1 ` +\n `but got rank ${$variance.rank}.`);\n if ($scale != null) {\n util.assert($scale.rank === 2 || $scale.rank === 1, () => `Error in batchNorm2D: scale must be rank 2 or rank 1 ` +\n `but got rank ${$scale.rank}.`);\n }\n if ($offset != null) {\n util.assert($offset.rank === 2 || $offset.rank === 1, () => `Error in batchNorm2D: offset must be rank 2 or rank 1 ` +\n `but got rank ${$offset.rank}.`);\n }\n return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\nexport const batchNorm2d = op({ batchNorm2d_ });\n//# 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{ convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { batchNorm } from './batchnorm';\nimport { op } from './operation';\n/**\n * Batch normalization, strictly for 3D. For the more relaxed version, see\n * `tf.batchNorm`.\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n */\nfunction batchNorm3d_(x, mean, variance, offset, scale, varianceEpsilon) {\n const $x = convertToTensor(x, 'x', 'batchNorm');\n const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n let $scale;\n if (scale != null) {\n $scale = convertToTensor(scale, 'scale', 'batchNorm');\n }\n let $offset;\n if (offset != null) {\n $offset = convertToTensor(offset, 'offset', 'batchNorm');\n }\n util.assert($x.rank === 3, () => `Error in batchNorm3D: x must be rank 3 but got rank ` +\n `${$x.rank}.`);\n util.assert($mean.rank === 3 || $mean.rank === 1, () => `Error in batchNorm3D: mean must be rank 3 or rank 1 but ` +\n `got rank ${$mean.rank}.`);\n util.assert($variance.rank === 3 || $variance.rank === 1, () => `Error in batchNorm3D: variance must be rank 3 or rank 1 ` +\n `but got rank ${$variance.rank}.`);\n if ($scale != null) {\n util.assert($scale.rank === 3 || $scale.rank === 1, () => `Error in batchNorm3D: scale must be rank 3 or rank 1 ` +\n `but got rank ${$scale.rank}.`);\n }\n if ($offset != null) {\n util.assert($offset.rank === 3 || $offset.rank === 1, () => `Error in batchNorm3D: offset must be rank 3 or rank 1 ` +\n `but got rank ${$offset.rank}.`);\n }\n return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\nexport const batchNorm3d = op({ batchNorm3d_ });\n//# sourceMappingURL=data:application/json;base64,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{ convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { batchNorm } from './batchnorm';\nimport { op } from './operation';\n/**\n * Batch normalization, strictly for 4D. For the more relaxed version, see\n * `tf.batchNorm`.\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n */\nfunction batchNorm4d_(x, mean, variance, offset, scale, varianceEpsilon) {\n const $x = convertToTensor(x, 'x', 'batchNorm');\n const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n let $scale;\n if (scale != null) {\n $scale = convertToTensor(scale, 'scale', 'batchNorm');\n }\n let $offset;\n if (offset != null) {\n $offset = convertToTensor(offset, 'offset', 'batchNorm');\n }\n util.assert($x.rank === 4, () => `Error in batchNorm4D: x must be rank 4 but got rank ` +\n `${$x.rank}.`);\n util.assert($mean.rank === 4 || $mean.rank === 1, () => `Error in batchNorm4D: mean must be rank 4 or rank 1 but ` +\n `got rank ${$mean.rank}.`);\n util.assert($variance.rank === 4 || $variance.rank === 1, () => `Error in batchNorm4D: variance must be rank 4 or rank 1 ` +\n `but got rank ${$variance.rank}.`);\n if ($scale != null) {\n util.assert($scale.rank === 4 || $scale.rank === 1, () => `Error in batchNorm4D: scale must be rank 4 or rank 1 ` +\n `but got rank ${$scale.rank}.`);\n }\n if ($offset != null) {\n util.assert($offset.rank === 4 || $offset.rank === 1, () => `Error in batchNorm4D: offset must be rank 4 or rank 1 ` +\n `but got rank ${$offset.rank}.`);\n }\n return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\nexport const batchNorm4d = op({ batchNorm4d_ });\n//# sourceMappingURL=data:application/json;base64,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{ concat } from './concat';\nimport { op } from './operation';\n/**\n * Concatenates a list of`tf.Tensor1D`s along an axis. See `concat` for details.\n *\n * For example, if:\n * A: shape(3) = |r1, g1, b1|\n * B: shape(2) = |r2, g2|\n * C = tf.concat1d([A, B]) == |r1, g1, b1, r2, g2|\n *\n * @param tensors A list of`tf.Tensor`s to concatenate.\n * @return The concatenated array.\n */\nfunction concat1d_(tensors) {\n return concat(tensors, 0 /* axis */);\n}\nexport const concat1d = op({ concat1d_ });\n//# sourceMappingURL=data:application/json;base64,eyJ2ZXJzaW9uIjozLCJmaWxlIjoiY29uY2F0XzFkLmpzIiwic291cmNlUm9vdCI6IiIsInNvdXJjZXMiOlsiLi4vLi4vLi4vLi4vLi4vLi4vdGZqcy1jb3JlL3NyYy9vcHMvY29uY2F0XzFkLnRzIl0sIm5hbWVzIjpbXSwibWFwcGluZ3MiOiJBQW1CQSxPQUFPLEVBQUMsTUFBTSxFQUFDLE1BQU0sVUFBVSxDQUFDO0FBQ2hDLE9BQU8sRUFBQyxFQUFFLEVBQUMsTUFBTSxhQUFhLENBQUM7QUFFL0I7Ozs7Ozs7Ozs7R0FVRztBQUNILFNBQVMsU0FBUyxDQUFDLE9BQW1DO0lBQ3BELE9BQU8sTUFBTSxDQUFDLE9BQU8sRUFBRSxDQUFDLENBQUMsVUFBVSxDQUFDLENBQUM7QUFDdkMsQ0FBQztBQUVELE1BQU0sQ0FBQyxNQUFNLFFBQVEsR0FBRyxFQUFFLENBQUMsRUFBQyxTQUFTLEVBQUMsQ0FBQyxDQUFDIiwic291cmNlc0NvbnRlbnQiOlsiLyoqXG4gKiBAbGljZW5zZVxuICogQ29weXJpZ2h0IDIwMjAgR29vZ2xlIExMQy4gQWxsIFJpZ2h0cyBSZXNlcnZlZC5cbiAqIExpY2Vuc2VkIHVuZGVyIHRoZSBBcGFjaGUgTGljZW5zZSwgVmVyc2lvbiAyLjAgKHRoZSBcIkxpY2Vuc2VcIik7XG4gKiB5b3UgbWF5IG5vdCB1c2UgdGhpcyBmaWxlIGV4Y2VwdCBpbiBjb21wbGlhbmNlIHdpdGggdGhlIExpY2Vuc2UuXG4gKiBZb3UgbWF5IG9idGFpbiBhIGNvcHkgb2YgdGhlIExpY2Vuc2UgYXRcbiAqXG4gKiBodHRwOi8vd3d3LmFwYWNoZS5vcmcvbGljZW5zZXMvTElDRU5TRS0yLjBcbiAqXG4gKiBVbmxlc3MgcmVxdWlyZWQgYnkgYXBwbGljYWJsZSBsYXcgb3IgYWdyZWVkIHRvIGluIHdyaXRpbmcsIHNvZnR3YXJlXG4gKiBkaXN0cmlidXRlZCB1bmRlciB0aGUgTGljZW5zZSBpcyBkaXN0cmlidXRlZCBvbiBhbiBcIkFTIElTXCIgQkFTSVMsXG4gKiBXSVRIT1VUIFdBUlJBTlRJRVMgT1IgQ09ORElUSU9OUyBPRiBBTlkgS0lORCwgZWl0aGVyIGV4cHJlc3Mgb3IgaW1wbGllZC5cbiAqIFNlZSB0aGUgTGljZW5zZSBmb3IgdGhlIHNwZWNpZmljIGxhbmd1YWdlIGdvdmVybmluZyBwZXJtaXNzaW9ucyBhbmRcbiAqIGxpbWl0YXRpb25zIHVuZGVyIHRoZSBMaWNlbnNlLlxuICogPT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT1cbiAqL1xuaW1wb3J0IHtUZW5zb3IxRH0gZnJvbSAnLi4vdGVuc29yJztcbmltcG9ydCB7VGVuc29yTGlrZX0gZnJvbSAnLi4vdHlwZXMnO1xuXG5pbXBvcnQge2NvbmNhdH0gZnJvbSAnLi9jb25jYXQnO1xuaW1wb3J0IHtvcH0gZnJvbSAnLi9vcGVyYXRpb24nO1xuXG4vKipcbiAqIENvbmNhdGVuYXRlcyBhIGxpc3Qgb2ZgdGYuVGVuc29yMURgcyBhbG9uZyBhbiBheGlzLiBTZWUgYGNvbmNhdGAgZm9yIGRldGFpbHMuXG4gKlxuICogRm9yIGV4YW1wbGUsIGlmOlxuICogQTogc2hhcGUoMykgPSB8cjEsIGcxLCBiMXxcbiAqIEI6IHNoYXBlKDIpID0gfHIyLCBnMnxcbiAqIEMgPSB0Zi5jb25jYXQxZChbQSwgQl0pID09IHxyMSwgZzEsIGIxLCByMiwgZzJ8XG4gKlxuICogQHBhcmFtIHRlbnNvcnMgQSBsaXN0IG9mYHRmLlRlbnNvcmBzIHRvIGNvbmNhdGVuYXRlLlxuICogQHJldHVybiBUaGUgY29uY2F0ZW5hdGVkIGFycmF5LlxuICovXG5mdW5jdGlvbiBjb25jYXQxZF8odGVuc29yczogQXJyYXk8VGVuc29yMUR8VGVuc29yTGlrZT4pOiBUZW5zb3IxRCB7XG4gIHJldHVybiBjb25jYXQodGVuc29ycywgMCAvKiBheGlzICovKTtcbn1cblxuZXhwb3J0IGNvbnN0IGNvbmNhdDFkID0gb3Aoe2NvbmNhdDFkX30pO1xuIl19","import { concat } from './concat';\nimport { op } from './operation';\n/**\n * Concatenates a list of`tf.Tensor2D`s along an axis. See `concat` for details.\n *\n * For example, if:\n * A: shape(2, 3) = | r1, g1, b1 |\n * | r2, g2, b2 |\n *\n * B: shape(2, 3) = | r3, g3, b3 |\n * | r4, g4, b4 |\n *\n * C = tf.concat2d([A, B], axis)\n *\n * if axis = 0:\n * C: shape(4, 3) = | r1, g1, b1 |\n * | r2, g2, b2 |\n * | r3, g3, b3 |\n * | r4, g4, b4 |\n *\n * if axis = 1:\n * C = shape(2, 6) = | r1, g1, b1, r3, g3, b3 |\n * | r2, g2, b2, r4, g4, b4 |\n *\n *\n * @param tensors A list of `tf.Tensor`s to concatenate.\n * @param axis The axis to concatenate along.\n * @return The concatenated array.\n */\nfunction concat2d_(tensors, axis) {\n return concat(tensors, axis);\n}\nexport const concat2d = op({ concat2d_ });\n//# sourceMappingURL=data:application/json;base64,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","import { concat } from './concat';\nimport { op } from './operation';\n/**\n * Concatenates a list of `tf.Tensor3D`s along an axis.\n * See `concat` for details.\n *\n * For example, if:\n * A: shape(2, 1, 3) = | r1, g1, b1 |\n * | r2, g2, b2 |\n *\n * B: shape(2, 1, 3) = | r3, g3, b3 |\n * | r4, g4, b4 |\n *\n * C = tf.concat3d([A, B], axis)\n *\n * if axis = 0:\n * C: shape(4, 1, 3) = | r1, g1, b1 |\n * | r2, g2, b2 |\n * | r3, g3, b3 |\n * | r4, g4, b4 |\n *\n * if axis = 1:\n * C: shape(2, 2, 3) = | r1, g1, b1, r3, g3, b3 |\n * | r2, g2, b2, r4, g4, b4 |\n *\n * if axis = 2:\n * C = shape(2, 1, 6) = | r1, g1, b1, r3, g3, b3 |\n * | r2, g2, b2, r4, g4, b4 |\n *\n * @param tensors A list of`tf.Tensor`s to concatenate.\n * @param axis The axis to concate along.\n * @return The concatenated array.\n */\nfunction concat3d_(tensors, axis) {\n return concat(tensors, axis);\n}\nexport const concat3d = op({ concat3d_ });\n//# 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{ concat } from './concat';\nimport { op } from './operation';\n/**\n * Concatenates a list of `tf.Tensor4D`s along an axis.\n * See `concat` for details.\n *\n * @param tensors A list of `tf.Tensor`s to concatenate.\n * @param axis The axis to concate along.\n * @return The concatenated array.\n */\nfunction concat4d_(tensors, axis) {\n return concat(tensors, axis);\n}\nexport const concat4d = op({ concat4d_ });\n//# sourceMappingURL=data:application/json;base64,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","import { convertToTensor } from '../tensor_util_env';\nimport { conv3DBackpropInput } from './conv3d_backprop_input';\nimport { op } from './operation';\n/**\n * Computes the transposed 3D convolution of a volume, also known as a\n * deconvolution.\n *\n * @param x The input image, of rank 5 or rank 4, of shape\n * `[batch, depth, height, width, inDepth]`. If rank 4, batch of 1 is assumed.\n * @param filter The filter, rank 4, of shape\n * `[depth, filterHeight, filterWidth, outDepth, inDepth]`.\n * `inDepth` must match `inDepth` in `x`.\n * @param outputShape Output shape, of rank 5 or rank 4:\n * `[batch, depth, height, width, outDepth]`. If rank 3, batch of 1 is\n * assumed.\n * @param strides The strides of the original convolution:\n * `[strideDepth, strideHeight, strideWidth]`.\n * @param pad The type of padding algorithm used in the non-transpose version\n * of the op.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv3dTranspose_(x, filter, outputShape, strides, pad) {\n const $x = convertToTensor(x, 'x', 'conv3dTranspose');\n const $filter = convertToTensor(filter, 'filter', 'conv3dTranspose');\n return conv3DBackpropInput(outputShape, $x, $filter, strides, pad);\n}\nexport const conv3dTranspose = op({ conv3dTranspose_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Diag } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns a diagonal tensor with a given diagonal values.\n *\n * Given a diagonal, this operation returns a tensor with the diagonal and\n * everything else padded with zeros.\n *\n * Assume the input has dimensions `[D1,..., Dk]`, then the output is a tensor\n * of rank 2k with dimensions `[D1,..., Dk, D1,..., Dk]`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * tf.diag(x).print()\n * ```\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4, 5, 6, 6, 8], [4, 2])\n *\n * tf.diag(x).print()\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction diag_(x) {\n const $x = convertToTensor(x, 'x', 'diag');\n const inputs = { x: $x };\n return ENGINE.runKernel(Diag, inputs);\n}\nexport const diag = op({ diag_ });\n//# 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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { matMul } from './mat_mul';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the dot product of two matrices and/or vectors, `t1` and `t2`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor2d([[1, 2], [3, 4]]);\n * const c = tf.tensor2d([[1, 2, 3], [4, 5, 6]]);\n *\n * a.dot(b).print(); // or tf.dot(a, b)\n * b.dot(a).print();\n * b.dot(c).print();\n * ```\n * @param t1 The first tensor in the dot operation.\n * @param t2 The second tensor in the dot operation.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction dot_(t1, t2) {\n const $t1 = convertToTensor(t1, 't1', 'dot');\n const $t2 = convertToTensor(t2, 't2', 'dot');\n util.assert(($t1.rank === 1 || $t1.rank === 2) && ($t2.rank === 1 || $t2.rank === 2), () => `Error in dot: inputs must all be rank 1 or 2, but got ranks ` +\n `${$t1.rank} and ${$t2.rank}.`);\n const t1Inner = ($t1.rank === 1 ? $t1.size : $t1.shape[1]);\n const t2Inner = ($t2.rank === 1 ? $t2.size : $t2.shape[0]);\n util.assert(t1Inner === t2Inner, () => `Error in dot: inner dimensions of inputs must match, but got ` +\n `${t1Inner} and ${t2Inner}.`);\n if ($t1.rank === 1 && $t2.rank === 1) {\n const t12D = reshape($t1, [1, -1]);\n const t22D = reshape($t2, [-1, 1]);\n const t1t2 = matMul(t12D, t22D);\n return reshape(t1t2, []);\n }\n else if ($t1.rank === 1 && $t2.rank === 2) {\n const t12D = reshape($t1, [1, -1]);\n const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);\n const t1t2 = matMul(t12D, t22D);\n return reshape(t1t2, [t1t2.size]);\n }\n else if ($t1.rank === 2 && $t2.rank === 1) {\n const t22D = reshape($t2, [-1, 1]);\n const t1t2 = matMul($t1, t22D);\n return reshape(t1t2, [t1t2.size]);\n }\n else {\n const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);\n const t1t2 = matMul($t1, t22D);\n return t1t2;\n }\n}\nexport const dot = op({ dot_ });\n//# 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BbNCwgNSwgNl1dKTtcbiAqXG4gKiBhLmRvdChiKS5wcmludCgpOyAgLy8gb3IgdGYuZG90KGEsIGIpXG4gKiBiLmRvdChhKS5wcmludCgpO1xuICogYi5kb3QoYykucHJpbnQoKTtcbiAqIGBgYFxuICogQHBhcmFtIHQxIFRoZSBmaXJzdCB0ZW5zb3IgaW4gdGhlIGRvdCBvcGVyYXRpb24uXG4gKiBAcGFyYW0gdDIgVGhlIHNlY29uZCB0ZW5zb3IgaW4gdGhlIGRvdCBvcGVyYXRpb24uXG4gKlxuICogQGRvYyB7aGVhZGluZzogJ09wZXJhdGlvbnMnLCBzdWJoZWFkaW5nOiAnTWF0cmljZXMnfVxuICovXG5mdW5jdGlvbiBkb3RfKHQxOiBUZW5zb3J8VGVuc29yTGlrZSwgdDI6IFRlbnNvcnxUZW5zb3JMaWtlKTogVGVuc29yIHtcbiAgY29uc3QgJHQxID0gY29udmVydFRvVGVuc29yKHQxLCAndDEnLCAnZG90Jyk7XG4gIGNvbnN0ICR0MiA9IGNvbnZlcnRUb1RlbnNvcih0MiwgJ3QyJywgJ2RvdCcpO1xuXG4gIHV0aWwuYXNzZXJ0KFxuICAgICAgKCR0MS5yYW5rID09PSAxIHx8ICR0MS5yYW5rID09PSAyKSAmJiAoJHQyLnJhbmsgPT09IDEgfHwgJHQyLnJhbmsgPT09IDIpLFxuICAgICAgKCkgPT4gYEVycm9yIGluIGRvdDogaW5wdXRzIG11c3QgYWxsIGJlIHJhbmsgMSBvciAyLCBidXQgZ290IHJhbmtzIGAgK1xuICAgICAgICAgIGAkeyR0MS5yYW5rfSBhbmQgJHskdDIucmFua30uYCk7XG5cbiAgY29uc3QgdDFJbm5lciA9ICgkdDEucmFuayA9PT0gMSA/ICR0MS5zaXplIDogJHQxLnNoYXBlWzFdKTtcbiAgY29uc3QgdDJJbm5lciA9ICgkdDIucmFuayA9PT0gMSA/ICR0Mi5zaXplIDogJHQyLnNoYXBlWzBdKTtcblxuICB1dGlsLmFzc2VydChcbiAgICAgIHQxSW5uZXIgPT09IHQySW5uZXIsXG4gICAgICAoKSA9PiBgRXJyb3IgaW4gZG90OiBpbm5lciBkaW1lbnNpb25zIG9mIGlucHV0cyBtdXN0IG1hdGNoLCBidXQgZ290IGAgK1xuICAgICAgICAgIGAke3QxSW5uZXJ9IGFuZCAke3QySW5uZXJ9LmApO1xuXG4gIGlmICgkdDEucmFuayA9PT0gMSAmJiAkdDIucmFuayA9PT0gMSkge1xuICAgIGNvbnN0IHQxMkQgPSByZXNoYXBlKCR0MSwgWzEsIC0xXSk7XG4gICAgY29uc3QgdDIyRCA9IHJlc2hhcGUoJHQyLCBbLTEsIDFdKTtcbiAgICBjb25zdCB0MXQyID0gbWF0TXVsKHQxMkQsIHQyMkQpO1xuICAgIHJldHVybiByZXNoYXBlKHQxdDIsIFtdKTtcbiAgfSBlbHNlIGlmICgkdDEucmFuayA9PT0gMSAmJiAkdDIucmFuayA9PT0gMikge1xuICAgIGNvbnN0IHQxMkQgPSByZXNoYXBlKCR0MSwgWzEsIC0xXSk7XG4gICAgY29uc3QgdDIyRCA9IHJlc2hhcGUoJHQyLCBbJHQyLnNoYXBlWzBdLCAkdDIuc2hhcGVbMV1dKTtcbiAgICBjb25zdCB0MXQyID0gbWF0TXVsKHQxMkQsIHQyMkQpO1xuICAgIHJldHVybiByZXNoYXBlKHQxdDIsIFt0MXQyLnNpemVdKTtcbiAgfSBlbHNlIGlmICgkdDEucmFuayA9PT0gMiAmJiAkdDIucmFuayA9PT0gMSkge1xuICAgIGNvbnN0IHQyMkQgPSByZXNoYXBlKCR0MiwgWy0xLCAxXSk7XG4gICAgY29uc3QgdDF0MiA9IG1hdE11bCgkdDEsIHQyMkQpO1xuICAgIHJldHVybiByZXNoYXBlKHQxdDIsIFt0MXQyLnNpemVdKTtcbiAgfSBlbHNlIHtcbiAgICBjb25zdCB0MjJEID0gcmVzaGFwZSgkdDIsIFskdDIuc2hhcGVbMF0sICR0Mi5zaGFwZVsxXV0pO1xuICAgIGNvbnN0IHQxdDIgPSBtYXRNdWwoJHQxLCB0MjJEKTtcbiAgICByZXR1cm4gdDF0MjtcbiAgfVxufVxuXG5leHBvcnQgY29uc3QgZG90ID0gb3Aoe2RvdF99KTtcbiJdfQ==","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from './buffer';\nimport { expandDims } from './expand_dims';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { tile } from './tile';\n/**\n * Create an identity matrix.\n *\n * @param numRows Number of rows.\n * @param numColumns Number of columns. Defaults to `numRows`.\n * @param batchShape If provided, will add the batch shape to the beginning\n * of the shape of the returned `tf.Tensor` by repeating the identity\n * matrix.\n * @param dtype Data type.\n * @returns Identity matrix of the specified size and data type, possibly\n * with batch repetition if `batchShape` is specified.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction eye_(numRows, numColumns, batchShape, dtype = 'float32') {\n if (numColumns == null) {\n numColumns = numRows;\n }\n const buff = buffer([numRows, numColumns], dtype);\n const n = numRows <= numColumns ? numRows : numColumns;\n for (let i = 0; i < n; ++i) {\n buff.set(1, i, i);\n }\n const out = reshape(buff.toTensor(), [numRows, numColumns]);\n if (batchShape == null) {\n return out;\n }\n else {\n if (batchShape.length === 1) {\n return tile(expandDims(out, 0), [batchShape[0], 1, 1]);\n }\n else if (batchShape.length === 2) {\n // tslint:disable-next-line:no-unnecessary-type-assertion\n return tile(expandDims(expandDims(out, 0), 0), [batchShape[0], batchShape[1], 1, 1]);\n }\n else if (batchShape.length === 3) {\n // tslint:disable-next-line:no-unnecessary-type-assertion\n return tile(expandDims(expandDims(expandDims(out, 0), 0), 0), [\n batchShape[0], batchShape[1], batchShape[2], 1, 1\n ]);\n }\n else {\n throw new Error(`eye() currently supports only 1D and 2D ` +\n // tslint:disable-next-line:no-any\n `batchShapes, but received ${batchShape.length}D.`);\n }\n }\n}\nexport const eye = op({ eye_ });\n//# 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* @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { IsFinite } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns which elements of x are finite.\n *\n * ```js\n * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);\n *\n * x.isFinite().print(); // or tf.isNaN(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction isFinite_(x) {\n const $x = convertToTensor(x, 'x', 'isFinite');\n const inputs = { x: $x };\n return ENGINE.runKernel(IsFinite, inputs);\n}\nexport const isFinite = op({ isFinite_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { IsInf } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns which elements of x are Infinity or -Infinity.\n *\n * ```js\n * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);\n *\n * x.isInf().print(); // or tf.isNaN(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction isInf_(x) {\n const $x = convertToTensor(x, 'x', 'isInf');\n const inputs = { x: $x };\n return ENGINE.runKernel(IsInf, inputs);\n}\nexport const isInf = op({ isInf_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { customGrad } from '../gradients';\nimport { convertToTensor } from '../tensor_util_env';\nimport { mul } from './mul';\nimport { neg } from './neg';\nimport { op } from './operation';\nimport { sigmoid } from './sigmoid';\nimport { softplus } from './softplus';\n/**\n * Computes log sigmoid of the input `tf.Tensor` element-wise:\n * `logSigmoid(x)`. For numerical stability, we use `-tf.softplus(-x)`.\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.logSigmoid().print(); // or tf.logSigmoid(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction logSigmoid_(x) {\n const $x = convertToTensor(x, 'x', 'logSigmoid');\n // Use a custom gradient to maintain previous implementation.\n // There is no LogSigmoid kernel in TF so we can't use engine.runKernel\n // directly\n const customOp = customGrad((x) => {\n // TODO(yassogba) we can remove the chained softplus call here only\n // after backends have modualrized softplus at which point we can call\n // engine runKernel(..., Sotfplus, ...) directly.\n const value = neg(softplus(neg(x)));\n const gradFunc = (dy) => {\n const derX = mul(dy, sigmoid(neg(x)));\n return derX;\n };\n return { value, gradFunc };\n });\n return customOp($x);\n}\nexport const logSigmoid = op({ logSigmoid_ });\n//# 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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { add } from './add';\nimport { expandShapeToKeepDim } from './axis_util';\nimport { exp } from './exp';\nimport { log } from './log';\nimport { max } from './max';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { sub } from './sub';\nimport { sum } from './sum';\n/**\n * Computes the log(sum(exp(elements across the reduction dimensions)).\n *\n * Reduces the input along the dimensions given in `axis`. Unless `keepDims`\n * is true, the rank of the array is reduced by 1 for each entry in `axis`.\n * If `keepDims` is true, the reduced dimensions are retained with length 1.\n * If `axis` has no entries, all dimensions are reduced, and an array with a\n * single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.logSumExp().print(); // or tf.logSumExp(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.logSumExp(axis).print(); // or tf.logSumExp(a, axis)\n * ```\n * @param x The input tensor.\n * @param axis The dimension(s) to reduce. If null (the default),\n * reduces all dimensions.\n * @param keepDims If true, retains reduced dimensions with length\n * of 1. Defaults to false.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction logSumExp_(x, axis = null, keepDims = false) {\n const $x = convertToTensor(x, 'x', 'logSumExp');\n const axes = parseAxisParam(axis, $x.shape);\n const xMax = max($x, axes, true /* keepDims */);\n const a = sub($x, xMax);\n const b = exp(a);\n const c = sum(b, axes);\n const d = log(c);\n const res = add(reshape(xMax, d.shape), d);\n if (keepDims) {\n const newShape = expandShapeToKeepDim(res.shape, axes);\n return reshape(res, newShape);\n }\n return res;\n}\nexport const logSumExp = op({ logSumExp_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { logicalAnd } from './logical_and';\nimport { logicalNot } from './logical_not';\nimport { logicalOr } from './logical_or';\nimport { op } from './operation';\n/**\n * Returns the truth value of `a XOR b` element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([false, false, true, true], 'bool');\n * const b = tf.tensor1d([false, true, false, true], 'bool');\n *\n * a.logicalXor(b).print();\n * ```\n *\n * @param a The first input tensor. Must be of dtype bool.\n * @param b The second input tensor. Must be of dtype bool.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalXor_(a, b) {\n const $a = convertToTensor(a, 'a', 'logicalXor', 'bool');\n const $b = convertToTensor(b, 'b', 'logicalXor', 'bool');\n assertAndGetBroadcastShape($a.shape, $b.shape);\n // x ^ y = (x | y) & ~(x & y)\n return logicalAnd(logicalOr(a, b), logicalNot(logicalAnd(a, b)));\n}\nexport const logicalXor = op({ logicalXor_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { matMul } from './mat_mul';\nimport { ones } from './ones';\nimport { reshape } from './reshape';\nimport { Tensor } from '../tensor';\nimport { convertToTensor } from '../tensor_util_env';\nimport { sizeFromShape } from '../util_base';\n/**\n * Broadcasts parameters for evaluation on an N-D grid.\n *\n * Given N one-dimensional coordinate arrays `*args`, returns a list `outputs`\n * of N-D coordinate arrays for evaluating expressions on an N-D grid.\n *\n * Notes:\n * `meshgrid` supports cartesian ('xy') and matrix ('ij') indexing conventions.\n * When the `indexing` argument is set to 'xy' (the default), the broadcasting\n * instructions for the first two dimensions are swapped.\n * Examples:\n * Calling `const [X, Y] = meshgrid(x, y)` with the tensors\n *\n * ```javascript\n * const x = [1, 2, 3];\n * const y = [4, 5, 6];\n * const [X, Y] = tf.meshgrid(x, y);\n * // X = [[1, 2, 3],\n * // [1, 2, 3],\n * // [1, 2, 3]]\n * // Y = [[4, 4, 4],\n * // [5, 5, 5],\n * // [6, 6, 6]]\n * ```\n *\n * @param x Tensor with rank geq 1.\n * @param y Tensor with rank geq 1.\n * @param indexing\n *\n * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}\n */\nexport function meshgrid(x, y, { indexing = 'xy' } = {}) {\n if (indexing !== 'xy' && indexing !== 'ij') {\n throw new TypeError(`${indexing} is not a valid third argument to meshgrid`);\n }\n if (x === undefined) {\n return [];\n }\n let $x = convertToTensor(x, 'x', 'meshgrid', x instanceof Tensor ? x.dtype : 'float32');\n if (y === undefined) {\n return [$x];\n }\n let $y = convertToTensor(y, 'y', 'meshgrid', y instanceof Tensor ? y.dtype : 'float32');\n const w = sizeFromShape($x.shape);\n const h = sizeFromShape($y.shape);\n if (indexing === 'xy') {\n $x = reshape($x, [1, -1]);\n $y = reshape($y, [-1, 1]);\n return [\n matMul(ones([h, 1], $x.dtype), $x),\n matMul($y, ones([1, w], $y.dtype)),\n ];\n }\n $x = reshape($x, [-1, 1]);\n $y = reshape($y, [1, -1]);\n return [\n matMul($x, ones([1, h], $x.dtype)),\n matMul(ones([w, 1], $y.dtype), $y),\n ];\n}\n//# 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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { expandShapeToKeepDim } from './axis_util';\nimport { cast } from './cast';\nimport { mean } from './mean';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { square } from './square';\nimport { sub } from './sub';\n/**\n * Calculates the mean and variance of `x`. The mean and variance are\n * calculated by aggregating the contents of `x` across `axes`. If `x` is\n * 1-D and `axes = [0]` this is just the mean and variance of a vector.\n *\n * @param x The input tensor.\n * @param axis The dimension(s) along with to compute mean and\n * variance. By default it reduces all dimensions.\n * @param keepDims If true, the moments have the same dimensionality as the\n * input.\n * @return An object with two keys: `mean` and `variance`.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction moments_(x, axis = null, keepDims = false) {\n x = convertToTensor(x, 'x', 'moments');\n const axes = parseAxisParam(axis, x.shape);\n const xMean = mean(x, axes, keepDims);\n let keepDimsShape = xMean.shape;\n if (!keepDims) {\n keepDimsShape = expandShapeToKeepDim(xMean.shape, axes);\n }\n const devSquared = square(sub(cast(x, 'float32'), reshape(xMean, keepDimsShape)));\n const variance = mean(devSquared, axes, keepDims);\n return { mean: xMean, variance };\n}\nexport const moments = op({ moments_ });\n//# sourceMappingURL=data:application/json;base64,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","import { convertToTensor, convertToTensorArray } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the next states and outputs of a stack of LSTMCells.\n *\n * Each cell output is used as input to the next cell.\n *\n * Returns `[cellState, cellOutput]`.\n *\n * Derived from tf.contrib.rn.MultiRNNCell.\n *\n * @param lstmCells Array of LSTMCell functions.\n * @param data The input to the cell.\n * @param c Array of previous cell states.\n * @param h Array of previous cell outputs.\n *\n * @doc {heading: 'Operations', subheading: 'RNN'}\n */\nfunction multiRNNCell_(lstmCells, data, c, h) {\n const $data = convertToTensor(data, 'data', 'multiRNNCell');\n const $c = convertToTensorArray(c, 'c', 'multiRNNCell');\n const $h = convertToTensorArray(h, 'h', 'multiRNNCell');\n let input = $data;\n const newStates = [];\n for (let i = 0; i < lstmCells.length; i++) {\n const output = lstmCells[i](input, $c[i], $h[i]);\n newStates.push(output[0]);\n newStates.push(output[1]);\n input = output[1];\n }\n const newC = [];\n const newH = [];\n for (let i = 0; i < newStates.length; i += 2) {\n newC.push(newStates[i]);\n newH.push(newStates[i + 1]);\n }\n return [newC, newH];\n}\nexport const multiRNNCell = op({ multiRNNCell_ });\n//# 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4gIH1cbiAgcmV0dXJuIFtuZXdDLCBuZXdIXTtcbn1cbmV4cG9ydCBjb25zdCBtdWx0aVJOTkNlbGwgPSBvcCh7bXVsdGlSTk5DZWxsX30pO1xuIl19","import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { matMul } from './mat_mul';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the outer product of two vectors, `v1` and `v2`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([3, 4, 5]);\n *\n * tf.outerProduct(a, b).print();\n * ```\n * @param v1 The first vector in the outer product operation.\n * @param v2 The second vector in the outer product operation.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction outerProduct_(v1, v2) {\n const $v1 = convertToTensor(v1, 'v1', 'outerProduct');\n const $v2 = convertToTensor(v2, 'v2', 'outerProduct');\n util.assert($v1.rank === 1 && $v2.rank === 1, () => `Error in outerProduct: inputs must be rank 1, but got ranks ` +\n `${$v1.rank} and ${$v2.rank}.`);\n const v12D = reshape($v1, [-1, 1]);\n const v22D = reshape($v2, [1, -1]);\n return matMul(v12D, v22D);\n}\nexport const outerProduct = op({ outerProduct_ });\n//# sourceMappingURL=data:application/json;base64,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","import { assert } from '../util';\nimport { op } from './operation';\nimport { pad } from './pad';\n/**\n * Pads a `tf.Tensor1D` with a given value and paddings. See `pad` for details.\n */\nfunction pad1d_(x, paddings, constantValue = 0) {\n assert(paddings.length === 2, () => 'Invalid number of paddings. Must be length of 2.');\n return pad(x, [paddings], constantValue);\n}\nexport const pad1d = op({ pad1d_ });\n//# sourceMappingURL=data:application/json;base64,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","import { assert } from '../util';\nimport { op } from './operation';\nimport { pad } from './pad';\n/**\n * Pads a `tf.Tensor2D` with a given value and paddings. See `pad` for details.\n */\nfunction pad2d_(x, paddings, constantValue = 0) {\n assert(paddings.length === 2 && paddings[0].length === 2 &&\n paddings[1].length === 2, () => 'Invalid number of paddings. Must be length of 2 each.');\n return pad(x, paddings, constantValue);\n}\nexport const pad2d = op({ pad2d_ });\n//# sourceMappingURL=data:application/json;base64,eyJ2ZXJzaW9uIjozLCJmaWxlIjoicGFkMmQuanMiLCJzb3VyY2VSb290IjoiIiwic291cmNlcyI6WyIuLi8uLi8uLi8uLi8uLi8uLi90ZmpzLWNvcmUvc3JjL29wcy9wYWQyZC50cyJdLCJuYW1lcyI6W10sIm1hcHBpbmdzIjoiQUFrQkEsT0FBTyxFQUFDLE1BQU0sRUFBQyxNQUFNLFNBQVMsQ0FBQztBQUMvQixPQUFPLEVBQUMsRUFBRSxFQUFDLE1BQU0sYUFBYSxDQUFDO0FBQy9CLE9BQU8sRUFBQyxHQUFHLEVBQUMsTUFBTSxPQUFPLENBQUM7QUFFMUI7O0dBRUc7QUFDSCxTQUFTLE1BQU0sQ0FDWCxDQUFzQixFQUFFLFFBQThDLEVBQ3RFLGFBQWEsR0FBRyxDQUFDO0lBQ25CLE1BQU0sQ0FDRixRQUFRLENBQUMsTUFBTSxLQUFLLENBQUMsSUFBSSxRQUFRLENBQUMsQ0FBQyxDQUFDLENBQUMsTUFBTSxLQUFLLENBQUM7UUFDN0MsUUFBUSxDQUFDLENBQUMsQ0FBQyxDQUFDLE1BQU0sS0FBSyxDQUFDLEVBQzVCLEdBQUcsRUFBRSxDQUFDLHVEQUF1RCxDQUFDLENBQUM7SUFDbkUsT0FBTyxHQUFHLENBQUMsQ0FBQyxFQUFFLFFBQVEsRUFBRSxhQUFhLENBQUMsQ0FBQztBQUN6QyxDQUFDO0FBRUQsTUFBTSxDQUFDLE1BQU0sS0FBSyxHQUFHLEVBQUUsQ0FBQyxFQUFDLE1BQU0sRUFBQyxDQUFDLENBQUMiLCJzb3VyY2VzQ29udGVudCI6WyIvKipcbiAqIEBsaWNlbnNlXG4gKiBDb3B5cmlnaHQgMjAyMCBHb29nbGUgTExDLiBBbGwgUmlnaHRzIFJlc2VydmVkLlxuICogTGljZW5zZWQgdW5kZXIgdGhlIEFwYWNoZSBMaWNlbnNlLCBWZXJzaW9uIDIuMCAodGhlIFwiTGljZW5zZVwiKTtcbiAqIHlvdSBtYXkgbm90IHVzZSB0aGlzIGZpbGUgZXhjZXB0IGluIGNvbXBsaWFuY2Ugd2l0aCB0aGUgTGljZW5zZS5cbiAqIFlvdSBtYXkgb2J0YWluIGEgY29weSBvZiB0aGUgTGljZW5zZSBhdFxuICpcbiAqIGh0dHA6Ly93d3cuYXBhY2hlLm9yZy9saWNlbnNlcy9MSUNFTlNFLTIuMFxuICpcbiAqIFVubGVzcyByZXF1aXJlZCBieSBhcHBsaWNhYmxlIGxhdyBvciBhZ3JlZWQgdG8gaW4gd3JpdGluZywgc29mdHdhcmVcbiAqIGRpc3RyaWJ1dGVkIHVuZGVyIHRoZSBMaWNlbnNlIGlzIGRpc3RyaWJ1dGVkIG9uIGFuIFwiQVMgSVNcIiBCQVNJUyxcbiAqIFdJVEhPVVQgV0FSUkFOVElFUyBPUiBDT05ESVRJT05TIE9GIEFOWSBLSU5ELCBlaXRoZXIgZXhwcmVzcyBvciBpbXBsaWVkLlxuICogU2VlIHRoZSBMaWNlbnNlIGZvciB0aGUgc3BlY2lmaWMgbGFuZ3VhZ2UgZ292ZXJuaW5nIHBlcm1pc3Npb25zIGFuZFxuICogbGltaXRhdGlvbnMgdW5kZXIgdGhlIExpY2Vuc2UuXG4gKiA9PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PVxuICovXG5pbXBvcnQge1RlbnNvcjJEfSBmcm9tICcuLi90ZW5zb3InO1xuaW1wb3J0IHtUZW5zb3JMaWtlfSBmcm9tICcuLi90eXBlcyc7XG5pbXBvcnQge2Fzc2VydH0gZnJvbSAnLi4vdXRpbCc7XG5pbXBvcnQge29wfSBmcm9tICcuL29wZXJhdGlvbic7XG5pbXBvcnQge3BhZH0gZnJvbSAnLi9wYWQnO1xuXG4vKipcbiAqIFBhZHMgYSBgdGYuVGVuc29yMkRgIHdpdGggYSBnaXZlbiB2YWx1ZSBhbmQgcGFkZGluZ3MuIFNlZSBgcGFkYCBmb3IgZGV0YWlscy5cbiAqL1xuZnVuY3Rpb24gcGFkMmRfKFxuICAgIHg6IFRlbnNvcjJEfFRlbnNvckxpa2UsIHBhZGRpbmdzOiBbW251bWJlciwgbnVtYmVyXSwgW251bWJlciwgbnVtYmVyXV0sXG4gICAgY29uc3RhbnRWYWx1ZSA9IDApOiBUZW5zb3IyRCB7XG4gIGFzc2VydChcbiAgICAgIHBhZGRpbmdzLmxlbmd0aCA9PT0gMiAmJiBwYWRkaW5nc1swXS5sZW5ndGggPT09IDIgJiZcbiAgICAgICAgICBwYWRkaW5nc1sxXS5sZW5ndGggPT09IDIsXG4gICAgICAoKSA9PiAnSW52YWxpZCBudW1iZXIgb2YgcGFkZGluZ3MuIE11c3QgYmUgbGVuZ3RoIG9mIDIgZWFjaC4nKTtcbiAgcmV0dXJuIHBhZCh4LCBwYWRkaW5ncywgY29uc3RhbnRWYWx1ZSk7XG59XG5cbmV4cG9ydCBjb25zdCBwYWQyZCA9IG9wKHtwYWQyZF99KTtcbiJdfQ==","import { assert } from '../util';\nimport { op } from './operation';\nimport { pad } from './pad';\n/**\n * Pads a `tf.Tensor3D` with a given value and paddings. See `pad` for details.\n */\nfunction pad3d_(x, paddings, constantValue = 0) {\n assert(paddings.length === 3 && paddings[0].length === 2 &&\n paddings[1].length === 2 && paddings[2].length === 2, () => 'Invalid number of paddings. Must be length of 2 each.');\n return pad(x, paddings, constantValue);\n}\nexport const pad3d = op({ pad3d_ });\n//# sourceMappingURL=data:application/json;base64,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","import { assert } from '../util';\nimport { op } from './operation';\nimport { pad } from './pad';\n/**\n * Pads a `tf.Tensor4D` with a given value and paddings. See `pad` for details.\n */\nfunction pad4d_(x, paddings, constantValue = 0) {\n assert(paddings.length === 4 && paddings[0].length === 2 &&\n paddings[1].length === 2 && paddings[2].length === 2 &&\n paddings[3].length === 2, () => 'Invalid number of paddings. Must be length of 2 each.');\n return pad(x, paddings, constantValue);\n}\nexport const pad4d = op({ pad4d_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { avgPool } from './avg_pool';\nimport { batchToSpaceND } from './batch_to_space_nd';\nimport * as conv_util from './conv_util';\nimport { maxPool } from './max_pool';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { spaceToBatchND } from './space_to_batch_nd';\n/**\n * Performs an N-D pooling operation\n *\n * @param input The input tensor, of rank 4 or rank 3 of shape\n * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param windowShape The filter size: `[filterHeight, filterWidth]`. If\n * `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param poolingType The type of pooling, either 'max' or 'avg'.\n * @param pad The type of padding algorithm:\n * - `same` and stride 1: output will be of same size as input,\n * regardless of filter size.\n * - `valid`: output will be smaller than input if filter is larger\n * than 1x1.\n * - For more info, see this guide:\n * [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n * https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n * in which we sample input values across the height and width dimensions\n * in dilated pooling. Defaults to `[1, 1]`. If `dilationRate` is a single\n * number, then `dilationHeight == dilationWidth`. If it is greater than\n * 1, then all values of `strides` must be 1.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n * `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n * provided, it will default to truncate.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction pool_(input, windowShape, poolingType, pad, dilations, strides, dimRoundingMode) {\n if (dilations == null) {\n dilations = [1, 1];\n }\n if (strides == null) {\n strides = 1;\n }\n if (pad === 0) {\n pad = 'valid';\n }\n const $x = convertToTensor(input, 'x', 'maxPool');\n let x4D = $x;\n let reshapedTo4D = false;\n if ($x.rank === 3) {\n reshapedTo4D = true;\n x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n }\n util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in pool: Either strides or dilations must be 1. ' +\n `Got strides ${strides} and dilations '${dilations}'`);\n const convInfo = conv_util.computePool2DInfo(x4D.shape, windowShape, strides, dilations, pad);\n const dilation = [convInfo.dilationHeight, convInfo.dilationWidth];\n // The following implementation does batchToSpace(pool(spaceToBatch(x)))\n // whenever dilation > 1 since the TF kernels do not support dilation > 1.\n // tslint:disable-next-line:max-line-length\n // https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/nn_ops.py#L1037\n let basePadding;\n if (pad === 'same') {\n basePadding = withSpaceToBatchBasePaddings([convInfo.filterHeight, convInfo.filterWidth], dilation);\n }\n else {\n basePadding = [[0, 0], [0, 0]];\n }\n const isDilationOne = dilation[0] === 1 && dilation[1] === 1;\n const [adjustedPadding, adjustedCrops] = requiredSpaceToBatchPaddings([convInfo.inHeight, convInfo.inWidth], dilation, basePadding);\n const convertedPad = isDilationOne ? pad : 'valid';\n const convertedX = isDilationOne ? x4D : spaceToBatchND(x4D, dilation, adjustedPadding);\n const forwardOp = poolingType === 'avg' ?\n () => avgPool(convertedX, windowShape, strides, convertedPad, dimRoundingMode) :\n () => maxPool(convertedX, windowShape, strides, convertedPad, dimRoundingMode);\n const y = forwardOp();\n const res = isDilationOne ? y : batchToSpaceND(y, dilation, adjustedCrops);\n if (reshapedTo4D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n }\n return res;\n}\n// Helper function to compute crops and paddings for pool with dilation > 1.\n// tslint:disable-next-line:max-line-length\n// https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/array_ops.py#L2184\nfunction requiredSpaceToBatchPaddings(inputShape, blockShape, basePadding) {\n const padStart = basePadding.map(b => b[0]);\n const origPadEnd = basePadding.map(b => b[1]);\n const fullInputShape = inputShape.concat(padStart, origPadEnd);\n const padEndExtra = blockShape.map((b, i) => (b - fullInputShape[i] % b) % b);\n const padEnd = origPadEnd.map((s, i) => s + padEndExtra[i]);\n const paddings = blockShape.map((_, i) => [padStart[i], padEnd[i]]);\n const crops = blockShape.map((_, i) => [0, padEndExtra[i]]);\n return [paddings, crops];\n}\n// Helper function to compute base paddings for pool with dilation > 1.\n// tslint:disable-next-line:max-line-length\n// https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/nn_ops.py#L524\nfunction withSpaceToBatchBasePaddings(filterShape, dilation) {\n // Spatial dimensions of the filters and the upsampled filters in which we\n // introduce (rate - 1) zeros between consecutive filter values.\n const dilatedFilterShape = filterShape.map((s, i) => {\n return s + (s - 1) * (dilation[i] - 1);\n });\n const padExtraShape = dilatedFilterShape.map(s => s - 1);\n // When padding is odd, we pad more at end, following the same\n // convention as conv2d.\n const padExtraStart = padExtraShape.map(s => Math.floor(s / 2));\n const padExtraEnd = padExtraShape.map((s, i) => s - padExtraStart[i]);\n return padExtraShape.map((_, i) => {\n return [padExtraStart[i], padExtraEnd[i]];\n });\n}\nexport const pool = op({ pool_ });\n//# sourceMappingURL=data:application/json;base64,{"version":3,"file":"pool.js","sourceRoot":"","sources":["../../../../../../tfjs-core/src/ops/pool.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAGH,OAAO,EAAC,eAAe,EAAC,MAAM,oBAAoB,CAAC;AAEnD,OAAO,KAAK,IAAI,MAAM,SAAS,CAAC;AAEhC,OAAO,EAAC,OAAO,EAAC,MAAM,YAAY,CAAC;AACnC,OAAO,EAAC,cAAc,EAAC,MAAM,qBAAqB,CAAC;AACnD,OAAO,KAAK,SAAS,MAAM,aAAa,CAAC;AACzC,OAAO,EAAC,OAAO,EAAC,MAAM,YAAY,CAAC;AACnC,OAAO,EAAC,EAAE,EAAC,MAAM,aAAa,CAAC;AAC/B,OAAO,EAAC,OAAO,EAAC,MAAM,WAAW,CAAC;AAClC,OAAO,EAAC,cAAc,EAAC,MAAM,qBAAqB,CAAC;AAEnD;;;;;;;;;;;;;;;;;;;;;;;;;;;GA2BG;AACH,SAAS,KAAK,CACV,KAAmB,EAAE,WAAoC,EACzD,WAAwB,EACxB,GAAoD,EACpD,SAAmC,EAAE,OAAiC,EACtE,eAAwC;IAC1C,IAAI,SAAS,IAAI,IAAI,EAAE;QACrB,SAAS,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;KACpB;IACD,IAAI,OAAO,IAAI,IAAI,EAAE;QACnB,OAAO,GAAG,CAAC,CAAC;KACb;IACD,IAAI,GAAG,KAAK,CAAC,EAAE;QACb,GAAG,GAAG,OAAO,CAAC;KACf;IAED,MAAM,EAAE,GAAG,eAAe,CAAC,KAAK,EAAE,GAAG,EAAE,SAAS,CAAC,CAAC;IAClD,IAAI,GAAG,GAAG,EAAc,CAAC;IACzB,IAAI,YAAY,GAAG,KAAK,CAAC;IAEzB,IAAI,EAAE,CAAC,IAAI,KAAK,CAAC,EAAE;QACjB,YAAY,GAAG,IAAI,CAAC;QACpB,GAAG,GAAG,OAAO,CAAC,EAAE,EAAE,CAAC,CAAC,EAAE,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;KAC/D;IAED,IAAI,CAAC,MAAM,CACP,SAAS,CAAC,8BAA8B,CAAC,OAAO,EAAE,SAAS,CAAC,EAC5D,GAAG,EAAE,CAAC,wDAAwD;QAC1D,eAAe,OAAO,mBAAmB,SAAS,GAAG,CAAC,CAAC;IAE/D,MAAM,QAAQ,GAAG,SAAS,CAAC,iBAAiB,CACxC,GAAG,CAAC,KAAK,EAAE,WAAW,EAAE,OAAO,EAAE,SAAS,EAAE,GAAG,CAAC,CAAC;IACrD,MAAM,QAAQ,GACV,CAAC,QAAQ,CAAC,cAAc,EAAE,QAAQ,CAAC,aAAa,CAAC,CAAC;IAEtD,wEAAwE;IACxE,0EAA0E;IAC1E,2CAA2C;IAC3C,+HAA+H;IAE/H,IAAI,WAAuB,CAAC;IAC5B,IAAI,GAAG,KAAK,MAAM,EAAE;QAClB,WAAW,GAAG,4BAA4B,CACtC,CAAC,QAAQ,CAAC,YAAY,EAAE,QAAQ,CAAC,WAAW,CAAC,EAAE,QAAQ,CAAC,CAAC;KAC9D;SAAM;QACL,WAAW,GAAG,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;KAChC;IAED,MAAM,aAAa,GAAG,QAAQ,CAAC,CAAC,CAAC,KAAK,CAAC,IAAI,QAAQ,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC;IAC7D,MAAM,CAAC,eAAe,EAAE,aAAa,CAAC,GAAG,4BAA4B,CACjE,CAAC,QAAQ,CAAC,QAAQ,EAAE,QAAQ,CAAC,OAAO,CAAC,EAAE,QAAQ,EAAE,WAAW,CAAC,CAAC;IAClE,MAAM,YAAY,GAAG,aAAa,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,OAAO,CAAC;IACnD,MAAM,UAAU,GACZ,aAAa,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,cAAc,CAAC,GAAG,EAAE,QAAQ,EAAE,eAAe,CAAC,CAAC;IAEzE,MAAM,SAAS,GAAG,WAAW,KAAK,KAAK,CAAC,CAAC;QACrC,GAAG,EAAE,CAAC,OAAO,CAAC,UAAU,EAAE,WAAW,EAAE,OAAO,EAAE,YAAY,EAC9C,eAAe,CAAC,CAAC,CAAC;QAChC,GAAG,EAAE,CAAC,OAAO,CAAC,UAAU,EAAE,WAAW,EAAE,OAAO,EAAE,YAAY,EAC9C,eAAe,CAAC,CAAC;IACnC,MAAM,CAAC,GAAG,SAAS,EAAE,CAAC;IAEtB,MAAM,GAAG,GAAG,aAAa,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,cAAc,CAAC,CAAC,EAAE,QAAQ,EAAE,aAAa,CAAC,CAAC;IAE3E,IAAI,YAAY,EAAE;QAChB,OAAO,OAAO,CAAC,GAAG,EAAE,CAAC,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAM,CAAC;KACtE;IAED,OAAO,GAAQ,CAAC;AAClB,CAAC;AAED,4EAA4E;AAC5E,2CAA2C;AAC3C,kIAAkI;AAClI,SAAS,4BAA4B,CACjC,UAA4B,EAAE,UAA4B,EAC1D,WAAuB;IACzB,MAAM,QAAQ,GAAG,WAAW,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;IAC5C,MAAM,UAAU,GAAG,WAAW,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;IAC9C,MAAM,cAAc,GAAG,UAAU,CAAC,MAAM,CAAC,QAAQ,EAAE,UAAU,CAAC,CAAC;IAC/D,MAAM,WAAW,GAAG,UAAU,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,CAAC,CAAC,GAAG,cAAc,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC;IAC9E,MAAM,MAAM,GAAG,UAAU,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,CAAC,GAAG,WAAW,CAAC,CAAC,CAAC,CAAC,CAAC;IAC5D,MAAM,QAAQ,GAAG,UAAU,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;IACpE,MAAM,KAAK,GAAG,UAAU,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,CAAC,CAAC,EAAE,WAAW,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;IAC5D,OAAO,CAAC,QAAQ,EAAE,KAAK,CAAC,CAAC;AAC3B,CAAC;AAED,uEAAuE;AACvE,2CAA2C;AAC3C,8HAA8H;AAC9H,SAAS,4BAA4B,CACjC,WAA6B,EAAE,QAA0B;IAC3D,0EAA0E;IAC1E,gEAAgE;IAChE,MAAM,kBAAkB,GAAG,WAAW,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,EAAE;QAClD,OAAO,CAAC,GAAG,CAAC,CAAC,GAAG,CAAC,CAAC,GAAG,CAAC,QAAQ,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC;IACzC,CAAC,CAAC,CAAC;IACH,MAAM,aAAa,GAAG,kBAAkB,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC;IAEzD,8DAA8D;IAC9D,wBAAwB;IACxB,MAAM,aAAa,GAAG,aAAa,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,CAAC,IAAI,CAAC,KAAK,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC;IAChE,MAAM,WAAW,GAAG,aAAa,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,CAAC,GAAG,aAAa,CAAC,CAAC,CAAC,CAAC,CAAC;IACtE,OAAO,aAAa,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,EAAE;QAChC,OAAO,CAAC,aAAa,CAAC,CAAC,CAAC,EAAE,WAAW,CAAC,CAAC,CAAC,CAAC,CAAC;IAC5C,CAAC,CAAC,CAAC;AACL,CAAC;AAED,MAAM,CAAC,MAAM,IAAI,GAAG,EAAE,CAAC,EAAC,KAAK,EAAC,CAAC,CAAC","sourcesContent":["/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor3D, Tensor4D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {avgPool} from './avg_pool';\nimport {batchToSpaceND} from './batch_to_space_nd';\nimport * as conv_util from './conv_util';\nimport {maxPool} from './max_pool';\nimport {op} from './operation';\nimport {reshape} from './reshape';\nimport {spaceToBatchND} from './space_to_batch_nd';\n\n/**\n * Performs an N-D pooling operation\n *\n * @param input The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param windowShape The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param poolingType The type of pooling, either 'max' or 'avg'.\n * @param pad The type of padding algorithm:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *         https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in dilated pooling. Defaults to `[1, 1]`. If `dilationRate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n *     provided, it will default to truncate.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction pool_<T extends Tensor3D|Tensor4D>(\n    input: T|TensorLike, windowShape: [number, number]|number,\n    poolingType: 'avg'|'max',\n    pad: 'valid'|'same'|number|conv_util.ExplicitPadding,\n    dilations?: [number, number]|number, strides?: [number, number]|number,\n    dimRoundingMode?: 'floor'|'round'|'ceil') {\n  if (dilations == null) {\n    dilations = [1, 1];\n  }\n  if (strides == null) {\n    strides = 1;\n  }\n  if (pad === 0) {\n    pad = 'valid';\n  }\n\n  const $x = convertToTensor(input, 'x', 'maxPool');\n  let x4D = $x as Tensor4D;\n  let reshapedTo4D = false;\n\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n\n  util.assert(\n      conv_util.eitherStridesOrDilationsAreOne(strides, dilations),\n      () => 'Error in pool: Either strides or dilations must be 1. ' +\n          `Got strides ${strides} and dilations '${dilations}'`);\n\n  const convInfo = conv_util.computePool2DInfo(\n      x4D.shape, windowShape, strides, dilations, pad);\n  const dilation: [number, number] =\n      [convInfo.dilationHeight, convInfo.dilationWidth];\n\n  // The following implementation does batchToSpace(pool(spaceToBatch(x)))\n  // whenever dilation > 1 since the TF kernels do not support dilation > 1.\n  // tslint:disable-next-line:max-line-length\n  // https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/nn_ops.py#L1037\n\n  let basePadding: number[][];\n  if (pad === 'same') {\n    basePadding = withSpaceToBatchBasePaddings(\n        [convInfo.filterHeight, convInfo.filterWidth], dilation);\n  } else {\n    basePadding = [[0, 0], [0, 0]];\n  }\n\n  const isDilationOne = dilation[0] === 1 && dilation[1] === 1;\n  const [adjustedPadding, adjustedCrops] = requiredSpaceToBatchPaddings(\n      [convInfo.inHeight, convInfo.inWidth], dilation, basePadding);\n  const convertedPad = isDilationOne ? pad : 'valid';\n  const convertedX =\n      isDilationOne ? x4D : spaceToBatchND(x4D, dilation, adjustedPadding);\n\n  const forwardOp = poolingType === 'avg' ?\n      () => avgPool(convertedX, windowShape, strides, convertedPad,\n                    dimRoundingMode) :\n      () => maxPool(convertedX, windowShape, strides, convertedPad,\n                    dimRoundingMode);\n  const y = forwardOp();\n\n  const res = isDilationOne ? y : batchToSpaceND(y, dilation, adjustedCrops);\n\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as T;\n  }\n\n  return res as T;\n}\n\n// Helper function to compute crops and paddings for pool with dilation > 1.\n// tslint:disable-next-line:max-line-length\n// https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/array_ops.py#L2184\nfunction requiredSpaceToBatchPaddings(\n    inputShape: [number, number], blockShape: [number, number],\n    basePadding: number[][]) {\n  const padStart = basePadding.map(b => b[0]);\n  const origPadEnd = basePadding.map(b => b[1]);\n  const fullInputShape = inputShape.concat(padStart, origPadEnd);\n  const padEndExtra = blockShape.map((b, i) => (b - fullInputShape[i] % b) % b);\n  const padEnd = origPadEnd.map((s, i) => s + padEndExtra[i]);\n  const paddings = blockShape.map((_, i) => [padStart[i], padEnd[i]]);\n  const crops = blockShape.map((_, i) => [0, padEndExtra[i]]);\n  return [paddings, crops];\n}\n\n// Helper function to compute base paddings for pool with dilation > 1.\n// tslint:disable-next-line:max-line-length\n// https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/nn_ops.py#L524\nfunction withSpaceToBatchBasePaddings(\n    filterShape: [number, number], dilation: [number, number]) {\n  // Spatial dimensions of the filters and the upsampled filters in which we\n  // introduce (rate - 1) zeros between consecutive filter values.\n  const dilatedFilterShape = filterShape.map((s, i) => {\n    return s + (s - 1) * (dilation[i] - 1);\n  });\n  const padExtraShape = dilatedFilterShape.map(s => s - 1);\n\n  // When padding is odd, we pad more at end, following the same\n  // convention as conv2d.\n  const padExtraStart = padExtraShape.map(s => Math.floor(s / 2));\n  const padExtraEnd = padExtraShape.map((s, i) => s - padExtraStart[i]);\n  return padExtraShape.map((_, i) => {\n    return [padExtraStart[i], padExtraEnd[i]];\n  });\n}\n\nexport const pool = op({pool_});\n"]}","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { sizeFromShape } from '../util';\nimport { op } from './operation';\n/**\n * Creates a `tf.Tensor` with values sampled from a random number generator\n * function defined by the user.\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param randFunction A random number generator function which is called\n * for each element in the output tensor.\n * @param dtype The data type of the output tensor. Defaults to 'float32'.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction rand_(shape, randFunction, dtype) {\n const size = sizeFromShape(shape);\n let values = null;\n if (dtype == null || dtype === 'float32') {\n values = new Float32Array(size);\n }\n else if (dtype === 'int32') {\n values = new Int32Array(size);\n }\n else if (dtype === 'bool') {\n values = new Uint8Array(size);\n }\n else {\n throw new Error(`Unknown data type ${dtype}`);\n }\n for (let i = 0; i < size; i++) {\n values[i] = randFunction();\n }\n return ENGINE.makeTensor(values, shape, dtype);\n}\nexport const rand = op({ rand_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from './buffer';\nimport { op } from './operation';\nimport { RandGamma } from './rand_util';\n/**\n * Creates a `tf.Tensor` with values sampled from a gamma distribution.\n *\n * ```js\n * tf.randomGamma([2, 2], 1).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param alpha The shape parameter of the gamma distribution.\n * @param beta The inverse scale parameter of the gamma distribution. Defaults\n * to 1.\n * @param dtype The data type of the output. Defaults to float32.\n * @param seed The seed for the random number generator.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction randomGamma_(shape, alpha, beta = 1, dtype = 'float32', seed) {\n if (beta == null) {\n beta = 1;\n }\n if (dtype == null) {\n dtype = 'float32';\n }\n if (dtype !== 'float32' && dtype !== 'int32') {\n throw new Error(`Unsupported data type ${dtype}`);\n }\n const rgamma = new RandGamma(alpha, beta, dtype, seed);\n const res = buffer(shape, dtype);\n for (let i = 0; i < res.values.length; i++) {\n res.values[i] = rgamma.nextValue();\n }\n return res.toTensor();\n}\nexport const randomGamma = op({ randomGamma_ });\n//# 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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from './buffer';\nimport { op } from './operation';\nimport { MPRandGauss } from './rand_util';\n/**\n * Creates a `tf.Tensor` with values sampled from a normal distribution.\n *\n * ```js\n * tf.randomNormal([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param mean The mean of the normal distribution.\n * @param stdDev The standard deviation of the normal distribution.\n * @param dtype The data type of the output.\n * @param seed The seed for the random number generator.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction randomNormal_(shape, mean = 0, stdDev = 1, dtype, seed) {\n if (dtype != null && dtype === 'bool') {\n throw new Error(`Unsupported data type ${dtype}`);\n }\n const randGauss = new MPRandGauss(mean, stdDev, dtype, false /* truncated */, seed);\n const res = buffer(shape, dtype);\n for (let i = 0; i < res.values.length; i++) {\n res.values[i] = randGauss.nextValue();\n }\n return res.toTensor();\n}\nexport const randomNormal = op({ randomNormal_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reverse } from './reverse';\n/**\n * Reverses a `tf.Tensor1D`.\n *\n * @param x The input tensor.\n */\nfunction reverse1d_(x) {\n const $x = convertToTensor(x, 'x', 'reverse');\n util.assert($x.rank === 1, () => `Error in reverse1D: x must be rank 1 but got rank ${$x.rank}.`);\n return reverse($x, 0);\n}\nexport const reverse1d = op({ reverse1d_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reverse } from './reverse';\n/**\n * Reverses a `tf.Tensor2D` along a specified axis.\n *\n * @param x The input tensor.\n * @param axis The set of dimensions to reverse. Must be in the\n * range [-rank(x), rank(x)). Defaults to all axes.\n */\nfunction reverse2d_(x, axis) {\n const $x = convertToTensor(x, 'x', 'reverse');\n util.assert($x.rank === 2, () => `Error in reverse2D: x must be rank 2 but got rank ${$x.rank}.`);\n return reverse($x, axis);\n}\nexport const reverse2d = op({ reverse2d_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reverse } from './reverse';\n/**\n * Reverses a `tf.Tensor3D` along a specified axis.\n *\n * @param x The input tensor.\n * @param axis The set of dimensions to reverse. Must be in the\n * range [-rank(x), rank(x)). Defaults to all axes.\n */\nfunction reverse3d_(x, axis) {\n const $x = convertToTensor(x, 'x', 'reverse');\n util.assert($x.rank === 3, () => `Error in reverse3D: x must be rank 3 but got rank ${$x.rank}.`);\n return reverse($x, axis);\n}\nexport const reverse3d = op({ reverse3d_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reverse } from './reverse';\n/**\n * Reverses a `tf.Tensor4D` along a specified axis.\n *\n * @param x The input tensor.\n * @param axis The set of dimensions to reverse. Must be in the\n * range [-rank(x), rank(x)). Defaults to all axes.\n */\nfunction reverse4d_(x, axis) {\n const $x = convertToTensor(x, 'x', 'reverse');\n util.assert($x.rank === 4, () => `Error in reverse4D: x must be rank 4 but got rank ${$x.rank}.`);\n return reverse($x, axis);\n}\nexport const reverse4d = op({ reverse4d_ });\n//# sourceMappingURL=data:application/json;base64,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","import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { conv2d } from './conv2d';\nimport { depthwiseConv2d } from './depthwise_conv2d';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * 2-D convolution with separable filters.\n *\n * Performs a depthwise convolution that acts separately on channels followed\n * by a pointwise convolution that mixes channels. Note that this is\n * separability between dimensions [1, 2] and 3, not spatial separability\n * between dimensions 1 and 2.\n *\n * See\n * [https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d](\n * https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d)\n * for more details.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param depthwiseFilter The depthwise filter tensor, rank 4, of shape\n * `[filterHeight, filterWidth, inChannels, channelMultiplier]`. This is\n * the filter used in the first step.\n * @param pointwiseFilter The pointwise filter tensor, rank 4, of shape\n * `[1, 1, inChannels * channelMultiplier, outChannels]`. This is\n * the filter used in the second step.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`. If strides is a single number, then `strideHeight ==\n * strideWidth`.\n * @param pad The type of padding algorithm.\n * - `same` and stride 1: output will be of same size as input,\n * regardless of filter size.\n * - `valid`: output will be smaller than input if filter is larger\n * than 1x1.\n * - For more info, see this guide:\n * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](\n * https://www.tensorflow.org/api_docs/python/tf/nn/convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n * in which we sample input values across the height and width dimensions\n * in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single\n * number, then `dilationHeight == dilationWidth`. If it is greater than\n * 1, then all values of `strides` must be 1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n * \"NHWC\". Specify the data format of the input and output data. With the\n * default format \"NHWC\", the data is stored in the order of: [batch,\n * height, width, channels]. Only \"NHWC\" is currently supported.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction separableConv2d_(x, depthwiseFilter, pointwiseFilter, strides, pad, dilation = [1, 1], dataFormat = 'NHWC') {\n const $x = convertToTensor(x, 'x', 'separableConv2d');\n const $depthwiseFilter = convertToTensor(depthwiseFilter, 'depthwiseFilter', 'separableConv2d');\n const $pointwiseFilter = convertToTensor(pointwiseFilter, 'pointwiseFilter', 'separableConv2d');\n let x4D = $x;\n let reshapedTo4D = false;\n if ($x.rank === 3) {\n reshapedTo4D = true;\n x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n }\n if (dataFormat === 'NCHW') {\n throw new Error('separableConv2d currently does not support dataFormat NCHW; only ' +\n 'NHWC is supported');\n }\n util.assert(x4D.rank === 4, () => `Error in separableConv2d: input must be rank 4, but got ` +\n `rank ${x4D.rank}.`);\n util.assert($depthwiseFilter.rank === 4, () => `Error in separableConv2d: depthwise filter must be rank 4, but ` +\n `got rank ${$depthwiseFilter.rank}.`);\n util.assert($pointwiseFilter.rank === 4, () => `Error in separableConv2d: pointwise filter must be rank 4, but ` +\n `got rank ${$depthwiseFilter.rank}.`);\n util.assert($pointwiseFilter.shape[0] === 1, () => `Error in separableConv2d: the first dimension of pointwise filter ` +\n ` must be 1, but got ${$pointwiseFilter.shape[0]}.`);\n util.assert($pointwiseFilter.shape[1] === 1, () => `Error in separableConv2d: the second dimension of pointwise ` +\n `filter must be 1, but got ${$pointwiseFilter.shape[1]}.`);\n const inChannels = $depthwiseFilter.shape[2];\n const channelMultiplier = $depthwiseFilter.shape[3];\n util.assert($pointwiseFilter.shape[2] === inChannels * channelMultiplier, () => `Error in separableConv2d: the third dimension of pointwise filter ` +\n `must be ${inChannels * channelMultiplier}, ` +\n `but got ${$pointwiseFilter.shape[2]}.`);\n const depthwise = depthwiseConv2d(x4D, $depthwiseFilter, strides, pad, dataFormat, dilation);\n const pointwiseStride = 1;\n const res = conv2d(depthwise, $pointwiseFilter, pointwiseStride, 'valid', dataFormat);\n if (reshapedTo4D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n }\n return res;\n}\nexport const separableConv2d = op({ separableConv2d_ });\n//# 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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor3D, Tensor4D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {conv2d} from './conv2d';\nimport {depthwiseConv2d} from './depthwise_conv2d';\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * 2-D convolution with separable filters.\n *\n * Performs a depthwise convolution that acts separately on channels followed\n * by a pointwise convolution that mixes channels. Note that this is\n * separability between dimensions [1, 2] and 3, not spatial separability\n * between dimensions 1 and 2.\n *\n * See\n * [https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d](\n *     https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d)\n * for more details.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param depthwiseFilter The depthwise filter tensor, rank 4, of shape\n *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`. This is\n *     the filter used in the first step.\n * @param pointwiseFilter The pointwise filter tensor, rank 4, of shape\n *     `[1, 1, inChannels * channelMultiplier, outChannels]`. This is\n *     the filter used in the second step.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`. If strides is a single number, then `strideHeight ==\n * strideWidth`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](\n *          https://www.tensorflow.org/api_docs/python/tf/nn/convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction separableConv2d_<T extends Tensor3D|Tensor4D>(\n    x: T|TensorLike, depthwiseFilter: Tensor4D|TensorLike,\n    pointwiseFilter: Tensor4D|TensorLike, strides: [number, number]|number,\n    pad: 'valid'|'same', dilation: [number, number]|number = [1, 1],\n    dataFormat: 'NHWC'|'NCHW' = 'NHWC'): T {\n  const $x = convertToTensor(x, 'x', 'separableConv2d');\n  const $depthwiseFilter =\n      convertToTensor(depthwiseFilter, 'depthwiseFilter', 'separableConv2d');\n  const $pointwiseFilter =\n      convertToTensor(pointwiseFilter, 'pointwiseFilter', 'separableConv2d');\n\n  let x4D = $x as Tensor4D;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n\n  if (dataFormat === 'NCHW') {\n    throw new Error(\n        'separableConv2d currently does not support dataFormat NCHW; only ' +\n        'NHWC is supported');\n  }\n\n  util.assert(\n      x4D.rank === 4,\n      () => `Error in separableConv2d: input must be rank 4, but got ` +\n          `rank ${x4D.rank}.`);\n  util.assert(\n      $depthwiseFilter.rank === 4,\n      () => `Error in separableConv2d: depthwise filter must be rank 4, but ` +\n          `got rank ${$depthwiseFilter.rank}.`);\n  util.assert(\n      $pointwiseFilter.rank === 4,\n      () => `Error in separableConv2d: pointwise filter must be rank 4, but ` +\n          `got rank ${$depthwiseFilter.rank}.`);\n  util.assert(\n      $pointwiseFilter.shape[0] === 1,\n      () =>\n          `Error in separableConv2d: the first dimension of pointwise filter ` +\n          ` must be 1, but got ${$pointwiseFilter.shape[0]}.`);\n  util.assert(\n      $pointwiseFilter.shape[1] === 1,\n      () => `Error in separableConv2d: the second dimension of pointwise ` +\n          `filter must be 1, but got ${$pointwiseFilter.shape[1]}.`);\n\n  const inChannels = $depthwiseFilter.shape[2];\n  const channelMultiplier = $depthwiseFilter.shape[3];\n  util.assert(\n      $pointwiseFilter.shape[2] === inChannels * channelMultiplier,\n      () =>\n          `Error in separableConv2d: the third dimension of pointwise filter ` +\n          `must be ${inChannels * channelMultiplier}, ` +\n          `but got ${$pointwiseFilter.shape[2]}.`);\n\n  const depthwise = depthwiseConv2d(\n      x4D, $depthwiseFilter, strides, pad, dataFormat, dilation);\n  const pointwiseStride = 1;\n  const res =\n      conv2d(depthwise, $pointwiseFilter, pointwiseStride, 'valid', dataFormat);\n\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as T;\n  }\n  return res as T;\n}\n\nexport const separableConv2d = op({separableConv2d_});\n"]}","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { slice } from './slice';\n/**\n * Extracts a 1D slice from 1D array starting at coordinates `begin` and is\n * of length `size`. See `slice` for details.\n */\nfunction slice1d_(x, begin, size) {\n const $x = convertToTensor(x, 'x', 'slice1d');\n util.assert($x.rank === 1, () => `slice1d expects a rank-1 tensor, but got a rank-${$x.rank} tensor`);\n return slice($x, [begin], [size]);\n}\nexport const slice1d = op({ slice1d_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { slice } from './slice';\n/**\n * Extracts a 2D slice from a 2D array starting at coordinates `begin` and\n * is of size `size`. See `slice` for details.\n */\nfunction slice2d_(x, begin, size) {\n const $x = convertToTensor(x, 'x', 'slice2d');\n util.assert($x.rank === 2, () => `slice2d expects a rank-2 tensor, but got a rank-${$x.rank} tensor`);\n return slice($x, begin, size);\n}\nexport const slice2d = op({ slice2d_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { slice } from './slice';\n/**\n * Extracts a 3D slice from a 3D array starting at coordinates `begin` and\n * is of size `size`. See `slice` for details.\n */\nfunction slice3d_(x, begin, size) {\n const $x = convertToTensor(x, 'x', 'slice3d');\n util.assert($x.rank === 3, () => `slice3d expects a rank-3 tensor, but got a rank-${$x.rank} tensor`);\n return slice($x, begin, size);\n}\nexport const slice3d = op({ slice3d_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { slice } from './slice';\n/**\n * Extracts a 4D slice from a 4D array starting at coordinates `begin` and\n * is of size `size`. See `slice` for details.\n */\nfunction slice4d_(x, begin, size) {\n const $x = convertToTensor(x, 'x', 'slice4d');\n util.assert($x.rank === 4, () => `slice4d expects a rank-4 tensor, but got a rank-${$x.rank} tensor`);\n return slice($x, begin, size);\n}\nexport const slice4d = op({ slice4d_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-2 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor2d` as it makes the code more readable.\n *\n * ```js\n * // Pass a nested array.\n * tf.tensor2d([[1, 2], [3, 4]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor2d([1, 2, 3, 4], [2, 2]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n * or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. If not provided, it is inferred from\n * `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor2d(values, shape, dtype) {\n assertNonNull(values);\n if (shape != null && shape.length !== 2) {\n throw new Error('tensor2d() requires shape to have two numbers');\n }\n const inferredShape = inferShape(values, dtype);\n if (inferredShape.length !== 2 && inferredShape.length !== 1) {\n throw new Error('tensor2d() requires values to be number[][] or flat/TypedArray');\n }\n if (inferredShape.length === 1 && shape == null) {\n throw new Error('tensor2d() requires shape to be provided when `values` ' +\n 'are a flat/TypedArray');\n }\n return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-4 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor4d` as it makes the code more readable.\n *\n * ```js\n * // Pass a nested array.\n * tf.tensor4d([[[[1], [2]], [[3], [4]]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n * or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n * it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor4d(values, shape, dtype) {\n assertNonNull(values);\n if (shape != null && shape.length !== 4) {\n throw new Error('tensor4d() requires shape to have four numbers');\n }\n const inferredShape = inferShape(values, dtype);\n if (inferredShape.length !== 4 && inferredShape.length !== 1) {\n throw new Error('tensor4d() requires values to be number[][][][] or flat/TypedArray');\n }\n if (inferredShape.length === 1 && shape == null) {\n throw new Error('tensor4d() requires shape to be provided when `values` ' +\n 'are a flat array');\n }\n return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-5 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor5d` as it makes the code more readable.\n *\n * ```js\n * // Pass a nested array.\n * tf.tensor5d([[[[[1],[2]],[[3],[4]]],[[[5],[6]],[[7],[8]]]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n * or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n * it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor5d(values, shape, dtype) {\n assertNonNull(values);\n if (shape != null && shape.length !== 5) {\n throw new Error('tensor5d() requires shape to have five numbers');\n }\n const inferredShape = inferShape(values, dtype);\n if (inferredShape.length !== 5 && inferredShape.length !== 1) {\n throw new Error('tensor5d() requires values to be ' +\n 'number[][][][][] or flat/TypedArray');\n }\n if (inferredShape.length === 1 && shape == null) {\n throw new Error('tensor5d() requires shape to be provided when `values` ' +\n 'are a flat array');\n }\n return makeTensor(values, shape, inferredShape, dtype);\n}\n//# 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* @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-6 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor6d` as it makes the code more readable.\n *\n * ```js\n * // Pass a nested array.\n * tf.tensor6d([[[[[[1],[2]],[[3],[4]]],[[[5],[6]],[[7],[8]]]]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor6d([1, 2, 3, 4, 5, 6, 7, 8], [1, 1, 2, 2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n * or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n * it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor6d(values, shape, dtype) {\n assertNonNull(values);\n if (shape != null && shape.length !== 6) {\n throw new Error('tensor6d() requires shape to have six numbers');\n }\n const inferredShape = inferShape(values, dtype);\n if (inferredShape.length !== 6 && inferredShape.length !== 1) {\n throw new Error('tensor6d() requires values to be number[][][][][][] or ' +\n 'flat/TypedArray');\n }\n if (inferredShape.length === 1 && shape == null) {\n throw new Error('tensor6d() requires shape to be provided when `values` ' +\n 'are a flat array');\n }\n shape = shape ||\n inferredShape;\n return makeTensor(values, shape, inferredShape, dtype);\n}\n//# 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* @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\n/**\n * Creates a new variable with the provided initial value.\n * ```js\n * const x = tf.variable(tf.tensor([1, 2, 3]));\n * x.assign(tf.tensor([4, 5, 6]));\n *\n * x.print();\n * ```\n *\n * @param initialValue Initial value for the tensor.\n * @param trainable If true, optimizers are allowed to update it.\n * @param name Name of the variable. Defaults to a unique id.\n * @param dtype If set, initialValue will be converted to the given type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function variable(initialValue, trainable = true, name, dtype) {\n return ENGINE.makeVariable(initialValue, trainable, name, dtype);\n}\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { gather } from './gather';\nimport { reshape } from './reshape';\nimport { squeeze } from './squeeze';\nimport { whereAsync } from './where_async';\n/**\n * Apply boolean mask to tensor.\n *\n * ```js\n * const tensor = tf.tensor2d([1, 2, 3, 4, 5, 6], [3, 2]);\n * const mask = tf.tensor1d([1, 0, 1], 'bool');\n * const result = await tf.booleanMaskAsync(tensor, mask);\n * result.print();\n * ```\n *\n * @param tensor N-D tensor.\n * @param mask K-D boolean tensor, K <= N and K must be known statically.\n * @param axis A 0-D int Tensor representing the axis in tensor to mask from.\n * By default, axis is 0 which will mask from the first dimension.\n * Otherwise K + axis <= N.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nasync function booleanMaskAsync_(tensor, mask, axis) {\n const $tensor = convertToTensor(tensor, 'tensor', 'boolMask');\n const $mask = convertToTensor(mask, 'mask', 'boolMask', 'bool');\n const axisFrom = axis == null ? 0 : axis;\n const maskDim = $mask.rank;\n const tensorShape = $tensor.shape;\n util.assert(maskDim > 0, () => 'mask cannot be scalar');\n util.assertShapesMatch(tensorShape.slice(axisFrom, axisFrom + maskDim), $mask.shape, `mask's shape must match the first K dimensions of tensor's shape,`);\n let leadingSize = 1;\n for (let i = axisFrom; i < axisFrom + maskDim; i++) {\n leadingSize *= tensorShape[i];\n }\n const targetTensorShape = tensorShape.slice(0, axisFrom)\n .concat([leadingSize], tensorShape.slice(axisFrom + maskDim));\n const reshapedTensor = reshape($tensor, targetTensorShape);\n const reshapedMask = reshape($mask, [-1]);\n const positivePositions = await whereAsync(reshapedMask);\n const indices = squeeze(positivePositions, [1]);\n const res = gather(reshapedTensor, indices, axisFrom);\n // Ensure no memory leak.\n if (tensor !== $tensor) {\n $tensor.dispose();\n }\n if (mask !== $mask) {\n $mask.dispose();\n }\n indices.dispose();\n reshapedTensor.dispose();\n reshapedMask.dispose();\n positivePositions.dispose();\n return res;\n}\nexport const booleanMaskAsync = booleanMaskAsync_;\n//# sourceMappingURL=data:application/json;base64,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* @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { abs } from './abs';\nimport * as axis_util from './axis_util';\nimport { max } from './max';\nimport { min } from './min';\nimport { op } from './operation';\nimport { pow } from './pow';\nimport { reshape } from './reshape';\nimport { scalar } from './scalar';\nimport { sqrt } from './sqrt';\nimport { square } from './square';\nimport { sum } from './sum';\n/**\n * Computes the norm of scalar, vectors, and matrices.\n * This function can compute several different vector norms (the 1-norm, the\n * Euclidean or 2-norm, the inf-norm, and in general the p-norm for p > 0)\n * and matrix norms (Frobenius, 1-norm, and inf-norm).\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * x.norm().print(); // or tf.norm(x)\n * ```\n *\n * @param x The input array.\n * @param ord Optional. Order of the norm. Supported norm types are\n * following:\n *\n * | ord | norm for matrices | norm for vectors\n * |------------|---------------------------|---------------------\n * |'euclidean' |Frobenius norm |2-norm\n * |'fro' |Frobenius norm\t |\n * |Infinity |max(sum(abs(x), axis=1)) |max(abs(x))\n * |-Infinity |min(sum(abs(x), axis=1)) |min(abs(x))\n * |1 |max(sum(abs(x), axis=0)) |sum(abs(x))\n * |2 | |sum(abs(x)^2)^1/2*\n *\n * @param axis Optional. If axis is null (the default), the input is\n * considered a vector and a single vector norm is computed over the entire\n * set of values in the Tensor, i.e. norm(x, ord) is equivalent\n * to norm(x.reshape([-1]), ord). If axis is a integer, the input\n * is considered a batch of vectors, and axis determines the axis in x\n * over which to compute vector norms. If axis is a 2-tuple of integer it is\n * considered a batch of matrices and axis determines the axes in NDArray\n * over which to compute a matrix norm.\n * @param keepDims Optional. If true, the norm have the same dimensionality\n * as the input.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction norm_(x, ord = 'euclidean', axis = null, keepDims = false) {\n x = convertToTensor(x, 'x', 'norm');\n const norm = normImpl(x, ord, axis);\n let keepDimsShape = norm.shape;\n if (keepDims) {\n const axes = parseAxisParam(axis, x.shape);\n keepDimsShape = axis_util.expandShapeToKeepDim(norm.shape, axes);\n }\n return reshape(norm, keepDimsShape);\n}\nfunction normImpl(x, p, axis = null) {\n if (x.rank === 0) {\n return abs(x);\n }\n // consider vector when no axis is specified\n if (x.rank !== 1 && axis === null) {\n return normImpl(reshape(x, [-1]), p, axis);\n }\n // vector\n if (x.rank === 1 || typeof axis === 'number' ||\n Array.isArray(axis) && axis.length === 1) {\n if (p === 1) {\n return sum(abs(x), axis);\n }\n if (p === Infinity) {\n return max(abs(x), axis);\n }\n if (p === -Infinity) {\n return min(abs(x), axis);\n }\n if (p === 'euclidean' || p === 2) {\n // norm(x, 2) = sum(abs(xi) ^ 2) ^ 1/2\n return sqrt(sum(pow(abs(x), scalar(2, 'int32')), axis));\n }\n throw new Error(`Error in norm: invalid ord value: ${p}`);\n }\n // matrix (assumption axis[0] < axis[1])\n if (Array.isArray(axis) && axis.length === 2) {\n if (p === 1) {\n return max(sum(abs(x), axis[0]), axis[1] - 1);\n }\n if (p === Infinity) {\n return max(sum(abs(x), axis[1]), axis[0]);\n }\n if (p === -Infinity) {\n return min(sum(abs(x), axis[1]), axis[0]);\n }\n if (p === 'fro' || p === 'euclidean') {\n // norm(x) = sqrt(sum(pow(x, 2)))\n return sqrt(sum(square(x), axis));\n }\n throw new Error(`Error in norm: invalid ord value: ${p}`);\n }\n throw new Error(`Error in norm: invalid axis: ${axis}`);\n}\nexport const norm = op({ norm_ });\n//# sourceMappingURL=data:application/json;base64,{"version":3,"file":"norm.js","sourceRoot":"","sources":["../../../../../../tfjs-core/src/ops/norm.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAGH,OAAO,EAAC,eAAe,EAAC,MAAM,oBAAoB,CAAC;AAEnD,OAAO,EAAC,cAAc,EAAC,MAAM,SAAS,CAAC;AAEvC,OAAO,EAAC,GAAG,EAAC,MAAM,OAAO,CAAC;AAC1B,OAAO,KAAK,SAAS,MAAM,aAAa,CAAC;AACzC,OAAO,EAAC,GAAG,EAAC,MAAM,OAAO,CAAC;AAC1B,OAAO,EAAC,GAAG,EAAC,MAAM,OAAO,CAAC;AAC1B,OAAO,EAAC,EAAE,EAAC,MAAM,aAAa,CAAC;AAC/B,OAAO,EAAC,GAAG,EAAC,MAAM,OAAO,CAAC;AAC1B,OAAO,EAAC,OAAO,EAAC,MAAM,WAAW,CAAC;AAClC,OAAO,EAAC,MAAM,EAAC,MAAM,UAAU,CAAC;AAChC,OAAO,EAAC,IAAI,EAAC,MAAM,QAAQ,CAAC;AAC5B,OAAO,EAAC,MAAM,EAAC,MAAM,UAAU,CAAC;AAChC,OAAO,EAAC,GAAG,EAAC,MAAM,OAAO,CAAC;AAE1B;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;GAqCG;AACH,SAAS,KAAK,CACV,CAAoB,EAAE,MAAgC,WAAW,EACjE,OAAwB,IAAI,EAAE,QAAQ,GAAG,KAAK;IAChD,CAAC,GAAG,eAAe,CAAC,CAAC,EAAE,GAAG,EAAE,MAAM,CAAC,CAAC;IAEpC,MAAM,IAAI,GAAG,QAAQ,CAAC,CAAC,EAAE,GAAG,EAAE,IAAI,CAAC,CAAC;IACpC,IAAI,aAAa,GAAG,IAAI,CAAC,KAAK,CAAC;IAC/B,IAAI,QAAQ,EAAE;QACZ,MAAM,IAAI,GAAG,cAAc,CAAC,IAAI,EAAE,CAAC,CAAC,KAAK,CAAC,CAAC;QAC3C,aAAa,GAAG,SAAS,CAAC,oBAAoB,CAAC,IAAI,CAAC,KAAK,EAAE,IAAI,CAAC,CAAC;KAClE;IACD,OAAO,OAAO,CAAC,IAAI,EAAE,aAAa,CAAC,CAAC;AACtC,CAAC;AAED,SAAS,QAAQ,CACb,CAAS,EAAE,CAAgB,EAAE,OAAwB,IAAI;IAC3D,IAAI,CAAC,CAAC,IAAI,KAAK,CAAC,EAAE;QAChB,OAAO,GAAG,CAAC,CAAC,CAAC,CAAC;KACf;IAED,4CAA4C;IAC5C,IAAI,CAAC,CAAC,IAAI,KAAK,CAAC,IAAI,IAAI,KAAK,IAAI,EAAE;QACjC,OAAO,QAAQ,CAAC,OAAO,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,IAAI,CAAC,CAAC;KAC5C;IAED,SAAS;IACT,IAAI,CAAC,CAAC,IAAI,KAAK,CAAC,IAAI,OAAO,IAAI,KAAK,QAAQ;QACxC,KAAK,CAAC,OAAO,CAAC,IAAI,CAAC,IAAI,IAAI,CAAC,MAAM,KAAK,CAAC,EAAE;QAC5C,IAAI,CAAC,KAAK,CAAC,EAAE;YACX,OAAO,GAAG,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,IAAI,CAAC,CAAC;SAC1B;QACD,IAAI,CAAC,KAAK,QAAQ,EAAE;YAClB,OAAO,GAAG,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,IAAI,CAAC,CAAC;SAC1B;QACD,IAAI,CAAC,KAAK,CAAC,QAAQ,EAAE;YACnB,OAAO,GAAG,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,IAAI,CAAC,CAAC;SAC1B;QACD,IAAI,CAAC,KAAK,WAAW,IAAI,CAAC,KAAK,CAAC,EAAE;YAChC,sCAAsC;YACtC,OAAO,IAAI,CAAC,GAAG,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,MAAM,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC,EAAE,IAAI,CAAC,CAAC,CAAC;SACzD;QAED,MAAM,IAAI,KAAK,CAAC,qCAAqC,CAAC,EAAE,CAAC,CAAC;KAC3D;IAED,wCAAwC;IACxC,IAAI,KAAK,CAAC,OAAO,CAAC,IAAI,CAAC,IAAI,IAAI,CAAC,MAAM,KAAK,CAAC,EAAE;QAC5C,IAAI,CAAC,KAAK,CAAC,EAAE;YACX,OAAO,GAAG,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,IAAI,CAAC,CAAC,CAAC,CAAC,EAAE,IAAI,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC;SAC/C;QACD,IAAI,CAAC,KAAK,QAAQ,EAAE;YAClB,OAAO,GAAG,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,IAAI,CAAC,CAAC,CAAC,CAAC,EAAE,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC;SAC3C;QACD,IAAI,CAAC,KAAK,CAAC,QAAQ,EAAE;YACnB,OAAO,GAAG,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,IAAI,CAAC,CAAC,CAAC,CAAC,EAAE,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC;SAC3C;QACD,IAAI,CAAC,KAAK,KAAK,IAAI,CAAC,KAAK,WAAW,EAAE;YACpC,iCAAiC;YACjC,OAAO,IAAI,CAAC,GAAG,CAAC,MAAM,CAAC,CAAC,CAAC,EAAE,IAAI,CAAC,CAAC,CAAC;SACnC;QAED,MAAM,IAAI,KAAK,CAAC,qCAAqC,CAAC,EAAE,CAAC,CAAC;KAC3D;IAED,MAAM,IAAI,KAAK,CAAC,gCAAgC,IAAI,EAAE,CAAC,CAAC;AAC1D,CAAC;AAED,MAAM,CAAC,MAAM,IAAI,GAAG,EAAE,CAAC,EAAC,KAAK,EAAC,CAAC,CAAC","sourcesContent":["/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {parseAxisParam} from '../util';\n\nimport {abs} from './abs';\nimport * as axis_util from './axis_util';\nimport {max} from './max';\nimport {min} from './min';\nimport {op} from './operation';\nimport {pow} from './pow';\nimport {reshape} from './reshape';\nimport {scalar} from './scalar';\nimport {sqrt} from './sqrt';\nimport {square} from './square';\nimport {sum} from './sum';\n\n/**\n * Computes the norm of scalar, vectors, and matrices.\n * This function can compute several different vector norms (the 1-norm, the\n * Euclidean or 2-norm, the inf-norm, and in general the p-norm for p > 0)\n * and matrix norms (Frobenius, 1-norm, and inf-norm).\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * x.norm().print();  // or tf.norm(x)\n * ```\n *\n * @param x The input array.\n * @param ord Optional. Order of the norm. Supported norm types are\n * following:\n *\n *  | ord        | norm for matrices         | norm for vectors\n *  |------------|---------------------------|---------------------\n *  |'euclidean' |Frobenius norm             |2-norm\n *  |'fro'       |Frobenius norm\t           |\n *  |Infinity    |max(sum(abs(x), axis=1))   |max(abs(x))\n *  |-Infinity   |min(sum(abs(x), axis=1))   |min(abs(x))\n *  |1           |max(sum(abs(x), axis=0))   |sum(abs(x))\n *  |2           |                           |sum(abs(x)^2)^1/2*\n *\n * @param axis Optional. If axis is null (the default), the input is\n * considered a vector and a single vector norm is computed over the entire\n * set of values in the Tensor, i.e. norm(x, ord) is equivalent\n * to norm(x.reshape([-1]), ord). If axis is a integer, the input\n * is considered a batch of vectors, and axis determines the axis in x\n * over which to compute vector norms. If axis is a 2-tuple of integer it is\n * considered a batch of matrices and axis determines the axes in NDArray\n * over which to compute a matrix norm.\n * @param keepDims Optional. If true, the norm have the same dimensionality\n * as the input.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction norm_(\n    x: Tensor|TensorLike, ord: number|'euclidean'|'fro' = 'euclidean',\n    axis: number|number[] = null, keepDims = false): Tensor {\n  x = convertToTensor(x, 'x', 'norm');\n\n  const norm = normImpl(x, ord, axis);\n  let keepDimsShape = norm.shape;\n  if (keepDims) {\n    const axes = parseAxisParam(axis, x.shape);\n    keepDimsShape = axis_util.expandShapeToKeepDim(norm.shape, axes);\n  }\n  return reshape(norm, keepDimsShape);\n}\n\nfunction normImpl(\n    x: Tensor, p: number|string, axis: number|number[] = null): Tensor {\n  if (x.rank === 0) {\n    return abs(x);\n  }\n\n  // consider vector when no axis is specified\n  if (x.rank !== 1 && axis === null) {\n    return normImpl(reshape(x, [-1]), p, axis);\n  }\n\n  // vector\n  if (x.rank === 1 || typeof axis === 'number' ||\n      Array.isArray(axis) && axis.length === 1) {\n    if (p === 1) {\n      return sum(abs(x), axis);\n    }\n    if (p === Infinity) {\n      return max(abs(x), axis);\n    }\n    if (p === -Infinity) {\n      return min(abs(x), axis);\n    }\n    if (p === 'euclidean' || p === 2) {\n      // norm(x, 2) = sum(abs(xi) ^ 2) ^ 1/2\n      return sqrt(sum(pow(abs(x), scalar(2, 'int32')), axis));\n    }\n\n    throw new Error(`Error in norm: invalid ord value: ${p}`);\n  }\n\n  // matrix (assumption axis[0] < axis[1])\n  if (Array.isArray(axis) && axis.length === 2) {\n    if (p === 1) {\n      return max(sum(abs(x), axis[0]), axis[1] - 1);\n    }\n    if (p === Infinity) {\n      return max(sum(abs(x), axis[1]), axis[0]);\n    }\n    if (p === -Infinity) {\n      return min(sum(abs(x), axis[1]), axis[0]);\n    }\n    if (p === 'fro' || p === 'euclidean') {\n      // norm(x) = sqrt(sum(pow(x, 2)))\n      return sqrt(sum(square(x), axis));\n    }\n\n    throw new Error(`Error in norm: invalid ord value: ${p}`);\n  }\n\n  throw new Error(`Error in norm: invalid axis: ${axis}`);\n}\n\nexport const norm = op({norm_});\n"]}","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { assertTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { add } from './add';\nimport { div } from './div';\nimport { mul } from './mul';\nimport { op } from './operation';\nimport { pow } from './pow';\nimport { scalar } from './scalar';\nimport { sub } from './sub';\n/**\n * Compute the moving average of a variable.\n *\n * Without zeroDebias, the moving average operation is defined by:\n * `v += delta`\n * where\n * `delta = (1 - decay) * (x - v)`\n *\n * With zeroDebias (default), the `delta` term is scaled to debias the\n * effect of the (assumed) zero-initialization of `v`.\n * `delta /= (1 - decay ^ step)`\n *\n * For more details on the zero-debiasing algorithm, see:\n * https://arxiv.org/abs/1412.6980\n *\n * Note that this function is completely stateless and does not keep track of\n * step count. The step count needs to be maintained by the caller and passed\n * in as `step`.\n *\n * @param v The current moving average value.\n * @param x New input value, must have the same shape and dtype as `v`.\n * @param decay The decay factor. Typical values are 0.95 and 0.99.\n * @param step Step count.\n * @param zeroDebias: Whether zeroDebias is to be performed (default: `true`).\n * @returns The new moving average value.\n *\n * @doc {heading: 'Operations', subheading: 'Moving Average'}\n */\nfunction movingAverage_(v, x, decay, step, zeroDebias = true) {\n const $v = convertToTensor(v, 'v', 'movingAverage');\n const $x = convertToTensor(x, 'x', 'movingAverage');\n const $decay = convertToTensor(decay, 'decay', 'movingAverage');\n assertTypesMatch($v, $x);\n util.assert(util.arraysEqual($v.shape, $x.shape), () => 'Shape mismatch in v and x');\n const one = scalar(1);\n const oneMinusDecay = sub(one, $decay);\n let update = mul(sub($x, $v), oneMinusDecay);\n if (zeroDebias) {\n util.assert(step != null, () => 'When using zeroDebias: true, step is required.');\n const $step = convertToTensor(step, 'step', 'movingAverage');\n update = div(update, sub(one, pow($decay, $step)));\n }\n return add($v, update);\n}\nexport const movingAverage = op({ movingAverage_ });\n//# sourceMappingURL=data:application/json;base64,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* @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\n/**\n * Normalize noise shape based on provided tensor and noise shape.\n *\n * @param x Tensor.\n * @param noiseShape The shape for the randomly generated keep/drop flags, as\n * an array of numbers. Optional.\n * @returns Normalized noise shape.\n */\nexport function getNoiseShape(x, noiseShape) {\n if (noiseShape == null) {\n return x.shape.slice();\n }\n if (util.arraysEqual(x.shape, noiseShape)) {\n return noiseShape;\n }\n if (x.shape.length === noiseShape.length) {\n const newDimension = [];\n for (let i = 0; i < x.shape.length; i++) {\n if (noiseShape[i] == null && x.shape[i] != null) {\n newDimension.push(x.shape[i]);\n }\n else {\n newDimension.push(noiseShape[i]);\n }\n }\n return newDimension;\n }\n return noiseShape;\n}\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tensor } from '../tensor';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { add } from './add';\nimport { div } from './div';\nimport { getNoiseShape } from './dropout_util';\nimport { floor } from './floor';\nimport { mul } from './mul';\nimport { op } from './operation';\nimport { randomUniform } from './random_uniform';\n/**\n * Computes dropout.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 2, 1]);\n * const rate = 0.75;\n * const output = tf.dropout(x, rate);\n * output.print();\n * ```\n *\n * @param x A floating point Tensor or TensorLike.\n * @param rate A float in the range [0, 1). The probability that each element\n * of x is discarded.\n * @param noiseShape An array of numbers of type int32, representing the\n * shape for randomly generated keep/drop flags. If the noiseShape has null\n * value, it will be automatically replaced with the x's relative dimension\n * size. Optional.\n * @param seed Used to create random seeds. Optional.\n * @returns A Tensor of the same shape of x.\n *\n * @doc {heading: 'Operations', subheading: 'Dropout'}\n */\nfunction dropout_(x, rate, noiseShape, seed) {\n const $x = convertToTensor(x, 'x', 'dropout');\n util.assert($x.dtype === 'float32', () => `x has to be a floating point tensor since it's going to be ` +\n `scaled, but got a ${$x.dtype} tensor instead.`);\n util.assert(rate >= 0 && rate < 1, () => `rate must be a float in the range [0, 1), but got ${rate}.`);\n if (rate === 0) {\n return x instanceof Tensor ? $x.clone() : $x;\n }\n const $noiseShape = getNoiseShape($x, noiseShape);\n const keepProb = 1 - rate;\n const multiplier = div(floor(add(randomUniform($noiseShape, 0, 1, 'float32', seed), keepProb)), keepProb);\n return mul($x, multiplier);\n}\nexport const dropout = op({ dropout_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { tensor1d } from './tensor1d';\nexport function enclosingPowerOfTwo(value) {\n // Return 2**N for integer N such that 2**N >= value.\n return Math.floor(Math.pow(2, Math.ceil(Math.log(value) / Math.log(2.0))));\n}\nexport function cosineWindow(windowLength, a, b) {\n const even = 1 - windowLength % 2;\n const newValues = new Float32Array(windowLength);\n for (let i = 0; i < windowLength; ++i) {\n const cosArg = (2.0 * Math.PI * i) / (windowLength + even - 1);\n newValues[i] = a - b * Math.cos(cosArg);\n }\n return tensor1d(newValues, 'float32');\n}\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { assert, assertShapesMatch, getTypedArrayFromDType } from '../util';\nimport { tensor } from './tensor';\n/**\n * Returns whether the targets are in the top K predictions.\n *\n * ```js\n * const predictions = tf.tensor2d([[20, 10, 40, 30], [30, 50, -20, 10]]);\n * const targets = tf.tensor1d([2, 0]);\n * const precision = await tf.inTopKAsync(predictions, targets);\n * precision.print();\n * ```\n * @param predictions 2-D or higher `tf.Tensor` with last dimension being\n * at least `k`.\n * @param targets 1-D or higher `tf.Tensor`.\n * @param k Optional Number of top elements to look at for computing precision,\n * default to 1.\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nasync function inTopKAsync_(predictions, targets, k = 1) {\n const $predictions = convertToTensor(predictions, 'predictions', 'inTopK');\n const $targets = convertToTensor(targets, 'targets', 'inTopK');\n assert($predictions.rank > 1, () => 'inTopK() expects the predictions to be of rank 2 or higher, ' +\n `but got ${$predictions.rank}`);\n assert($predictions.rank - 1 === $targets.rank, () => `predictions rank should be 1 larger than ` +\n `targets rank, but got predictions rank ` +\n `${$predictions.rank} and targets rank ${$targets.rank}`);\n assertShapesMatch($predictions.shape.slice(0, $predictions.shape.length - 1), $targets.shape, `predictions's shape should be align with the targets' shape, ` +\n 'except the last dimension.');\n const lastDim = $predictions.shape[$predictions.shape.length - 1];\n assert(k > 0 && k <= lastDim, () => `'k' passed to inTopK() must be > 0 && <= the predictions last ` +\n `dimension (${lastDim}), but got ${k}`);\n const predictionsVals = await $predictions.data();\n const targetsVals = await $targets.data();\n // Reshape predictionsVals into a 2d tensor [batch, lastDim]\n // and look up topK along lastDim.\n const [batch, size] = [predictionsVals.length / lastDim, lastDim];\n const precision = getTypedArrayFromDType('bool', batch);\n for (let b = 0; b < batch; b++) {\n const offset = b * size;\n const vals = predictionsVals.subarray(offset, offset + size);\n const valAndInd = [];\n for (let i = 0; i < vals.length; i++) {\n valAndInd.push({ value: vals[i], index: i });\n }\n valAndInd.sort((a, b) => b.value - a.value);\n precision[b] = 0;\n for (let i = 0; i < k; i++) {\n if (valAndInd[i].index === targetsVals[b]) {\n precision[b] = 1;\n break;\n }\n }\n }\n if (predictions !== $predictions) {\n $predictions.dispose();\n }\n if (targets !== $targets) {\n $targets.dispose();\n }\n // Output precision has the same shape as targets.\n return tensor(precision, $targets.shape, 'bool');\n}\nexport const inTopKAsync = inTopKAsync_;\n//# sourceMappingURL=data:application/json;base64,{"version":3,"file":"in_top_k.js","sourceRoot":"","sources":["../../../../../../tfjs-core/src/ops/in_top_k.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAGH,OAAO,EAAC,eAAe,EAAC,MAAM,oBAAoB,CAAC;AAEnD,OAAO,EAAC,MAAM,EAAE,iBAAiB,EAAE,sBAAsB,EAAC,MAAM,SAAS,CAAC;AAC1E,OAAO,EAAC,MAAM,EAAC,MAAM,UAAU,CAAC;AAEhC;;;;;;;;;;;;;;;;GAgBG;AACH,KAAK,UAAU,YAAY,CACvB,WAAyB,EAAE,OAAqB,EAAE,CAAC,GAAG,CAAC;IACzD,MAAM,YAAY,GAAG,eAAe,CAAC,WAAW,EAAE,aAAa,EAAE,QAAQ,CAAC,CAAC;IAC3E,MAAM,QAAQ,GAAG,eAAe,CAAC,OAAO,EAAE,SAAS,EAAE,QAAQ,CAAC,CAAC;IAE/D,MAAM,CACF,YAAY,CAAC,IAAI,GAAG,CAAC,EACrB,GAAG,EAAE,CAAC,8DAA8D;QAChE,WAAW,YAAY,CAAC,IAAI,EAAE,CAAC,CAAC;IACxC,MAAM,CACF,YAAY,CAAC,IAAI,GAAG,CAAC,KAAK,QAAQ,CAAC,IAAI,EACvC,GAAG,EAAE,CAAC,2CAA2C;QAC7C,yCAAyC;QACzC,GAAG,YAAY,CAAC,IAAI,qBAAqB,QAAQ,CAAC,IAAI,EAAE,CAAC,CAAC;IAClE,iBAAiB,CACb,YAAY,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,EAAE,YAAY,CAAC,KAAK,CAAC,MAAM,GAAG,CAAC,CAAC,EAC1D,QAAQ,CAAC,KAAK,EACd,+DAA+D;QAC3D,4BAA4B,CAAC,CAAC;IACtC,MAAM,OAAO,GAAG,YAAY,CAAC,KAAK,CAAC,YAAY,CAAC,KAAK,CAAC,MAAM,GAAG,CAAC,CAAC,CAAC;IAClE,MAAM,CACF,CAAC,GAAG,CAAC,IAAI,CAAC,IAAI,OAAO,EACrB,GAAG,EAAE,CAAC,gEAAgE;QAClE,cAAc,OAAO,cAAc,CAAC,EAAE,CAAC,CAAC;IAEhD,MAAM,eAAe,GAAG,MAAM,YAAY,CAAC,IAAI,EAAE,CAAC;IAClD,MAAM,WAAW,GAAG,MAAM,QAAQ,CAAC,IAAI,EAAE,CAAC;IAE1C,4DAA4D;IAC5D,kCAAkC;IAClC,MAAM,CAAC,KAAK,EAAE,IAAI,CAAC,GAAG,CAAC,eAAe,CAAC,MAAM,GAAG,OAAO,EAAE,OAAO,CAAC,CAAC;IAClE,MAAM,SAAS,GAAG,sBAAsB,CAAC,MAAM,EAAE,KAAK,CAAC,CAAC;IAExD,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,KAAK,EAAE,CAAC,EAAE,EAAE;QAC9B,MAAM,MAAM,GAAG,CAAC,GAAG,IAAI,CAAC;QACxB,MAAM,IAAI,GAAG,eAAe,CAAC,QAAQ,CAAC,MAAM,EAAE,MAAM,GAAG,IAAI,CAAC,CAAC;QAC7D,MAAM,SAAS,GAA0C,EAAE,CAAC;QAC5D,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,IAAI,CAAC,MAAM,EAAE,CAAC,EAAE,EAAE;YACpC,SAAS,CAAC,IAAI,CAAC,EAAC,KAAK,EAAE,IAAI,CAAC,CAAC,CAAC,EAAE,KAAK,EAAE,CAAC,EAAC,CAAC,CAAC;SAC5C;QACD,SAAS,CAAC,IAAI,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,CAAC,CAAC,KAAK,GAAG,CAAC,CAAC,KAAK,CAAC,CAAC;QAE5C,SAAS,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC;QACjB,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,EAAE,EAAE;YAC1B,IAAI,SAAS,CAAC,CAAC,CAAC,CAAC,KAAK,KAAK,WAAW,CAAC,CAAC,CAAC,EAAE;gBACzC,SAAS,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC;gBACjB,MAAM;aACP;SACF;KACF;IAED,IAAI,WAAW,KAAK,YAAY,EAAE;QAChC,YAAY,CAAC,OAAO,EAAE,CAAC;KACxB;IACD,IAAI,OAAO,KAAK,QAAQ,EAAE;QACxB,QAAQ,CAAC,OAAO,EAAE,CAAC;KACpB;IAED,kDAAkD;IAClD,OAAO,MAAM,CAAC,SAAS,EAAE,QAAQ,CAAC,KAAK,EAAE,MAAM,CAAM,CAAC;AACxD,CAAC;AAED,MAAM,CAAC,MAAM,WAAW,GAAG,YAAY,CAAC","sourcesContent":["/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {assert, assertShapesMatch, getTypedArrayFromDType} from '../util';\nimport {tensor} from './tensor';\n\n/**\n * Returns whether the targets are in the top K predictions.\n *\n * ```js\n * const predictions = tf.tensor2d([[20, 10, 40, 30], [30, 50, -20, 10]]);\n * const targets = tf.tensor1d([2, 0]);\n * const precision = await tf.inTopKAsync(predictions, targets);\n * precision.print();\n * ```\n * @param predictions 2-D or higher `tf.Tensor` with last dimension being\n *     at least `k`.\n * @param targets 1-D or higher `tf.Tensor`.\n * @param k Optional Number of top elements to look at for computing precision,\n *     default to 1.\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nasync function inTopKAsync_<T extends Tensor, U extends Tensor>(\n    predictions: T|TensorLike, targets: U|TensorLike, k = 1): Promise<U> {\n  const $predictions = convertToTensor(predictions, 'predictions', 'inTopK');\n  const $targets = convertToTensor(targets, 'targets', 'inTopK');\n\n  assert(\n      $predictions.rank > 1,\n      () => 'inTopK() expects the predictions to be of rank 2 or higher, ' +\n          `but got ${$predictions.rank}`);\n  assert(\n      $predictions.rank - 1 === $targets.rank,\n      () => `predictions rank should be 1 larger than ` +\n          `targets rank, but got predictions rank ` +\n          `${$predictions.rank} and targets rank ${$targets.rank}`);\n  assertShapesMatch(\n      $predictions.shape.slice(0, $predictions.shape.length - 1),\n      $targets.shape,\n      `predictions's shape should be align with the targets' shape, ` +\n          'except the last dimension.');\n  const lastDim = $predictions.shape[$predictions.shape.length - 1];\n  assert(\n      k > 0 && k <= lastDim,\n      () => `'k' passed to inTopK() must be > 0 && <= the predictions last ` +\n          `dimension (${lastDim}), but got ${k}`);\n\n  const predictionsVals = await $predictions.data();\n  const targetsVals = await $targets.data();\n\n  // Reshape predictionsVals into a 2d tensor [batch, lastDim]\n  // and look up topK along lastDim.\n  const [batch, size] = [predictionsVals.length / lastDim, lastDim];\n  const precision = getTypedArrayFromDType('bool', batch);\n\n  for (let b = 0; b < batch; b++) {\n    const offset = b * size;\n    const vals = predictionsVals.subarray(offset, offset + size);\n    const valAndInd: Array<{value: number, index: number}> = [];\n    for (let i = 0; i < vals.length; i++) {\n      valAndInd.push({value: vals[i], index: i});\n    }\n    valAndInd.sort((a, b) => b.value - a.value);\n\n    precision[b] = 0;\n    for (let i = 0; i < k; i++) {\n      if (valAndInd[i].index === targetsVals[b]) {\n        precision[b] = 1;\n        break;\n      }\n    }\n  }\n\n  if (predictions !== $predictions) {\n    $predictions.dispose();\n  }\n  if (targets !== $targets) {\n    $targets.dispose();\n  }\n\n  // Output precision has the same shape as targets.\n  return tensor(precision, $targets.shape, 'bool') as U;\n}\n\nexport const inTopKAsync = inTopKAsync_;\n"]}","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { customGrad } from '../../gradients';\nimport { FusedConv2D } from '../../kernel_names';\nimport { makeTypesMatch } from '../../tensor_util';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { add } from '../add';\nimport * as broadcast_util from '../broadcast_util';\nimport { conv2d as unfusedConv2d } from '../conv2d';\nimport { conv2DBackpropFilter } from '../conv2d_backprop_filter';\nimport { conv2DBackpropInput } from '../conv2d_backprop_input';\nimport * as conv_util from '../conv_util';\nimport { applyActivation, getFusedBiasGradient, getFusedDyActivation, shouldFuse } from '../fused_util';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * Computes a 2D convolution over the input x, optionally fused with adding a\n * bias and applying an activation.\n *\n * ```js\n * const inputDepth = 2;\n * const inShape = [2, 2, 2, inputDepth];\n * const outputDepth = 2;\n * const fSize = 1;\n * const pad = 0;\n * const strides = 1;\n *\n * const x = tf.tensor4d( [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,\n * 16], inShape);\n * const w = tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth,\n * outputDepth]);\n *\n * tf.fused.conv2d({ x, filter: w, strides, pad, dataFormat: 'NHWC',\n * dilations: [1, 1], bias: tf.scalar(5), activation: 'relu' }).print();\n * ```\n *\n * @param obj An object with the following properties:\n * @param x The input tensor, of rank 4 or rank 3, of shape\n * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter, rank 4, of shape\n * `[filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n * - `same` and stride 1: output will be of same size as input,\n * regardless of filter size.\n * - `valid` output will be smaller than input if filter is larger\n * than 1x1.\n * - For more info, see this guide:\n * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](\n * https://www.tensorflow.org/api_docs/python/tf/nn/convolution)\n * @param dataFormat An optional string from: \"NHWC\", \"NCHW\". Defaults to\n * \"NHWC\". Specify the data format of the input and output data. With the\n * default format \"NHWC\", the data is stored in the order of: [batch,\n * height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n * in which we sample input values across the height and width dimensions\n * in atrous convolution. Defaults to `[1, 1]`. If `dilations` is a single\n * number, then `dilationHeight == dilationWidth`. If it is greater than\n * 1, then all values of `strides` must be 1.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n * provided, it will default to truncate.\n * @param bias Tensor to be added to the result.\n * @param activation Name of activation kernel (defaults to `linear`) to be\n * applied\n * after biasAdd.\n * @param preluActivationWeights Tensor of prelu weights to be applied as part\n * of a `prelu` activation, typically the same shape as `x`.\n * @param leakyreluAlpha Optional. Alpha to be applied as part of a `leakyrelu`\n * activation.\n */\nfunction fusedConv2d_({ x, filter, strides, pad, dataFormat = 'NHWC', dilations = [1, 1], dimRoundingMode, bias, activation = 'linear', preluActivationWeights, leakyreluAlpha }) {\n activation = activation || 'linear';\n if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {\n let result = unfusedConv2d(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n if (bias != null) {\n result = add(result, bias);\n }\n return applyActivation(result, activation, preluActivationWeights, leakyreluAlpha);\n }\n const $x = convertToTensor(x, 'x', 'conv2d', 'float32');\n const $filter = convertToTensor(filter, 'filter', 'conv2d', 'float32');\n let x4D = $x;\n let reshapedTo4D = false;\n if ($x.rank === 3) {\n reshapedTo4D = true;\n x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n }\n util.assert(x4D.rank === 4, () => `Error in fused conv2d: input must be rank 4, but got rank ` +\n `${x4D.rank}.`);\n util.assert($filter.rank === 4, () => `Error in fused conv2d: filter must be rank 4, but got rank ` +\n `${$filter.rank}.`);\n conv_util.checkPadOnDimRoundingMode('fused conv2d', pad, dimRoundingMode);\n util.assert(x4D.shape[3] === $filter.shape[2], () => `Error in conv2d: depth of input (${x4D.shape[3]}) must match ` +\n `input depth for filter ${$filter.shape[2]}.`);\n util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in conv2D: Either strides or dilations must be 1. ' +\n `Got strides ${strides} and dilations '${dilations}'`);\n util.assert(dataFormat === 'NHWC', () => `Error in conv2d: got dataFormat of ${dataFormat} but only NHWC is currently supported.`);\n const convInfo = conv_util.computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode);\n let $bias;\n if (bias != null) {\n $bias = convertToTensor(bias, 'bias', 'fused conv2d');\n [$bias] = makeTypesMatch($bias, $x);\n broadcast_util.assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);\n }\n let $preluActivationWeights;\n if (preluActivationWeights != null) {\n $preluActivationWeights = convertToTensor(preluActivationWeights, 'prelu weights', 'fused conv2d');\n }\n const grad = (dy, saved) => {\n const [$filter, x4D, y, $bias] = saved;\n const dyActivation = getFusedDyActivation(dy, y, activation);\n util.assert(conv_util.tupleValuesAreOne(dilations), () => 'Error in gradient of fused conv2D: ' +\n `dilation rates greater than 1 ` +\n `are not yet supported in gradients. Got dilations '${dilations}'`);\n const xDer = conv2DBackpropInput(x4D.shape, dyActivation, $filter, strides, pad);\n const filterDer = conv2DBackpropFilter(x4D, dyActivation, $filter.shape, strides, pad);\n const der = [xDer, filterDer];\n if ($bias != null) {\n const biasDer = getFusedBiasGradient($bias, dyActivation);\n der.push(biasDer);\n }\n return der;\n };\n const inputs = {\n x: x4D,\n filter: $filter,\n bias: $bias,\n preluActivationWeights: $preluActivationWeights\n };\n const attrs = {\n strides,\n pad,\n dataFormat,\n dilations,\n dimRoundingMode,\n activation,\n leakyreluAlpha\n };\n // Depending on the the params passed in we will have different number of\n // inputs and thus a a different number of elements in the gradient.\n if (bias == null) {\n const customOp = customGrad((x4D, filter, save) => {\n let res = \n // tslint:disable-next-line: no-unnecessary-type-assertion\n ENGINE.runKernel(FusedConv2D, inputs, attrs);\n save([filter, x4D, res]);\n if (reshapedTo4D) {\n // tslint:disable-next-line: no-unnecessary-type-assertion\n res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n }\n return { value: res, gradFunc: grad };\n });\n return customOp(x4D, $filter);\n }\n else {\n const customOpWithBias = customGrad((x4D, filter, bias, save) => {\n let res = ENGINE.runKernel(FusedConv2D, inputs, attrs);\n save([filter, x4D, res, bias]);\n if (reshapedTo4D) {\n // tslint:disable-next-line: no-unnecessary-type-assertion\n res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n }\n return { value: res, gradFunc: grad };\n });\n return customOpWithBias(x4D, $filter, $bias);\n }\n}\nexport const conv2d = op({ fusedConv2d_ });\n//# 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* @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../../engine';\nimport {customGrad} from '../../gradients';\nimport {FusedConv2D, FusedConv2DAttrs, FusedConv2DInputs} from '../../kernel_names';\nimport {NamedAttrMap} from '../../kernel_registry';\nimport {Tensor, Tensor3D, Tensor4D} from '../../tensor';\nimport {GradSaveFunc, NamedTensorMap} from '../../tensor_types';\nimport {makeTypesMatch} from '../../tensor_util';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport * as util from '../../util';\nimport {add} from '../add';\nimport * as broadcast_util from '../broadcast_util';\nimport {conv2d as unfusedConv2d} from '../conv2d';\nimport {conv2DBackpropFilter} from '../conv2d_backprop_filter';\nimport {conv2DBackpropInput} from '../conv2d_backprop_input';\nimport * as conv_util from '../conv_util';\nimport {Activation} from '../fused_types';\nimport {applyActivation, getFusedBiasGradient, getFusedDyActivation, shouldFuse} from '../fused_util';\nimport {op} from '../operation';\nimport {reshape} from '../reshape';\n\n/**\n * Computes a 2D convolution over the input x, optionally fused with adding a\n * bias and applying an activation.\n *\n * ```js\n * const inputDepth = 2;\n * const inShape = [2, 2, 2, inputDepth];\n * const outputDepth = 2;\n * const fSize = 1;\n * const pad = 0;\n * const strides = 1;\n *\n * const x = tf.tensor4d( [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,\n * 16], inShape);\n * const w = tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth,\n * outputDepth]);\n *\n * tf.fused.conv2d({ x, filter: w, strides, pad, dataFormat: 'NHWC',\n * dilations: [1, 1], bias: tf.scalar(5), activation: 'relu' }).print();\n * ```\n *\n * @param obj An object with the following properties:\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid` output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](\n *          https://www.tensorflow.org/api_docs/python/tf/nn/convolution)\n * @param dataFormat An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `dilations` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n *     provided, it will default to truncate.\n * @param bias Tensor to be added to the result.\n * @param activation Name of activation kernel (defaults to `linear`) to be\n *     applied\n *      after biasAdd.\n * @param preluActivationWeights Tensor of prelu weights to be applied as part\n *     of a `prelu` activation, typically the same shape as `x`.\n * @param leakyreluAlpha Optional. Alpha to be applied as part of a `leakyrelu`\n *     activation.\n */\nfunction fusedConv2d_<T extends Tensor3D|Tensor4D>({\n  x,\n  filter,\n  strides,\n  pad,\n  dataFormat = 'NHWC',\n  dilations = [1, 1],\n  dimRoundingMode,\n  bias,\n  activation = 'linear',\n  preluActivationWeights,\n  leakyreluAlpha\n}: {\n  x: T|TensorLike,\n  filter: Tensor4D|TensorLike,\n  strides: [number, number]|number,\n  pad: 'valid'|'same'|number|conv_util.ExplicitPadding,\n  dataFormat?: 'NHWC'|'NCHW',\n  dilations?: [number, number]|number,\n  dimRoundingMode?: 'floor'|'round'|'ceil',\n  bias?: Tensor|TensorLike,\n  activation?: Activation,\n  preluActivationWeights?: Tensor,\n  leakyreluAlpha?: number\n}): T {\n  activation = activation || 'linear';\n\n  if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {\n    let result = unfusedConv2d(\n        x, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n    if (bias != null) {\n      result = add(result, bias);\n    }\n\n    return applyActivation(\n               result, activation, preluActivationWeights, leakyreluAlpha) as T;\n  }\n\n  const $x = convertToTensor(x, 'x', 'conv2d', 'float32');\n  const $filter = convertToTensor(filter, 'filter', 'conv2d', 'float32');\n\n  let x4D = $x as Tensor4D;\n  let reshapedTo4D = false;\n\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n  util.assert(\n      x4D.rank === 4,\n      () => `Error in fused conv2d: input must be rank 4, but got rank ` +\n          `${x4D.rank}.`);\n  util.assert(\n      $filter.rank === 4,\n      () => `Error in fused conv2d: filter must be rank 4, but got rank ` +\n          `${$filter.rank}.`);\n  conv_util.checkPadOnDimRoundingMode('fused conv2d', pad, dimRoundingMode);\n  util.assert(\n      x4D.shape[3] === $filter.shape[2],\n      () => `Error in conv2d: depth of input (${x4D.shape[3]}) must match ` +\n          `input depth for filter ${$filter.shape[2]}.`);\n  util.assert(\n      conv_util.eitherStridesOrDilationsAreOne(strides, dilations),\n      () => 'Error in conv2D: Either strides or dilations must be 1. ' +\n          `Got strides ${strides} and dilations '${dilations}'`);\n  util.assert(\n      dataFormat === 'NHWC',\n      () => `Error in conv2d: got dataFormat of ${\n          dataFormat} but only NHWC is currently supported.`);\n\n  const convInfo = conv_util.computeConv2DInfo(\n      x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode);\n\n  let $bias: Tensor;\n  if (bias != null) {\n    $bias = convertToTensor(bias, 'bias', 'fused conv2d');\n    [$bias] = makeTypesMatch($bias, $x);\n\n    broadcast_util.assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);\n  }\n\n  let $preluActivationWeights: Tensor;\n  if (preluActivationWeights != null) {\n    $preluActivationWeights = convertToTensor(\n        preluActivationWeights, 'prelu weights', 'fused conv2d');\n  }\n\n  const grad = (dy: Tensor4D, saved: Tensor[]) => {\n    const [$filter, x4D, y, $bias] =\n        saved as [Tensor4D, Tensor4D, Tensor4D, Tensor];\n\n    const dyActivation = getFusedDyActivation(dy, y, activation) as Tensor4D;\n\n    util.assert(\n        conv_util.tupleValuesAreOne(dilations),\n        () => 'Error in gradient of fused conv2D: ' +\n            `dilation rates greater than 1 ` +\n            `are not yet supported in gradients. Got dilations '${dilations}'`);\n\n    const xDer =\n        conv2DBackpropInput(x4D.shape, dyActivation, $filter, strides, pad);\n    const filterDer =\n        conv2DBackpropFilter(x4D, dyActivation, $filter.shape, strides, pad);\n    const der: Tensor[] = [xDer, filterDer];\n\n    if ($bias != null) {\n      const biasDer = getFusedBiasGradient($bias, dyActivation);\n      der.push(biasDer);\n    }\n    return der;\n  };\n\n  const inputs: FusedConv2DInputs = {\n    x: x4D,\n    filter: $filter,\n    bias: $bias,\n    preluActivationWeights: $preluActivationWeights\n  };\n\n  const attrs: FusedConv2DAttrs = {\n    strides,\n    pad,\n    dataFormat,\n    dilations,\n    dimRoundingMode,\n    activation,\n    leakyreluAlpha\n  };\n\n  // Depending on the the params passed in we will have different number of\n  // inputs and thus a a different number of elements in the gradient.\n  if (bias == null) {\n    const customOp =\n        customGrad((x4D: Tensor4D, filter: Tensor4D, save: GradSaveFunc) => {\n          let res: Tensor4D|Tensor3D =\n              // tslint:disable-next-line: no-unnecessary-type-assertion\n              ENGINE.runKernel(\n                  FusedConv2D, inputs as {} as NamedTensorMap,\n                  attrs as {} as NamedAttrMap);\n\n          save([filter, x4D, res]);\n\n          if (reshapedTo4D) {\n            // tslint:disable-next-line: no-unnecessary-type-assertion\n            res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as\n                Tensor3D;\n          }\n\n          return {value: res, gradFunc: grad};\n        });\n    return customOp(x4D, $filter) as T;\n  } else {\n    const customOpWithBias = customGrad(\n        (x4D: Tensor4D, filter: Tensor4D, bias: Tensor, save: GradSaveFunc) => {\n          let res: Tensor4D|Tensor3D = ENGINE.runKernel(\n              FusedConv2D, inputs as {} as NamedTensorMap,\n              attrs as {} as NamedAttrMap);\n\n          save([filter, x4D, res, bias]);\n\n          if (reshapedTo4D) {\n            // tslint:disable-next-line: no-unnecessary-type-assertion\n            res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as\n                Tensor3D;\n          }\n\n          return {value: res, gradFunc: grad};\n        });\n\n    return customOpWithBias(x4D, $filter, $bias) as T;\n  }\n}\nexport const conv2d = op({fusedConv2d_});\n"]}","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { customGrad } from '../../gradients';\nimport { FusedDepthwiseConv2D } from '../../kernel_names';\nimport { makeTypesMatch } from '../../tensor_util';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { add } from '../add';\nimport * as broadcast_util from '../broadcast_util';\nimport * as conv_util from '../conv_util';\nimport { depthwiseConv2d as unfusedDepthwiseConv2d } from '../depthwise_conv2d';\nimport { depthwiseConv2dNativeBackpropFilter } from '../depthwise_conv2d_native_backprop_filter';\nimport { depthwiseConv2dNativeBackpropInput } from '../depthwise_conv2d_native_backprop_input';\nimport { applyActivation, getFusedBiasGradient, getFusedDyActivation, shouldFuse } from '../fused_util';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * Computes depthwise 2D convolution, optionally fused with adding a\n * bias and applying an activation.\n *\n * Given a 4D `input` array and a `filter` array of shape\n * `[filterHeight, filterWidth, inChannels, channelMultiplier]` containing\n * `inChannels` convolutional filters of depth 1, this op applies a\n * different filter to each input channel (expanding from 1 channel to\n * `channelMultiplier` channels for each), then concatenates the results\n * together. The output has `inChannels * channelMultiplier` channels.\n *\n * See\n * [https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d](\n * https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d)\n * for more details.\n *\n * @param obj An object with the following properties:\n * @param x The input tensor, of rank 4 or rank 3, of shape\n * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter tensor, rank 4, of shape\n * `[filterHeight, filterWidth, inChannels, channelMultiplier]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`. If strides is a single number, then `strideHeight ==\n * strideWidth`.\n * @param pad The type of padding algorithm.\n * - `same` and stride 1: output will be of same size as input,\n * regardless of filter size.\n * - `valid`: output will be smaller than input if filter is larger\n * than 1x1.\n * - For more info, see this guide:\n * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](\n * https://www.tensorflow.org/api_docs/python/tf/nn/convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n * in which we sample input values across the height and width dimensions\n * in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single\n * number, then `dilationHeight == dilationWidth`. If it is greater than\n * 1, then all values of `strides` must be 1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n * \"NHWC\". Specify the data format of the input and output data. With the\n * default format \"NHWC\", the data is stored in the order of: [batch,\n * height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n * provided, it will default to truncate.\n * @param bias Tensor to be added to the result.\n * @param activation Name of activation kernel (defaults to `linear`).\n * @param preluActivationWeights Tensor of prelu weights to be applied as part\n * of a `prelu` activation, typically the same shape as `x`.\n * @param leakyreluAlpha Optional. Alpha to be applied as part of a `leakyrelu`\n * activation.\n */\nfunction fusedDepthwiseConv2d_({ x, filter, strides, pad, dataFormat = 'NHWC', dilations = [1, 1], dimRoundingMode, bias, activation = 'linear', preluActivationWeights, leakyreluAlpha }) {\n if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {\n let result = unfusedDepthwiseConv2d(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n if (bias != null) {\n result = add(result, bias);\n }\n return applyActivation(result, activation, preluActivationWeights, leakyreluAlpha);\n }\n const $x = convertToTensor(x, 'x', 'depthwiseConv2d', 'float32');\n const $filter = convertToTensor(filter, 'filter', 'depthwiseConv2d', 'float32');\n let x4D = $x;\n let reshapedTo4D = false;\n if ($x.rank === 3) {\n reshapedTo4D = true;\n x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n }\n util.assert(x4D.rank === 4, () => `Error in fused depthwiseConv2d: input must be rank 4, but got ` +\n `rank ${x4D.rank}.`);\n util.assert($filter.rank === 4, () => `Error in fused depthwiseConv2d: filter must be rank 4, ` +\n `but got rank ${$filter.rank}.`);\n util.assert(x4D.shape[3] === $filter.shape[2], () => `Error in fused depthwiseConv2d: number of input channels ` +\n `(${x4D.shape[3]}) must match the inChannels dimension in ` +\n `filter ${$filter.shape[2]}.`);\n if (dilations == null) {\n dilations = [1, 1];\n }\n util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in fused depthwiseConv2d: Either strides or dilations must ' +\n `be 1. Got strides ${strides} and dilations '${dilations}'`);\n conv_util.checkPadOnDimRoundingMode('fused depthwiseConv2d', pad, dimRoundingMode);\n const convInfo = conv_util.computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode, true /* depthwise */);\n let $bias;\n if (bias != null) {\n $bias = convertToTensor(bias, 'bias', 'fused conv2d');\n [$bias] = makeTypesMatch($bias, $x);\n broadcast_util.assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);\n }\n let $preluActivationWeights;\n if (preluActivationWeights != null) {\n $preluActivationWeights = convertToTensor(preluActivationWeights, 'prelu weights', 'fused depthwiseConv2d');\n }\n const grad = (dy, saved) => {\n util.assert(conv_util.tupleValuesAreOne(dilations), () => 'Error in gradient of fused depthwiseConv2d: dilation rates ' +\n `greater than 1 are not yet supported. Got dilations ` +\n `'${dilations}'`);\n const [$filter, x4D, y, bias] = saved;\n const dyActivation = getFusedDyActivation(dy, y, activation);\n const xDer = depthwiseConv2dNativeBackpropInput(x4D.shape, dyActivation, $filter, strides, pad, dilations, dimRoundingMode);\n const filterDer = depthwiseConv2dNativeBackpropFilter(x4D, dyActivation, $filter.shape, strides, pad, dilations, dimRoundingMode);\n if (bias != null) {\n const biasDer = getFusedBiasGradient($bias, dyActivation);\n return [xDer, filterDer, biasDer];\n }\n return [xDer, filterDer];\n };\n const inputs = {\n x: x4D,\n filter: $filter,\n bias: $bias,\n preluActivationWeights: $preluActivationWeights\n };\n const attrs = {\n strides,\n pad,\n dataFormat,\n dilations,\n dimRoundingMode,\n activation,\n leakyreluAlpha\n };\n // Depending on the the params passed in we will have different number of\n // inputs and thus a a different number of elements in the gradient.\n if (bias == null) {\n const customOp = customGrad((x4D, filter, save) => {\n // tslint:disable-next-line: no-unnecessary-type-assertion\n let res = ENGINE.runKernel(FusedDepthwiseConv2D, inputs, attrs);\n save([filter, x4D, res]);\n if (reshapedTo4D) {\n // tslint:disable-next-line: no-unnecessary-type-assertion\n res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n }\n return { value: res, gradFunc: grad };\n });\n return customOp(x4D, $filter);\n }\n else {\n const customOpWithBias = customGrad((x4D, filter, bias, save) => {\n // tslint:disable-next-line: no-unnecessary-type-assertion\n let res = ENGINE.runKernel(FusedDepthwiseConv2D, inputs, attrs);\n save([filter, x4D, res, bias]);\n if (reshapedTo4D) {\n // tslint:disable-next-line: no-unnecessary-type-assertion\n res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n }\n return { value: res, gradFunc: grad };\n });\n return customOpWithBias(x4D, $filter, $bias);\n }\n}\nexport const depthwiseConv2d = op({ fusedDepthwiseConv2d_ });\n//# 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* @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../../engine';\nimport {customGrad} from '../../gradients';\nimport {FusedDepthwiseConv2D, FusedDepthwiseConv2DAttrs, FusedDepthwiseConv2DInputs} from '../../kernel_names';\nimport {NamedAttrMap} from '../../kernel_registry';\nimport {Tensor, Tensor3D, Tensor4D} from '../../tensor';\nimport {GradSaveFunc, NamedTensorMap} from '../../tensor_types';\nimport {makeTypesMatch} from '../../tensor_util';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport * as util from '../../util';\nimport {add} from '../add';\nimport * as broadcast_util from '../broadcast_util';\nimport * as conv_util from '../conv_util';\nimport {depthwiseConv2d as unfusedDepthwiseConv2d} from '../depthwise_conv2d';\nimport {depthwiseConv2dNativeBackpropFilter} from '../depthwise_conv2d_native_backprop_filter';\nimport {depthwiseConv2dNativeBackpropInput} from '../depthwise_conv2d_native_backprop_input';\nimport {Activation} from '../fused_types';\nimport {applyActivation, getFusedBiasGradient, getFusedDyActivation, shouldFuse} from '../fused_util';\nimport {op} from '../operation';\nimport {reshape} from '../reshape';\n\n/**\n * Computes depthwise 2D convolution, optionally fused with adding a\n * bias and applying an activation.\n *\n * Given a 4D `input` array and a `filter` array of shape\n * `[filterHeight, filterWidth, inChannels, channelMultiplier]` containing\n * `inChannels` convolutional filters of depth 1, this op applies a\n * different filter to each input channel (expanding from 1 channel to\n * `channelMultiplier` channels for each), then concatenates the results\n * together. The output has `inChannels * channelMultiplier` channels.\n *\n * See\n * [https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d](\n *     https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d)\n * for more details.\n *\n * @param obj An object with the following properties:\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter tensor, rank 4, of shape\n *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`. If strides is a single number, then `strideHeight ==\n * strideWidth`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](\n *          https://www.tensorflow.org/api_docs/python/tf/nn/convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n *     provided, it will default to truncate.\n * @param bias Tensor to be added to the result.\n * @param activation Name of activation kernel (defaults to `linear`).\n * @param preluActivationWeights Tensor of prelu weights to be applied as part\n *     of a `prelu` activation, typically the same shape as `x`.\n * @param leakyreluAlpha Optional. Alpha to be applied as part of a `leakyrelu`\n *     activation.\n */\nfunction fusedDepthwiseConv2d_<T extends Tensor3D|Tensor4D>({\n  x,\n  filter,\n  strides,\n  pad,\n  dataFormat = 'NHWC',\n  dilations = [1, 1],\n  dimRoundingMode,\n  bias,\n  activation = 'linear',\n  preluActivationWeights,\n  leakyreluAlpha\n}: {\n  x: T|TensorLike,\n  filter: Tensor4D|TensorLike,\n  strides: [number, number]|number,\n  pad: 'valid'|'same'|number,\n  dataFormat?: 'NHWC'|'NCHW',\n  dilations?: [number, number]|number,\n  dimRoundingMode?: 'floor'|'round'|'ceil',\n  bias?: Tensor|TensorLike,\n  activation?: Activation,\n  preluActivationWeights?: Tensor,\n  leakyreluAlpha?: number\n}): T {\n  if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {\n    let result = unfusedDepthwiseConv2d(\n        x, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n    if (bias != null) {\n      result = add(result, bias);\n    }\n\n    return applyActivation(\n               result, activation, preluActivationWeights, leakyreluAlpha) as T;\n  }\n\n  const $x = convertToTensor(x, 'x', 'depthwiseConv2d', 'float32');\n  const $filter =\n      convertToTensor(filter, 'filter', 'depthwiseConv2d', 'float32');\n\n  let x4D = $x as Tensor4D;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n  util.assert(\n      x4D.rank === 4,\n      () => `Error in fused depthwiseConv2d: input must be rank 4, but got ` +\n          `rank ${x4D.rank}.`);\n  util.assert(\n      $filter.rank === 4,\n      () => `Error in fused depthwiseConv2d: filter must be rank 4, ` +\n          `but got rank ${$filter.rank}.`);\n  util.assert(\n      x4D.shape[3] === $filter.shape[2],\n      () => `Error in fused depthwiseConv2d: number of input channels ` +\n          `(${x4D.shape[3]}) must match the inChannels dimension in ` +\n          `filter ${$filter.shape[2]}.`);\n  if (dilations == null) {\n    dilations = [1, 1];\n  }\n  util.assert(\n      conv_util.eitherStridesOrDilationsAreOne(strides, dilations),\n      () =>\n          'Error in fused depthwiseConv2d: Either strides or dilations must ' +\n          `be 1. Got strides ${strides} and dilations '${dilations}'`);\n  conv_util.checkPadOnDimRoundingMode(\n      'fused depthwiseConv2d', pad, dimRoundingMode);\n  const convInfo = conv_util.computeConv2DInfo(\n      x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode,\n      true /* depthwise */);\n\n  let $bias: Tensor;\n  if (bias != null) {\n    $bias = convertToTensor(bias, 'bias', 'fused conv2d');\n    [$bias] = makeTypesMatch($bias, $x);\n\n    broadcast_util.assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);\n  }\n\n  let $preluActivationWeights: Tensor;\n  if (preluActivationWeights != null) {\n    $preluActivationWeights = convertToTensor(\n        preluActivationWeights, 'prelu weights', 'fused depthwiseConv2d');\n  }\n\n  const grad = (dy: Tensor4D, saved: Tensor[]) => {\n    util.assert(\n        conv_util.tupleValuesAreOne(dilations),\n        () => 'Error in gradient of fused depthwiseConv2d: dilation rates ' +\n            `greater than 1 are not yet supported. Got dilations ` +\n            `'${dilations}'`);\n    const [$filter, x4D, y, bias] = saved;\n\n    const dyActivation = getFusedDyActivation(dy, y, activation) as Tensor4D;\n\n    const xDer = depthwiseConv2dNativeBackpropInput(\n        (x4D as Tensor4D).shape, dyActivation, $filter as Tensor4D, strides,\n        pad, dilations, dimRoundingMode);\n    const filterDer = depthwiseConv2dNativeBackpropFilter(\n        x4D as Tensor4D, dyActivation, ($filter as Tensor4D).shape, strides,\n        pad, dilations, dimRoundingMode);\n\n    if (bias != null) {\n      const biasDer = getFusedBiasGradient($bias, dyActivation);\n      return [xDer, filterDer, biasDer];\n    }\n    return [xDer, filterDer];\n  };\n\n  const inputs: FusedDepthwiseConv2DInputs = {\n    x: x4D,\n    filter: $filter,\n    bias: $bias,\n    preluActivationWeights: $preluActivationWeights\n  };\n  const attrs: FusedDepthwiseConv2DAttrs = {\n    strides,\n    pad,\n    dataFormat,\n    dilations,\n    dimRoundingMode,\n    activation,\n    leakyreluAlpha\n  };\n\n  // Depending on the the params passed in we will have different number of\n  // inputs and thus a a different number of elements in the gradient.\n  if (bias == null) {\n    const customOp =\n        customGrad((x4D: Tensor4D, filter: Tensor4D, save: GradSaveFunc) => {\n          // tslint:disable-next-line: no-unnecessary-type-assertion\n          let res: Tensor4D|Tensor3D = ENGINE.runKernel(\n              FusedDepthwiseConv2D, inputs as {} as NamedTensorMap,\n              attrs as {} as NamedAttrMap);\n\n          save([filter, x4D, res]);\n\n          if (reshapedTo4D) {\n            // tslint:disable-next-line: no-unnecessary-type-assertion\n            res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as\n                Tensor3D;\n          }\n\n          return {value: res, gradFunc: grad};\n        });\n    return customOp(x4D, $filter) as T;\n  } else {\n    const customOpWithBias = customGrad(\n        (x4D: Tensor4D, filter: Tensor4D, bias: Tensor, save: GradSaveFunc) => {\n          // tslint:disable-next-line: no-unnecessary-type-assertion\n          let res: Tensor4D|Tensor3D = ENGINE.runKernel(\n              FusedDepthwiseConv2D, inputs as {} as NamedTensorMap,\n              attrs as {} as NamedAttrMap);\n\n          save([filter, x4D, res, bias]);\n\n          if (reshapedTo4D) {\n            // tslint:disable-next-line: no-unnecessary-type-assertion\n            res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as\n                Tensor3D;\n          }\n\n          return {value: res, gradFunc: grad};\n        });\n\n    return customOpWithBias(x4D, $filter, $bias) as T;\n  }\n}\nexport const depthwiseConv2d = op({fusedDepthwiseConv2d_});\n"]}","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { customGrad } from '../../gradients';\nimport { _FusedMatMul } from '../../kernel_names';\nimport { makeTypesMatch } from '../../tensor_util';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { add } from '../add';\nimport * as broadcast_util from '../broadcast_util';\nimport { applyActivation, getFusedBiasGradient, getFusedDyActivation, shouldFuse } from '../fused_util';\nimport { matMul as unfusedMatMul } from '../mat_mul';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * Computes the dot product of two matrices with optional activation and bias.\n *\n * ```js\n * const a = tf.tensor2d([-1, -2], [1, 2]);\n * const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const bias = tf.tensor2d([1, 2], [1, 2]);\n *\n * tf.fused.matMul({a, b, bias, activation: 'relu'}).print();\n * ```\n *\n * @param obj An object with the following properties:\n * - `a` First matrix in dot product operation.\n * - `b` Second matrix in dot product operation.\n * - `transposeA` If true, `a` is transposed before multiplication.\n * - `transposeB` If true, `b` is transposed before multiplication.\n * - `bias` Matrix to be added to the result.\n * - `activation` Name of activation kernel (defaults to `linear`).\n * - `preluActivationWeights` Tensor of prelu weights.\n * - `leakyreluAlpha` Alpha of leakyrelu.\n */\nfunction fusedMatMul_({ a, b, transposeA = false, transposeB = false, bias, activation = 'linear', preluActivationWeights, leakyreluAlpha, }) {\n if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {\n let result = unfusedMatMul(a, b, transposeA, transposeB);\n if (bias != null) {\n result = add(result, bias);\n }\n return applyActivation(result, activation, preluActivationWeights, leakyreluAlpha);\n }\n let $a = convertToTensor(a, 'a', 'fused matMul');\n let $b = convertToTensor(b, 'b', 'fused matMul');\n [$a, $b] = makeTypesMatch($a, $b);\n const innerShapeA = transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];\n const innerShapeB = transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];\n const outerShapeA = transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];\n const outerShapeB = transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];\n const outerDimsA = $a.shape.slice(0, -2);\n const outerDimsB = $b.shape.slice(0, -2);\n const batchDimA = util.sizeFromShape(outerDimsA);\n const batchDimB = util.sizeFromShape(outerDimsB);\n util.assert(innerShapeA === innerShapeB, () => `Error in fused matMul: inner shapes (${innerShapeA}) and (` +\n `${innerShapeB}) of Tensors with shapes ${$a.shape} and ` +\n `${$b.shape} and transposeA=${transposeA}` +\n ` and transposeB=${transposeB} must match.`);\n const outShapeOuterDims = broadcast_util.assertAndGetBroadcastShape($a.shape.slice(0, -2), $b.shape.slice(0, -2));\n const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);\n const a3D = transposeA ?\n reshape($a, [batchDimA, innerShapeA, outerShapeA]) :\n reshape($a, [batchDimA, outerShapeA, innerShapeA]);\n const b3D = transposeB ?\n reshape($b, [batchDimB, outerShapeB, innerShapeB]) :\n reshape($b, [batchDimB, innerShapeB, outerShapeB]);\n let $bias;\n if (bias != null) {\n $bias = convertToTensor(bias, 'bias', 'fused matMul');\n [$bias] = makeTypesMatch($bias, $a);\n broadcast_util.assertAndGetBroadcastShape(outShape, $bias.shape);\n }\n let $preluActivationWeights;\n if (preluActivationWeights != null) {\n $preluActivationWeights = convertToTensor(preluActivationWeights, 'prelu weights', 'fused matMul');\n }\n const grad = (dy, saved) => {\n const [a3D, b3D, y, $bias] = saved;\n // we reshape dy because the result of the forward is not\n // necessarily going to be a 3d tensor due to a reshape done at the end of\n // the customOp.\n const dyActivation = getFusedDyActivation(reshape(dy, y.shape), y, activation);\n let aDer;\n let bDer;\n if (!transposeA && !transposeB) {\n aDer = unfusedMatMul(dyActivation, b3D, false, true);\n bDer = unfusedMatMul(a3D, dyActivation, true, false);\n }\n else if (!transposeA && transposeB) {\n aDer = unfusedMatMul(dyActivation, b3D, false, false);\n bDer = unfusedMatMul(dyActivation, a3D, true, false);\n }\n else if (transposeA && !transposeB) {\n aDer = unfusedMatMul(b3D, dyActivation, false, true);\n bDer = unfusedMatMul(a3D, dyActivation, false, false);\n }\n else {\n aDer = unfusedMatMul(b3D, dyActivation, true, true);\n bDer = unfusedMatMul(dyActivation, a3D, true, true);\n }\n if (bias != null) {\n const biasDer = getFusedBiasGradient($bias, dyActivation);\n return [aDer, bDer, biasDer];\n }\n else {\n return [aDer, bDer];\n }\n };\n const inputs = {\n a: a3D,\n b: b3D,\n bias: $bias,\n preluActivationWeights: $preluActivationWeights\n };\n const attrs = { transposeA, transposeB, activation, leakyreluAlpha };\n // Depending on the the params passed in we will have different number of\n // inputs and thus a a different number of elements in the gradient.\n if (bias == null) {\n const customOp = customGrad((a3D, b3D, save) => {\n const res = \n // tslint:disable-next-line: no-unnecessary-type-assertion\n ENGINE.runKernel(_FusedMatMul, inputs, attrs);\n save([a3D, b3D, res]);\n return { value: reshape(res, outShape), gradFunc: grad };\n });\n return customOp(a3D, b3D);\n }\n else {\n const customOpWithBias = customGrad((a3D, b3D, $bias, save) => {\n const res = \n // tslint:disable-next-line: no-unnecessary-type-assertion\n ENGINE.runKernel(_FusedMatMul, inputs, attrs);\n save([a3D, b3D, res, $bias]);\n return { value: reshape(res, outShape), gradFunc: grad };\n });\n return customOpWithBias(a3D, b3D, $bias);\n }\n}\nexport const matMul = op({ fusedMatMul_ });\n//# 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* @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../../engine';\nimport {customGrad} from '../../gradients';\nimport {_FusedMatMul, _FusedMatMulAttrs, _FusedMatMulInputs} from '../../kernel_names';\nimport {NamedAttrMap} from '../../kernel_registry';\nimport {Tensor, Tensor3D} from '../../tensor';\nimport {GradSaveFunc, NamedTensorMap} from '../../tensor_types';\nimport {makeTypesMatch} from '../../tensor_util';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport * as util from '../../util';\n\nimport {add} from '../add';\nimport * as broadcast_util from '../broadcast_util';\nimport {Activation} from '../fused_types';\nimport {applyActivation, getFusedBiasGradient, getFusedDyActivation, shouldFuse} from '../fused_util';\nimport {matMul as unfusedMatMul} from '../mat_mul';\nimport {op} from '../operation';\nimport {reshape} from '../reshape';\n\n/**\n * Computes the dot product of two matrices with optional activation and bias.\n *\n * ```js\n * const a = tf.tensor2d([-1, -2], [1, 2]);\n * const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const bias = tf.tensor2d([1, 2], [1, 2]);\n *\n * tf.fused.matMul({a, b, bias, activation: 'relu'}).print();\n * ```\n *\n * @param obj An object with the following properties:\n * - `a` First matrix in dot product operation.\n * - `b` Second matrix in dot product operation.\n * - `transposeA` If true, `a` is transposed before multiplication.\n * - `transposeB` If true, `b` is transposed before multiplication.\n * - `bias` Matrix to be added to the result.\n * - `activation` Name of activation kernel (defaults to `linear`).\n * - `preluActivationWeights` Tensor of prelu weights.\n * - `leakyreluAlpha` Alpha of leakyrelu.\n */\nfunction fusedMatMul_({\n  a,\n  b,\n  transposeA = false,\n  transposeB = false,\n  bias,\n  activation = 'linear',\n  preluActivationWeights,\n  leakyreluAlpha,\n}: {\n  a: Tensor|TensorLike,\n  b: Tensor|TensorLike,\n  transposeA?: boolean,\n  transposeB?: boolean,\n  bias?: Tensor|TensorLike,\n  activation?: Activation,\n  preluActivationWeights?: Tensor\n  leakyreluAlpha?: number\n}): Tensor {\n    if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {\n      let result = unfusedMatMul(a, b, transposeA, transposeB);\n      if (bias != null) {\n        result = add(result, bias);\n      }\n\n      return applyActivation(\n                 result, activation, preluActivationWeights, leakyreluAlpha);\n    }\n\n    let $a = convertToTensor(a, 'a', 'fused matMul');\n    let $b = convertToTensor(b, 'b', 'fused matMul');\n    [$a, $b] = makeTypesMatch($a, $b);\n\n    const innerShapeA =\n        transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];\n    const innerShapeB =\n        transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];\n\n    const outerShapeA =\n        transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];\n    const outerShapeB =\n        transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];\n\n    const outerDimsA = $a.shape.slice(0, -2);\n    const outerDimsB = $b.shape.slice(0, -2);\n    const batchDimA = util.sizeFromShape(outerDimsA);\n    const batchDimB = util.sizeFromShape(outerDimsB);\n\n    util.assert(\n        innerShapeA === innerShapeB,\n        () => `Error in fused matMul: inner shapes (${innerShapeA}) and (` +\n            `${innerShapeB}) of Tensors with shapes ${$a.shape} and ` +\n            `${$b.shape} and transposeA=${transposeA}` +\n            ` and transposeB=${transposeB} must match.`);\n\n    const outShapeOuterDims = broadcast_util.assertAndGetBroadcastShape(\n        $a.shape.slice(0, -2), $b.shape.slice(0, -2));\n    const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);\n\n    const a3D: Tensor3D = transposeA ?\n        reshape($a, [batchDimA, innerShapeA, outerShapeA]) :\n        reshape($a, [batchDimA, outerShapeA, innerShapeA]);\n    const b3D: Tensor3D = transposeB ?\n        reshape($b, [batchDimB, outerShapeB, innerShapeB]) :\n        reshape($b, [batchDimB, innerShapeB, outerShapeB]);\n\n    let $bias: Tensor;\n    if (bias != null) {\n      $bias = convertToTensor(bias, 'bias', 'fused matMul');\n      [$bias] = makeTypesMatch($bias, $a);\n\n      broadcast_util.assertAndGetBroadcastShape(outShape, $bias.shape);\n    }\n\n    let $preluActivationWeights: Tensor;\n    if (preluActivationWeights != null) {\n      $preluActivationWeights = convertToTensor(\n          preluActivationWeights, 'prelu weights', 'fused matMul');\n    }\n\n    const grad = (dy: Tensor3D, saved: Tensor[]) => {\n      const [a3D, b3D, y, $bias] = saved;\n      // we reshape dy because the result of the forward is not\n      // necessarily going to be a 3d tensor due to a reshape done at the end of\n      // the customOp.\n      const dyActivation =\n          getFusedDyActivation(reshape(dy, y.shape), y, activation);\n      let aDer: Tensor;\n      let bDer: Tensor;\n\n      if (!transposeA && !transposeB) {\n        aDer = unfusedMatMul(dyActivation, b3D, false, true);\n        bDer = unfusedMatMul(a3D, dyActivation, true, false);\n      } else if (!transposeA && transposeB) {\n        aDer = unfusedMatMul(dyActivation, b3D, false, false);\n        bDer = unfusedMatMul(dyActivation, a3D, true, false);\n      } else if (transposeA && !transposeB) {\n        aDer = unfusedMatMul(b3D, dyActivation, false, true);\n        bDer = unfusedMatMul(a3D, dyActivation, false, false);\n      } else {\n        aDer = unfusedMatMul(b3D, dyActivation, true, true);\n        bDer = unfusedMatMul(dyActivation, a3D, true, true);\n      }\n\n      if (bias != null) {\n        const biasDer = getFusedBiasGradient($bias, dyActivation);\n        return [aDer, bDer, biasDer];\n      } else {\n        return [aDer, bDer];\n      }\n    };\n\n    const inputs: _FusedMatMulInputs = {\n      a: a3D,\n      b: b3D,\n      bias: $bias,\n      preluActivationWeights: $preluActivationWeights\n    };\n    const attrs: _FusedMatMulAttrs =\n        {transposeA, transposeB, activation, leakyreluAlpha};\n\n    // Depending on the the params passed in we will have different number of\n    // inputs and thus a a different number of elements in the gradient.\n    if (bias == null) {\n      const customOp =\n          customGrad((a3D: Tensor3D, b3D: Tensor3D, save: GradSaveFunc) => {\n            const res =\n                // tslint:disable-next-line: no-unnecessary-type-assertion\n                ENGINE.runKernel(\n                    _FusedMatMul, inputs as {} as NamedTensorMap,\n                    attrs as {} as NamedAttrMap) as Tensor;\n\n            save([a3D, b3D, res]);\n\n            return {value: reshape(res, outShape), gradFunc: grad};\n          });\n      return customOp(a3D, b3D);\n    } else {\n      const customOpWithBias = customGrad(\n          (a3D: Tensor3D, b3D: Tensor3D, $bias: Tensor, save: GradSaveFunc) => {\n            const res =\n                // tslint:disable-next-line: no-unnecessary-type-assertion\n                ENGINE.runKernel(\n                    _FusedMatMul, inputs as {} as NamedTensorMap,\n                    attrs as {} as NamedAttrMap) as Tensor;\n\n            save([a3D, b3D, res, $bias]);\n\n            return {value: reshape(res, outShape), gradFunc: grad};\n          });\n\n      return customOpWithBias(a3D, b3D, $bias);\n    }\n  }\n\n  export const matMul = op({fusedMatMul_});\n"]}","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { conv2d } from './fused/conv2d';\nimport { depthwiseConv2d } from './fused/depthwise_conv2d';\nimport { matMul } from './fused/mat_mul';\nexport { conv2d, depthwiseConv2d, matMul };\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { op } from '../operation';\nimport { cosineWindow } from '../signal_ops_util';\n/**\n * Generate a hamming window.\n *\n * See: https://en.wikipedia.org/wiki/Window_function#Hann_and_Hamming_windows\n *\n * ```js\n * tf.signal.hammingWindow(10).print();\n * ```\n * @param The length of window\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction hammingWindow_(windowLength) {\n return cosineWindow(windowLength, 0.54, 0.46);\n}\nexport const hammingWindow = op({ hammingWindow_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { op } from '../operation';\nimport { cosineWindow } from '../signal_ops_util';\n/**\n * Generate a Hann window.\n *\n * See: https://en.wikipedia.org/wiki/Window_function#Hann_and_Hamming_windows\n *\n * ```js\n * tf.signal.hannWindow(10).print();\n * ```\n * @param The length of window\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction hannWindow_(windowLength) {\n return cosineWindow(windowLength, 0.5, 0.5);\n}\nexport const hannWindow = op({ hannWindow_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { concat } from '../concat';\nimport { fill } from '../fill';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\nimport { slice } from '../slice';\nimport { tensor2d } from '../tensor2d';\n/**\n * Expands input into frames of frameLength.\n * Slides a window size with frameStep.\n *\n * ```js\n * tf.signal.frame([1, 2, 3], 2, 1).print();\n * ```\n * @param signal The input tensor to be expanded\n * @param frameLength Length of each frame\n * @param frameStep The frame hop size in samples.\n * @param padEnd Whether to pad the end of signal with padValue.\n * @param padValue An number to use where the input signal does\n * not exist when padEnd is True.\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction frame_(signal, frameLength, frameStep, padEnd = false, padValue = 0) {\n let start = 0;\n const output = [];\n while (start + frameLength <= signal.size) {\n output.push(slice(signal, start, frameLength));\n start += frameStep;\n }\n if (padEnd) {\n while (start < signal.size) {\n const padLen = (start + frameLength) - signal.size;\n const pad = concat([\n slice(signal, start, frameLength - padLen), fill([padLen], padValue)\n ]);\n output.push(pad);\n start += frameStep;\n }\n }\n if (output.length === 0) {\n return tensor2d([], [0, frameLength]);\n }\n return reshape(concat(output), [output.length, frameLength]);\n}\nexport const frame = op({ frame_ });\n//# sourceMappingURL=data:application/json;base64,eyJ2ZXJzaW9uIjozLCJmaWxlIjoiZnJhbWUuanMiLCJzb3VyY2VSb290IjoiIiwic291cmNlcyI6WyIuLi8uLi8uLi8uLi8uLi8uLi8uLi90ZmpzLWNvcmUvc3JjL29wcy9zaWduYWwvZnJhbWUudHMiXSwibmFtZXMiOltdLCJtYXBwaW5ncyI6IkFBQUE7Ozs7Ozs7Ozs7Ozs7OztHQWVHO0FBR0gsT0FBTyxFQUFDLE1BQU0sRUFBQyxNQUFNLFdBQVcsQ0FBQztBQUNqQyxPQUFPLEVBQUMsSUFBSSxFQUFDLE1BQU0sU0FBUyxDQUFDO0FBQzdCLE9BQU8sRUFBQyxFQUFFLEVBQUMsTUFBTSxjQUFjLENBQUM7QUFDaEMsT0FBTyxFQUFDLE9BQU8sRUFBQyxNQUFNLFlBQVksQ0FBQztBQUNuQyxPQUFPLEVBQUMsS0FBSyxFQUFDLE1BQU0sVUFBVSxDQUFDO0FBQy9CLE9BQU8sRUFBQyxRQUFRLEVBQUMsTUFBTSxhQUFhLENBQUM7QUFFckM7Ozs7Ozs7Ozs7Ozs7OztHQWVHO0FBQ0gsU0FBUyxNQUFNLENBQ1gsTUFBZ0IsRUFBRSxXQUFtQixFQUFFLFNBQWlCLEVBQUUsTUFBTSxHQUFHLEtBQUssRUFDeEUsUUFBUSxHQUFHLENBQUM7SUFDZCxJQUFJLEtBQUssR0FBRyxDQUFDLENBQUM7SUFDZCxNQUFNLE1BQU0sR0FBYSxFQUFFLENBQUM7SUFDNUIsT0FBTyxLQUFLLEdBQUcsV0FBVyxJQUFJLE1BQU0sQ0FBQyxJQUFJLEVBQUU7UUFDekMsTUFBTSxDQUFDLElBQUksQ0FBQyxLQUFLLENBQUMsTUFBTSxFQUFFLEtBQUssRUFBRSxXQUFXLENBQUMsQ0FBQyxDQUFDO1FBQy9DLEtBQUssSUFBSSxTQUFTLENBQUM7S0FDcEI7SUFFRCxJQUFJLE1BQU0sRUFBRTtRQUNWLE9BQU8sS0FBSyxHQUFHLE1BQU0sQ0FBQyxJQUFJLEVBQUU7WUFDMUIsTUFBTSxNQUFNLEdBQUcsQ0FBQyxLQUFLLEdBQUcsV0FBVyxDQUFDLEdBQUcsTUFBTSxDQUFDLElBQUksQ0FBQztZQUNuRCxNQUFNLEdBQUcsR0FBRyxNQUFNLENBQUM7Z0JBQ2pCLEtBQUssQ0FBQyxNQUFNLEVBQUUsS0FBSyxFQUFFLFdBQVcsR0FBRyxNQUFNLENBQUMsRUFBRSxJQUFJLENBQUMsQ0FBQyxNQUFNLENBQUMsRUFBRSxRQUFRLENBQUM7YUFDckUsQ0FBQyxDQUFDO1lBQ0gsTUFBTSxDQUFDLElBQUksQ0FBQyxHQUFHLENBQUMsQ0FBQztZQUNqQixLQUFLLElBQUksU0FBUyxDQUFDO1NBQ3BCO0tBQ0Y7SUFFRCxJQUFJLE1BQU0sQ0FBQyxNQUFNLEtBQUssQ0FBQyxFQUFFO1FBQ3ZCLE9BQU8sUUFBUSxDQUFDLEVBQUUsRUFBRSxDQUFDLENBQUMsRUFBRSxXQUFXLENBQUMsQ0FBQyxDQUFDO0tBQ3ZDO0lBRUQsT0FBTyxPQUFPLENBQUMsTUFBTSxDQUFDLE1BQU0sQ0FBQyxFQUFFLENBQUMsTUFBTSxDQUFDLE1BQU0sRUFBRSxXQUFXLENBQUMsQ0FBQyxDQUFDO0FBQy9ELENBQUM7QUFDRCxNQUFNLENBQUMsTUFBTSxLQUFLLEdBQUcsRUFBRSxDQUFDLEVBQUMsTUFBTSxFQUFDLENBQUMsQ0FBQyIsInNvdXJjZXNDb250ZW50IjpbIi8qKlxuICogQGxpY2Vuc2VcbiAqIENvcHlyaWdodCAyMDE5IEdvb2dsZSBMTEMuIEFsbCBSaWdodHMgUmVzZXJ2ZWQuXG4gKiBMaWNlbnNlZCB1bmRlciB0aGUgQXBhY2hlIExpY2Vuc2UsIFZlcnNpb24gMi4wICh0aGUgXCJMaWNlbnNlXCIpO1xuICogeW91IG1heSBub3QgdXNlIHRoaXMgZmlsZSBleGNlcHQgaW4gY29tcGxpYW5jZSB3aXRoIHRoZSBMaWNlbnNlLlxuICogWW91IG1heSBvYnRhaW4gYSBjb3B5IG9mIHRoZSBMaWNlbnNlIGF0XG4gKlxuICogaHR0cDovL3d3dy5hcGFjaGUub3JnL2xpY2Vuc2VzL0xJQ0VOU0UtMi4wXG4gKlxuICogVW5sZXNzIHJlcXVpcmVkIGJ5IGFwcGxpY2FibGUgbGF3IG9yIGFncmVlZCB0byBpbiB3cml0aW5nLCBzb2Z0d2FyZVxuICogZGlzdHJpYnV0ZWQgdW5kZXIgdGhlIExpY2Vuc2UgaXMgZGlzdHJpYnV0ZWQgb24gYW4gXCJBUyBJU1wiIEJBU0lTLFxuICogV0lUSE9VVCBXQVJSQU5USUVTIE9SIENPTkRJVElPTlMgT0YgQU5ZIEtJTkQsIGVpdGhlciBleHByZXNzIG9yIGltcGxpZWQuXG4gKiBTZWUgdGhlIExpY2Vuc2UgZm9yIHRoZSBzcGVjaWZpYyBsYW5ndWFnZSBnb3Zlcm5pbmcgcGVybWlzc2lvbnMgYW5kXG4gKiBsaW1pdGF0aW9ucyB1bmRlciB0aGUgTGljZW5zZS5cbiAqID09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09XG4gKi9cblxuaW1wb3J0IHtUZW5zb3IsIFRlbnNvcjFEfSBmcm9tICcuLi8uLi90ZW5zb3InO1xuaW1wb3J0IHtjb25jYXR9IGZyb20gJy4uL2NvbmNhdCc7XG5pbXBvcnQge2ZpbGx9IGZyb20gJy4uL2ZpbGwnO1xuaW1wb3J0IHtvcH0gZnJvbSAnLi4vb3BlcmF0aW9uJztcbmltcG9ydCB7cmVzaGFwZX0gZnJvbSAnLi4vcmVzaGFwZSc7XG5pbXBvcnQge3NsaWNlfSBmcm9tICcuLi9zbGljZSc7XG5pbXBvcnQge3RlbnNvcjJkfSBmcm9tICcuLi90ZW5zb3IyZCc7XG5cbi8qKlxuICogRXhwYW5kcyBpbnB1dCBpbnRvIGZyYW1lcyBvZiBmcmFtZUxlbmd0aC5cbiAqIFNsaWRlcyBhIHdpbmRvdyBzaXplIHdpdGggZnJhbWVTdGVwLlxuICpcbiAqIGBgYGpzXG4gKiB0Zi5zaWduYWwuZnJhbWUoWzEsIDIsIDNdLCAyLCAxKS5wcmludCgpO1xuICogYGBgXG4gKiBAcGFyYW0gc2lnbmFsIFRoZSBpbnB1dCB0ZW5zb3IgdG8gYmUgZXhwYW5kZWRcbiAqIEBwYXJhbSBmcmFtZUxlbmd0aCBMZW5ndGggb2YgZWFjaCBmcmFtZVxuICogQHBhcmFtIGZyYW1lU3RlcCBUaGUgZnJhbWUgaG9wIHNpemUgaW4gc2FtcGxlcy5cbiAqIEBwYXJhbSBwYWRFbmQgV2hldGhlciB0byBwYWQgdGhlIGVuZCBvZiBzaWduYWwgd2l0aCBwYWRWYWx1ZS5cbiAqIEBwYXJhbSBwYWRWYWx1ZSBBbiBudW1iZXIgdG8gdXNlIHdoZXJlIHRoZSBpbnB1dCBzaWduYWwgZG9lc1xuICogICAgIG5vdCBleGlzdCB3aGVuIHBhZEVuZCBpcyBUcnVlLlxuICpcbiAqIEBkb2Mge2hlYWRpbmc6ICdPcGVyYXRpb25zJywgc3ViaGVhZGluZzogJ1NpZ25hbCcsIG5hbWVzcGFjZTogJ3NpZ25hbCd9XG4gKi9cbmZ1bmN0aW9uIGZyYW1lXyhcbiAgICBzaWduYWw6IFRlbnNvcjFELCBmcmFtZUxlbmd0aDogbnVtYmVyLCBmcmFtZVN0ZXA6IG51bWJlciwgcGFkRW5kID0gZmFsc2UsXG4gICAgcGFkVmFsdWUgPSAwKTogVGVuc29yIHtcbiAgbGV0IHN0YXJ0ID0gMDtcbiAgY29uc3Qgb3V0cHV0OiBUZW5zb3JbXSA9IFtdO1xuICB3aGlsZSAoc3RhcnQgKyBmcmFtZUxlbmd0aCA8PSBzaWduYWwuc2l6ZSkge1xuICAgIG91dHB1dC5wdXNoKHNsaWNlKHNpZ25hbCwgc3RhcnQsIGZyYW1lTGVuZ3RoKSk7XG4gICAgc3RhcnQgKz0gZnJhbWVTdGVwO1xuICB9XG5cbiAgaWYgKHBhZEVuZCkge1xuICAgIHdoaWxlIChzdGFydCA8IHNpZ25hbC5zaXplKSB7XG4gICAgICBjb25zdCBwYWRMZW4gPSAoc3RhcnQgKyBmcmFtZUxlbmd0aCkgLSBzaWduYWwuc2l6ZTtcbiAgICAgIGNvbnN0IHBhZCA9IGNvbmNhdChbXG4gICAgICAgIHNsaWNlKHNpZ25hbCwgc3RhcnQsIGZyYW1lTGVuZ3RoIC0gcGFkTGVuKSwgZmlsbChbcGFkTGVuXSwgcGFkVmFsdWUpXG4gICAgICBdKTtcbiAgICAgIG91dHB1dC5wdXNoKHBhZCk7XG4gICAgICBzdGFydCArPSBmcmFtZVN0ZXA7XG4gICAgfVxuICB9XG5cbiAgaWYgKG91dHB1dC5sZW5ndGggPT09IDApIHtcbiAgICByZXR1cm4gdGVuc29yMmQoW10sIFswLCBmcmFtZUxlbmd0aF0pO1xuICB9XG5cbiAgcmV0dXJuIHJlc2hhcGUoY29uY2F0KG91dHB1dCksIFtvdXRwdXQubGVuZ3RoLCBmcmFtZUxlbmd0aF0pO1xufVxuZXhwb3J0IGNvbnN0IGZyYW1lID0gb3Aoe2ZyYW1lX30pO1xuIl19","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { mul } from '../mul';\nimport { op } from '../operation';\nimport { enclosingPowerOfTwo } from '../signal_ops_util';\nimport { rfft } from '../spectral/rfft';\nimport { frame } from './frame';\nimport { hannWindow } from './hann_window';\n/**\n * Computes the Short-time Fourier Transform of signals\n * See: https://en.wikipedia.org/wiki/Short-time_Fourier_transform\n *\n * ```js\n * const input = tf.tensor1d([1, 1, 1, 1, 1])\n * tf.signal.stft(input, 3, 1).print();\n * ```\n * @param signal 1-dimensional real value tensor.\n * @param frameLength The window length of samples.\n * @param frameStep The number of samples to step.\n * @param fftLength The size of the FFT to apply.\n * @param windowFn A callable that takes a window length and returns 1-d tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction stft_(signal, frameLength, frameStep, fftLength, windowFn = hannWindow) {\n if (fftLength == null) {\n fftLength = enclosingPowerOfTwo(frameLength);\n }\n const framedSignal = frame(signal, frameLength, frameStep);\n const windowedSignal = mul(framedSignal, windowFn(frameLength));\n return rfft(windowedSignal, fftLength);\n}\nexport const stft = op({ stft_ });\n//# 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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { CropAndResize } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\n/**\n * Extracts crops from the input image tensor and resizes them using bilinear\n * sampling or nearest neighbor sampling (possibly with aspect ratio change)\n * to a common output size specified by cropSize.\n *\n * @param image 4d tensor of shape `[batch,imageHeight,imageWidth, depth]`,\n * where imageHeight and imageWidth must be positive, specifying the\n * batch of images from which to take crops\n * @param boxes 2d float32 tensor of shape `[numBoxes, 4]`. Each entry is\n * `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the normalized\n * coordinates of the box in the boxInd[i]'th image in the batch\n * @param boxInd 1d int32 tensor of shape `[numBoxes]` with values in range\n * `[0, batch)` that specifies the image that the `i`-th box refers to.\n * @param cropSize 1d int32 tensor of 2 elements `[cropHeigh, cropWidth]`\n * specifying the size to which all crops are resized to.\n * @param method Optional string from `'bilinear' | 'nearest'`,\n * defaults to bilinear, which specifies the sampling method for resizing\n * @param extrapolationValue A threshold for deciding when to remove boxes based\n * on score. Defaults to 0.\n * @return A 4D tensor of the shape `[numBoxes,cropHeight,cropWidth,depth]`\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction cropAndResize_(image, boxes, boxInd, cropSize, method = 'bilinear', extrapolationValue = 0) {\n const $image = convertToTensor(image, 'image', 'cropAndResize');\n const $boxes = convertToTensor(boxes, 'boxes', 'cropAndResize', 'float32');\n const $boxInd = convertToTensor(boxInd, 'boxInd', 'cropAndResize', 'int32');\n const numBoxes = $boxes.shape[0];\n util.assert($image.rank === 4, () => 'Error in cropAndResize: image must be rank 4,' +\n `but got rank ${$image.rank}.`);\n util.assert($boxes.rank === 2 && $boxes.shape[1] === 4, () => `Error in cropAndResize: boxes must be have size [${numBoxes},4] ` +\n `but had shape ${$boxes.shape}.`);\n util.assert($boxInd.rank === 1 && $boxInd.shape[0] === numBoxes, () => `Error in cropAndResize: boxInd must be have size [${numBoxes}] ` +\n `but had shape ${$boxes.shape}.`);\n util.assert(cropSize.length === 2, () => `Error in cropAndResize: cropSize must be of length 2, but got ` +\n `length ${cropSize.length}.`);\n util.assert(cropSize[0] >= 1 && cropSize[1] >= 1, () => `cropSize must be atleast [1,1], but was ${cropSize}`);\n util.assert(method === 'bilinear' || method === 'nearest', () => `method must be bilinear or nearest, but was ${method}`);\n const inputs = { image: $image, boxes: $boxes, boxInd: $boxInd };\n const attrs = { method, extrapolationValue, cropSize };\n const res = ENGINE.runKernel(CropAndResize, inputs, attrs);\n return res;\n}\nexport const cropAndResize = op({ cropAndResize_ });\n//# sourceMappingURL=data:application/json;base64,{"version":3,"file":"crop_and_resize.js","sourceRoot":"","sources":["../../../../../../../tfjs-core/src/ops/image/crop_and_resize.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAEH,OAAO,EAAC,MAAM,EAAC,MAAM,cAAc,CAAC;AACpC,OAAO,EAAC,aAAa,EAA0C,MAAM,oBAAoB,CAAC;AAI1F,OAAO,EAAC,eAAe,EAAC,MAAM,uBAAuB,CAAC;AAEtD,OAAO,KAAK,IAAI,MAAM,YAAY,CAAC;AAEnC,OAAO,EAAC,EAAE,EAAC,MAAM,cAAc,CAAC;AAEhC;;;;;;;;;;;;;;;;;;;;;;GAsBG;AACH,SAAS,cAAc,CACnB,KAA0B,EAC1B,KAA0B,EAC1B,MAA2B,EAC3B,QAA0B,EAC1B,SAA+B,UAAU,EACzC,kBAAkB,GAAG,CAAC;IAExB,MAAM,MAAM,GAAG,eAAe,CAAC,KAAK,EAAE,OAAO,EAAE,eAAe,CAAC,CAAC;IAChE,MAAM,MAAM,GAAG,eAAe,CAAC,KAAK,EAAE,OAAO,EAAE,eAAe,EAAE,SAAS,CAAC,CAAC;IAC3E,MAAM,OAAO,GAAG,eAAe,CAAC,MAAM,EAAE,QAAQ,EAAE,eAAe,EAAE,OAAO,CAAC,CAAC;IAE5E,MAAM,QAAQ,GAAG,MAAM,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC;IAEjC,IAAI,CAAC,MAAM,CACP,MAAM,CAAC,IAAI,KAAK,CAAC,EACjB,GAAG,EAAE,CAAC,+CAA+C;QACjD,gBAAgB,MAAM,CAAC,IAAI,GAAG,CAAC,CAAC;IACxC,IAAI,CAAC,MAAM,CACP,MAAM,CAAC,IAAI,KAAK,CAAC,IAAI,MAAM,CAAC,KAAK,CAAC,CAAC,CAAC,KAAK,CAAC,EAC1C,GAAG,EAAE,CAAC,oDAAoD,QAAQ,MAAM;QACpE,iBAAiB,MAAM,CAAC,KAAK,GAAG,CAAC,CAAC;IAC1C,IAAI,CAAC,MAAM,CACP,OAAO,CAAC,IAAI,KAAK,CAAC,IAAI,OAAO,CAAC,KAAK,CAAC,CAAC,CAAC,KAAK,QAAQ,EACnD,GAAG,EAAE,CAAC,qDAAqD,QAAQ,IAAI;QACnE,iBAAiB,MAAM,CAAC,KAAK,GAAG,CAAC,CAAC;IAC1C,IAAI,CAAC,MAAM,CACP,QAAQ,CAAC,MAAM,KAAK,CAAC,EACrB,GAAG,EAAE,CAAC,gEAAgE;QAClE,UAAU,QAAQ,CAAC,MAAM,GAAG,CAAC,CAAC;IACtC,IAAI,CAAC,MAAM,CACP,QAAQ,CAAC,CAAC,CAAC,IAAI,CAAC,IAAI,QAAQ,CAAC,CAAC,CAAC,IAAI,CAAC,EACpC,GAAG,EAAE,CAAC,2CAA2C,QAAQ,EAAE,CAAC,CAAC;IACjE,IAAI,CAAC,MAAM,CACP,MAAM,KAAK,UAAU,IAAI,MAAM,KAAK,SAAS,EAC7C,GAAG,EAAE,CAAC,+CAA+C,MAAM,EAAE,CAAC,CAAC;IAEnE,MAAM,MAAM,GACc,EAAC,KAAK,EAAE,MAAM,EAAE,KAAK,EAAE,MAAM,EAAE,MAAM,EAAE,OAAO,EAAC,CAAC;IAC1E,MAAM,KAAK,GAAuB,EAAC,MAAM,EAAE,kBAAkB,EAAE,QAAQ,EAAC,CAAC;IACzE,MAAM,GAAG,GAAG,MAAM,CAAC,SAAS,CACxB,aAAa,EAAE,MAA8B,EAC7C,KAA2B,CAAC,CAAC;IACjC,OAAO,GAAe,CAAC;AACzB,CAAC;AAED,MAAM,CAAC,MAAM,aAAa,GAAG,EAAE,CAAC,EAAC,cAAc,EAAC,CAAC,CAAC","sourcesContent":["/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../../engine';\nimport {CropAndResize, CropAndResizeAttrs, CropAndResizeInputs} from '../../kernel_names';\nimport {NamedAttrMap} from '../../kernel_registry';\nimport {Tensor1D, Tensor2D, Tensor4D} from '../../tensor';\nimport {NamedTensorMap} from '../../tensor_types';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport * as util from '../../util';\n\nimport {op} from '../operation';\n\n/**\n * Extracts crops from the input image tensor and resizes them using bilinear\n * sampling or nearest neighbor sampling (possibly with aspect ratio change)\n * to a common output size specified by cropSize.\n *\n * @param image 4d tensor of shape `[batch,imageHeight,imageWidth, depth]`,\n *     where imageHeight and imageWidth must be positive, specifying the\n *     batch of images from which to take crops\n * @param boxes 2d float32 tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the normalized\n *     coordinates of the box in the boxInd[i]'th image in the batch\n * @param boxInd 1d int32 tensor of shape `[numBoxes]` with values in range\n *     `[0, batch)` that specifies the image that the `i`-th box refers to.\n * @param cropSize 1d int32 tensor of 2 elements `[cropHeigh, cropWidth]`\n *     specifying the size to which all crops are resized to.\n * @param method Optional string from `'bilinear' | 'nearest'`,\n *     defaults to bilinear, which specifies the sampling method for resizing\n * @param extrapolationValue A threshold for deciding when to remove boxes based\n *     on score. Defaults to 0.\n * @return A 4D tensor of the shape `[numBoxes,cropHeight,cropWidth,depth]`\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction cropAndResize_(\n    image: Tensor4D|TensorLike,\n    boxes: Tensor2D|TensorLike,\n    boxInd: Tensor1D|TensorLike,\n    cropSize: [number, number],\n    method: 'bilinear'|'nearest' = 'bilinear',\n    extrapolationValue = 0,\n    ): Tensor4D {\n  const $image = convertToTensor(image, 'image', 'cropAndResize');\n  const $boxes = convertToTensor(boxes, 'boxes', 'cropAndResize', 'float32');\n  const $boxInd = convertToTensor(boxInd, 'boxInd', 'cropAndResize', 'int32');\n\n  const numBoxes = $boxes.shape[0];\n\n  util.assert(\n      $image.rank === 4,\n      () => 'Error in cropAndResize: image must be rank 4,' +\n          `but got rank ${$image.rank}.`);\n  util.assert(\n      $boxes.rank === 2 && $boxes.shape[1] === 4,\n      () => `Error in cropAndResize: boxes must be have size [${numBoxes},4] ` +\n          `but had shape ${$boxes.shape}.`);\n  util.assert(\n      $boxInd.rank === 1 && $boxInd.shape[0] === numBoxes,\n      () => `Error in cropAndResize: boxInd must be have size [${numBoxes}] ` +\n          `but had shape ${$boxes.shape}.`);\n  util.assert(\n      cropSize.length === 2,\n      () => `Error in cropAndResize: cropSize must be of length 2, but got ` +\n          `length ${cropSize.length}.`);\n  util.assert(\n      cropSize[0] >= 1 && cropSize[1] >= 1,\n      () => `cropSize must be atleast [1,1], but was ${cropSize}`);\n  util.assert(\n      method === 'bilinear' || method === 'nearest',\n      () => `method must be bilinear or nearest, but was ${method}`);\n\n  const inputs:\n      CropAndResizeInputs = {image: $image, boxes: $boxes, boxInd: $boxInd};\n  const attrs: CropAndResizeAttrs = {method, extrapolationValue, cropSize};\n  const res = ENGINE.runKernel(\n      CropAndResize, inputs as {} as NamedTensorMap,\n      attrs as {} as NamedAttrMap);\n  return res as Tensor4D;\n}\n\nexport const cropAndResize = op({cropAndResize_});\n"]}","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { FlipLeftRight } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\n/**\n * Flips the image left to right. Currently available in the CPU, WebGL, and\n * WASM backends.\n *\n * @param image 4d tensor of shape `[batch, imageHeight, imageWidth, depth]`.\n */\n/** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */\nfunction flipLeftRight_(image) {\n const $image = convertToTensor(image, 'image', 'flipLeftRight', 'float32');\n util.assert($image.rank === 4, () => 'Error in flipLeftRight: image must be rank 4,' +\n `but got rank ${$image.rank}.`);\n const inputs = { image: $image };\n const res = ENGINE.runKernel(FlipLeftRight, inputs, {});\n return res;\n}\nexport const flipLeftRight = op({ flipLeftRight_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\nimport { tile } from '../tile';\n/**\n * Converts images from grayscale to RGB format.\n *\n * @param image A grayscale tensor to convert. The `image`'s last dimension must\n * be size 1 with at least a two-dimensional shape.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction grayscaleToRGB_(image) {\n const $image = convertToTensor(image, 'image', 'grayscaleToRGB');\n const lastDimsIdx = $image.rank - 1;\n const lastDims = $image.shape[lastDimsIdx];\n util.assert($image.rank >= 2, () => 'Error in grayscaleToRGB: images must be at least rank 2, ' +\n `but got rank ${$image.rank}.`);\n util.assert(lastDims === 1, () => 'Error in grayscaleToRGB: last dimension of a grayscale image ' +\n `should be size 1, but got size ${lastDims}.`);\n const reps = new Array($image.rank);\n reps.fill(1, 0, lastDimsIdx);\n reps[lastDimsIdx] = 3;\n return tile($image, reps);\n}\nexport const grayscaleToRGB = op({ grayscaleToRGB_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { RotateWithOffset } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\n/**\n * Rotates the input image tensor counter-clockwise with an optional offset\n * center of rotation. Currently available in the CPU, WebGL, and WASM backends.\n *\n * @param image 4d tensor of shape `[batch, imageHeight, imageWidth, depth]`.\n * @param radians The amount of rotation.\n * @param fillValue The value to fill in the empty space leftover\n * after rotation. Can be either a single grayscale value (0-255), or an\n * array of three numbers `[red, green, blue]` specifying the red, green,\n * and blue channels. Defaults to `0` (black).\n * @param center The center of rotation. Can be either a single value (0-1), or\n * an array of two numbers `[centerX, centerY]`. Defaults to `0.5` (rotates\n * the image around its center).\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction rotateWithOffset_(image, radians, fillValue = 0, center = 0.5) {\n const $image = convertToTensor(image, 'image', 'rotateWithOffset', 'float32');\n util.assert($image.rank === 4, () => 'Error in rotateWithOffset: image must be rank 4,' +\n `but got rank ${$image.rank}.`);\n const inputs = { image: $image };\n const attrs = { radians, fillValue, center };\n const res = ENGINE.runKernel(RotateWithOffset, inputs, attrs);\n return res;\n}\nexport const rotateWithOffset = op({ rotateWithOffset_ });\n//# 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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\nfunction nonMaxSuppSanityCheck(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {\n if (iouThreshold == null) {\n iouThreshold = 0.5;\n }\n if (scoreThreshold == null) {\n scoreThreshold = Number.NEGATIVE_INFINITY;\n }\n if (softNmsSigma == null) {\n softNmsSigma = 0.0;\n }\n const numBoxes = boxes.shape[0];\n maxOutputSize = Math.min(maxOutputSize, numBoxes);\n util.assert(0 <= iouThreshold && iouThreshold <= 1, () => `iouThreshold must be in [0, 1], but was '${iouThreshold}'`);\n util.assert(boxes.rank === 2, () => `boxes must be a 2D tensor, but was of rank '${boxes.rank}'`);\n util.assert(boxes.shape[1] === 4, () => `boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`);\n util.assert(scores.rank === 1, () => 'scores must be a 1D tensor');\n util.assert(scores.shape[0] === numBoxes, () => `scores has incompatible shape with boxes. Expected ${numBoxes}, ` +\n `but was ${scores.shape[0]}`);\n util.assert(0 <= softNmsSigma && softNmsSigma <= 1, () => `softNmsSigma must be in [0, 1], but was '${softNmsSigma}'`);\n return { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma };\n}\nexport { nonMaxSuppSanityCheck };\n//# 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tcbiJdfQ==","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { NonMaxSuppressionV3 } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\nimport { op } from '../operation';\n/**\n * Performs non maximum suppression of bounding boxes based on\n * iou (intersection over union).\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n * `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n * the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n * boxes overlap too much with respect to IOU. Must be between [0, 1].\n * Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n * on score. Defaults to -inf, which means any score is accepted.\n * @return A 1D tensor with the selected box indices.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction nonMaxSuppression_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) {\n const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression', 'float32');\n const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression', 'float32');\n const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);\n maxOutputSize = inputs.maxOutputSize;\n iouThreshold = inputs.iouThreshold;\n scoreThreshold = inputs.scoreThreshold;\n const attrs = { maxOutputSize, iouThreshold, scoreThreshold };\n return ENGINE.runKernel(NonMaxSuppressionV3, { boxes: $boxes, scores: $scores }, attrs);\n}\nexport const nonMaxSuppression = op({ nonMaxSuppression_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { nonMaxSuppressionV3Impl } from '../../backends/non_max_suppression_impl';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\nimport { tensor1d } from '../tensor1d';\n/**\n * Performs non maximum suppression of bounding boxes based on\n * iou (intersection over union).\n *\n * This is the async version of `nonMaxSuppression`\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n * `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n * the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n * boxes overlap too much with respect to IOU. Must be between [0, 1].\n * Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n * on score. Defaults to -inf, which means any score is accepted.\n * @return A 1D tensor with the selected box indices.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nasync function nonMaxSuppressionAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) {\n const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');\n const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');\n const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);\n maxOutputSize = inputs.maxOutputSize;\n iouThreshold = inputs.iouThreshold;\n scoreThreshold = inputs.scoreThreshold;\n const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);\n const boxesVals = boxesAndScores[0];\n const scoresVals = boxesAndScores[1];\n // We call a cpu based impl directly with the typedarray data here rather\n // than a kernel because all kernels are synchronous (and thus cannot await\n // .data()).\n const { selectedIndices } = nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);\n if ($boxes !== boxes) {\n $boxes.dispose();\n }\n if ($scores !== scores) {\n $scores.dispose();\n }\n return tensor1d(selectedIndices, 'int32');\n}\nexport const nonMaxSuppressionAsync = nonMaxSuppressionAsync_;\n//# sourceMappingURL=data:application/json;base64,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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { NonMaxSuppressionV5 } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\nimport { op } from '../operation';\n/**\n * Performs non maximum suppression of bounding boxes based on\n * iou (intersection over union).\n *\n * This op also supports a Soft-NMS mode (c.f.\n * Bodla et al, https://arxiv.org/abs/1704.04503) where boxes reduce the score\n * of other overlapping boxes, therefore favoring different regions of the image\n * with high scores. To enable this Soft-NMS mode, set the `softNmsSigma`\n * parameter to be larger than 0.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n * `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n * the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n * boxes overlap too much with respect to IOU. Must be between [0, 1].\n * Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n * on score. Defaults to -inf, which means any score is accepted.\n * @param softNmsSigma A float representing the sigma parameter for Soft NMS.\n * When sigma is 0, it falls back to nonMaxSuppression.\n * @return A map with the following properties:\n * - selectedIndices: A 1D tensor with the selected box indices.\n * - selectedScores: A 1D tensor with the corresponding scores for each\n * selected box.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction nonMaxSuppressionWithScore_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0.0) {\n const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');\n const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');\n const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n maxOutputSize = params.maxOutputSize;\n iouThreshold = params.iouThreshold;\n scoreThreshold = params.scoreThreshold;\n softNmsSigma = params.softNmsSigma;\n const inputs = { boxes: $boxes, scores: $scores };\n const attrs = { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma };\n // tslint:disable-next-line: no-unnecessary-type-assertion\n const result = ENGINE.runKernel(NonMaxSuppressionV5, inputs, attrs);\n return { selectedIndices: result[0], selectedScores: result[1] };\n}\nexport const nonMaxSuppressionWithScore = op({ nonMaxSuppressionWithScore_ });\n//# sourceMappingURL=data:application/json;base64,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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { nonMaxSuppressionV5Impl } from '../../backends/non_max_suppression_impl';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\nimport { tensor1d } from '../tensor1d';\n/**\n * Asynchronously performs non maximum suppression of bounding boxes based on\n * iou (intersection over union).\n *\n * This op also supports a Soft-NMS mode (c.f.\n * Bodla et al, https://arxiv.org/abs/1704.04503) where boxes reduce the score\n * of other overlapping boxes, therefore favoring different regions of the image\n * with high scores. To enable this Soft-NMS mode, set the `softNmsSigma`\n * parameter to be larger than 0.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n * `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n * the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n * boxes overlap too much with respect to IOU. Must be between [0, 1].\n * Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n * on score. Defaults to -inf, which means any score is accepted.\n * @param softNmsSigma A float representing the sigma parameter for Soft NMS.\n * When sigma is 0, it falls back to nonMaxSuppression.\n * @return A map with the following properties:\n * - selectedIndices: A 1D tensor with the selected box indices.\n * - selectedScores: A 1D tensor with the corresponding scores for each\n * selected box.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nasync function nonMaxSuppressionWithScoreAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0.0) {\n const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');\n const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');\n const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n maxOutputSize = params.maxOutputSize;\n iouThreshold = params.iouThreshold;\n scoreThreshold = params.scoreThreshold;\n softNmsSigma = params.softNmsSigma;\n const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);\n const boxesVals = boxesAndScores[0];\n const scoresVals = boxesAndScores[1];\n // We call a cpu based impl directly with the typedarray data here rather\n // than a kernel because all kernels are synchronous (and thus cannot await\n // .data()).\n const { selectedIndices, selectedScores } = nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n if ($boxes !== boxes) {\n $boxes.dispose();\n }\n if ($scores !== scores) {\n $scores.dispose();\n }\n return {\n selectedIndices: tensor1d(selectedIndices, 'int32'),\n selectedScores: tensor1d(selectedScores)\n };\n}\nexport const nonMaxSuppressionWithScoreAsync = nonMaxSuppressionWithScoreAsync_;\n//# sourceMappingURL=data:application/json;base64,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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { NonMaxSuppressionV4 } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\nimport { op } from '../operation';\n/**\n * Asynchronously performs non maximum suppression of bounding boxes based on\n * iou (intersection over union), with an option to pad results.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n * `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n * the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n * boxes overlap too much with respect to IOU. Must be between [0, 1].\n * Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n * on score. Defaults to -inf, which means any score is accepted.\n * @param padToMaxOutputSize Defalts to false. If true, size of output\n * `selectedIndices` is padded to maxOutputSize.\n * @return A map with the following properties:\n * - selectedIndices: A 1D tensor with the selected box indices.\n * - validOutputs: A scalar denoting how many elements in `selectedIndices`\n * are valid. Valid elements occur first, then padding.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction nonMaxSuppressionPadded_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) {\n const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');\n const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');\n const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null /* softNmsSigma */);\n const $maxOutputSize = params.maxOutputSize;\n const $iouThreshold = params.iouThreshold;\n const $scoreThreshold = params.scoreThreshold;\n const inputs = { boxes: $boxes, scores: $scores };\n const attrs = {\n maxOutputSize: $maxOutputSize,\n iouThreshold: $iouThreshold,\n scoreThreshold: $scoreThreshold,\n padToMaxOutputSize\n };\n // tslint:disable-next-line: no-unnecessary-type-assertion\n const result = ENGINE.runKernel(NonMaxSuppressionV4, inputs, attrs);\n return { selectedIndices: result[0], validOutputs: result[1] };\n}\nexport const nonMaxSuppressionPadded = op({ nonMaxSuppressionPadded_ });\n//# 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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { nonMaxSuppressionV4Impl } from '../../backends/non_max_suppression_impl';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\nimport { scalar } from '../scalar';\nimport { tensor1d } from '../tensor1d';\n/**\n * Asynchronously performs non maximum suppression of bounding boxes based on\n * iou (intersection over union), with an option to pad results.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n * `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n * the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n * boxes overlap too much with respect to IOU. Must be between [0, 1].\n * Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n * on score. Defaults to -inf, which means any score is accepted.\n * @param padToMaxOutputSize Defalts to false. If true, size of output\n * `selectedIndices` is padded to maxOutputSize.\n * @return A map with the following properties:\n * - selectedIndices: A 1D tensor with the selected box indices.\n * - validOutputs: A scalar denoting how many elements in `selectedIndices`\n * are valid. Valid elements occur first, then padding.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nasync function nonMaxSuppressionPaddedAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) {\n const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');\n const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');\n const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null /* softNmsSigma */);\n const $maxOutputSize = params.maxOutputSize;\n const $iouThreshold = params.iouThreshold;\n const $scoreThreshold = params.scoreThreshold;\n const [boxesVals, scoresVals] = await Promise.all([$boxes.data(), $scores.data()]);\n // We call a cpu based impl directly with the typedarray data here rather\n // than a kernel because all kernels are synchronous (and thus cannot await\n // .data()).\n const { selectedIndices, validOutputs } = nonMaxSuppressionV4Impl(boxesVals, scoresVals, $maxOutputSize, $iouThreshold, $scoreThreshold, padToMaxOutputSize);\n if ($boxes !== boxes) {\n $boxes.dispose();\n }\n if ($scores !== scores) {\n $scores.dispose();\n }\n return {\n selectedIndices: tensor1d(selectedIndices, 'int32'),\n validOutputs: scalar(validOutputs, 'int32')\n };\n}\nexport const nonMaxSuppressionPaddedAsync = nonMaxSuppressionPaddedAsync_;\n//# sourceMappingURL=data:application/json;base64,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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { ResizeBilinear } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * Bilinear resize a single 3D image or a batch of 3D images to a new shape.\n *\n * @param images The images, of rank 4 or rank 3, of shape\n * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param size The new shape `[newHeight, newWidth]` to resize the\n * images to. Each channel is resized individually.\n * @param alignCorners Defaults to `false`. If true, rescale\n * input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4\n * corners of images and resized images. If false, rescale by\n * `new_height / height`. Treat similarly the width dimension.\n * @param halfPixelCenters Defaults to `false`. Whether to assume pixel centers\n * are at 0.5, which would make the floating point coordinates of the top\n * left pixel 0.5, 0.5.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction resizeBilinear_(images, size, alignCorners = false, halfPixelCenters = false) {\n const $images = convertToTensor(images, 'images', 'resizeBilinear');\n util.assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeBilinear: x must be rank 3 or 4, but got ` +\n `rank ${$images.rank}.`);\n util.assert(size.length === 2, () => `Error in resizeBilinear: new shape must 2D, but got shape ` +\n `${size}.`);\n util.assert(halfPixelCenters === false || alignCorners === false, () => `Error in resizeBilinear: If halfPixelCenters is true, ` +\n `alignCorners must be false.`);\n let batchImages = $images;\n let reshapedTo4D = false;\n if ($images.rank === 3) {\n reshapedTo4D = true;\n batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);\n }\n const [] = size;\n const inputs = { images: batchImages };\n const attrs = { alignCorners, halfPixelCenters, size };\n // tslint:disable-next-line: no-unnecessary-type-assertion\n const res = ENGINE.runKernel(ResizeBilinear, inputs, attrs);\n if (reshapedTo4D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n }\n return res;\n}\nexport const resizeBilinear = op({ resizeBilinear_ });\n//# sourceMappingURL=data:application/json;base64,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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { ResizeNearestNeighbor } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * NearestNeighbor resize a batch of 3D images to a new shape.\n *\n * @param images The images, of rank 4 or rank 3, of shape\n * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param size The new shape `[newHeight, newWidth]` to resize the\n * images to. Each channel is resized individually.\n * @param alignCorners Defaults to False. If true, rescale\n * input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4\n * corners of images and resized images. If false, rescale by\n * `new_height / height`. Treat similarly the width dimension.\n * @param halfPixelCenters Defaults to `false`. Whether to assumes pixels are of\n * half the actual dimensions, and yields more accurate resizes. This flag\n * would also make the floating point coordinates of the top left pixel\n * 0.5, 0.5.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction resizeNearestNeighbor_(images, size, alignCorners = false, halfPixelCenters = false) {\n const $images = convertToTensor(images, 'images', 'resizeNearestNeighbor');\n util.assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeNearestNeighbor: x must be rank 3 or 4, but got ` +\n `rank ${$images.rank}.`);\n util.assert(size.length === 2, () => `Error in resizeNearestNeighbor: new shape must 2D, but got shape ` +\n `${size}.`);\n util.assert($images.dtype === 'float32' || $images.dtype === 'int32', () => '`images` must have `int32` or `float32` as dtype');\n util.assert(halfPixelCenters === false || alignCorners === false, () => `Error in resizeNearestNeighbor: If halfPixelCenters is true, ` +\n `alignCorners must be false.`);\n let batchImages = $images;\n let reshapedTo4D = false;\n if ($images.rank === 3) {\n reshapedTo4D = true;\n batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);\n }\n const [] = size;\n const inputs = { images: batchImages };\n const attrs = { alignCorners, halfPixelCenters, size };\n // tslint:disable-next-line: no-unnecessary-type-assertion\n const res = ENGINE.runKernel(ResizeNearestNeighbor, inputs, attrs);\n if (reshapedTo4D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n }\n return res;\n}\nexport const resizeNearestNeighbor = op({ resizeNearestNeighbor_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * https://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { tensor1d } from '../tensor1d';\nimport { op } from '../operation';\nimport { cast } from '../cast';\nimport { split } from '../split';\nimport { bincount } from '../bincount';\nimport { lessEqual } from '../less_equal';\nimport { greater } from '../greater';\nimport { sum } from '../sum';\nimport { add } from '../add';\nimport { mul } from '../mul';\nimport { div } from '../div';\nimport { sub } from '../sub';\nimport { round } from '../round';\nimport { where } from '../where';\nimport { fill } from '../fill';\nimport { slice } from '../slice';\nimport { range } from '../range';\nimport { tensor } from '../tensor';\nimport * as util from '../../util';\nimport { convertToTensor } from '../../tensor_util_env';\n/**\n * Performs image binarization with corresponding threshold\n * (depends on the method)value, which creates a binary image from a grayscale.\n * @param image 3d tensor of shape [imageHeight,imageWidth, depth],\n * where imageHeight and imageWidth must be positive.The image color\n * range should be [0, 255].\n * @param method Optional string from `'binary' | 'otsu'`\n * which specifies the method for thresholding. Defaults to 'binary'.\n * @param inverted Optional boolean whichspecifies\n * if colours should be inverted. Defaults to false.\n * @param threshValue Optional number which defines threshold value from 0 to 1.\n * Defaults to 0.5.\n * @return A 3d tensor of shape [imageHeight,imageWidth, depth], which\n * contains binarized image.\n */\nfunction threshold_(image, method = 'binary', inverted = false, threshValue = 0.5) {\n const $image = convertToTensor(image, 'image', 'threshold');\n /* 0.2989, 0.5870, 0.1140 are represent luma coefficients in CCIR601.\n Reference for converting between RGB and grayscale: https://en.wikipedia.org/wiki/Luma_%28video%29 */\n const RED_INTENCITY_COEF = 0.2989;\n const GREEN_INTENCITY_COEF = 0.5870;\n const BLUE_INTENCITY_COEF = 0.1140;\n const totalPixelsInImage = $image.shape[0] * $image.shape[1];\n let $threshold = mul(tensor1d([threshValue]), 255);\n let r, g, b, grayscale;\n util.assert($image.rank === 3, () => 'Error in threshold: image must be rank 3,' +\n `but got rank ${$image.rank}.`);\n util.assert($image.shape[2] === 3 || $image.shape[2] === 1, () => 'Error in threshold: ' +\n 'image color channel must be equal to 3 or 1' +\n `but got ${$image.shape[2]}.`);\n util.assert($image.dtype === 'int32' || $image.dtype === 'float32', () => 'Error in dtype: image dtype must be int32 or float32,' +\n `but got dtype ${$image.dtype}.`);\n util.assert(method === 'otsu' || method === 'binary', () => `Method must be binary or otsu, but was ${method}`);\n if ($image.shape[2] === 3) {\n [r, g, b] = split($image, [1, 1, 1], -1);\n const $r = mul(r, RED_INTENCITY_COEF);\n const $g = mul(g, GREEN_INTENCITY_COEF);\n const $b = mul(b, BLUE_INTENCITY_COEF);\n grayscale = add(add($r, $g), $b);\n }\n else {\n grayscale = image;\n }\n if (method === 'otsu') {\n const $histogram = bincount(cast(round(grayscale), 'int32'), tensor([]), 256);\n $threshold = otsu($histogram, totalPixelsInImage);\n }\n const invCondition = inverted ?\n lessEqual(grayscale, $threshold) : greater(grayscale, $threshold);\n const result = cast(mul(invCondition, 255), 'int32');\n return result;\n}\nfunction otsu(histogram, total) {\n let bestThresh = tensor1d([-1]);\n let bestInBetVar = tensor1d([0]);\n let cInBetVar = tensor1d([0]);\n let classFirst, classSecond, meanFirst, meanSec, weightForeground, weightBack;\n for (let index = 0; index < histogram.size - 1; index++) {\n classFirst = slice(histogram, 0, index + 1);\n classSecond = slice(histogram, index + 1);\n weightForeground = div(sum(classFirst), total);\n weightBack = div(sum(classSecond), total);\n const meanFirstDivA = sum(mul(classFirst, range(0, classFirst.size)));\n meanFirst = div(meanFirstDivA, sum(classFirst));\n const meanSecFill = fill(classSecond.shape, classFirst.size);\n const meanSecAdd = add(range(0, classSecond.size), meanSecFill);\n const meanSecMul = mul(classSecond, (meanSecAdd));\n meanSec = div(sum(meanSecMul), sum(classSecond));\n const cInBetVarSubA = sub(meanFirst, meanSec);\n const cInBetVarSubB = sub(meanFirst, meanSec);\n const cInBetVarMul = mul(weightForeground, weightBack);\n cInBetVar = mul(mul(cInBetVarMul, cInBetVarSubA), cInBetVarSubB);\n const condition = greater(cInBetVar, bestInBetVar);\n bestInBetVar = where(condition, cInBetVar, bestInBetVar);\n bestThresh = where(condition, tensor1d([index]), bestThresh);\n }\n return bestThresh;\n}\nexport const threshold = op({ threshold_ });\n//# 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* @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * https://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport { Tensor1D, Tensor3D } from '../../tensor';\nimport { tensor1d } from '../tensor1d';\nimport { TensorLike } from '../../types';\nimport { op } from '../operation';\nimport { cast } from '../cast';\nimport { split } from '../split';\nimport { bincount } from '../bincount';\nimport { lessEqual } from '../less_equal';\nimport { greater } from '../greater';\nimport { sum } from '../sum';\nimport { add } from '../add';\nimport { mul } from '../mul';\nimport { div } from '../div';\nimport { sub } from '../sub';\nimport { round } from '../round';\nimport { where } from '../where';\nimport { fill } from '../fill';\nimport {slice} from '../slice';\nimport { range } from '../range';\nimport { tensor } from '../tensor';\nimport * as util from '../../util';\nimport { convertToTensor } from '../../tensor_util_env';\n\n/**\n * Performs image binarization with corresponding threshold\n * (depends on the method)value, which creates a binary image from a grayscale.\n * @param image 3d tensor of shape [imageHeight,imageWidth, depth],\n * where imageHeight and imageWidth must be positive.The image color\n * range should be [0, 255].\n * @param method Optional string from `'binary' | 'otsu'`\n * which specifies the method for thresholding. Defaults to 'binary'.\n * @param inverted Optional boolean whichspecifies\n * if colours should be inverted. Defaults to false.\n * @param threshValue Optional number which defines threshold value from 0 to 1.\n * Defaults to 0.5.\n * @return A 3d tensor of shape [imageHeight,imageWidth, depth], which\n * contains binarized image.\n */\n\nfunction threshold_(\n    image: Tensor3D | TensorLike,\n    method = 'binary',\n    inverted = false,\n    threshValue = 0.5\n): Tensor3D {\n    const $image = convertToTensor(image, 'image', 'threshold');\n\n    /* 0.2989, 0.5870, 0.1140 are represent luma coefficients in CCIR601.\n\tReference for converting between RGB and grayscale: https://en.wikipedia.org/wiki/Luma_%28video%29  */\n\n    const RED_INTENCITY_COEF = 0.2989;\n    const GREEN_INTENCITY_COEF = 0.5870;\n    const BLUE_INTENCITY_COEF = 0.1140;\n    const totalPixelsInImage = $image.shape[0] * $image.shape[1];\n\n    let $threshold = mul(tensor1d([threshValue]), 255);\n    let r, g, b, grayscale;\n\n    util.assert(\n        $image.rank === 3,\n        () => 'Error in threshold: image must be rank 3,' +\n            `but got rank ${$image.rank}.`);\n\n    util.assert(\n        $image.shape[2] === 3 || $image.shape[2]=== 1,\n        () => 'Error in threshold: ' +\n            'image color channel must be equal to 3 or 1' +\n            `but got ${$image.shape[2]}.`);\n\n    util.assert(\n      $image.dtype === 'int32' || $image.dtype === 'float32',\n      () => 'Error in dtype: image dtype must be int32 or float32,' +\n          `but got dtype ${$image.dtype}.`);\n\n    util.assert(\n      method === 'otsu' || method === 'binary',\n      () => `Method must be binary or otsu, but was ${method}`);\n\n    if ($image.shape[2] === 3) {\n        [r, g, b] = split($image, [1, 1, 1], -1);\n        const $r = mul(r,RED_INTENCITY_COEF);\n        const $g = mul(g,GREEN_INTENCITY_COEF);\n        const $b = mul(b,BLUE_INTENCITY_COEF);\n        grayscale = add(add($r, $g), $b);\n    } else {\n        grayscale = image;\n    }\n\n    if (method === 'otsu') {\n        const $histogram = bincount(cast(round(grayscale), 'int32') as Tensor1D,\n            tensor([]),\n            256);\n        $threshold = otsu($histogram, totalPixelsInImage);\n    }\n\n    const invCondition = inverted ?\n        lessEqual(grayscale, $threshold) : greater(grayscale, $threshold);\n\n    const result = cast(mul(invCondition,255), 'int32');\n\n    return result as Tensor3D;\n}\n\nfunction otsu(histogram: Tensor1D, total: number):Tensor1D {\n\n    let bestThresh = tensor1d([-1]);\n    let bestInBetVar = tensor1d([0]);\n    let cInBetVar = tensor1d([0]);\n    let classFirst, classSecond, meanFirst,\n        meanSec, weightForeground, weightBack;\n\n    for (let index = 0; index < histogram.size-1; index++) {\n\n        classFirst = slice(histogram, 0, index + 1);\n\n        classSecond = slice(histogram,index + 1);\n\n        weightForeground = div(sum(classFirst),total);\n\n        weightBack = div(sum(classSecond),total);\n\n        const meanFirstDivA = sum(mul(classFirst, range(0, classFirst.size)));\n\n        meanFirst = div(meanFirstDivA, sum(classFirst) );\n\n        const meanSecFill = fill(classSecond.shape, classFirst.size);\n        const meanSecAdd = add(range(0,classSecond.size),meanSecFill);\n        const meanSecMul = mul(classSecond, (meanSecAdd));\n        meanSec = div(sum(meanSecMul), sum(classSecond));\n\n        const cInBetVarSubA = sub(meanFirst, meanSec);\n        const cInBetVarSubB = sub(meanFirst, meanSec);\n        const cInBetVarMul = mul(weightForeground, weightBack);\n        cInBetVar = mul(mul(cInBetVarMul,cInBetVarSubA), cInBetVarSubB);\n\n        const condition = greater(cInBetVar, bestInBetVar);\n\n        bestInBetVar = where(condition, cInBetVar, bestInBetVar);\n\n        bestThresh = where(condition, tensor1d([index]), bestThresh);\n\n    }\n    return bestThresh;\n}\n\nexport const threshold = op({ threshold_ });\n"]}","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { Transform } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\n/**\n * Applies the given transform(s) to the image(s).\n *\n * @param image 4d tensor of shape `[batch, imageHeight, imageWidth, depth]`.\n * @param transforms Projective transform matrix/matrices. A tensor1d of length\n * 8 or tensor of size N x 8. If one row of transforms is [a0, a1, a2, b0\n * b1, b2, c0, c1], then it maps the output point (x, y) to a transformed\n * input point (x', y') = ((a0 x + a1 y + a2) / k, (b0 x + b1 y + b2) / k),\n * where k = c0 x + c1 y + 1. The transforms are inverted compared to the\n * transform mapping input points to output points.\n * @param interpolation Interpolation mode.\n * Supported values: 'nearest', 'bilinear'. Default to 'nearest'.\n * @param fillMode Points outside the boundaries of the input are filled\n * according to the given mode, one of 'constant', 'reflect', 'wrap',\n * 'nearest'. Default to 'constant'.\n * 'reflect': (d c b a | a b c d | d c b a ) The input is extended by\n * reflecting about the edge of the last pixel.\n * 'constant': (k k k k | a b c d | k k k k) The input is extended by\n * filling all values beyond the edge with the same constant value k.\n * 'wrap': (a b c d | a b c d | a b c d) The input is extended by\n * wrapping around to the opposite edge.\n * 'nearest': (a a a a | a b c d | d d d d) The input is extended by\n * the nearest pixel.\n * @param fillValue A float represents the value to be filled outside the\n * boundaries when fillMode is 'constant'.\n * @param Output dimension after the transform, [height, width]. If undefined,\n * output is the same size as input image.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction transform_(image, transforms, interpolation = 'nearest', fillMode = 'constant', fillValue = 0, outputShape) {\n const $image = convertToTensor(image, 'image', 'transform', 'float32');\n const $transforms = convertToTensor(transforms, 'transforms', 'transform', 'float32');\n util.assert($image.rank === 4, () => 'Error in transform: image must be rank 4,' +\n `but got rank ${$image.rank}.`);\n util.assert($transforms.rank === 2 &&\n ($transforms.shape[0] === $image.shape[0] ||\n $transforms.shape[0] === 1) &&\n $transforms.shape[1] === 8, () => `Error in transform: Input transform should be batch x 8 or 1 x 8`);\n util.assert(outputShape == null || outputShape.length === 2, () => 'Error in transform: outputShape must be [height, width] or null, ' +\n `but got ${outputShape}.`);\n const inputs = { image: $image, transforms: $transforms };\n const attrs = { interpolation, fillMode, fillValue, outputShape };\n return ENGINE.runKernel(Transform, inputs, attrs);\n}\nexport const transform = op({ transform_ });\n//# 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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assert } from '../../util';\nimport { greaterEqual } from '../greater_equal';\nimport { lessEqual } from '../less_equal';\nimport { logicalAnd } from '../logical_and';\nimport { op } from '../operation';\nimport { range } from '../range';\nimport { reshape } from '../reshape';\nimport { scalar } from '../scalar';\nimport { stack } from '../stack';\nimport { sub } from '../sub';\nimport { unstack } from '../unstack';\nimport { where } from '../where';\nimport { zeros } from '../zeros';\n/**\n * Copy a tensor setting everything outside a central band in each innermost\n * matrix to zero.\n *\n * The band part is computed as follows: Assume input has `k` dimensions\n * `[I, J, K, ..., M, N]`, then the output is a tensor with the same shape where\n * `band[i, j, k, ..., m, n] = in_band(m, n) * input[i, j, k, ..., m, n]`.\n * The indicator function\n * `in_band(m, n) = (num_lower < 0 || (m-n) <= num_lower))`\n * `&& (num_upper < 0 || (n-m) <= num_upper)`\n *\n * ```js\n * const x = tf.tensor2d([[ 0, 1, 2, 3],\n * [-1, 0, 1, 2],\n * [-2, -1, 0, 1],\n * [-3, -2, -1, 0]]);\n * let y = tf.linalg.bandPart(x, 1, -1);\n * y.print(); // [[ 0, 1, 2, 3],\n * // [-1, 0, 1, 2],\n * // [ 0, -1, 0, 1],\n * // [ 0, 0 , -1, 0]]\n * let z = tf.linalg.bandPart(x, 2, 1);\n * z.print(); // [[ 0, 1, 0, 0],\n * // [-1, 0, 1, 0],\n * // [-2, -1, 0, 1],\n * // [ 0, -2, -1, 0]]\n * ```\n *\n * @param x Rank `k` tensor\n * @param numLower Number of subdiagonals to keep.\n * If negative, keep entire lower triangle.\n * @param numUpper Number of subdiagonals to keep.\n * If negative, keep entire upper triangle.\n * @returns Rank `k` tensor of the same shape as input.\n * The extracted banded tensor.\n *\n * @doc {heading:'Operations', subheading:'Linear Algebra', namespace:'linalg'}\n */\nfunction bandPart_(a, numLower, numUpper) {\n assert(numLower % 1 === 0, () => `bandPart(): numLower must be an integer, got ${numLower}.`);\n assert(numUpper % 1 === 0, () => `bandPart(): numUpper must be an integer, got ${numUpper}.`);\n const $a = convertToTensor(a, 'a', 'bandPart');\n assert($a.rank >= 2, () => `bandPart(): Rank must be at least 2, got ${$a.rank}.`);\n const shape = $a.shape;\n const [M, N] = $a.shape.slice(-2);\n if (!(numLower <= M)) {\n throw new Error(`bandPart(): numLower (${numLower})` +\n ` must not be greater than the number of rows (${M}).`);\n }\n if (!(numUpper <= N)) {\n throw new Error(`bandPart(): numUpper (${numUpper})` +\n ` must not be greater than the number of columns (${N}).`);\n }\n if (numLower < 0) {\n numLower = M;\n }\n if (numUpper < 0) {\n numUpper = N;\n }\n const i = reshape(range(0, M, 1, 'int32'), [-1, 1]);\n const j = range(0, N, 1, 'int32');\n const ij = sub(i, j);\n const inBand = logicalAnd(lessEqual(ij, scalar(+numLower, 'int32')), greaterEqual(ij, scalar(-numUpper, 'int32')));\n const zero = zeros([M, N], $a.dtype);\n return reshape(stack(unstack(reshape($a, [-1, M, N]))\n .map(mat => where(inBand, mat, zero))), shape);\n}\nexport const bandPart = op({ bandPart_ });\n//# sourceMappingURL=data:application/json;base64,{"version":3,"file":"band_part.js","sourceRoot":"","sources":["../../../../../../../tfjs-core/src/ops/linalg/band_part.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAGH,OAAO,EAAC,eAAe,EAAC,MAAM,uBAAuB,CAAC;AAEtD,OAAO,EAAC,MAAM,EAAC,MAAM,YAAY,CAAC;AAElC,OAAO,EAAC,YAAY,EAAC,MAAM,kBAAkB,CAAC;AAC9C,OAAO,EAAC,SAAS,EAAC,MAAM,eAAe,CAAC;AACxC,OAAO,EAAC,UAAU,EAAC,MAAM,gBAAgB,CAAC;AAC1C,OAAO,EAAC,EAAE,EAAC,MAAM,cAAc,CAAC;AAChC,OAAO,EAAC,KAAK,EAAC,MAAM,UAAU,CAAC;AAC/B,OAAO,EAAC,OAAO,EAAC,MAAM,YAAY,CAAC;AACnC,OAAO,EAAC,MAAM,EAAC,MAAM,WAAW,CAAC;AACjC,OAAO,EAAC,KAAK,EAAC,MAAM,UAAU,CAAC;AAC/B,OAAO,EAAC,GAAG,EAAC,MAAM,QAAQ,CAAC;AAC3B,OAAO,EAAC,OAAO,EAAC,MAAM,YAAY,CAAC;AACnC,OAAO,EAAC,KAAK,EAAC,MAAM,UAAU,CAAC;AAC/B,OAAO,EAAC,KAAK,EAAC,MAAM,UAAU,CAAC;AAE/B;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;GAqCG;AACH,SAAS,SAAS,CACd,CAAe,EAAE,QAAgB,EAAE,QAAgB;IACrD,MAAM,CACF,QAAQ,GAAG,CAAC,KAAK,CAAC,EAClB,GAAG,EAAE,CAAC,gDAAgD,QAAQ,GAAG,CAAC,CAAC;IACvE,MAAM,CACF,QAAQ,GAAG,CAAC,KAAK,CAAC,EAClB,GAAG,EAAE,CAAC,gDAAgD,QAAQ,GAAG,CAAC,CAAC;IAEvE,MAAM,EAAE,GAAG,eAAe,CAAC,CAAC,EAAE,GAAG,EAAE,UAAU,CAAC,CAAC;IAE/C,MAAM,CACF,EAAE,CAAC,IAAI,IAAI,CAAC,EACZ,GAAG,EAAE,CAAC,4CAA4C,EAAE,CAAC,IAAI,GAAG,CAAC,CAAC;IAElE,MAAM,KAAK,GAAG,EAAE,CAAC,KAAK,CAAC;IACvB,MAAM,CAAC,CAAC,EAAE,CAAC,CAAC,GAAG,EAAE,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC;IAElC,IAAI,CAAC,CAAC,QAAQ,IAAI,CAAC,CAAC,EAAE;QACpB,MAAM,IAAI,KAAK,CACX,yBAAyB,QAAQ,GAAG;YACpC,iDAAiD,CAAC,IAAI,CAAC,CAAC;KAC7D;IACD,IAAI,CAAC,CAAC,QAAQ,IAAI,CAAC,CAAC,EAAE;QACpB,MAAM,IAAI,KAAK,CACX,yBAAyB,QAAQ,GAAG;YACpC,oDAAoD,CAAC,IAAI,CAAC,CAAC;KAChE;IAED,IAAI,QAAQ,GAAG,CAAC,EAAE;QAChB,QAAQ,GAAG,CAAC,CAAC;KACd;IACD,IAAI,QAAQ,GAAG,CAAC,EAAE;QAChB,QAAQ,GAAG,CAAC,CAAC;KACd;IAED,MAAM,CAAC,GAAG,OAAO,CAAC,KAAK,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,OAAO,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;IACpD,MAAM,CAAC,GAAG,KAAK,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,OAAO,CAAC,CAAC;IAClC,MAAM,EAAE,GAAG,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;IAErB,MAAM,MAAM,GAAG,UAAU,CACrB,SAAS,CAAC,EAAE,EAAE,MAAM,CAAC,CAAC,QAAQ,EAAE,OAAO,CAAC,CAAC,EACzC,YAAY,CAAC,EAAE,EAAE,MAAM,CAAC,CAAC,QAAQ,EAAE,OAAO,CAAC,CAAC,CAAC,CAAC;IAElD,MAAM,IAAI,GAAG,KAAK,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,EAAE,CAAC,KAAK,CAAC,CAAC;IAErC,OAAO,OAAO,CACH,KAAK,CAAC,OAAO,CAAC,OAAO,CAAC,EAAE,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;SAC3B,GAAG,CAAC,GAAG,CAAC,EAAE,CAAC,KAAK,CAAC,MAAM,EAAE,GAAG,EAAE,IAAI,CAAC,CAAC,CAAC,EAChD,KAAK,CAAM,CAAC;AACzB,CAAC;AAED,MAAM,CAAC,MAAM,QAAQ,GAAG,EAAE,CAAC,EAAC,SAAS,EAAC,CAAC,CAAC","sourcesContent":["/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../../tensor';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport {assert} from '../../util';\n\nimport {greaterEqual} from '../greater_equal';\nimport {lessEqual} from '../less_equal';\nimport {logicalAnd} from '../logical_and';\nimport {op} from '../operation';\nimport {range} from '../range';\nimport {reshape} from '../reshape';\nimport {scalar} from '../scalar';\nimport {stack} from '../stack';\nimport {sub} from '../sub';\nimport {unstack} from '../unstack';\nimport {where} from '../where';\nimport {zeros} from '../zeros';\n\n/**\n * Copy a tensor setting everything outside a central band in each innermost\n * matrix to zero.\n *\n * The band part is computed as follows: Assume input has `k` dimensions\n * `[I, J, K, ..., M, N]`, then the output is a tensor with the same shape where\n * `band[i, j, k, ..., m, n] = in_band(m, n) * input[i, j, k, ..., m, n]`.\n * The indicator function\n * `in_band(m, n) = (num_lower < 0 || (m-n) <= num_lower))`\n * `&& (num_upper < 0 || (n-m) <= num_upper)`\n *\n * ```js\n * const x = tf.tensor2d([[ 0,  1,  2, 3],\n *                        [-1,  0,  1, 2],\n *                        [-2, -1,  0, 1],\n *                        [-3, -2, -1, 0]]);\n * let y = tf.linalg.bandPart(x, 1, -1);\n * y.print(); // [[ 0,  1,  2, 3],\n *            //  [-1,  0,  1, 2],\n *            //  [ 0, -1,  0, 1],\n *            //  [ 0, 0 , -1, 0]]\n * let z = tf.linalg.bandPart(x, 2, 1);\n * z.print(); // [[ 0,  1,  0, 0],\n *            //  [-1,  0,  1, 0],\n *            //  [-2, -1,  0, 1],\n *            //  [ 0, -2, -1, 0]]\n * ```\n *\n * @param x Rank `k` tensor\n * @param numLower Number of subdiagonals to keep.\n *   If negative, keep entire lower triangle.\n * @param numUpper Number of subdiagonals to keep.\n *   If negative, keep entire upper triangle.\n * @returns Rank `k` tensor of the same shape as input.\n *   The extracted banded tensor.\n *\n * @doc {heading:'Operations', subheading:'Linear Algebra', namespace:'linalg'}\n */\nfunction bandPart_<T extends Tensor>(\n    a: T|TensorLike, numLower: number, numUpper: number): T {\n  assert(\n      numLower % 1 === 0,\n      () => `bandPart(): numLower must be an integer, got ${numLower}.`);\n  assert(\n      numUpper % 1 === 0,\n      () => `bandPart(): numUpper must be an integer, got ${numUpper}.`);\n\n  const $a = convertToTensor(a, 'a', 'bandPart');\n\n  assert(\n      $a.rank >= 2,\n      () => `bandPart(): Rank must be at least 2, got ${$a.rank}.`);\n\n  const shape = $a.shape;\n  const [M, N] = $a.shape.slice(-2);\n\n  if (!(numLower <= M)) {\n    throw new Error(\n        `bandPart(): numLower (${numLower})` +\n        ` must not be greater than the number of rows (${M}).`);\n  }\n  if (!(numUpper <= N)) {\n    throw new Error(\n        `bandPart(): numUpper (${numUpper})` +\n        ` must not be greater than the number of columns (${N}).`);\n  }\n\n  if (numLower < 0) {\n    numLower = M;\n  }\n  if (numUpper < 0) {\n    numUpper = N;\n  }\n\n  const i = reshape(range(0, M, 1, 'int32'), [-1, 1]);\n  const j = range(0, N, 1, 'int32');\n  const ij = sub(i, j);\n\n  const inBand = logicalAnd(\n      lessEqual(ij, scalar(+numLower, 'int32')),\n      greaterEqual(ij, scalar(-numUpper, 'int32')));\n\n  const zero = zeros([M, N], $a.dtype);\n\n  return reshape(\n             stack(unstack(reshape($a, [-1, M, N]))\n                       .map(mat => where(inBand, mat, zero))),\n             shape) as T;\n}\n\nexport const bandPart = op({bandPart_});\n"]}","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { assert } from '../../util';\nimport { div } from '../div';\nimport { mul } from '../mul';\nimport { norm } from '../norm';\nimport { op } from '../operation';\nimport { split } from '../split';\nimport { squeeze } from '../squeeze';\nimport { stack } from '../stack';\nimport { sub } from '../sub';\nimport { sum } from '../sum';\n/**\n * Gram-Schmidt orthogonalization.\n *\n * ```js\n * const x = tf.tensor2d([[1, 2], [3, 4]]);\n * let y = tf.linalg.gramSchmidt(x);\n * y.print();\n * console.log('Othogonalized:');\n * y.dot(y.transpose()).print(); // should be nearly the identity matrix.\n * console.log('First row direction maintained:');\n * const data = await y.array();\n * console.log(data[0][1] / data[0][0]); // should be nearly 2.\n * ```\n *\n * @param xs The vectors to be orthogonalized, in one of the two following\n * formats:\n * - An Array of `tf.Tensor1D`.\n * - A `tf.Tensor2D`, i.e., a matrix, in which case the vectors are the rows\n * of `xs`.\n * In each case, all the vectors must have the same length and the length\n * must be greater than or equal to the number of vectors.\n * @returns The orthogonalized and normalized vectors or matrix.\n * Orthogonalization means that the vectors or the rows of the matrix\n * are orthogonal (zero inner products). Normalization means that each\n * vector or each row of the matrix has an L2 norm that equals `1`.\n *\n * @doc {heading:'Operations', subheading:'Linear Algebra', namespace:'linalg'}\n */\nfunction gramSchmidt_(xs) {\n let inputIsTensor2D;\n if (Array.isArray(xs)) {\n inputIsTensor2D = false;\n assert(xs != null && xs.length > 0, () => 'Gram-Schmidt process: input must not be null, undefined, or ' +\n 'empty');\n const dim = xs[0].shape[0];\n for (let i = 1; i < xs.length; ++i) {\n assert(xs[i].shape[0] === dim, () => 'Gram-Schmidt: Non-unique lengths found in the input vectors: ' +\n `(${xs[i].shape[0]} vs. ${dim})`);\n }\n }\n else {\n inputIsTensor2D = true;\n xs = split(xs, xs.shape[0], 0).map(x => squeeze(x, [0]));\n }\n assert(xs.length <= xs[0].shape[0], () => `Gram-Schmidt: Number of vectors (${xs.length}) exceeds ` +\n `number of dimensions (${xs[0].shape[0]}).`);\n const ys = [];\n const xs1d = xs;\n for (let i = 0; i < xs.length; ++i) {\n ys.push(ENGINE.tidy(() => {\n let x = xs1d[i];\n if (i > 0) {\n for (let j = 0; j < i; ++j) {\n const proj = mul(sum(mul(ys[j], x)), ys[j]);\n x = sub(x, proj);\n }\n }\n return div(x, norm(x, 'euclidean'));\n }));\n }\n if (inputIsTensor2D) {\n return stack(ys, 0);\n }\n else {\n return ys;\n }\n}\nexport const gramSchmidt = op({ gramSchmidt_ });\n//# sourceMappingURL=data:application/json;base64,{"version":3,"file":"gram_schmidt.js","sourceRoot":"","sources":["../../../../../../../tfjs-core/src/ops/linalg/gram_schmidt.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAEH,OAAO,EAAC,MAAM,EAAC,MAAM,cAAc,CAAC;AAEpC,OAAO,EAAC,MAAM,EAAC,MAAM,YAAY,CAAC;AAElC,OAAO,EAAC,GAAG,EAAC,MAAM,QAAQ,CAAC;AAC3B,OAAO,EAAC,GAAG,EAAC,MAAM,QAAQ,CAAC;AAC3B,OAAO,EAAC,IAAI,EAAC,MAAM,SAAS,CAAC;AAC7B,OAAO,EAAC,EAAE,EAAC,MAAM,cAAc,CAAC;AAChC,OAAO,EAAC,KAAK,EAAC,MAAM,UAAU,CAAC;AAC/B,OAAO,EAAC,OAAO,EAAC,MAAM,YAAY,CAAC;AACnC,OAAO,EAAC,KAAK,EAAC,MAAM,UAAU,CAAC;AAC/B,OAAO,EAAC,GAAG,EAAC,MAAM,QAAQ,CAAC;AAC3B,OAAO,EAAC,GAAG,EAAC,MAAM,QAAQ,CAAC;AAE3B;;;;;;;;;;;;;;;;;;;;;;;;;;;GA2BG;AACH,SAAS,YAAY,CAAC,EAAuB;IAC3C,IAAI,eAAwB,CAAC;IAC7B,IAAI,KAAK,CAAC,OAAO,CAAC,EAAE,CAAC,EAAE;QACrB,eAAe,GAAG,KAAK,CAAC;QACxB,MAAM,CACF,EAAE,IAAI,IAAI,IAAI,EAAE,CAAC,MAAM,GAAG,CAAC,EAC3B,GAAG,EAAE,CAAC,8DAA8D;YAChE,OAAO,CAAC,CAAC;QACjB,MAAM,GAAG,GAAG,EAAE,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC;QAC3B,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,EAAE,CAAC,MAAM,EAAE,EAAE,CAAC,EAAE;YAClC,MAAM,CACF,EAAE,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,KAAK,GAAG,EACtB,GAAG,EAAE,CACD,+DAA+D;gBAC/D,IAAK,EAAiB,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,QAAQ,GAAG,GAAG,CAAC,CAAC;SAC3D;KACF;SAAM;QACL,eAAe,GAAG,IAAI,CAAC;QACvB,EAAE,GAAG,KAAK,CAAC,EAAE,EAAE,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,CAAC,OAAO,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;KAC1D;IAED,MAAM,CACF,EAAE,CAAC,MAAM,IAAI,EAAE,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,EAC3B,GAAG,EAAE,CAAC,oCACK,EAAiB,CAAC,MAAM,YAAY;QAC3C,yBAA0B,EAAiB,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC;IAErE,MAAM,EAAE,GAAe,EAAE,CAAC;IAC1B,MAAM,IAAI,GAAG,EAAE,CAAC;IAChB,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,EAAE,CAAC,MAAM,EAAE,EAAE,CAAC,EAAE;QAClC,EAAE,CAAC,IAAI,CAAC,MAAM,CAAC,IAAI,CAAC,GAAG,EAAE;YACvB,IAAI,CAAC,GAAG,IAAI,CAAC,CAAC,CAAC,CAAC;YAChB,IAAI,CAAC,GAAG,CAAC,EAAE;gBACT,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,EAAE,EAAE,CAAC,EAAE;oBAC1B,MAAM,IAAI,GAAG,GAAG,CAAC,GAAG,CAAC,GAAG,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC;oBAC5C,CAAC,GAAG,GAAG,CAAC,CAAC,EAAE,IAAI,CAAC,CAAC;iBAClB;aACF;YACD,OAAO,GAAG,CAAC,CAAC,EAAE,IAAI,CAAC,CAAC,EAAE,WAAW,CAAC,CAAC,CAAC;QACtC,CAAC,CAAC,CAAC,CAAC;KACL;IAED,IAAI,eAAe,EAAE;QACnB,OAAO,KAAK,CAAC,EAAE,EAAE,CAAC,CAAa,CAAC;KACjC;SAAM;QACL,OAAO,EAAE,CAAC;KACX;AACH,CAAC;AAED,MAAM,CAAC,MAAM,WAAW,GAAG,EAAE,CAAC,EAAC,YAAY,EAAC,CAAC,CAAC","sourcesContent":["/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../../engine';\nimport {Tensor1D, Tensor2D} from '../../tensor';\nimport {assert} from '../../util';\n\nimport {div} from '../div';\nimport {mul} from '../mul';\nimport {norm} from '../norm';\nimport {op} from '../operation';\nimport {split} from '../split';\nimport {squeeze} from '../squeeze';\nimport {stack} from '../stack';\nimport {sub} from '../sub';\nimport {sum} from '../sum';\n\n/**\n * Gram-Schmidt orthogonalization.\n *\n * ```js\n * const x = tf.tensor2d([[1, 2], [3, 4]]);\n * let y = tf.linalg.gramSchmidt(x);\n * y.print();\n * console.log('Othogonalized:');\n * y.dot(y.transpose()).print();  // should be nearly the identity matrix.\n * console.log('First row direction maintained:');\n * const data = await y.array();\n * console.log(data[0][1] / data[0][0]);  // should be nearly 2.\n * ```\n *\n * @param xs The vectors to be orthogonalized, in one of the two following\n *   formats:\n *   - An Array of `tf.Tensor1D`.\n *   - A `tf.Tensor2D`, i.e., a matrix, in which case the vectors are the rows\n *     of `xs`.\n *   In each case, all the vectors must have the same length and the length\n *   must be greater than or equal to the number of vectors.\n * @returns The orthogonalized and normalized vectors or matrix.\n *   Orthogonalization means that the vectors or the rows of the matrix\n *   are orthogonal (zero inner products). Normalization means that each\n *   vector or each row of the matrix has an L2 norm that equals `1`.\n *\n * @doc {heading:'Operations', subheading:'Linear Algebra', namespace:'linalg'}\n */\nfunction gramSchmidt_(xs: Tensor1D[]|Tensor2D): Tensor1D[]|Tensor2D {\n  let inputIsTensor2D: boolean;\n  if (Array.isArray(xs)) {\n    inputIsTensor2D = false;\n    assert(\n        xs != null && xs.length > 0,\n        () => 'Gram-Schmidt process: input must not be null, undefined, or ' +\n            'empty');\n    const dim = xs[0].shape[0];\n    for (let i = 1; i < xs.length; ++i) {\n      assert(\n          xs[i].shape[0] === dim,\n          () =>\n              'Gram-Schmidt: Non-unique lengths found in the input vectors: ' +\n              `(${(xs as Tensor1D[])[i].shape[0]} vs. ${dim})`);\n    }\n  } else {\n    inputIsTensor2D = true;\n    xs = split(xs, xs.shape[0], 0).map(x => squeeze(x, [0]));\n  }\n\n  assert(\n      xs.length <= xs[0].shape[0],\n      () => `Gram-Schmidt: Number of vectors (${\n                (xs as Tensor1D[]).length}) exceeds ` +\n          `number of dimensions (${(xs as Tensor1D[])[0].shape[0]}).`);\n\n  const ys: Tensor1D[] = [];\n  const xs1d = xs;\n  for (let i = 0; i < xs.length; ++i) {\n    ys.push(ENGINE.tidy(() => {\n      let x = xs1d[i];\n      if (i > 0) {\n        for (let j = 0; j < i; ++j) {\n          const proj = mul(sum(mul(ys[j], x)), ys[j]);\n          x = sub(x, proj);\n        }\n      }\n      return div(x, norm(x, 'euclidean'));\n    }));\n  }\n\n  if (inputIsTensor2D) {\n    return stack(ys, 0) as Tensor2D;\n  } else {\n    return ys;\n  }\n}\n\nexport const gramSchmidt = op({gramSchmidt_});\n"]}","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { dispose } from '../../globals';\nimport { assert } from '../../util';\nimport { clone } from '../clone';\nimport { concat } from '../concat';\nimport { div } from '../div';\nimport { eye } from '../eye';\nimport { greater } from '../greater';\nimport { matMul } from '../mat_mul';\nimport { mul } from '../mul';\nimport { neg } from '../neg';\nimport { norm } from '../norm';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\nimport { slice } from '../slice';\nimport { stack } from '../stack';\nimport { sub } from '../sub';\nimport { tensor2d } from '../tensor2d';\nimport { transpose } from '../transpose';\nimport { unstack } from '../unstack';\nimport { where } from '../where';\n/**\n * Compute QR decomposition of m-by-n matrix using Householder transformation.\n *\n * Implementation based on\n * [http://www.cs.cornell.edu/~bindel/class/cs6210-f09/lec18.pdf]\n * (http://www.cs.cornell.edu/~bindel/class/cs6210-f09/lec18.pdf)\n *\n * ```js\n * const a = tf.tensor2d([[1, 2], [3, 4]]);\n * let [q, r] = tf.linalg.qr(a);\n * console.log('Q');\n * q.print();\n * console.log('R');\n * r.print();\n * console.log('Orthogonalized');\n * q.dot(q.transpose()).print() // should be nearly the identity matrix.\n * console.log('Reconstructed');\n * q.dot(r).print(); // should be nearly [[1, 2], [3, 4]];\n * ```\n *\n * @param x The `tf.Tensor` to be QR-decomposed. Must have rank >= 2. Suppose\n * it has the shape `[..., M, N]`.\n * @param fullMatrices An optional boolean parameter. Defaults to `false`.\n * If `true`, compute full-sized `Q`. If `false` (the default),\n * compute only the leading N columns of `Q` and `R`.\n * @returns An `Array` of two `tf.Tensor`s: `[Q, R]`. `Q` is a unitary matrix,\n * i.e., its columns all have unit norm and are mutually orthogonal.\n * If `M >= N`,\n * If `fullMatrices` is `false` (default),\n * - `Q` has a shape of `[..., M, N]`,\n * - `R` has a shape of `[..., N, N]`.\n * If `fullMatrices` is `true` (default),\n * - `Q` has a shape of `[..., M, M]`,\n * - `R` has a shape of `[..., M, N]`.\n * If `M < N`,\n * - `Q` has a shape of `[..., M, M]`,\n * - `R` has a shape of `[..., M, N]`.\n * @throws If the rank of `x` is less than 2.\n *\n * @doc {heading:'Operations',\n * subheading:'Linear Algebra',\n * namespace:'linalg'}\n */\nfunction qr_(x, fullMatrices = false) {\n assert(x.rank >= 2, () => `qr() requires input tensor to have a rank >= 2, but got rank ${x.rank}`);\n if (x.rank === 2) {\n return qr2d(x, fullMatrices);\n }\n else {\n // Rank > 2.\n // TODO(cais): Below we split the input into individual 2D tensors,\n // perform QR decomposition on them and then stack the results back\n // together. We should explore whether this can be parallelized.\n const outerDimsProd = x.shape.slice(0, x.shape.length - 2)\n .reduce((value, prev) => value * prev);\n const x2ds = unstack(reshape(x, [\n outerDimsProd, x.shape[x.shape.length - 2],\n x.shape[x.shape.length - 1]\n ]), 0);\n const q2ds = [];\n const r2ds = [];\n x2ds.forEach(x2d => {\n const [q2d, r2d] = qr2d(x2d, fullMatrices);\n q2ds.push(q2d);\n r2ds.push(r2d);\n });\n const q = reshape(stack(q2ds, 0), x.shape);\n const r = reshape(stack(r2ds, 0), x.shape);\n return [q, r];\n }\n}\nfunction qr2d(x, fullMatrices = false) {\n return ENGINE.tidy(() => {\n assert(x.shape.length === 2, () => `qr2d() requires a 2D Tensor, but got a ${x.shape.length}D Tensor.`);\n const m = x.shape[0];\n const n = x.shape[1];\n let q = eye(m); // Orthogonal transform so far.\n let r = clone(x); // Transformed matrix so far.\n const one2D = tensor2d([[1]], [1, 1]);\n let w = clone(one2D);\n const iters = m >= n ? n : m;\n for (let j = 0; j < iters; ++j) {\n // This tidy within the for-loop ensures we clean up temporary\n // tensors as soon as they are no longer needed.\n const rTemp = r;\n const wTemp = w;\n const qTemp = q;\n [w, r, q] = ENGINE.tidy(() => {\n // Find H = I - tau * w * w', to put zeros below R(j, j).\n const rjEnd1 = slice(r, [j, j], [m - j, 1]);\n const normX = norm(rjEnd1);\n const rjj = slice(r, [j, j], [1, 1]);\n // The sign() function returns 0 on 0, which causes division by zero.\n const s = where(greater(rjj, 0), tensor2d([[-1]]), tensor2d([[1]]));\n const u1 = sub(rjj, mul(s, normX));\n const wPre = div(rjEnd1, u1);\n if (wPre.shape[0] === 1) {\n w = clone(one2D);\n }\n else {\n w = concat([\n one2D,\n slice(wPre, [1, 0], [wPre.shape[0] - 1, wPre.shape[1]])\n ], 0);\n }\n const tau = neg(div(matMul(s, u1), normX));\n // -- R := HR, Q := QH.\n const rjEndAll = slice(r, [j, 0], [m - j, n]);\n const tauTimesW = mul(tau, w);\n const wT = transpose(w);\n if (j === 0) {\n r = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));\n }\n else {\n const rTimesTau = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));\n r = concat([slice(r, [0, 0], [j, n]), rTimesTau], 0);\n }\n const tawTimesWT = transpose(tauTimesW);\n const qAllJEnd = slice(q, [0, j], [m, q.shape[1] - j]);\n if (j === 0) {\n q = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));\n }\n else {\n const qTimesTau = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));\n q = concat([slice(q, [0, 0], [m, j]), qTimesTau], 1);\n }\n return [w, r, q];\n });\n dispose([rTemp, wTemp, qTemp]);\n }\n if (!fullMatrices && m > n) {\n q = slice(q, [0, 0], [m, n]);\n r = slice(r, [0, 0], [n, n]);\n }\n return [q, r];\n });\n}\nexport const qr = op({ qr_ });\n//# 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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../../engine';\nimport {dispose} from '../../globals';\nimport {Tensor, Tensor2D} from '../../tensor';\nimport {assert} from '../../util';\n\nimport {clone} from '../clone';\nimport {concat} from '../concat';\nimport {div} from '../div';\nimport {eye} from '../eye';\nimport {greater} from '../greater';\nimport {matMul} from '../mat_mul';\nimport {mul} from '../mul';\nimport {neg} from '../neg';\nimport {norm} from '../norm';\nimport {op} from '../operation';\nimport {reshape} from '../reshape';\nimport {slice} from '../slice';\nimport {stack} from '../stack';\nimport {sub} from '../sub';\nimport {tensor2d} from '../tensor2d';\nimport {transpose} from '../transpose';\nimport {unstack} from '../unstack';\nimport {where} from '../where';\n\n/**\n * Compute QR decomposition of m-by-n matrix using Householder transformation.\n *\n * Implementation based on\n *   [http://www.cs.cornell.edu/~bindel/class/cs6210-f09/lec18.pdf]\n * (http://www.cs.cornell.edu/~bindel/class/cs6210-f09/lec18.pdf)\n *\n * ```js\n * const a = tf.tensor2d([[1, 2], [3, 4]]);\n * let [q, r] = tf.linalg.qr(a);\n * console.log('Q');\n * q.print();\n * console.log('R');\n * r.print();\n * console.log('Orthogonalized');\n * q.dot(q.transpose()).print()  // should be nearly the identity matrix.\n * console.log('Reconstructed');\n * q.dot(r).print(); // should be nearly [[1, 2], [3, 4]];\n * ```\n *\n * @param x The `tf.Tensor` to be QR-decomposed. Must have rank >= 2. Suppose\n *   it has the shape `[..., M, N]`.\n * @param fullMatrices An optional boolean parameter. Defaults to `false`.\n *   If `true`, compute full-sized `Q`. If `false` (the default),\n *   compute only the leading N columns of `Q` and `R`.\n * @returns An `Array` of two `tf.Tensor`s: `[Q, R]`. `Q` is a unitary matrix,\n *   i.e., its columns all have unit norm and are mutually orthogonal.\n *   If `M >= N`,\n *     If `fullMatrices` is `false` (default),\n *       - `Q` has a shape of `[..., M, N]`,\n *       - `R` has a shape of `[..., N, N]`.\n *     If `fullMatrices` is `true` (default),\n *       - `Q` has a shape of `[..., M, M]`,\n *       - `R` has a shape of `[..., M, N]`.\n *   If `M < N`,\n *     - `Q` has a shape of `[..., M, M]`,\n *     - `R` has a shape of `[..., M, N]`.\n * @throws If the rank of `x` is less than 2.\n *\n * @doc {heading:'Operations',\n *       subheading:'Linear Algebra',\n *       namespace:'linalg'}\n */\nfunction qr_(x: Tensor, fullMatrices = false): [Tensor, Tensor] {\n  assert(\n      x.rank >= 2,\n      () => `qr() requires input tensor to have a rank >= 2, but got rank ${\n          x.rank}`);\n\n  if (x.rank === 2) {\n    return qr2d(x as Tensor2D, fullMatrices);\n  } else {\n    // Rank > 2.\n    // TODO(cais): Below we split the input into individual 2D tensors,\n    //   perform QR decomposition on them and then stack the results back\n    //   together. We should explore whether this can be parallelized.\n    const outerDimsProd = x.shape.slice(0, x.shape.length - 2)\n                              .reduce((value, prev) => value * prev);\n    const x2ds = unstack(\n        reshape(\n            x,\n            [\n              outerDimsProd, x.shape[x.shape.length - 2],\n              x.shape[x.shape.length - 1]\n            ]),\n        0);\n    const q2ds: Tensor2D[] = [];\n    const r2ds: Tensor2D[] = [];\n    x2ds.forEach(x2d => {\n      const [q2d, r2d] = qr2d(x2d as Tensor2D, fullMatrices);\n      q2ds.push(q2d);\n      r2ds.push(r2d);\n    });\n    const q = reshape(stack(q2ds, 0), x.shape);\n    const r = reshape(stack(r2ds, 0), x.shape);\n    return [q, r];\n  }\n}\n\nfunction qr2d(x: Tensor2D, fullMatrices = false): [Tensor2D, Tensor2D] {\n  return ENGINE.tidy(() => {\n    assert(\n        x.shape.length === 2,\n        () => `qr2d() requires a 2D Tensor, but got a ${\n            x.shape.length}D Tensor.`);\n\n    const m = x.shape[0];\n    const n = x.shape[1];\n\n    let q = eye(m);    // Orthogonal transform so far.\n    let r = clone(x);  // Transformed matrix so far.\n\n    const one2D = tensor2d([[1]], [1, 1]);\n    let w: Tensor2D = clone(one2D);\n\n    const iters = m >= n ? n : m;\n    for (let j = 0; j < iters; ++j) {\n      // This tidy within the for-loop ensures we clean up temporary\n      // tensors as soon as they are no longer needed.\n      const rTemp = r;\n      const wTemp = w;\n      const qTemp = q;\n      [w, r, q] = ENGINE.tidy((): [Tensor2D, Tensor2D, Tensor2D] => {\n        // Find H = I - tau * w * w', to put zeros below R(j, j).\n        const rjEnd1 = slice(r, [j, j], [m - j, 1]);\n        const normX = norm(rjEnd1);\n        const rjj = slice(r, [j, j], [1, 1]);\n\n        // The sign() function returns 0 on 0, which causes division by zero.\n        const s = where(greater(rjj, 0), tensor2d([[-1]]), tensor2d([[1]]));\n\n        const u1 = sub(rjj, mul(s, normX));\n        const wPre = div(rjEnd1, u1);\n        if (wPre.shape[0] === 1) {\n          w = clone(one2D);\n        } else {\n          w = concat(\n              [\n                one2D,\n                slice(wPre, [1, 0], [wPre.shape[0] - 1, wPre.shape[1]]) as\n                    Tensor2D\n              ],\n              0);\n        }\n        const tau = neg(div(matMul(s, u1), normX)) as Tensor2D;\n\n        // -- R := HR, Q := QH.\n        const rjEndAll = slice(r, [j, 0], [m - j, n]);\n        const tauTimesW: Tensor2D = mul(tau, w);\n        const wT: Tensor2D = transpose(w);\n        if (j === 0) {\n          r = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));\n        } else {\n          const rTimesTau: Tensor2D =\n              sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));\n          r = concat([slice(r, [0, 0], [j, n]), rTimesTau], 0);\n        }\n        const tawTimesWT: Tensor2D = transpose(tauTimesW);\n        const qAllJEnd = slice(q, [0, j], [m, q.shape[1] - j]);\n        if (j === 0) {\n          q = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));\n        } else {\n          const qTimesTau: Tensor2D =\n              sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));\n          q = concat([slice(q, [0, 0], [m, j]), qTimesTau], 1);\n        }\n        return [w, r, q];\n      });\n      dispose([rTemp, wTemp, qTemp]);\n    }\n\n    if (!fullMatrices && m > n) {\n      q = slice(q, [0, 0], [m, n]);\n      r = slice(r, [0, 0], [n, n]);\n    }\n\n    return [q, r];\n  }) as [Tensor2D, Tensor2D];\n}\n\nexport const qr = op({qr_});\n"]}","import { convertToTensor } from '../../tensor_util_env';\nimport { cast } from '../cast';\nimport { div } from '../div';\nimport { Reduction } from '../loss_ops_utils';\nimport { mean } from '../mean';\nimport { mul } from '../mul';\nimport { notEqual } from '../not_equal';\nimport { ones } from '../ones';\nimport { op } from '../operation';\nimport { scalar } from '../scalar';\nimport { sum } from '../sum';\n/**\n * Computes the weighted loss between two tensors.\n *\n * @param losses Tensor of shape `[batch_size, d1, ... dN]`.\n * @param weights Tensor whose rank is either 0, or the same rank as\n * `losses`, and must be broadcastable to `losses` (i.e., all\n * dimensions must be either `1`, or the same as the corresponding\n * `losses` dimension).\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction computeWeightedLoss_(losses, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n const $losses = convertToTensor(losses, 'losses', 'computeWeightedLoss');\n let $weights = null;\n if (weights != null) {\n $weights = convertToTensor(weights, 'weights', 'computeWeightedLoss');\n }\n const weightedLoss = ($weights == null) ? $losses : mul($losses, $weights);\n if (reduction === Reduction.NONE) {\n return weightedLoss;\n }\n if (reduction === Reduction.SUM) {\n return sum(weightedLoss);\n }\n if (reduction === Reduction.MEAN) {\n if ($weights == null) {\n return mean(weightedLoss);\n }\n else {\n const broadcastFactor = $losses.size / $weights.size;\n const result = div(sum(weightedLoss), sum($weights));\n return broadcastFactor > 1 ? div(result, scalar(broadcastFactor)) :\n result;\n }\n }\n if (reduction === Reduction.SUM_BY_NONZERO_WEIGHTS) {\n if ($weights == null) {\n return div(sum(weightedLoss), scalar($losses.size));\n }\n else {\n const broadcastedWeights = mul($weights, ones($losses.shape));\n const numNonZeros = cast(sum(notEqual(broadcastedWeights, scalar(0))), 'float32');\n return div(sum(weightedLoss), numNonZeros);\n }\n }\n throw Error(`Unknown reduction: ${reduction}`);\n}\nexport const computeWeightedLoss = op({ computeWeightedLoss_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { abs } from '../abs';\nimport { Reduction } from '../loss_ops_utils';\nimport { op } from '../operation';\nimport { sub } from '../sub';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the absolute difference loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n * 'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n * `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n * must be either `1`, or the same as the corresponding `losses`\n * dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n * `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction absoluteDifference_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n const $labels = convertToTensor(labels, 'labels', 'absoluteDifference');\n const $predictions = convertToTensor(predictions, 'predictions', 'absoluteDifference');\n let $weights = null;\n if (weights != null) {\n $weights = convertToTensor(weights, 'weights', 'absoluteDifference');\n }\n assertShapesMatch($labels.shape, $predictions.shape, 'Error in absoluteDifference: ');\n const losses = abs(sub($labels, $predictions));\n return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const absoluteDifference = op({ absoluteDifference_ });\n//# 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{ convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { Reduction } from '../loss_ops_utils';\nimport { mul } from '../mul';\nimport { op } from '../operation';\nimport { scalar } from '../scalar';\nimport { sub } from '../sub';\nimport { sum } from '../sum';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the cosine distance loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n * 'predictions'.\n * @param predictions The predicted outputs.\n * @param axis The dimension along which the cosine distance is computed.\n * @param weights Tensor whose rank is either 0, or the same rank as\n * `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n * must be either `1`, or the same as the corresponding `losses`\n * dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n * `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction cosineDistance_(labels, predictions, axis, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n const $labels = convertToTensor(labels, 'labels', 'cosineDistance');\n const $predictions = convertToTensor(predictions, 'predictions', 'cosineDistance');\n let $weights = null;\n if (weights != null) {\n $weights = convertToTensor(weights, 'weights', 'cosineDistance');\n }\n assertShapesMatch($labels.shape, $predictions.shape, 'Error in cosineDistance: ');\n const one = scalar(1);\n const losses = sub(one, sum(mul($labels, $predictions), axis, true));\n return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const cosineDistance = op({ cosineDistance_ });\n//# 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{ convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { Reduction } from '../loss_ops_utils';\nimport { mul } from '../mul';\nimport { op } from '../operation';\nimport { relu } from '../relu';\nimport { scalar } from '../scalar';\nimport { sub } from '../sub';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the Hinge loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n * 'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n * `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n * must be either `1`, or the same as the corresponding `losses`\n * dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n * `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction hingeLoss_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n let $labels = convertToTensor(labels, 'labels', 'hingeLoss');\n const $predictions = convertToTensor(predictions, 'predictions', 'hingeLoss');\n let $weights = null;\n if (weights != null) {\n $weights = convertToTensor(weights, 'weights', 'hingeLoss');\n }\n assertShapesMatch($labels.shape, $predictions.shape, 'Error in hingeLoss: ');\n const one = scalar(1);\n // Convert binary labels to (-1, 1)\n $labels = sub(mul(scalar(2), $labels), one);\n const losses = relu(sub(one, mul($labels, $predictions)));\n return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const hingeLoss = op({ hingeLoss_ });\n//# 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9uc3Qgb25lID0gc2NhbGFyKDEpO1xuICAvLyBDb252ZXJ0IGJpbmFyeSBsYWJlbHMgdG8gKC0xLCAxKVxuICAkbGFiZWxzID0gc3ViKG11bChzY2FsYXIoMiksICRsYWJlbHMpLCBvbmUpO1xuICBjb25zdCBsb3NzZXMgPSByZWx1KHN1YihvbmUsIG11bCgkbGFiZWxzLCAkcHJlZGljdGlvbnMpKSk7XG4gIHJldHVybiBjb21wdXRlV2VpZ2h0ZWRMb3NzKGxvc3NlcywgJHdlaWdodHMsIHJlZHVjdGlvbik7XG59XG5leHBvcnQgY29uc3QgaGluZ2VMb3NzID0gb3Aoe2hpbmdlTG9zc199KTtcbiJdfQ==","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { abs } from '../abs';\nimport { add } from '../add';\nimport { Reduction } from '../loss_ops_utils';\nimport { minimum } from '../minimum';\nimport { mul } from '../mul';\nimport { op } from '../operation';\nimport { scalar } from '../scalar';\nimport { square } from '../square';\nimport { sub } from '../sub';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the huber loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n * 'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n * `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n * must be either `1`, or the same as the corresponding `losses`\n * dimension).\n * @param delta Point where huber loss changes from quadratic to linear.\n * @param reduction Type of reduction to apply to loss. Should be of type\n * `Reduction`.\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction huberLoss_(labels, predictions, weights, delta = 1.0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n const $labels = convertToTensor(labels, 'labels', 'huberLoss');\n const $predictions = convertToTensor(predictions, 'predictions', 'huberLoss');\n let $weights = null;\n if (weights != null) {\n $weights = convertToTensor(weights, 'weights', 'huberLoss');\n }\n assertShapesMatch($labels.shape, $predictions.shape, 'Error in huberLoss: ');\n const deltaScalar = scalar(delta);\n const error = abs(sub($predictions, $labels));\n const quadratic = minimum(error, deltaScalar);\n const linear = sub(error, quadratic);\n const losses = add(mul(scalar(0.5), square(quadratic)), mul(deltaScalar, linear));\n return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const huberLoss = op({ huberLoss_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { add } from '../add';\nimport { log } from '../log';\nimport { Reduction } from '../loss_ops_utils';\nimport { mul } from '../mul';\nimport { neg } from '../neg';\nimport { op } from '../operation';\nimport { scalar } from '../scalar';\nimport { sub } from '../sub';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the log loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n * 'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n * `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n * must be either `1`, or the same as the corresponding `losses`\n * dimension).\n * @param epsilon A small increment to avoid taking log of zero\n * @param reduction Type of reduction to apply to loss. Should be of type\n * `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction logLoss_(labels, predictions, weights, epsilon = 1e-7, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n const $labels = convertToTensor(labels, 'labels', 'logLoss');\n const $predictions = convertToTensor(predictions, 'predictions', 'logLoss');\n let $weights = null;\n if (weights != null) {\n $weights = convertToTensor(weights, 'weights', 'logLoss');\n }\n assertShapesMatch($labels.shape, $predictions.shape, 'Error in logLoss: ');\n const one = scalar(1);\n const epsilonScalar = scalar(epsilon);\n const l1 = neg(mul($labels, log(add($predictions, epsilonScalar))));\n const l2 = mul(sub(one, $labels), log(add(sub(one, $predictions), epsilonScalar)));\n const losses = sub(l1, l2);\n return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const logLoss = op({ logLoss_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { Reduction } from '../loss_ops_utils';\nimport { op } from '../operation';\nimport { squaredDifference } from '../squared_difference';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the mean squared error between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n * 'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n * `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n * must be either `1`, or the same as the corresponding `losses`\n * dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n * `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction meanSquaredError_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n const $labels = convertToTensor(labels, 'labels', 'meanSquaredError');\n const $predictions = convertToTensor(predictions, 'predictions', 'meanSquaredError');\n let $weights = null;\n if (weights != null) {\n $weights = convertToTensor(weights, 'weights', 'meanSquaredError');\n }\n assertShapesMatch($labels.shape, $predictions.shape, 'Error in meanSquaredError: ');\n const losses = squaredDifference($labels, $predictions);\n return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const meanSquaredError = op({ meanSquaredError_ });\n//# 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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { abs } from '../abs';\nimport { add } from '../add';\nimport { exp } from '../exp';\nimport { log1p } from '../log1p';\nimport { Reduction } from '../loss_ops_utils';\nimport { mul } from '../mul';\nimport { neg } from '../neg';\nimport { op } from '../operation';\nimport { relu } from '../relu';\nimport { scalar } from '../scalar';\nimport { sub } from '../sub';\nimport { computeWeightedLoss } from './compute_weighted_loss';\nfunction sigmoidCrossEntropyWithLogits_(labels, logits) {\n const $labels = convertToTensor(labels, 'labels', 'sigmoidCrossEntropyWithLogits');\n const $logits = convertToTensor(logits, 'logits', 'sigmoidCrossEntropyWithLogits');\n assertShapesMatch($labels.shape, $logits.shape, 'Error in sigmoidCrossEntropyWithLogits: ');\n /**\n * Implementation Details:\n *\n * For brevity, let `x = logits`, `z = labels`. The logistic loss is\n * z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))\n * = z * -log(1 / (1 + exp(-x))) + (1 - z) * -log(exp(-x) / (1 + exp(-x)))\n * = z * log(1 + exp(-x)) + (1 - z) * (-log(exp(-x)) + log(1 + exp(-x)))\n * = z * log(1 + exp(-x)) + (1 - z) * (x + log(1 + exp(-x))\n * = (1 - z) * x + log(1 + exp(-x))\n * = x - x * z + log(1 + exp(-x))\n *\n * For x < 0, to avoid overflow in exp(-x), we reformulate the above\n * x - x * z + log(1 + exp(-x))\n * = log(exp(x)) - x * z + log(1 + exp(-x))\n * = - x * z + log(1 + exp(x))\n *\n * Hence, to ensure stability and avoid overflow, the implementation uses\n * this equivalent formulation:\n * max(x, 0) - x * z + log(1 + exp(-abs(x)))\n */\n const maxOutput = relu($logits);\n const outputXTarget = mul($logits, $labels);\n const sigmoidOutput = log1p(exp(neg(abs($logits))));\n return add(sub(maxOutput, outputXTarget), sigmoidOutput);\n}\n/**\n * Computes the sigmoid cross entropy loss between two tensors.\n *\n * If labelSmoothing is nonzero, smooth the labels towards 1/2:\n *\n * newMulticlassLabels = multiclassLabels * (1 - labelSmoothing)\n * + 0.5 * labelSmoothing\n *\n * @param multiClassLabels The ground truth output tensor of shape\n * [batch_size, num_classes], same dimensions as 'predictions'.\n * @param logits The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n * `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n * must be either `1`, or the same as the corresponding `losses`\n * dimension).\n * @param labelSmoothing If greater than 0, then smooth the labels.\n * @param reduction Type of reduction to apply to loss. Should be of type\n * `Reduction`\n *\n * @doc { heading: 'Training', subheading: 'Losses', namespace: 'losses' }\n */\nfunction sigmoidCrossEntropy_(multiClassLabels, logits, weights, labelSmoothing = 0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n let $multiClassLabels = convertToTensor(multiClassLabels, 'multiClassLabels', 'sigmoidCrossEntropy');\n const $logits = convertToTensor(logits, 'logits', 'sigmoidCrossEntropy');\n let $weights = null;\n if (weights != null) {\n $weights = convertToTensor(weights, 'weights', 'sigmoidCrossEntropy');\n }\n assertShapesMatch($multiClassLabels.shape, $logits.shape, 'Error in sigmoidCrossEntropy: ');\n if (labelSmoothing > 0) {\n const labelSmoothingScalar = scalar(labelSmoothing);\n const one = scalar(1);\n const half = scalar(0.5);\n $multiClassLabels =\n add(mul($multiClassLabels, sub(one, labelSmoothingScalar)), mul(half, labelSmoothingScalar));\n }\n const losses = sigmoidCrossEntropyWithLogits_($multiClassLabels, $logits);\n return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const sigmoidCrossEntropy = op({ sigmoidCrossEntropy_ });\n//# sourceMappingURL=data:application/json;base64,{"version":3,"file":"sigmoid_cross_entropy.js","sourceRoot":"","sources":["../../../../../../../tfjs-core/src/ops/losses/sigmoid_cross_entropy.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAGH,OAAO,EAAC,eAAe,EAAC,MAAM,uBAAuB,CAAC;AAEtD,OAAO,EAAC,iBAAiB,EAAC,MAAM,YAAY,CAAC;AAC7C,OAAO,EAAC,GAAG,EAAC,MAAM,QAAQ,CAAC;AAC3B,OAAO,EAAC,GAAG,EAAC,MAAM,QAAQ,CAAC;AAC3B,OAAO,EAAC,GAAG,EAAC,MAAM,QAAQ,CAAC;AAC3B,OAAO,EAAC,KAAK,EAAC,MAAM,UAAU,CAAC;AAC/B,OAAO,EAAC,SAAS,EAAC,MAAM,mBAAmB,CAAC;AAC5C,OAAO,EAAC,GAAG,EAAC,MAAM,QAAQ,CAAC;AAC3B,OAAO,EAAC,GAAG,EAAC,MAAM,QAAQ,CAAC;AAC3B,OAAO,EAAC,EAAE,EAAC,MAAM,cAAc,CAAC;AAChC,OAAO,EAAC,IAAI,EAAC,MAAM,SAAS,CAAC;AAC7B,OAAO,EAAC,MAAM,EAAC,MAAM,WAAW,CAAC;AACjC,OAAO,EAAC,GAAG,EAAC,MAAM,QAAQ,CAAC;AAE3B,OAAO,EAAC,mBAAmB,EAAC,MAAM,yBAAyB,CAAC;AAE5D,SAAS,8BAA8B,CACnC,MAAoB,EAAE,MAAoB;IAC5C,MAAM,OAAO,GACT,eAAe,CAAC,MAAM,EAAE,QAAQ,EAAE,+BAA+B,CAAC,CAAC;IACvE,MAAM,OAAO,GACT,eAAe,CAAC,MAAM,EAAE,QAAQ,EAAE,+BAA+B,CAAC,CAAC;IACvE,iBAAiB,CACb,OAAO,CAAC,KAAK,EAAE,OAAO,CAAC,KAAK,EAAE,0CAA0C,CAAC,CAAC;IAE9E;;;;;;;;;;;;;;;;;;;OAmBG;IACH,MAAM,SAAS,GAAG,IAAI,CAAC,OAAO,CAAC,CAAC;IAChC,MAAM,aAAa,GAAG,GAAG,CAAC,OAAO,EAAE,OAAO,CAAC,CAAC;IAC5C,MAAM,aAAa,GAAG,KAAK,CAAC,GAAG,CAAC,GAAG,CAAC,GAAG,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC;IAEpD,OAAO,GAAG,CAAC,GAAG,CAAC,SAAS,EAAE,aAAa,CAAC,EAAE,aAAa,CAAC,CAAC;AAC3D,CAAC;AAED;;;;;;;;;;;;;;;;;;;;GAoBG;AACH,SAAS,oBAAoB,CACzB,gBAA8B,EAAE,MAAoB,EACpD,OAA2B,EAAE,cAAc,GAAG,CAAC,EAC/C,SAAS,GAAG,SAAS,CAAC,sBAAsB;IAC9C,IAAI,iBAAiB,GAAG,eAAe,CACnC,gBAAgB,EAAE,kBAAkB,EAAE,qBAAqB,CAAC,CAAC;IACjE,MAAM,OAAO,GAAG,eAAe,CAAC,MAAM,EAAE,QAAQ,EAAE,qBAAqB,CAAC,CAAC;IACzE,IAAI,QAAQ,GAAW,IAAI,CAAC;IAC5B,IAAI,OAAO,IAAI,IAAI,EAAE;QACnB,QAAQ,GAAG,eAAe,CAAC,OAAO,EAAE,SAAS,EAAE,qBAAqB,CAAC,CAAC;KACvE;IACD,iBAAiB,CACb,iBAAiB,CAAC,KAAK,EAAE,OAAO,CAAC,KAAK,EAAE,gCAAgC,CAAC,CAAC;IAE9E,IAAI,cAAc,GAAG,CAAC,EAAE;QACtB,MAAM,oBAAoB,GAAG,MAAM,CAAC,cAAc,CAAC,CAAC;QACpD,MAAM,GAAG,GAAG,MAAM,CAAC,CAAC,CAAC,CAAC;QACtB,MAAM,IAAI,GAAG,MAAM,CAAC,GAAG,CAAC,CAAC;QAEzB,iBAAiB;YACb,GAAG,CAAC,GAAG,CAAC,iBAAiB,EAAE,GAAG,CAAC,GAAG,EAAE,oBAAoB,CAAC,CAAC,EACtD,GAAG,CAAC,IAAI,EAAE,oBAAoB,CAAC,CAAC,CAAC;KAC1C;IACD,MAAM,MAAM,GAAG,8BAA8B,CAAC,iBAAiB,EAAE,OAAO,CAAC,CAAC;IAE1E,OAAO,mBAAmB,CAAC,MAAM,EAAE,QAAQ,EAAE,SAAS,CAAC,CAAC;AAC1D,CAAC;AAED,MAAM,CAAC,MAAM,mBAAmB,GAAG,EAAE,CAAC,EAAC,oBAAoB,EAAC,CAAC,CAAC","sourcesContent":["/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../../tensor';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport {assertShapesMatch} from '../../util';\nimport {abs} from '../abs';\nimport {add} from '../add';\nimport {exp} from '../exp';\nimport {log1p} from '../log1p';\nimport {Reduction} from '../loss_ops_utils';\nimport {mul} from '../mul';\nimport {neg} from '../neg';\nimport {op} from '../operation';\nimport {relu} from '../relu';\nimport {scalar} from '../scalar';\nimport {sub} from '../sub';\n\nimport {computeWeightedLoss} from './compute_weighted_loss';\n\nfunction sigmoidCrossEntropyWithLogits_<T extends Tensor, O extends Tensor>(\n    labels: T|TensorLike, logits: T|TensorLike): O {\n  const $labels =\n      convertToTensor(labels, 'labels', 'sigmoidCrossEntropyWithLogits');\n  const $logits =\n      convertToTensor(logits, 'logits', 'sigmoidCrossEntropyWithLogits');\n  assertShapesMatch(\n      $labels.shape, $logits.shape, 'Error in sigmoidCrossEntropyWithLogits: ');\n\n  /**\n   * Implementation Details:\n   *\n   * For brevity, let `x = logits`, `z = labels`.  The logistic loss is\n   *     z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))\n   *   = z * -log(1 / (1 + exp(-x))) + (1 - z) * -log(exp(-x) / (1 + exp(-x)))\n   *   = z * log(1 + exp(-x)) + (1 - z) * (-log(exp(-x)) + log(1 + exp(-x)))\n   *   = z * log(1 + exp(-x)) + (1 - z) * (x + log(1 + exp(-x))\n   *   = (1 - z) * x + log(1 + exp(-x))\n   *   = x - x * z + log(1 + exp(-x))\n   *\n   *   For x < 0, to avoid overflow in exp(-x), we reformulate the above\n   *     x - x * z + log(1 + exp(-x))\n   *   = log(exp(x)) - x * z + log(1 + exp(-x))\n   *   = - x * z + log(1 + exp(x))\n   *\n   * Hence, to ensure stability and avoid overflow, the implementation uses\n   * this equivalent formulation:\n   *     max(x, 0) - x * z + log(1 + exp(-abs(x)))\n   */\n  const maxOutput = relu($logits);\n  const outputXTarget = mul($logits, $labels);\n  const sigmoidOutput = log1p(exp(neg(abs($logits))));\n\n  return add(sub(maxOutput, outputXTarget), sigmoidOutput);\n}\n\n/**\n * Computes the sigmoid cross entropy loss between two tensors.\n *\n * If labelSmoothing is nonzero, smooth the labels towards 1/2:\n *\n *   newMulticlassLabels = multiclassLabels * (1 - labelSmoothing)\n *                         + 0.5 * labelSmoothing\n *\n * @param multiClassLabels The ground truth output tensor of shape\n * [batch_size, num_classes], same dimensions as 'predictions'.\n * @param logits The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param labelSmoothing If greater than 0, then smooth the labels.\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc { heading: 'Training', subheading: 'Losses', namespace: 'losses' }\n */\nfunction sigmoidCrossEntropy_<T extends Tensor, O extends Tensor>(\n    multiClassLabels: T|TensorLike, logits: T|TensorLike,\n    weights?: Tensor|TensorLike, labelSmoothing = 0,\n    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n  let $multiClassLabels = convertToTensor(\n      multiClassLabels, 'multiClassLabels', 'sigmoidCrossEntropy');\n  const $logits = convertToTensor(logits, 'logits', 'sigmoidCrossEntropy');\n  let $weights: Tensor = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, 'weights', 'sigmoidCrossEntropy');\n  }\n  assertShapesMatch(\n      $multiClassLabels.shape, $logits.shape, 'Error in sigmoidCrossEntropy: ');\n\n  if (labelSmoothing > 0) {\n    const labelSmoothingScalar = scalar(labelSmoothing);\n    const one = scalar(1);\n    const half = scalar(0.5);\n\n    $multiClassLabels =\n        add(mul($multiClassLabels, sub(one, labelSmoothingScalar)),\n            mul(half, labelSmoothingScalar));\n  }\n  const losses = sigmoidCrossEntropyWithLogits_($multiClassLabels, $logits);\n\n  return computeWeightedLoss(losses, $weights, reduction);\n}\n\nexport const sigmoidCrossEntropy = op({sigmoidCrossEntropy_});\n"]}","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { customGrad } from '../../gradients';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { add } from '../add';\nimport { expandShapeToKeepDim } from '../axis_util';\nimport { cast } from '../cast';\nimport { div } from '../div';\nimport { exp } from '../exp';\nimport { logSumExp } from '../log_sum_exp';\nimport { Reduction } from '../loss_ops_utils';\nimport { mul } from '../mul';\nimport { neg } from '../neg';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\nimport { scalar } from '../scalar';\nimport { sub } from '../sub';\nimport { sum } from '../sum';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes softmax cross entropy between logits and labels.\n *\n * Measures the probability error in discrete classification tasks in which\n * the classes are mutually exclusive (each entry is in exactly one class).\n * For example, each CIFAR-10 image is labeled with one and only one label: an\n * image can be a dog or a truck, but not both.\n *\n * `NOTE`: While the classes are mutually exclusive, their probabilities need\n * not be. All that is required is that each row of labels is a valid\n * probability distribution. If they are not, the computation of the gradient\n * will be incorrect.\n *\n * `WARNING`: This op expects unscaled logits, since it performs a softmax on\n * logits internally for efficiency. Do not call this op with the output of\n * softmax, as it will produce incorrect results.\n *\n * logits and labels must have the same shape, e.g. [batch_size, num_classes]\n * and the same dtype.\n * @param labels The labels array.\n * @param logits The logits array.\n * @param dim The dimension softmax would be performed on. Defaults to `-1`\n * which indicates the last dimension.\n */\nfunction softmaxCrossEntropyWithLogits_(labels, logits, dim = -1) {\n if (dim === -1) {\n dim = logits.rank - 1;\n }\n if (dim !== logits.rank - 1) {\n throw Error(`Softmax cross entropy along a non-last dimension is not yet ` +\n `supported. Labels / logits was rank ${logits.rank} ` +\n `and dim was ${dim}`);\n }\n // Use a custom gradient for numerical stability.\n const customOp = customGrad((labels, logits, save) => {\n // Reference:\n // 1. http://cs231n.github.io/linear-classify/#softmax\n // 2. https://blog.feedly.com/tricks-of-the-trade-logsumexp/\n const keepDims = true;\n const lse = logSumExp(logits, [dim], keepDims);\n const logResult = sub(cast(logits, 'float32'), lse);\n save([labels, logResult]);\n const costVector = neg(mul(logResult, labels));\n const value = sum(costVector, [dim]);\n const gradFunc = (dy, saved) => {\n const [labels, logResult] = saved;\n const dyShape = expandShapeToKeepDim(dy.shape, [dim]);\n return [\n mul(reshape(dy, dyShape), sub(cast(labels, 'float32'), exp(logResult))),\n mul(reshape(dy, dyShape), sub(exp(logResult), cast(labels, 'float32'))),\n ];\n };\n return { value, gradFunc };\n });\n return customOp(labels, logits);\n}\n/**\n * Computes the softmax cross entropy loss between two tensors.\n *\n * If labelSmoothing is nonzero, smooth the labels towards 1/2:\n *\n * newOnehotLabels = onehotLabels * (1 - labelSmoothing)\n * + labelSmoothing / numClasses\n *\n * @param onehotLabels One hot encoded labels\n * [batch_size, num_classes], same dimensions as 'predictions'.\n * @param logits The predicted outputs.\n * @param weights Tensor whose rank is either 0, or 1, and must be\n * broadcastable to `loss` of shape [batch_size]\n * @param labelSmoothing If greater than 0, then smooth the labels.\n * @param reduction Type of reduction to apply to loss. Should be of type\n * `Reduction`\n *\n * @doc { heading: 'Training', subheading: 'Losses', namespace: 'losses' }\n */\nfunction softmaxCrossEntropy_(onehotLabels, logits, weights, labelSmoothing = 0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n let $onehotLabels = convertToTensor(onehotLabels, 'onehotLabels', 'softmaxCrossEntropy');\n const $logits = convertToTensor(logits, 'logits', 'softmaxCrossEntropy');\n let $weights = null;\n if (weights != null) {\n $weights = convertToTensor(weights, 'weights', 'softmaxCrossEntropy');\n }\n assertShapesMatch($onehotLabels.shape, $logits.shape, 'Error in softmaxCrossEntropy: ');\n if (labelSmoothing > 0) {\n const labelSmoothingScalar = scalar(labelSmoothing);\n const one = scalar(1);\n const numClasses = scalar($onehotLabels.shape[1]);\n $onehotLabels =\n add(mul($onehotLabels, sub(one, labelSmoothingScalar)), div(labelSmoothingScalar, numClasses));\n }\n const losses = softmaxCrossEntropyWithLogits_($onehotLabels, $logits);\n return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const softmaxCrossEntropy = op({ softmaxCrossEntropy_ });\n//# sourceMappingURL=data:application/json;base64,{"version":3,"file":"softmax_cross_entropy.js","sourceRoot":"","sources":["../../../../../../../tfjs-core/src/ops/losses/softmax_cross_entropy.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AACH,OAAO,EAAC,UAAU,EAAC,MAAM,iBAAiB,CAAC;AAG3C,OAAO,EAAC,eAAe,EAAC,MAAM,uBAAuB,CAAC;AAEtD,OAAO,EAAC,iBAAiB,EAAC,MAAM,YAAY,CAAC;AAC7C,OAAO,EAAC,GAAG,EAAC,MAAM,QAAQ,CAAC;AAC3B,OAAO,EAAC,oBAAoB,EAAC,MAAM,cAAc,CAAC;AAClD,OAAO,EAAC,IAAI,EAAC,MAAM,SAAS,CAAC;AAC7B,OAAO,EAAC,GAAG,EAAC,MAAM,QAAQ,CAAC;AAC3B,OAAO,EAAC,GAAG,EAAC,MAAM,QAAQ,CAAC;AAC3B,OAAO,EAAC,SAAS,EAAC,MAAM,gBAAgB,CAAC;AACzC,OAAO,EAAC,SAAS,EAAC,MAAM,mBAAmB,CAAC;AAC5C,OAAO,EAAC,GAAG,EAAC,MAAM,QAAQ,CAAC;AAC3B,OAAO,EAAC,GAAG,EAAC,MAAM,QAAQ,CAAC;AAC3B,OAAO,EAAC,EAAE,EAAC,MAAM,cAAc,CAAC;AAChC,OAAO,EAAC,OAAO,EAAC,MAAM,YAAY,CAAC;AACnC,OAAO,EAAC,MAAM,EAAC,MAAM,WAAW,CAAC;AACjC,OAAO,EAAC,GAAG,EAAC,MAAM,QAAQ,CAAC;AAC3B,OAAO,EAAC,GAAG,EAAC,MAAM,QAAQ,CAAC;AAE3B,OAAO,EAAC,mBAAmB,EAAC,MAAM,yBAAyB,CAAC;AAE5D;;;;;;;;;;;;;;;;;;;;;;;GAuBG;AACH,SAAS,8BAA8B,CACnC,MAAS,EAAE,MAAS,EAAE,GAAG,GAAG,CAAC,CAAC;IAChC,IAAI,GAAG,KAAK,CAAC,CAAC,EAAE;QACd,GAAG,GAAG,MAAM,CAAC,IAAI,GAAG,CAAC,CAAC;KACvB;IAED,IAAI,GAAG,KAAK,MAAM,CAAC,IAAI,GAAG,CAAC,EAAE;QAC3B,MAAM,KAAK,CACP,8DAA8D;YAC9D,uCAAuC,MAAM,CAAC,IAAI,GAAG;YACrD,eAAe,GAAG,EAAE,CAAC,CAAC;KAC3B;IACD,iDAAiD;IACjD,MAAM,QAAQ,GACV,UAAU,CAAC,CAAC,MAAc,EAAE,MAAc,EAAE,IAAkB,EAAE,EAAE;QAChE,aAAa;QACb,wDAAwD;QACxD,8DAA8D;QAC9D,MAAM,QAAQ,GAAG,IAAI,CAAC;QACtB,MAAM,GAAG,GAAG,SAAS,CAAC,MAAM,EAAE,CAAC,GAAG,CAAC,EAAE,QAAQ,CAAC,CAAC;QAC/C,MAAM,SAAS,GAAG,GAAG,CAAC,IAAI,CAAC,MAAM,EAAE,SAAS,CAAC,EAAE,GAAG,CAAC,CAAC;QACpD,IAAI,CAAC,CAAC,MAAM,EAAE,SAAS,CAAC,CAAC,CAAC;QAE1B,MAAM,UAAU,GAAG,GAAG,CAAC,GAAG,CAAC,SAAS,EAAE,MAAM,CAAC,CAAC,CAAC;QAC/C,MAAM,KAAK,GAAM,GAAG,CAAC,UAAU,EAAE,CAAC,GAAG,CAAC,CAAC,CAAC;QAExC,MAAM,QAAQ,GAAG,CAAC,EAAK,EAAE,KAAe,EAAE,EAAE;YAC1C,MAAM,CAAC,MAAM,EAAE,SAAS,CAAC,GAAG,KAAK,CAAC;YAClC,MAAM,OAAO,GAAG,oBAAoB,CAAC,EAAE,CAAC,KAAK,EAAE,CAAC,GAAG,CAAC,CAAC,CAAC;YACtD,OAAO;gBACL,GAAG,CAAC,OAAO,CAAC,EAAE,EAAE,OAAO,CAAC,EACpB,GAAG,CAAC,IAAI,CAAC,MAAM,EAAE,SAAS,CAAC,EAAE,GAAG,CAAC,SAAS,CAAC,CAAC,CAAC;gBACjD,GAAG,CAAC,OAAO,CAAC,EAAE,EAAE,OAAO,CAAC,EACpB,GAAG,CAAC,GAAG,CAAC,SAAS,CAAC,EAAE,IAAI,CAAC,MAAM,EAAE,SAAS,CAAC,CAAC,CAAC;aAClD,CAAC;QACJ,CAAC,CAAC;QACF,OAAO,EAAC,KAAK,EAAE,QAAQ,EAAC,CAAC;IAC3B,CAAC,CAAC,CAAC;IAEP,OAAO,QAAQ,CAAC,MAAM,EAAE,MAAM,CAAC,CAAC;AAClC,CAAC;AAED;;;;;;;;;;;;;;;;;;GAkBG;AACH,SAAS,oBAAoB,CACzB,YAA0B,EAAE,MAAoB,EAChD,OAA2B,EAAE,cAAc,GAAG,CAAC,EAC/C,SAAS,GAAG,SAAS,CAAC,sBAAsB;IAC9C,IAAI,aAAa,GACb,eAAe,CAAC,YAAY,EAAE,cAAc,EAAE,qBAAqB,CAAC,CAAC;IACzE,MAAM,OAAO,GAAG,eAAe,CAAC,MAAM,EAAE,QAAQ,EAAE,qBAAqB,CAAC,CAAC;IACzE,IAAI,QAAQ,GAAW,IAAI,CAAC;IAE5B,IAAI,OAAO,IAAI,IAAI,EAAE;QACnB,QAAQ,GAAG,eAAe,CAAC,OAAO,EAAE,SAAS,EAAE,qBAAqB,CAAC,CAAC;KACvE;IAED,iBAAiB,CACb,aAAa,CAAC,KAAK,EAAE,OAAO,CAAC,KAAK,EAAE,gCAAgC,CAAC,CAAC;IAE1E,IAAI,cAAc,GAAG,CAAC,EAAE;QACtB,MAAM,oBAAoB,GAAG,MAAM,CAAC,cAAc,CAAC,CAAC;QACpD,MAAM,GAAG,GAAG,MAAM,CAAC,CAAC,CAAC,CAAC;QACtB,MAAM,UAAU,GAAG,MAAM,CAAC,aAAa,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC;QAElD,aAAa;YACT,GAAG,CAAC,GAAG,CAAC,aAAa,EAAE,GAAG,CAAC,GAAG,EAAE,oBAAoB,CAAC,CAAC,EAClD,GAAG,CAAC,oBAAoB,EAAE,UAAU,CAAC,CAAC,CAAC;KAChD;IAED,MAAM,MAAM,GAAG,8BAA8B,CAAC,aAAa,EAAE,OAAO,CAAC,CAAC;IAEtE,OAAO,mBAAmB,CAAC,MAAM,EAAE,QAAQ,EAAE,SAAS,CAAC,CAAC;AAC1D,CAAC;AAED,MAAM,CAAC,MAAM,mBAAmB,GAAG,EAAE,CAAC,EAAC,oBAAoB,EAAC,CAAC,CAAC","sourcesContent":["/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {customGrad} from '../../gradients';\nimport {Tensor} from '../../tensor';\nimport {GradSaveFunc} from '../../tensor_types';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport {assertShapesMatch} from '../../util';\nimport {add} from '../add';\nimport {expandShapeToKeepDim} from '../axis_util';\nimport {cast} from '../cast';\nimport {div} from '../div';\nimport {exp} from '../exp';\nimport {logSumExp} from '../log_sum_exp';\nimport {Reduction} from '../loss_ops_utils';\nimport {mul} from '../mul';\nimport {neg} from '../neg';\nimport {op} from '../operation';\nimport {reshape} from '../reshape';\nimport {scalar} from '../scalar';\nimport {sub} from '../sub';\nimport {sum} from '../sum';\n\nimport {computeWeightedLoss} from './compute_weighted_loss';\n\n/**\n * Computes softmax cross entropy between logits and labels.\n *\n * Measures the probability error in discrete classification tasks in which\n * the classes are mutually exclusive (each entry is in exactly one class).\n * For example, each CIFAR-10 image is labeled with one and only one label: an\n * image can be a dog or a truck, but not both.\n *\n * `NOTE`: While the classes are mutually exclusive, their probabilities need\n * not be. All that is required is that each row of labels is a valid\n * probability distribution. If they are not, the computation of the gradient\n * will be incorrect.\n *\n * `WARNING`: This op expects unscaled logits, since it performs a softmax on\n * logits internally for efficiency. Do not call this op with the output of\n * softmax, as it will produce incorrect results.\n *\n * logits and labels must have the same shape, e.g. [batch_size, num_classes]\n * and the same dtype.\n * @param labels The labels array.\n * @param logits The logits array.\n * @param dim The dimension softmax would be performed on. Defaults to `-1`\n *     which indicates the last dimension.\n */\nfunction softmaxCrossEntropyWithLogits_<T extends Tensor, O extends Tensor>(\n    labels: T, logits: T, dim = -1): O {\n  if (dim === -1) {\n    dim = logits.rank - 1;\n  }\n\n  if (dim !== logits.rank - 1) {\n    throw Error(\n        `Softmax cross entropy along a non-last dimension is not yet ` +\n        `supported. Labels / logits was rank ${logits.rank} ` +\n        `and dim was ${dim}`);\n  }\n  // Use a custom gradient for numerical stability.\n  const customOp =\n      customGrad((labels: Tensor, logits: Tensor, save: GradSaveFunc) => {\n        // Reference:\n        //   1. http://cs231n.github.io/linear-classify/#softmax\n        //   2. https://blog.feedly.com/tricks-of-the-trade-logsumexp/\n        const keepDims = true;\n        const lse = logSumExp(logits, [dim], keepDims);\n        const logResult = sub(cast(logits, 'float32'), lse);\n        save([labels, logResult]);\n\n        const costVector = neg(mul(logResult, labels));\n        const value: O = sum(costVector, [dim]);\n\n        const gradFunc = (dy: O, saved: Tensor[]) => {\n          const [labels, logResult] = saved;\n          const dyShape = expandShapeToKeepDim(dy.shape, [dim]);\n          return [\n            mul(reshape(dy, dyShape),\n                sub(cast(labels, 'float32'), exp(logResult))),\n            mul(reshape(dy, dyShape),\n                sub(exp(logResult), cast(labels, 'float32'))),\n          ];\n        };\n        return {value, gradFunc};\n      });\n\n  return customOp(labels, logits);\n}\n\n/**\n * Computes the softmax cross entropy loss between two tensors.\n *\n * If labelSmoothing is nonzero, smooth the labels towards 1/2:\n *\n *   newOnehotLabels = onehotLabels * (1 - labelSmoothing)\n *                         + labelSmoothing / numClasses\n *\n * @param onehotLabels One hot encoded labels\n *    [batch_size, num_classes], same dimensions as 'predictions'.\n * @param logits The predicted outputs.\n * @param weights Tensor whose rank is either 0, or 1, and must be\n *    broadcastable to `loss`  of shape [batch_size]\n * @param labelSmoothing If greater than 0, then smooth the labels.\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc { heading: 'Training', subheading: 'Losses', namespace: 'losses' }\n */\nfunction softmaxCrossEntropy_<T extends Tensor, O extends Tensor>(\n    onehotLabels: T|TensorLike, logits: T|TensorLike,\n    weights?: Tensor|TensorLike, labelSmoothing = 0,\n    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n  let $onehotLabels =\n      convertToTensor(onehotLabels, 'onehotLabels', 'softmaxCrossEntropy');\n  const $logits = convertToTensor(logits, 'logits', 'softmaxCrossEntropy');\n  let $weights: Tensor = null;\n\n  if (weights != null) {\n    $weights = convertToTensor(weights, 'weights', 'softmaxCrossEntropy');\n  }\n\n  assertShapesMatch(\n      $onehotLabels.shape, $logits.shape, 'Error in softmaxCrossEntropy: ');\n\n  if (labelSmoothing > 0) {\n    const labelSmoothingScalar = scalar(labelSmoothing);\n    const one = scalar(1);\n    const numClasses = scalar($onehotLabels.shape[1]);\n\n    $onehotLabels =\n        add(mul($onehotLabels, sub(one, labelSmoothingScalar)),\n            div(labelSmoothingScalar, numClasses));\n  }\n\n  const losses = softmaxCrossEntropyWithLogits_($onehotLabels, $logits);\n\n  return computeWeightedLoss(losses, $weights, reduction);\n}\n\nexport const softmaxCrossEntropy = op({softmaxCrossEntropy_});\n"]}","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { SparseFillEmptyRows } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { op } from '../operation';\n/**\n * The input SparseTensor is represented via the map of inputs {`indices`,\n * `values`, `denseShape`}. The output SparseTensor has the same `denseShape`\n * but with indices `outputIndices` and values `outputValues`. This op inserts a\n * single entry for every row that doesn't have any values. The index is created\n * as `[row, 0, ..., 0]` and the inserted value is `defaultValue`.\n *\n * For example, suppose `spInput` has shape [5, 6] and non-empty values:\n * [0, 1]: a\n * [0, 3]: b\n * [2, 0]: c\n * [3, 1]: d\n *\n * Rows 1 and 4 are empty, so the output will be of shape [5, 6] with values:\n * [0, 1]: a\n * [0, 3]: b\n * [1, 0]: `defaultValue`\n * [2, 0]: c\n * [3, 1]: d\n * [4, 0]: `defaultValue`\n *\n * The output SparseTensor will be in row-major order and will have the same\n * shape as the input.\n *\n * This op also returns an indicator vector shaped [dense_shape[0]] such that\n * emptyRowIndicator[i] = True iff row i was an empty row.\n *\n * And a reverse index map vector shaped [indices.shape[0]] that is used during\n * backpropagation, reverseIndexMap[i] = outi s.t. indices[i, j] ==\n * outputIndices[outi, j] for all j\n *\n * ```js\n * const result = tf.sparse.sparseFillEmptyRows(\n * [[0, 0], [1, 0], [1, 3], [1, 4], [3, 2], [3, 3]],\n * [0, 10, 13, 14, 32, 33], [5, 6], -1);\n * console.log(result);\n * result['outputIndices'].print(); // [[0, 0], [1, 0], [1, 3], [1, 4],\n * // [2, 0], [3, 2], [3, 3], [4, 0]]\n * result['outputValues'].print(); // [0, 10, 13, 14,-1, 32, 33, -1]\n * result['emptyRowIndicator'].print(); // [false, false, true, false, true]\n * result['reverseIndexMap'].print(); // [0, 1, 2, 3, 5, 6]\n * ```\n * @param indices: 2-D. the indices of the sparse tensor.\n * @param values: 1-D. the values of the sparse tensor.\n * @param denseShape: 1-D. the shape of the sparse tensor.\n * @param defaultValue: 0-D. default value to insert into location [row, 0, ...,\n * 0] for rows missing from the input sparse tensor.\n * @return A map with the following properties:\n * - outputIndices\n * - outputValues: 1-D. the values of the filled sparse tensor.\n * - emptyRowIndicator: 1-D. whether the dense row was missing in the input\n * sparse tensor.\n * - reverseIndexMap: 1-D. a map from the input indices to the output\n * indices.\n * @doc {heading: 'Operations', subheading: 'Sparse'}\n */\nfunction sparseFillEmptyRows_(indices, values, denseShape, defaultValue) {\n const $indices = convertToTensor(indices, 'indices', 'sparseFillEmptyRows', 'int32');\n const $values = convertToTensor(values, 'values', 'sparseFillEmptyRows');\n const $denseShape = convertToTensor(denseShape, 'denseShape', 'sparseFillEmptyRows', 'int32');\n const $defaultValue = convertToTensor(defaultValue, 'defaultValue', 'sparseFillEmptyRows', $values.dtype);\n if ($indices.rank !== 2) {\n throw new Error(`Indices should be Tensor2D but received shape\n ${$indices.shape}`);\n }\n if ($values.rank !== 1) {\n throw new Error(`Values should be Tensor1D but received shape ${$values.shape}`);\n }\n if ($denseShape.rank !== 1) {\n throw new Error(`Dense shape should be Tensor1D but received shape ${$denseShape.shape}`);\n }\n if ($defaultValue.rank !== 0) {\n throw new Error(`Default value should be a scalar but received shape ${$defaultValue.shape}`);\n }\n const inputs = {\n indices: $indices,\n values: $values,\n denseShape: $denseShape,\n defaultValue: $defaultValue\n };\n const result = ENGINE.runKernel(SparseFillEmptyRows, inputs);\n return {\n outputIndices: result[0],\n outputValues: result[1],\n emptyRowIndicator: result[2],\n reverseIndexMap: result[3]\n };\n}\nexport const sparseFillEmptyRows = op({ sparseFillEmptyRows_ });\n//# sourceMappingURL=data:application/json;base64,{"version":3,"file":"sparse_fill_empty_rows.js","sourceRoot":"","sources":["../../../../../../../tfjs-core/src/ops/sparse/sparse_fill_empty_rows.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAEH,OAAO,EAAC,MAAM,EAAC,MAAM,cAAc,CAAC;AACpC,OAAO,EAAC,mBAAmB,EAA4B,MAAM,oBAAoB,CAAC;AAGlF,OAAO,EAAC,eAAe,EAAC,MAAM,uBAAuB,CAAC;AAEtD,OAAO,EAAC,EAAE,EAAC,MAAM,cAAc,CAAC;AAEhC;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;GAuDG;AACH,SAAS,oBAAoB,CACzB,OAA4B,EAAE,MAA2B,EACzD,UAA+B,EAC/B,YAA+B;IACjC,MAAM,QAAQ,GACV,eAAe,CAAC,OAAO,EAAE,SAAS,EAAE,qBAAqB,EAAE,OAAO,CAAC,CAAC;IACxE,MAAM,OAAO,GAAG,eAAe,CAAC,MAAM,EAAE,QAAQ,EAAE,qBAAqB,CAAC,CAAC;IACzE,MAAM,WAAW,GACb,eAAe,CAAC,UAAU,EAAE,YAAY,EAAE,qBAAqB,EAAE,OAAO,CAAC,CAAC;IAC9E,MAAM,aAAa,GAAG,eAAe,CACjC,YAAY,EAAE,cAAc,EAAE,qBAAqB,EAAE,OAAO,CAAC,KAAK,CAAC,CAAC;IAExE,IAAI,QAAQ,CAAC,IAAI,KAAK,CAAC,EAAE;QACvB,MAAM,IAAI,KAAK,CAAC;UACV,QAAQ,CAAC,KAAK,EAAE,CAAC,CAAC;KACzB;IACD,IAAI,OAAO,CAAC,IAAI,KAAK,CAAC,EAAE;QACtB,MAAM,IAAI,KAAK,CACX,gDAAgD,OAAO,CAAC,KAAK,EAAE,CAAC,CAAC;KACtE;IACD,IAAI,WAAW,CAAC,IAAI,KAAK,CAAC,EAAE;QAC1B,MAAM,IAAI,KAAK,CAAC,qDACZ,WAAW,CAAC,KAAK,EAAE,CAAC,CAAC;KAC1B;IACD,IAAI,aAAa,CAAC,IAAI,KAAK,CAAC,EAAE;QAC5B,MAAM,IAAI,KAAK,CAAC,uDACZ,aAAa,CAAC,KAAK,EAAE,CAAC,CAAC;KAC5B;IAED,MAAM,MAAM,GAA8B;QACxC,OAAO,EAAE,QAAQ;QACjB,MAAM,EAAE,OAAO;QACf,UAAU,EAAE,WAAW;QACvB,YAAY,EAAE,aAAa;KAC5B,CAAC;IAEF,MAAM,MAAM,GAAa,MAAM,CAAC,SAAS,CAAC,mBAAmB,EAAE,MAAY,CAAC,CAAC;IAC7E,OAAO;QACL,aAAa,EAAE,MAAM,CAAC,CAAC,CAAC;QACxB,YAAY,EAAE,MAAM,CAAC,CAAC,CAAC;QACvB,iBAAiB,EAAE,MAAM,CAAC,CAAC,CAAC;QAC5B,eAAe,EAAE,MAAM,CAAC,CAAC,CAAC;KAC3B,CAAC;AACJ,CAAC;AAED,MAAM,CAAC,MAAM,mBAAmB,GAAG,EAAE,CAAC,EAAC,oBAAoB,EAAC,CAAC,CAAC","sourcesContent":["/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../../engine';\nimport {SparseFillEmptyRows, SparseFillEmptyRowsInputs} from '../../kernel_names';\nimport {Scalar, Tensor, Tensor1D, Tensor2D} from '../../tensor';\nimport {NamedTensorMap} from '../../tensor_types';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {ScalarLike, TensorLike} from '../../types';\nimport {op} from '../operation';\n\n/**\n * The input SparseTensor is represented via the map of inputs {`indices`,\n * `values`, `denseShape`}. The output SparseTensor has the same `denseShape`\n * but with indices `outputIndices` and values `outputValues`. This op inserts a\n * single entry for every row that doesn't have any values. The index is created\n * as `[row, 0, ..., 0]` and the inserted value is `defaultValue`.\n *\n * For example, suppose `spInput` has shape [5, 6] and non-empty values:\n * [0, 1]: a\n * [0, 3]: b\n * [2, 0]: c\n * [3, 1]: d\n *\n * Rows 1 and 4 are empty, so the output will be of shape [5, 6] with values:\n * [0, 1]: a\n * [0, 3]: b\n * [1, 0]: `defaultValue`\n * [2, 0]: c\n * [3, 1]: d\n * [4, 0]: `defaultValue`\n *\n * The output SparseTensor will be in row-major order and will have the same\n * shape as the input.\n *\n * This op also returns an indicator vector shaped [dense_shape[0]] such that\n * emptyRowIndicator[i] = True iff row i was an empty row.\n *\n * And a reverse index map vector shaped [indices.shape[0]] that is used during\n * backpropagation, reverseIndexMap[i] = outi s.t. indices[i, j] ==\n * outputIndices[outi, j] for all j\n *\n * ```js\n * const result = tf.sparse.sparseFillEmptyRows(\n *   [[0, 0], [1, 0], [1, 3], [1, 4], [3, 2], [3, 3]],\n *   [0, 10, 13, 14, 32, 33], [5, 6], -1);\n * console.log(result);\n * result['outputIndices'].print(); // [[0, 0], [1, 0], [1, 3], [1, 4],\n *                                  //  [2, 0], [3, 2], [3, 3], [4, 0]]\n * result['outputValues'].print(); // [0, 10, 13, 14,-1, 32, 33, -1]\n * result['emptyRowIndicator'].print(); // [false, false, true, false, true]\n * result['reverseIndexMap'].print(); // [0, 1, 2, 3, 5, 6]\n * ```\n * @param indices: 2-D. the indices of the sparse tensor.\n * @param values: 1-D. the values of the sparse tensor.\n * @param denseShape: 1-D. the shape of the sparse tensor.\n * @param defaultValue: 0-D. default value to insert into location [row, 0, ...,\n *     0] for rows missing from the input sparse tensor.\n * @return A map with the following properties:\n *     - outputIndices\n *     - outputValues: 1-D. the values of the filled sparse tensor.\n *     - emptyRowIndicator: 1-D. whether the dense row was missing in the input\n * sparse tensor.\n *     - reverseIndexMap: 1-D. a map from the input indices to the output\n * indices.\n * @doc {heading: 'Operations', subheading: 'Sparse'}\n */\nfunction sparseFillEmptyRows_(\n    indices: Tensor2D|TensorLike, values: Tensor1D|TensorLike,\n    denseShape: Tensor1D|TensorLike,\n    defaultValue: Scalar|ScalarLike): NamedTensorMap {\n  const $indices =\n      convertToTensor(indices, 'indices', 'sparseFillEmptyRows', 'int32');\n  const $values = convertToTensor(values, 'values', 'sparseFillEmptyRows');\n  const $denseShape =\n      convertToTensor(denseShape, 'denseShape', 'sparseFillEmptyRows', 'int32');\n  const $defaultValue = convertToTensor(\n      defaultValue, 'defaultValue', 'sparseFillEmptyRows', $values.dtype);\n\n  if ($indices.rank !== 2) {\n    throw new Error(`Indices should be Tensor2D but received shape\n        ${$indices.shape}`);\n  }\n  if ($values.rank !== 1) {\n    throw new Error(\n        `Values should be Tensor1D but received shape ${$values.shape}`);\n  }\n  if ($denseShape.rank !== 1) {\n    throw new Error(`Dense shape should be Tensor1D but received shape ${\n        $denseShape.shape}`);\n  }\n  if ($defaultValue.rank !== 0) {\n    throw new Error(`Default value should be a scalar but received shape ${\n        $defaultValue.shape}`);\n  }\n\n  const inputs: SparseFillEmptyRowsInputs = {\n    indices: $indices,\n    values: $values,\n    denseShape: $denseShape,\n    defaultValue: $defaultValue\n  };\n\n  const result: Tensor[] = ENGINE.runKernel(SparseFillEmptyRows, inputs as {});\n  return {\n    outputIndices: result[0],\n    outputValues: result[1],\n    emptyRowIndicator: result[2],\n    reverseIndexMap: result[3]\n  };\n}\n\nexport const sparseFillEmptyRows = op({sparseFillEmptyRows_});\n"]}","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { SparseReshape } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { op } from '../operation';\n/**\n * This operation has the same semantics as reshape on the represented dense\n * tensor. The `inputIndices` are recomputed based on the requested `newShape`.\n * If one component of `newShape` is the special value -1, the size of that\n * dimension is computed so that the total dense size remains constant. At most\n * one component of `newShape` can be -1. The number of dense elements implied\n * by `newShape` must be the same as the number of dense elements originally\n * implied by `inputShape`. Reshaping does not affect the order of values in the\n * SparseTensor. If the input tensor has rank R_in and N non-empty values, and\n * `newShape` has length R_out, then `inputIndices` has shape [N, R_in],\n * `inputShape` has length R_in, `outputIndices` has shape [N, R_out], and\n * `outputShape` has length R_out.\n *\n * ```js\n * const result = tf.sparse.sparseReshape(\n * [[0, 0, 0], [0, 0, 1], [0, 1, 0], [1, 0, 0], [1, 2, 3]],\n * [2, 3, 6], [9, -1]);\n * console.log(result);\n * result['outputIndices'].print(); //[[0, 0], [0, 1], [1, 2], [4, 2], [8, 1]]\n * result['outputShape'].print(); // [9, 4]\n * ```\n * @param inputIndices: 2-D. N x R_in matrix with the indices of non-empty\n * values in a SparseTensor.\n * @param inputShape: 1-D. R_in Tensor1D with the input SparseTensor's dense\n * shape.\n * @param newShape: 1-D. R_out Tensor1D with the requested new dense shape.\n * @return A map with the following properties:\n * - outputIndices: 2-D. N x R_out matrix with the updated indices of\n * non-empty values in the output SparseTensor.\n * - outputShape: 1-D. R_out vector with the full dense shape of the output\n * SparseTensor. This is the same as newShape but with any -1 dimensions\n * filled in.\n * @doc {heading: 'Operations', subheading: 'Sparse'}\n */\nfunction sparseReshape_(inputIndices, inputShape, newShape) {\n const $inputIndices = convertToTensor(inputIndices, 'inputIndices', 'sparseReshape', 'int32');\n const $inputShape = convertToTensor(inputShape, 'inputShape', 'sparseReshape', 'int32');\n const $newShape = convertToTensor(newShape, 'newShape', 'sparseReshape', 'int32');\n if ($inputIndices.rank !== 2) {\n throw new Error(`Input indices should be Tensor2D but received shape\n ${$inputIndices.shape}`);\n }\n if ($inputShape.rank !== 1) {\n throw new Error(`Input shape should be Tensor1D but received shape ${$inputShape.shape}`);\n }\n if ($newShape.rank !== 1) {\n throw new Error(`New shape should be Tensor1D but received shape ${$newShape.shape}`);\n }\n const inputs = {\n inputIndices: $inputIndices,\n inputShape: $inputShape,\n newShape: $newShape\n };\n const result = ENGINE.runKernel(SparseReshape, inputs);\n return { outputIndices: result[0], outputShape: result[1] };\n}\nexport const sparseReshape = op({ sparseReshape_ });\n//# sourceMappingURL=data:application/json;base64,eyJ2ZXJzaW9uIjozLCJmaWxlIjoic3BhcnNlX3Jlc2hhcGUuanMiLCJzb3VyY2VSb290IjoiIiwic291cmNlcyI6WyIuLi8uLi8uLi8uLi8uLi8uLi8uLi90ZmpzLWNvcmUvc3JjL29wcy9zcGFyc2Uvc3BhcnNlX3Jlc2hhcGUudHMiXSwibmFtZXMiOltdLCJtYXBwaW5ncyI6IkFBQUE7Ozs7Ozs7Ozs7Ozs7OztHQWVHO0FBRUgsT0FBTyxFQUFDLE1BQU0sRUFBQyxNQUFNLGNBQWMsQ0FBQztBQUNwQyxPQUFPLEVBQUMsYUFBYSxFQUFzQixNQUFNLG9CQUFvQixDQUFDO0FBR3RFLE9BQU8sRUFBQyxlQUFlLEVBQUMsTUFBTSx1QkFBdUIsQ0FBQztBQUV0RCxPQUFPLEVBQUMsRUFBRSxFQUFDLE1BQU0sY0FBYyxDQUFDO0FBRWhDOzs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7R0FpQ0c7QUFDSCxTQUFTLGNBQWMsQ0FDbkIsWUFBaUMsRUFBRSxVQUErQixFQUNsRSxRQUE2QjtJQUMvQixNQUFNLGFBQWEsR0FDZixlQUFlLENBQUMsWUFBWSxFQUFFLGNBQWMsRUFBRSxlQUFlLEVBQUUsT0FBTyxDQUFDLENBQUM7SUFDNUUsTUFBTSxXQUFXLEdBQ2IsZUFBZSxDQUFDLFVBQVUsRUFBRSxZQUFZLEVBQUUsZUFBZSxFQUFFLE9BQU8sQ0FBQyxDQUFDO0lBQ3hFLE1BQU0sU0FBUyxHQUNYLGVBQWUsQ0FBQyxRQUFRLEVBQUUsVUFBVSxFQUFFLGVBQWUsRUFBRSxPQUFPLENBQUMsQ0FBQztJQUVwRSxJQUFJLGFBQWEsQ0FBQyxJQUFJLEtBQUssQ0FBQyxFQUFFO1FBQzVCLE1BQU0sSUFBSSxLQUFLLENBQUM7VUFDVixhQUFhLENBQUMsS0FBSyxFQUFFLENBQUMsQ0FBQztLQUM5QjtJQUNELElBQUksV0FBVyxDQUFDLElBQUksS0FBSyxDQUFDLEVBQUU7UUFDMUIsTUFBTSxJQUFJLEtBQUssQ0FBQyxxREFDWixXQUFXLENBQUMsS0FBSyxFQUFFLENBQUMsQ0FBQztLQUMxQjtJQUNELElBQUksU0FBUyxDQUFDLElBQUksS0FBSyxDQUFDLEVBQUU7UUFDeEIsTUFBTSxJQUFJLEtBQUssQ0FDWCxtREFBbUQsU0FBUyxDQUFDLEtBQUssRUFBRSxDQUFDLENBQUM7S0FDM0U7SUFFRCxNQUFNLE1BQU0sR0FBd0I7UUFDbEMsWUFBWSxFQUFFLGFBQWE7UUFDM0IsVUFBVSxFQUFFLFdBQVc7UUFDdkIsUUFBUSxFQUFFLFNBQVM7S0FDcEIsQ0FBQztJQUNGLE1BQU0sTUFBTSxHQUFhLE1BQU0sQ0FBQyxTQUFTLENBQUMsYUFBYSxFQUFFLE1BQVksQ0FBQyxDQUFDO0lBQ3ZFLE9BQU8sRUFBQyxhQUFhLEVBQUUsTUFBTSxDQUFDLENBQUMsQ0FBQyxFQUFFLFdBQVcsRUFBRSxNQUFNLENBQUMsQ0FBQyxDQUFDLEVBQUMsQ0FBQztBQUM1RCxDQUFDO0FBRUQsTUFBTSxDQUFDLE1BQU0sYUFBYSxHQUFHLEVBQUUsQ0FBQyxFQUFDLGNBQWMsRUFBQyxDQUFDLENBQUMiLCJzb3VyY2VzQ29udGVudCI6WyIvKipcbiAqIEBsaWNlbnNlXG4gKiBDb3B5cmlnaHQgMjAyMSBHb29nbGUgTExDLiBBbGwgUmlnaHRzIFJlc2VydmVkLlxuICogTGljZW5zZWQgdW5kZXIgdGhlIEFwYWNoZSBMaWNlbnNlLCBWZXJzaW9uIDIuMCAodGhlIFwiTGljZW5zZVwiKTtcbiAqIHlvdSBtYXkgbm90IHVzZSB0aGlzIGZpbGUgZXhjZXB0IGluIGNvbXBsaWFuY2Ugd2l0aCB0aGUgTGljZW5zZS5cbiAqIFlvdSBtYXkgb2J0YWluIGEgY29weSBvZiB0aGUgTGljZW5zZSBhdFxuICpcbiAqIGh0dHA6Ly93d3cuYXBhY2hlLm9yZy9saWNlbnNlcy9MSUNFTlNFLTIuMFxuICpcbiAqIFVubGVzcyByZXF1aXJlZCBieSBhcHBsaWNhYmxlIGxhdyBvciBhZ3JlZWQgdG8gaW4gd3JpdGluZywgc29mdHdhcmVcbiAqIGRpc3RyaWJ1dGVkIHVuZGVyIHRoZSBMaWNlbnNlIGlzIGRpc3RyaWJ1dGVkIG9uIGFuIFwiQVMgSVNcIiBCQVNJUyxcbiAqIFdJVEhPVVQgV0FSUkFOVElFUyBPUiBDT05ESVRJT05TIE9GIEFOWSBLSU5ELCBlaXRoZXIgZXhwcmVzcyBvciBpbXBsaWVkLlxuICogU2VlIHRoZSBMaWNlbnNlIGZvciB0aGUgc3BlY2lmaWMgbGFuZ3VhZ2UgZ292ZXJuaW5nIHBlcm1pc3Npb25zIGFuZFxuICogbGltaXRhdGlvbnMgdW5kZXIgdGhlIExpY2Vuc2UuXG4gKiA9PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PVxuICovXG5cbmltcG9ydCB7RU5HSU5FfSBmcm9tICcuLi8uLi9lbmdpbmUnO1xuaW1wb3J0IHtTcGFyc2VSZXNoYXBlLCBTcGFyc2VSZXNoYXBlSW5wdXRzfSBmcm9tICcuLi8uLi9rZXJuZWxfbmFtZXMnO1xuaW1wb3J0IHtUZW5zb3IsIFRlbnNvcjFELCBUZW5zb3IyRH0gZnJvbSAnLi4vLi4vdGVuc29yJztcbmltcG9ydCB7TmFtZWRUZW5zb3JNYXB9IGZyb20gJy4uLy4uL3RlbnNvcl90eXBlcyc7XG5pbXBvcnQge2NvbnZlcnRUb1RlbnNvcn0gZnJvbSAnLi4vLi4vdGVuc29yX3V0aWxfZW52JztcbmltcG9ydCB7VGVuc29yTGlrZX0gZnJvbSAnLi4vLi4vdHlwZXMnO1xuaW1wb3J0IHtvcH0gZnJvbSAnLi4vb3BlcmF0aW9uJztcblxuLyoqXG4gKiBUaGlzIG9wZXJhdGlvbiBoYXMgdGhlIHNhbWUgc2VtYW50aWNzIGFzIHJlc2hhcGUgb24gdGhlIHJlcHJlc2VudGVkIGRlbnNlXG4gKiB0ZW5zb3IuIFRoZSBgaW5wdXRJbmRpY2VzYCBhcmUgcmVjb21wdXRlZCBiYXNlZCBvbiB0aGUgcmVxdWVzdGVkIGBuZXdTaGFwZWAuXG4gKiBJZiBvbmUgY29tcG9uZW50IG9mIGBuZXdTaGFwZWAgaXMgdGhlIHNwZWNpYWwgdmFsdWUgLTEsIHRoZSBzaXplIG9mIHRoYXRcbiAqIGRpbWVuc2lvbiBpcyBjb21wdXRlZCBzbyB0aGF0IHRoZSB0b3RhbCBkZW5zZSBzaXplIHJlbWFpbnMgY29uc3RhbnQuIEF0IG1vc3RcbiAqIG9uZSBjb21wb25lbnQgb2YgYG5ld1NoYXBlYCBjYW4gYmUgLTEuIFRoZSBudW1iZXIgb2YgZGVuc2UgZWxlbWVudHMgaW1wbGllZFxuICogYnkgYG5ld1NoYXBlYCBtdXN0IGJlIHRoZSBzYW1lIGFzIHRoZSBudW1iZXIgb2YgZGVuc2UgZWxlbWVudHMgb3JpZ2luYWxseVxuICogaW1wbGllZCBieSBgaW5wdXRTaGFwZWAuIFJlc2hhcGluZyBkb2VzIG5vdCBhZmZlY3QgdGhlIG9yZGVyIG9mIHZhbHVlcyBpbiB0aGVcbiAqIFNwYXJzZVRlbnNvci4gSWYgdGhlIGlucHV0IHRlbnNvciBoYXMgcmFuayBSX2luIGFuZCBOIG5vbi1lbXB0eSB2YWx1ZXMsIGFuZFxuICogYG5ld1NoYXBlYCBoYXMgbGVuZ3RoIFJfb3V0LCB0aGVuIGBpbnB1dEluZGljZXNgIGhhcyBzaGFwZSBbTiwgUl9pbl0sXG4gKiBgaW5wdXRTaGFwZWAgaGFzIGxlbmd0aCBSX2luLCBgb3V0cHV0SW5kaWNlc2AgaGFzIHNoYXBlIFtOLCBSX291dF0sIGFuZFxuICogYG91dHB1dFNoYXBlYCBoYXMgbGVuZ3RoIFJfb3V0LlxuICpcbiAqIGBgYGpzXG4gKiBjb25zdCByZXN1bHQgPSB0Zi5zcGFyc2Uuc3BhcnNlUmVzaGFwZShcbiAqICAgW1swLCAwLCAwXSwgWzAsIDAsIDFdLCBbMCwgMSwgMF0sIFsxLCAwLCAwXSwgWzEsIDIsIDNdXSxcbiAqICAgWzIsIDMsIDZdLCBbOSwgLTFdKTtcbiAqIGNvbnNvbGUubG9nKHJlc3VsdCk7XG4gKiByZXN1bHRbJ291dHB1dEluZGljZXMnXS5wcmludCgpOyAvL1tbMCwgMF0sIFswLCAxXSwgWzEsIDJdLCBbNCwgMl0sIFs4LCAxXV1cbiAqIHJlc3VsdFsnb3V0cHV0U2hhcGUnXS5wcmludCgpOyAvLyBbOSwgNF1cbiAqIGBgYFxuICogQHBhcmFtIGlucHV0SW5kaWNlczogMi1ELiBOIHggUl9pbiBtYXRyaXggd2l0aCB0aGUgaW5kaWNlcyBvZiBub24tZW1wdHlcbiAqIHZhbHVlcyBpbiBhIFNwYXJzZVRlbnNvci5cbiAqIEBwYXJhbSBpbnB1dFNoYXBlOiAxLUQuIFJfaW4gVGVuc29yMUQgd2l0aCB0aGUgaW5wdX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* @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { SparseSegmentMean } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { op } from '../operation';\n/**\n * Computes the mean along sparse segments of a tensor.\n *\n * ```js\n * const c = tf.tensor2d([[1,2,3,4], [-1,-2,-3,-4], [6,7,8,9]]);\n * // Select two rows, one segment.\n * const result1 = tf.sparse.sparseSegmentMean(c,\n * tf.tensor1d([0, 1], 'int32'),\n * tf.tensor1d([0, 0], 'int32'));\n * result1.print(); // [[0, 0, 0, 0]]\n *\n * // Select two rows, two segments.\n * const result2 = tf.sparse.sparseSegmentMean(c,\n * tf.tensor1d([0, 1], 'int32'),\n * tf.tensor1d([0, 1], 'int32'));\n * result2.print(); // [[1, 2, 3, 4], [-1, -2, -3, -4]]\n *\n * // Select all rows, two segments.\n * const result3 = tf.sparse.sparseSegmentMean(c,\n * tf.tensor1d([0, 1, 2], 'int32'),\n * tf.tensor1d([0, 1, 1], 'int32'));\n * result3.print(); // [[1.0, 2.0, 3.0, 4.0], [2.5, 2.5, 2.5, 2.5]]\n * ```\n * @param data: A Tensor of at least one dimension with data that will be\n * assembled in the output.\n * @param indices: A 1-D Tensor with indices into data. Has same rank as\n * segmentIds.\n * @param segmentIds: A 1-D Tensor with indices into the output Tensor. Values\n * should be sorted and can be repeated.\n * @return Has same shape as data, except for dimension 0 which has equal to\n * the number of segments.\n *\n * @doc {heading: 'Operations', subheading: 'Sparse'}\n */\nfunction sparseSegmentMean_(data, indices, segmentIds) {\n const $data = convertToTensor(data, 'data', 'sparseSegmentMean');\n const $indices = convertToTensor(indices, 'indices', 'sparseSegmentMean', 'int32');\n const $segmentIds = convertToTensor(segmentIds, 'segmentIds', 'sparseSegmentMean', 'int32');\n if ($data.rank < 1) {\n throw new Error(`Data should be at least 1 dimensional but received scalar`);\n }\n if ($indices.rank !== 1) {\n throw new Error(`Indices should be Tensor1D but received shape\n ${$indices.shape}`);\n }\n if ($segmentIds.rank !== 1) {\n throw new Error(`Segment ids should be Tensor1D but received shape\n ${$segmentIds.shape}`);\n }\n const inputs = {\n data: $data,\n indices: $indices,\n segmentIds: $segmentIds\n };\n return ENGINE.runKernel(SparseSegmentMean, inputs);\n}\nexport const sparseSegmentMean = op({ sparseSegmentMean_ });\n//# 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* @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { SparseSegmentSum } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { op } from '../operation';\n/**\n * Computes the sum along sparse segments of a tensor.\n *\n * ```js\n * const c = tf.tensor2d([[1,2,3,4], [-1,-2,-3,-4], [5,6,7,8]]);\n * // Select two rows, one segment.\n * const result1 = tf.sparse.sparseSegmentSum(c,\n * tf.tensor1d([0, 1], 'int32'),\n * tf.tensor1d([0, 0], 'int32'));\n * result1.print(); // [[0, 0, 0, 0]]\n *\n * // Select two rows, two segment.\n * const result2 = tf.sparse.sparseSegmentSum(c,\n * tf.tensor1d([0, 1], 'int32'),\n * tf.tensor1d([0, 1], 'int32'));\n * result2.print(); // [[1, 2, 3, 4], [-1, -2, -3, -4]]\n *\n * // Select all rows, two segments.\n * const result3 = tf.sparse.sparseSegmentSum(c,\n * tf.tensor1d([0, 1, 2], 'int32'),\n * tf.tensor1d([0, 0, 1], 'int32'));\n * result3.print(); // [[0, 0, 0, 0], [5, 6, 7, 8]]\n * ```\n * @param data: A Tensor of at least one dimension with data that will be\n * assembled in the output.\n * @param indices: A 1-D Tensor with indices into data. Has same rank as\n * segmentIds.\n * @param segmentIds: A 1-D Tensor with indices into the output Tensor. Values\n * should be sorted and can be repeated.\n * @return Has same shape as data, except for dimension 0 which has equal to\n * the number of segments.\n *\n * @doc {heading: 'Operations', subheading: 'Sparse'}\n */\nfunction sparseSegmentSum_(data, indices, segmentIds) {\n const $data = convertToTensor(data, 'data', 'sparseSegmentSum');\n const $indices = convertToTensor(indices, 'indices', 'sparseSegmentSum', 'int32');\n const $segmentIds = convertToTensor(segmentIds, 'segmentIds', 'sparseSegmentSum', 'int32');\n if ($data.rank < 1) {\n throw new Error(`Data should be at least 1 dimensional but received scalar`);\n }\n if ($indices.rank !== 1) {\n throw new Error(`Indices should be Tensor1D but received shape\n ${$indices.shape}`);\n }\n if ($segmentIds.rank !== 1) {\n throw new Error(`Segment ids should be Tensor1D but received shape\n ${$segmentIds.shape}`);\n }\n const inputs = {\n data: $data,\n indices: $indices,\n segmentIds: $segmentIds\n };\n return ENGINE.runKernel(SparseSegmentSum, inputs);\n}\nexport const sparseSegmentSum = op({ sparseSegmentSum_ });\n//# 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* @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { StringNGrams } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { op } from '../operation';\n/**\n * Creates ngrams from ragged string data.\n *\n * This op accepts a ragged tensor with 1 ragged dimension containing only\n * strings and outputs a ragged tensor with 1 ragged dimension containing ngrams\n * of that string, joined along the innermost axis.\n *\n * ```js\n * const result = tf.string.stringNGrams(\n * ['a', 'b', 'c', 'd'], tf.tensor1d([0, 2, 4], 'int32'),\n * '|', [1, 2], 'LP', 'RP', -1, false);\n * result['nGrams'].print(); // ['a', 'b', 'LP|a', 'a|b', 'b|RP',\n * // 'c', 'd', 'LP|c', 'c|d', 'd|RP']\n * result['nGramsSplits'].print(); // [0, 5, 10]\n * ```\n * @param data: The values tensor of the ragged string tensor to make ngrams out\n * of. Must be a 1D string tensor.\n * @param dataSplits: The splits tensor of the ragged string tensor to make\n * ngrams out of.\n * @param separator: The string to append between elements of the token. Use \"\"\n * for no separator.\n * @param nGramWidths: The sizes of the ngrams to create.\n * @param leftPad: The string to use to pad the left side of the ngram sequence.\n * Only used if pad_width !== 0.\n * @param rightPad: The string to use to pad the right side of the ngram\n * sequence. Only used if pad_width !== 0.\n * @param padWidth: The number of padding elements to add to each side of each\n * sequence. Note that padding will never be greater than `nGramWidths`-1\n * regardless of this value. If `padWidth`=-1 , then add max(`nGramWidths)-1\n * elements.\n * @param preserveShortSequences: If true, then ensure that at least one ngram\n * is generated for each input sequence. In particular, if an input sequence\n * is shorter than min(ngramWidth) + 2*padWidth, then generate a single\n * ngram containing the entire sequence. If false, then no ngrams are\n * generated for these short input sequences.\n * @return A map with the following properties:\n * - nGrams: The values tensor of the output ngrams ragged tensor.\n * - nGramsSplits: The splits tensor of the output ngrams ragged tensor.\n *\n * @doc {heading: 'Operations', subheading: 'String'}\n */\nfunction stringNGrams_(data, dataSplits, separator, nGramWidths, leftPad, rightPad, padWidth, preserveShortSequences) {\n const $data = convertToTensor(data, 'data', 'stringNGrams', 'string');\n if ($data.dtype !== 'string') {\n throw new Error('Data must be of datatype string');\n }\n if ($data.shape.length !== 1) {\n throw new Error(`Data must be a vector, saw: ${$data.shape}`);\n }\n const $dataSplits = convertToTensor(dataSplits, 'dataSplits', 'stringNGrams');\n if ($dataSplits.dtype !== 'int32') {\n throw new Error('Data splits must be of datatype int32');\n }\n const attrs = {\n separator,\n nGramWidths,\n leftPad,\n rightPad,\n padWidth,\n preserveShortSequences\n };\n const inputs = { data: $data, dataSplits: $dataSplits };\n const result = ENGINE.runKernel(StringNGrams, inputs, attrs);\n return { nGrams: result[0], nGramsSplits: result[1] };\n}\nexport const stringNGrams = op({ stringNGrams_ });\n//# 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* @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { StringSplit } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { op } from '../operation';\n/**\n * Split elements of `input` based on `delimiter` into a SparseTensor .\n *\n * Let N be the size of source (typically N will be the batch size). Split each\n * element of `input` based on `delimiter` and return a SparseTensor containing\n * the splitted tokens. Empty tokens are ignored if `skipEmpty` is set to True.\n *\n * `delimiter` can be empty, or a string of split characters. If `delimiter` is\n * an empty string, each element of `input` is split into individual\n * character strings. Otherwise every character of `delimiter` is a potential\n * split point.\n *\n * ```js\n * const result = tf.string.stringSplit(['hello world', 'a b c'], ' ');\n * result['indices'].print(); // [[0, 0], [0, 1], [1, 0], [1, 1], [1, 2]]\n * result['values'].print(); // ['hello', 'world', 'a', 'b', 'c']\n * result['shape'].print(); // [2, 3]\n * ```\n * @param input: 1-D. Strings to split.\n * @param delimiter: 0-D. Delimiter characters, or empty string.\n * @param skipEmpty: Optional. If true, skip the empty strings from the result.\n * Defaults to true.\n * @return A map with the following properties:\n * - indices: A dense matrix of int32 representing the indices of the sparse\n * tensor.\n * - values: A vector of strings corresponding to the splited values.\n * - shape: a length-2 vector of int32 representing the shape of the sparse\n * tensor, where the first value is N and the second value is the maximum number\n * of tokens in a single input entry.\n *\n * @doc {heading: 'Operations', subheading: 'String'}\n */\nfunction stringSplit_(input, delimiter, skipEmpty = true) {\n const $input = convertToTensor(input, 'input', 'stringSplit', 'string');\n const $delimiter = convertToTensor(delimiter, 'delimiter', 'stringSplit', 'string');\n if ($input.rank !== 1) {\n throw new Error(`Input should be Tensor1D but received shape ${$input.shape}`);\n }\n if ($delimiter.rank !== 0) {\n throw new Error(`Delimiter should be a scalar but received shape ${$delimiter.shape}`);\n }\n const attrs = { skipEmpty };\n const inputs = { input: $input, delimiter: $delimiter };\n const result = ENGINE.runKernel(StringSplit, inputs, attrs);\n return { indices: result[0], values: result[1], shape: result[2] };\n}\nexport const stringSplit = op({ stringSplit_ });\n//# 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* @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { StringToHashBucketFast } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { op } from '../operation';\n/**\n * Converts each string in the input Tensor to its hash mod by a number of\n * buckets.\n *\n * The hash function is deterministic on the content of the string within the\n * process and will never change. However, it is not suitable for cryptography.\n * This function may be used when CPU time is scarce and inputs are trusted or\n * unimportant. There is a risk of adversaries constructing inputs that all hash\n * to the same bucket.\n *\n * ```js\n * const result = tf.string.stringToHashBucketFast(\n * ['Hello', 'TensorFlow', '2.x'], 3);\n * result.print(); // [0, 2, 2]\n * ```\n * @param input: The strings to assign a hash bucket.\n * @param numBuckets: The number of buckets.\n * @return A Tensor of the same shape as the input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'String'}\n */\nfunction stringToHashBucketFast_(input, numBuckets) {\n const $input = convertToTensor(input, 'input', 'stringToHashBucketFast', 'string');\n const attrs = { numBuckets };\n if (numBuckets <= 0) {\n throw new Error(`Number of buckets must be at least 1`);\n }\n const inputs = { input: $input };\n return ENGINE.runKernel(StringToHashBucketFast, inputs, attrs);\n}\nexport const stringToHashBucketFast = op({ stringToHashBucketFast_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Modularized ops.\nexport { abs } from './abs';\nexport { acos } from './acos';\nexport { acosh } from './acosh';\nexport { add } from './add';\nexport { addN } from './add_n';\nexport { all } from './all';\nexport { any } from './any';\nexport { argMax } from './arg_max';\nexport { argMin } from './arg_min';\nexport { asin } from './asin';\nexport { asinh } from './asinh';\nexport { atan } from './atan';\nexport { atan2 } from './atan2';\nexport { atanh } from './atanh';\nexport { avgPool } from './avg_pool';\nexport { avgPool3d } from './avg_pool_3d';\nexport { basicLSTMCell } from './basic_lstm_cell';\nexport { batchToSpaceND } from './batch_to_space_nd';\nexport { batchNorm } from './batchnorm';\nexport { batchNorm2d } from './batchnorm2d';\nexport { batchNorm3d } from './batchnorm3d';\nexport { batchNorm4d } from './batchnorm4d';\nexport { bincount } from './bincount';\nexport { broadcastArgs } from './broadcast_args';\nexport { broadcastTo } from './broadcast_to';\nexport { buffer } from './buffer';\nexport { cast } from './cast';\nexport { ceil } from './ceil';\nexport { clipByValue } from './clip_by_value';\nexport { clone } from './clone';\nexport { complex } from './complex';\nexport { concat } from './concat';\nexport { concat1d } from './concat_1d';\nexport { concat2d } from './concat_2d';\nexport { concat3d } from './concat_3d';\nexport { concat4d } from './concat_4d';\nexport { conv1d } from './conv1d';\nexport { conv2d } from './conv2d';\nexport { conv2dTranspose } from './conv2d_transpose';\nexport { conv3d } from './conv3d';\nexport { conv3dTranspose } from './conv3d_transpose';\nexport { cos } from './cos';\nexport { cosh } from './cosh';\nexport { cumprod } from './cumprod';\nexport { cumsum } from './cumsum';\nexport { denseBincount } from './dense_bincount';\nexport { depthToSpace } from './depth_to_space';\nexport { depthwiseConv2d } from './depthwise_conv2d';\nexport { diag } from './diag';\nexport { dilation2d } from './dilation2d';\nexport { div } from './div';\nexport { divNoNan } from './div_no_nan';\nexport { dot } from './dot';\nexport { einsum } from './einsum';\nexport { elu } from './elu';\nexport { equal } from './equal';\nexport { erf } from './erf';\nexport { exp } from './exp';\nexport { expandDims } from './expand_dims';\nexport { expm1 } from './expm1';\nexport { eye } from './eye';\nexport { fill } from './fill';\nexport { floor } from './floor';\nexport { floorDiv } from './floorDiv';\nexport { gather } from './gather';\nexport { greater } from './greater';\nexport { greaterEqual } from './greater_equal';\nexport { imag } from './imag';\nexport { isFinite } from './is_finite';\nexport { isInf } from './is_inf';\nexport { isNaN } from './is_nan';\nexport { leakyRelu } from './leaky_relu';\nexport { less } from './less';\nexport { lessEqual } from './less_equal';\nexport { linspace } from './linspace';\nexport { localResponseNormalization } from './local_response_normalization';\nexport { log } from './log';\nexport { log1p } from './log1p';\nexport { logSigmoid } from './log_sigmoid';\nexport { logSoftmax } from './log_softmax';\nexport { logSumExp } from './log_sum_exp';\nexport { logicalAnd } from './logical_and';\nexport { logicalNot } from './logical_not';\nexport { logicalOr } from './logical_or';\nexport { logicalXor } from './logical_xor';\nexport { matMul } from './mat_mul';\nexport { max } from './max';\nexport { maxPool } from './max_pool';\nexport { maxPool3d } from './max_pool_3d';\nexport { maxPoolWithArgmax } from './max_pool_with_argmax';\nexport { maximum } from './maximum';\nexport { mean } from './mean';\nexport { meshgrid } from './meshgrid';\nexport { min } from './min';\nexport { minimum } from './minimum';\nexport { mirrorPad } from './mirror_pad';\nexport { mod } from './mod';\nexport { moments } from './moments';\nexport { mul } from './mul';\nexport { multiRNNCell } from './multi_rnn_cell';\nexport { multinomial } from './multinomial';\nexport { neg } from './neg';\nexport { notEqual } from './not_equal';\nexport { oneHot } from './one_hot';\nexport { ones } from './ones';\nexport { onesLike } from './ones_like';\nexport { outerProduct } from './outer_product';\nexport { pad } from './pad';\nexport { pad1d } from './pad1d';\nexport { pad2d } from './pad2d';\nexport { pad3d } from './pad3d';\nexport { pad4d } from './pad4d';\nexport { pool } from './pool';\nexport { pow } from './pow';\nexport { prelu } from './prelu';\nexport { print } from './print';\nexport { prod } from './prod';\nexport { rand } from './rand';\nexport { randomGamma } from './random_gamma';\nexport { randomNormal } from './random_normal';\nexport { randomUniform } from './random_uniform';\nexport { range } from './range';\nexport { real } from './real';\nexport { reciprocal } from './reciprocal';\nexport { relu } from './relu';\nexport { relu6 } from './relu6';\nexport { reshape } from './reshape';\nexport { reverse } from './reverse';\nexport { reverse1d } from './reverse_1d';\nexport { reverse2d } from './reverse_2d';\nexport { reverse3d } from './reverse_3d';\nexport { reverse4d } from './reverse_4d';\nexport { round } from './round';\nexport { rsqrt } from './rsqrt';\nexport { scalar } from './scalar';\nexport { selu } from './selu';\nexport { separableConv2d } from './separable_conv2d';\nexport { setdiff1dAsync } from './setdiff1d_async';\nexport { sigmoid } from './sigmoid';\nexport { sign } from './sign';\nexport { sin } from './sin';\nexport { sinh } from './sinh';\nexport { slice } from './slice';\nexport { slice1d } from './slice1d';\nexport { slice2d } from './slice2d';\nexport { slice3d } from './slice3d';\nexport { slice4d } from './slice4d';\nexport { softmax } from './softmax';\nexport { softplus } from './softplus';\nexport { spaceToBatchND } from './space_to_batch_nd';\nexport { fft } from './spectral/fft';\nexport { ifft } from './spectral/ifft';\nexport { irfft } from './spectral/irfft';\nexport { rfft } from './spectral/rfft';\nexport { split } from './split';\nexport { sqrt } from './sqrt';\nexport { square } from './square';\nexport { squaredDifference } from './squared_difference';\nexport { squeeze } from './squeeze';\nexport { stack } from './stack';\nexport { step } from './step';\nexport { stridedSlice } from './strided_slice';\nexport { sub } from './sub';\nexport { sum } from './sum';\nexport { tan } from './tan';\nexport { tanh } from './tanh';\nexport { tensor } from './tensor';\nexport { tensor1d } from './tensor1d';\nexport { tensor2d } from './tensor2d';\nexport { tensor3d } from './tensor3d';\nexport { tensor4d } from './tensor4d';\nexport { tensor5d } from './tensor5d';\nexport { tensor6d } from './tensor6d';\nexport { tile } from './tile';\nexport { topk } from './topk';\nexport { truncatedNormal } from './truncated_normal';\nexport { unique } from './unique';\nexport { unsortedSegmentSum } from './unsorted_segment_sum';\nexport { unstack } from './unstack';\nexport { variable } from './variable';\nexport { where } from './where';\nexport { whereAsync } from './where_async';\nexport { zeros } from './zeros';\nexport { zerosLike } from './zeros_like';\nexport * from './boolean_mask';\nexport * from './transpose';\nexport * from './norm';\nexport * from './moving_average';\nexport * from './scatter_nd';\nexport * from './sparse_to_dense';\nexport * from './gather_nd';\nexport * from './dropout';\nexport * from './signal_ops_util';\nexport * from './in_top_k';\nexport { op, OP_SCOPE_SUFFIX } from './operation';\nimport { rfft } from './spectral/rfft';\nimport { fft } from './spectral/fft';\nimport { ifft } from './spectral/ifft';\nimport { irfft } from './spectral/irfft';\nconst spectral = {\n fft,\n ifft,\n rfft,\n irfft\n};\nimport * as fused from './fused_ops';\nimport { hammingWindow } from './signal/hamming_window';\nimport { hannWindow } from './signal/hann_window';\nimport { frame } from './signal/frame';\nimport { stft } from './signal/stft';\nconst signal = {\n hammingWindow,\n hannWindow,\n frame,\n stft,\n};\n// Image Ops namespace\nimport { cropAndResize } from './image/crop_and_resize';\nimport { flipLeftRight } from './image/flip_left_right';\nimport { grayscaleToRGB } from './image/grayscale_to_rgb';\nimport { rotateWithOffset } from './image/rotate_with_offset';\nimport { nonMaxSuppression } from './image/non_max_suppression';\nimport { nonMaxSuppressionAsync } from './image/non_max_suppression_async';\nimport { nonMaxSuppressionWithScore } from './image/non_max_suppression_with_score';\nimport { nonMaxSuppressionWithScoreAsync } from './image/non_max_suppression_with_score_async';\nimport { nonMaxSuppressionPadded } from './image/non_max_suppression_padded';\nimport { nonMaxSuppressionPaddedAsync } from './image/non_max_suppression_padded_async';\nimport { resizeBilinear } from './image/resize_bilinear';\nimport { resizeNearestNeighbor } from './image/resize_nearest_neighbor';\nimport { threshold } from './image/threshold';\nimport { transform } from './image/transform';\nconst image = {\n flipLeftRight,\n grayscaleToRGB,\n resizeNearestNeighbor,\n resizeBilinear,\n rotateWithOffset,\n cropAndResize,\n nonMaxSuppression,\n nonMaxSuppressionAsync,\n nonMaxSuppressionWithScore,\n nonMaxSuppressionWithScoreAsync,\n nonMaxSuppressionPadded,\n nonMaxSuppressionPaddedAsync,\n threshold,\n transform\n};\n// linalg namespace\nimport { bandPart } from './linalg/band_part';\nimport { gramSchmidt } from './linalg/gram_schmidt';\nimport { qr } from './linalg/qr';\nconst linalg = {\n bandPart,\n gramSchmidt,\n qr\n};\n// losses namespace;\nimport { absoluteDifference } from './losses/absolute_difference';\nimport { computeWeightedLoss } from './losses/compute_weighted_loss';\nimport { cosineDistance } from './losses/cosine_distance';\nimport { hingeLoss } from './losses/hinge_loss';\nimport { huberLoss } from './losses/huber_loss';\nimport { logLoss } from './losses/log_loss';\nimport { meanSquaredError } from './losses/mean_squared_error';\nimport { sigmoidCrossEntropy } from './losses/sigmoid_cross_entropy';\nimport { softmaxCrossEntropy } from './losses/softmax_cross_entropy';\nconst losses = {\n absoluteDifference,\n computeWeightedLoss,\n cosineDistance,\n hingeLoss,\n huberLoss,\n logLoss,\n meanSquaredError,\n sigmoidCrossEntropy,\n softmaxCrossEntropy\n};\nimport { sparseFillEmptyRows } from './sparse/sparse_fill_empty_rows';\nimport { sparseReshape } from './sparse/sparse_reshape';\nimport { sparseSegmentMean } from './sparse/sparse_segment_mean';\nimport { sparseSegmentSum } from './sparse/sparse_segment_sum';\nconst sparse = {\n sparseFillEmptyRows,\n sparseReshape,\n sparseSegmentMean,\n sparseSegmentSum\n};\nimport { stringNGrams } from './string/string_n_grams';\nimport { stringSplit } from './string/string_split';\nimport { stringToHashBucketFast } from './string/string_to_hash_bucket_fast';\n// tslint:disable-next-line:variable-name\nconst string = {\n stringNGrams,\n stringSplit,\n stringToHashBucketFast\n};\n// Second level exports.\nexport { image, linalg, losses, spectral, fused, signal, sparse, string };\n//# 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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// Modularized ops.\nexport {abs} from './abs';\nexport {acos} from './acos';\nexport {acosh} from './acosh';\nexport {add} from './add';\nexport {addN} from './add_n';\nexport {all} from './all';\nexport {any} from './any';\nexport {argMax} from './arg_max';\nexport {argMin} from './arg_min';\nexport {asin} from './asin';\nexport {asinh} from './asinh';\nexport {atan} from './atan';\nexport {atan2} from './atan2';\nexport {atanh} from './atanh';\nexport {avgPool} from './avg_pool';\nexport {avgPool3d} from './avg_pool_3d';\nexport {basicLSTMCell} from './basic_lstm_cell';\nexport {batchToSpaceND} from './batch_to_space_nd';\nexport {batchNorm} from './batchnorm';\nexport {batchNorm2d} from './batchnorm2d';\nexport {batchNorm3d} from './batchnorm3d';\nexport {batchNorm4d} from './batchnorm4d';\nexport {bincount} from './bincount';\nexport {broadcastArgs} from './broadcast_args';\nexport {broadcastTo} from './broadcast_to';\nexport {buffer} from './buffer';\nexport {cast} from './cast';\nexport {ceil} from './ceil';\nexport {clipByValue} from './clip_by_value';\nexport {clone} from './clone';\nexport {complex} from './complex';\nexport {concat} from './concat';\nexport {concat1d} from './concat_1d';\nexport {concat2d} from './concat_2d';\nexport {concat3d} from './concat_3d';\nexport {concat4d} from './concat_4d';\nexport {conv1d} from './conv1d';\nexport {conv2d} from './conv2d';\nexport {conv2dTranspose} from './conv2d_transpose';\nexport {conv3d} from './conv3d';\nexport {conv3dTranspose} from './conv3d_transpose';\nexport {cos} from './cos';\nexport {cosh} from './cosh';\nexport {cumprod} from './cumprod';\nexport {cumsum} from './cumsum';\nexport {denseBincount} from './dense_bincount';\nexport {depthToSpace} from './depth_to_space';\nexport {depthwiseConv2d} from './depthwise_conv2d';\nexport {diag} from './diag';\nexport {dilation2d} from './dilation2d';\nexport {div} from './div';\nexport {divNoNan} from './div_no_nan';\nexport {dot} from './dot';\nexport {einsum} from './einsum';\nexport {elu} from './elu';\nexport {equal} from './equal';\nexport {erf} from './erf';\nexport {exp} from './exp';\nexport {expandDims} from './expand_dims';\nexport {expm1} from './expm1';\nexport {eye} from './eye';\nexport {fill} from './fill';\nexport {floor} from './floor';\nexport {floorDiv} from './floorDiv';\nexport {gather} from './gather';\nexport {greater} from './greater';\nexport {greaterEqual} from './greater_equal';\nexport {imag} from './imag';\nexport {isFinite} from './is_finite';\nexport {isInf} from './is_inf';\nexport {isNaN} from './is_nan';\nexport {leakyRelu} from './leaky_relu';\nexport {less} from './less';\nexport {lessEqual} from './less_equal';\nexport {linspace} from './linspace';\nexport {localResponseNormalization} from './local_response_normalization';\nexport {log} from './log';\nexport {log1p} from './log1p';\nexport {logSigmoid} from './log_sigmoid';\nexport {logSoftmax} from './log_softmax';\nexport {logSumExp} from './log_sum_exp';\nexport {logicalAnd} from './logical_and';\nexport {logicalNot} from './logical_not';\nexport {logicalOr} from './logical_or';\nexport {logicalXor} from './logical_xor';\nexport {matMul} from './mat_mul';\nexport {max} from './max';\nexport {maxPool} from './max_pool';\nexport {maxPool3d} from './max_pool_3d';\nexport {maxPoolWithArgmax} from './max_pool_with_argmax';\nexport {maximum} from './maximum';\nexport {mean} from './mean';\nexport {meshgrid} from './meshgrid';\nexport {min} from './min';\nexport {minimum} from './minimum';\nexport {mirrorPad} from './mirror_pad';\nexport {mod} from './mod';\nexport {moments} from './moments';\nexport {mul} from './mul';\nexport {LSTMCellFunc, multiRNNCell} from './multi_rnn_cell';\nexport {multinomial} from './multinomial';\nexport {neg} from './neg';\nexport {notEqual} from './not_equal';\nexport {oneHot} from './one_hot';\nexport {ones} from './ones';\nexport {onesLike} from './ones_like';\nexport {outerProduct} from './outer_product';\nexport {pad} from './pad';\nexport {pad1d} from './pad1d';\nexport {pad2d} from './pad2d';\nexport {pad3d} from './pad3d';\nexport {pad4d} from './pad4d';\nexport {pool} from './pool';\nexport {pow} from './pow';\nexport {prelu} from './prelu';\nexport {print} from './print';\nexport {prod} from './prod';\nexport {rand} from './rand';\nexport {randomGamma} from './random_gamma';\nexport {randomNormal} from './random_normal';\nexport {randomUniform} from './random_uniform';\nexport {range} from './range';\nexport {real} from './real';\nexport {reciprocal} from './reciprocal';\nexport {relu} from './relu';\nexport {relu6} from './relu6';\nexport {reshape} from './reshape';\nexport {reverse} from './reverse';\nexport {reverse1d} from './reverse_1d';\nexport {reverse2d} from './reverse_2d';\nexport {reverse3d} from './reverse_3d';\nexport {reverse4d} from './reverse_4d';\nexport {round} from './round';\nexport {rsqrt} from './rsqrt';\nexport {scalar} from './scalar';\nexport {selu} from './selu';\nexport {separableConv2d} from './separable_conv2d';\nexport {setdiff1dAsync} from './setdiff1d_async';\nexport {sigmoid} from './sigmoid';\nexport {sign} from './sign';\nexport {sin} from './sin';\nexport {sinh} from './sinh';\nexport {slice} from './slice';\nexport {slice1d} from './slice1d';\nexport {slice2d} from './slice2d';\nexport {slice3d} from './slice3d';\nexport {slice4d} from './slice4d';\nexport {softmax} from './softmax';\nexport {softplus} from './softplus';\nexport {spaceToBatchND} from './space_to_batch_nd';\nexport {fft} from './spectral/fft';\nexport {ifft} from './spectral/ifft';\nexport {irfft} from './spectral/irfft';\nexport {rfft} from './spectral/rfft';\nexport {split} from './split';\nexport {sqrt} from './sqrt';\nexport {square} from './square';\nexport {squaredDifference} from './squared_difference';\nexport {squeeze} from './squeeze';\nexport {stack} from './stack';\nexport {step} from './step';\nexport {stridedSlice} from './strided_slice';\nexport {sub} from './sub';\nexport {sum} from './sum';\nexport {tan} from './tan';\nexport {tanh} from './tanh';\nexport {tensor} from './tensor';\nexport {tensor1d} from './tensor1d';\nexport {tensor2d} from './tensor2d';\nexport {tensor3d} from './tensor3d';\nexport {tensor4d} from './tensor4d';\nexport {tensor5d} from './tensor5d';\nexport {tensor6d} from './tensor6d';\nexport {tile} from './tile';\nexport {topk} from './topk';\nexport {truncatedNormal} from './truncated_normal';\nexport {unique} from './unique';\nexport {unsortedSegmentSum} from './unsorted_segment_sum';\nexport {unstack} from './unstack';\nexport {variable} from './variable';\nexport {where} from './where';\nexport {whereAsync} from './where_async';\nexport {zeros} from './zeros';\nexport {zerosLike} from './zeros_like';\n\nexport * from './boolean_mask';\nexport * from './transpose';\nexport * from './norm';\nexport * from './moving_average';\nexport * from './scatter_nd';\nexport * from './sparse_to_dense';\nexport * from './gather_nd';\nexport * from './dropout';\nexport * from './signal_ops_util';\nexport * from './in_top_k';\n\nexport {op, OP_SCOPE_SUFFIX} from './operation';\n\nimport {rfft} from './spectral/rfft';\nimport {fft} from './spectral/fft';\nimport {ifft} from './spectral/ifft';\nimport {irfft} from './spectral/irfft';\nconst spectral = {\n  fft,\n  ifft,\n  rfft,\n  irfft\n};\n\nimport * as fused from './fused_ops';\n\nimport {hammingWindow} from './signal/hamming_window';\nimport {hannWindow} from './signal/hann_window';\nimport {frame} from './signal/frame';\nimport {stft} from './signal/stft';\nconst signal = {\n  hammingWindow,\n  hannWindow,\n  frame,\n  stft,\n};\n\n// Image Ops namespace\nimport {cropAndResize} from './image/crop_and_resize';\nimport {flipLeftRight} from './image/flip_left_right';\nimport {grayscaleToRGB} from './image/grayscale_to_rgb';\nimport {rotateWithOffset} from './image/rotate_with_offset';\nimport {nonMaxSuppression} from './image/non_max_suppression';\nimport {nonMaxSuppressionAsync} from './image/non_max_suppression_async';\nimport {nonMaxSuppressionWithScore} from './image/non_max_suppression_with_score';\nimport {nonMaxSuppressionWithScoreAsync} from './image/non_max_suppression_with_score_async';\nimport {nonMaxSuppressionPadded} from './image/non_max_suppression_padded';\nimport {nonMaxSuppressionPaddedAsync} from './image/non_max_suppression_padded_async';\nimport {resizeBilinear} from './image/resize_bilinear';\nimport {resizeNearestNeighbor} from './image/resize_nearest_neighbor';\nimport {threshold} from './image/threshold';\nimport {transform} from './image/transform';\nconst image = {\n  flipLeftRight,\n  grayscaleToRGB,\n  resizeNearestNeighbor,\n  resizeBilinear,\n  rotateWithOffset,\n  cropAndResize,\n  nonMaxSuppression,\n  nonMaxSuppressionAsync,\n  nonMaxSuppressionWithScore,\n  nonMaxSuppressionWithScoreAsync,\n  nonMaxSuppressionPadded,\n  nonMaxSuppressionPaddedAsync,\n  threshold,\n  transform\n};\n\n// linalg namespace\nimport {bandPart} from './linalg/band_part';\nimport {gramSchmidt} from './linalg/gram_schmidt';\nimport {qr} from './linalg/qr';\nconst linalg = {\n  bandPart,\n  gramSchmidt,\n  qr\n};\n\n// losses namespace;\nimport {absoluteDifference} from './losses/absolute_difference';\nimport {computeWeightedLoss} from './losses/compute_weighted_loss';\nimport {cosineDistance} from './losses/cosine_distance';\nimport {hingeLoss} from './losses/hinge_loss';\nimport {huberLoss} from './losses/huber_loss';\nimport {logLoss} from './losses/log_loss';\nimport {meanSquaredError} from './losses/mean_squared_error';\nimport {sigmoidCrossEntropy} from './losses/sigmoid_cross_entropy';\nimport {softmaxCrossEntropy} from './losses/softmax_cross_entropy';\nconst losses = {\n  absoluteDifference,\n  computeWeightedLoss,\n  cosineDistance,\n  hingeLoss,\n  huberLoss,\n  logLoss,\n  meanSquaredError,\n  sigmoidCrossEntropy,\n  softmaxCrossEntropy\n};\n\nimport {sparseFillEmptyRows} from './sparse/sparse_fill_empty_rows';\nimport {sparseReshape} from './sparse/sparse_reshape';\nimport {sparseSegmentMean} from './sparse/sparse_segment_mean';\nimport {sparseSegmentSum} from './sparse/sparse_segment_sum';\nconst sparse = {\n  sparseFillEmptyRows,\n  sparseReshape,\n  sparseSegmentMean,\n  sparseSegmentSum\n};\n\nimport {stringNGrams} from './string/string_n_grams';\nimport {stringSplit} from './string/string_split';\nimport {stringToHashBucketFast} from './string/string_to_hash_bucket_fast';\n// tslint:disable-next-line:variable-name\nconst string = {\n  stringNGrams,\n  stringSplit,\n  stringToHashBucketFast\n};\n\n// Second level exports.\nexport {image, linalg, losses, spectral, fused, signal, sparse, string};\n"]}","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line:no-any\nfunction _isNavigatorDefined() {\n return typeof navigator !== 'undefined' && navigator != null;\n}\nlet isMobileMockValue;\nexport function mockIsMobile(value) {\n isMobileMockValue = value;\n}\nexport function isMobile(nav) {\n if (isMobileMockValue !== undefined) {\n return isMobileMockValue;\n }\n if (nav || _isNavigatorDefined()) {\n if (!nav) {\n nav = navigator;\n }\n if (nav.product === 'ReactNative') {\n return true;\n }\n const a = nav.userAgent || nav.vendor ||\n // tslint:disable-next-line:no-any\n (typeof window !== 'undefined' ? window.opera : '');\n // Use `navigator.userAgentData.mobile` as fallback.\n if (!a) {\n // tslint:disable-next-line:no-any\n const navAny = nav;\n return navAny.userAgentData && navAny.userAgentData.mobile;\n }\n // tslint:disable-next-line:max-line-length\n return /(android|bb\\d+|meego).+mobile|avantgo|bada\\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i\n .test(a) ||\n // tslint:disable-next-line:max-line-length\n /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a 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* @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Prints information about the `tf.Tensor` including its data.\n *\n * ```js\n * const verbose = true;\n * tf.tensor2d([1, 2, 3, 4], [2, 2]).print(verbose);\n * ```\n * @param x The tensor to be printed.\n * @param verbose Whether to print verbose information about the ` Tensor`,\n * including dtype and size.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function print(x, verbose = false) {\n console.log(x.toString(verbose));\n}\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { FloorDiv } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting.\n * The result is rounded with floor function.\n *\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 9, 16]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n *\n * a.floorDiv(b).print(); // or tf.div(a, b)\n * ```\n *\n * ```js\n * // Broadcast div a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(2);\n *\n * a.floorDiv(b).print(); // or tf.floorDiv(a, b)\n * ```\n *\n * @param a The first tensor as the numerator.\n * @param b The second tensor as the denominator. Must have the same dtype as\n * `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction floorDiv_(a, b) {\n let $a = convertToTensor(a, 'a', 'floorDiv');\n let $b = convertToTensor(b, 'b', 'floorDiv');\n [$a, $b] = makeTypesMatch($a, $b);\n const inputs = { a: $a, b: $b };\n return ENGINE.runKernel(FloorDiv, inputs);\n}\nexport const floorDiv = op({ floorDiv_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { AvgPool } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { cast } from './cast';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the 2D average pooling of an image.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n * `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n * `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad The type of padding algorithm:\n * - `same` and stride 1: output will be of same size as input,\n * regardless of filter size.\n * - `valid`: output will be smaller than input if filter is larger\n * than 1x1.\n * - For more info, see this guide:\n * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](\n * https://www.tensorflow.org/api_docs/python/tf/nn/convolution)\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n * provided, it will default to truncate.\n */\nfunction avgPool_(x, filterSize, strides, pad, dimRoundingMode) {\n const $x = convertToTensor(x, 'x', 'avgPool', 'float32');\n const dilations = 1;\n util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in avgPool: Either strides or dilations must be 1. ' +\n `Got strides ${strides} and dilations '${dilations}'`);\n let x4D = $x;\n let reshapedTo4D = false;\n if ($x.rank === 3) {\n reshapedTo4D = true;\n x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n }\n util.assert(x4D.rank === 4, () => `Error in avgPool: x must be rank 4 but got rank ${x4D.rank}.`);\n conv_util.checkPadOnDimRoundingMode('avgPool', pad, dimRoundingMode);\n const inputs = { x: x4D };\n const attrs = { filterSize, strides, pad, dimRoundingMode };\n // tslint:disable-next-line: no-unnecessary-type-assertion\n let res = ENGINE.runKernel(AvgPool, inputs, attrs);\n res = cast(res, $x.dtype);\n if (reshapedTo4D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n }\n return res;\n}\nexport const avgPool = op({ avgPool_ });\n//# 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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Bincount } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Outputs a vector with length `size` and the same dtype as `weights`.\n *\n * If `weights` are empty, then index `i` stores the number of times the value\n * `i` is counted in `x`. If `weights` are non-empty, then index `i` stores the\n * sum of the value in `weights` at each index where the corresponding value in\n * `x` is `i`.\n *\n * Values in `x` outside of the range [0, size) are ignored.\n *\n * @param x The input int tensor, rank 1.\n * @param weights The weights tensor, must have the same shape as x, or a\n * length-0 Tensor, in which case it acts as all weights equal to 1.\n * @param size Non-negative integer.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction bincount_(x, weights, size) {\n const $x = convertToTensor(x, 'x', 'bincount');\n const $weights = convertToTensor(weights, 'weights', 'bincount');\n util.assert($x.dtype === 'int32', () => `Error in bincount: input ` +\n `dtype must be int32, but got ${$x.dtype}`);\n util.assert(size >= 0, () => `size must be non-negative, but got ${size}.`);\n util.assert($weights.size === $x.size || $weights.size === 0, () => `Error in bincount: weights must have the same size as input or` +\n `0-length, but got input shape: ${$x.shape}, weights shape: ` +\n `${$weights.shape}.`);\n const inputs = { x: $x, weights: $weights };\n const attrs = { size };\n return ENGINE.runKernel(Bincount, inputs, attrs);\n}\nexport const bincount = op({ bincount_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Cosh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes hyperbolic cos of the input `tf.Tensor` element-wise: `cosh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.cosh().print(); // or tf.cosh(x)\n * ```\n * @param x The input tensor. Must be float32 type.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction cosh_(x) {\n const $x = convertToTensor(x, 'x', 'cosh', 'float32');\n const inputs = { x: $x };\n return ENGINE.runKernel(Cosh, inputs);\n}\nexport const cosh = op({ cosh_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Cumsum } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the cumulative sum of a `tf.Tensor` along `axis`.\n *\n * ```js\n * const x = tf.tensor([1, 2, 3, 4]);\n * x.cumsum().print();\n * ```\n * ```js\n * const x = tf.tensor([[1, 2], [3, 4]]);\n * x.cumsum().print();\n * ```\n *\n * @param x The input tensor to be summed.\n * @param axis The axis along which to sum. Optional. Defaults to 0.\n * @param exclusive Whether to perform exclusive cumulative sum. Optional.\n * Defaults to false. If set to true then the sum of each tensor entry\n * does not include its own value, but only the values previous to it\n * along the specified axis.\n * @param reverse Whether to sum in the opposite direction. Optional.\n * Defaults to false.\n *\n * @doc {heading: 'Operations', subheading: 'Scan'}\n */\nfunction cumsum_(x, axis = 0, exclusive = false, reverse = false) {\n const $x = convertToTensor(x, 'x', 'cumsum');\n const inputs = { x: $x };\n const attrs = { axis, exclusive, reverse };\n return ENGINE.runKernel(Cumsum, inputs, attrs);\n}\nexport const cumsum = op({ cumsum_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Elu } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes exponential linear element-wise: `x > 0 ? x : (e ^ x) - 1`.\n *\n * ```js\n * const x = tf.tensor1d([-1, 1, -3, 2]);\n *\n * x.elu().print(); // or tf.elu(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction elu_(x) {\n const $x = convertToTensor(x, 'x', 'elu', 'float32');\n const inputs = { x: $x };\n return ENGINE.runKernel(Elu, inputs);\n}\nexport const elu = op({ elu_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LeakyRelu } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes leaky rectified linear element-wise.\n *\n * See\n * [http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf](\n * http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf)\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.leakyRelu(0.1).print(); // or tf.leakyRelu(x, 0.1)\n * ```\n * @param x The input tensor.\n * @param alpha The scaling factor for negative values, defaults to 0.2.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction leakyRelu_(x, alpha = 0.2) {\n const $x = convertToTensor(x, 'x', 'leakyRelu');\n const inputs = { x: $x };\n const attrs = { alpha };\n return ENGINE.runKernel(LeakyRelu, inputs, attrs);\n}\nexport const leakyRelu = op({ leakyRelu_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Less } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a < b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.less(b).print();\n * ```\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction less_(a, b) {\n let $a = convertToTensor(a, 'a', 'less', 'string_or_numeric');\n let $b = convertToTensor(b, 'b', 'less', 'string_or_numeric');\n [$a, $b] = makeTypesMatch($a, $b);\n assertAndGetBroadcastShape($a.shape, $b.shape);\n const inputs = { a: $a, b: $b };\n return ENGINE.runKernel(Less, inputs);\n}\nexport const less = op({ less_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Log1p } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes natural logarithm of the input `tf.Tensor` plus one\n * element-wise: `ln(1 + x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, Math.E - 1]);\n *\n * x.log1p().print(); // or tf.log1p(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction log1p_(x) {\n const $x = convertToTensor(x, 'x', 'log1p');\n const inputs = { x: $x };\n return ENGINE.runKernel(Log1p, inputs);\n}\nexport const log1p = op({ log1p_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Softplus } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes softplus of the input `tf.Tensor` element-wise: `log(exp(x) + 1)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.softplus().print(); // or tf.softplus(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction softplus_(x) {\n const $x = convertToTensor(x, 'x', 'softplus');\n const inputs = { x: $x };\n return ENGINE.runKernel(Softplus, inputs);\n}\nexport const softplus = op({ softplus_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LogicalOr } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of `a OR b` element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([false, false, true, true], 'bool');\n * const b = tf.tensor1d([false, true, false, true], 'bool');\n *\n * a.logicalOr(b).print();\n * ```\n * @param a The first input tensor. Must be of dtype bool.\n * @param b The second input tensor. Must be of dtype bool.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalOr_(a, b) {\n const $a = convertToTensor(a, 'a', 'logicalOr', 'bool');\n const $b = convertToTensor(b, 'b', 'logicalOr', 'bool');\n assertAndGetBroadcastShape($a.shape, $b.shape);\n const inputs = { a: $a, b: $b };\n return ENGINE.runKernel(LogicalOr, inputs);\n}\nexport const logicalOr = op({ logicalOr_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { MaxPool } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the 2D max pooling of an image.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n * `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n * `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n * in which we sample input values across the height and width dimensions\n * in dilated pooling. Defaults to `[1, 1]`. If `dilations` is a single\n * number, then `dilationHeight == dilationWidth`. If it is greater than\n * 1, then all values of `strides` must be 1.\n * @param pad The type of padding algorithm.\n * - `same` and stride 1: output will be of same size as input,\n * regardless of filter size.\n * - `valid`: output will be smaller than input if filter is larger\n * than 1x1.\n * - For more info, see this guide:\n * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](\n * https://www.tensorflow.org/api_docs/python/tf/nn/convolution)\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n * provided, it will default to truncate.\n */\nfunction maxPool_(x, filterSize, strides, pad, dimRoundingMode) {\n const $x = convertToTensor(x, 'x', 'maxPool');\n const dilations = 1;\n let x4D = $x;\n let reshapedTo4D = false;\n if ($x.rank === 3) {\n reshapedTo4D = true;\n x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n }\n util.assert(x4D.rank === 4, () => `Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`);\n util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool: Either strides or dilations must be 1. ' +\n `Got strides ${strides} and dilations '${dilations}'`);\n conv_util.checkPadOnDimRoundingMode('maxPool', pad, dimRoundingMode);\n const inputs = { x: x4D };\n const attrs = { filterSize, strides, pad, dimRoundingMode };\n // tslint:disable-next-line: no-unnecessary-type-assertion\n const res = ENGINE.runKernel(MaxPool, inputs, attrs);\n if (reshapedTo4D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n }\n return res;\n}\nexport const maxPool = op({ maxPool_ });\n//# 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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Minimum } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { cast } from './cast';\nimport { op } from './operation';\n/**\n * Returns the min of a and b (`a < b ? a : b`) element-wise.\n * Supports broadcasting.\n *\n * We also expose `minimumStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.minimum(b).print(); // or tf.minimum(a, b)\n * ```\n *\n * ```js\n * // Broadcast minimum a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.minimum(b).print(); // or tf.minimum(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction minimum_(a, b) {\n let $a = convertToTensor(a, 'a', 'minimum');\n let $b = convertToTensor(b, 'b', 'minimum');\n [$a, $b] = makeTypesMatch($a, $b);\n if ($a.dtype === 'bool') {\n $a = cast($a, 'int32');\n $b = cast($b, 'int32');\n }\n assertAndGetBroadcastShape($a.shape, $b.shape);\n const inputs = { a: $a, b: $b };\n return ENGINE.runKernel(Minimum, inputs);\n}\nexport const minimum = op({ minimum_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { NotEqual } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a != b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([0, 2, 3]);\n *\n * a.notEqual(b).print();\n * ```\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction notEqual_(a, b) {\n let $a = convertToTensor(a, 'a', 'notEqual', 'string_or_numeric');\n let $b = convertToTensor(b, 'b', 'notEqual', 'string_or_numeric');\n [$a, $b] = makeTypesMatch($a, $b);\n assertAndGetBroadcastShape($a.shape, $b.shape);\n const inputs = { a: $a, b: $b };\n return ENGINE.runKernel(NotEqual, inputs);\n}\nexport const notEqual = op({ notEqual_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Prelu } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes leaky rectified linear element-wise with parametric alphas.\n *\n * `x < 0 ? alpha * x : f(x) = x`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n * const alpha = tf.scalar(0.1);\n *\n * x.prelu(alpha).print(); // or tf.prelu(x, alpha)\n * ```\n * @param x The input tensor.\n * @param alpha Scaling factor for negative values.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction prelu_(x, alpha) {\n const $x = convertToTensor(x, 'x', 'prelu');\n const $alpha = convertToTensor(alpha, 'alpha', 'prelu');\n const inputs = { x: $x, alpha: $alpha };\n return ENGINE.runKernel(Prelu, inputs);\n}\nexport const prelu = op({ prelu_ });\n//# sourceMappingURL=data:application/json;base64,eyJ2ZXJzaW9uIjozLCJmaWxlIjoicHJlbHUuanMiLCJzb3VyY2VSb290IjoiIiwic291cmNlcyI6WyIuLi8uLi8uLi8uLi8uLi8uLi90ZmpzLWNvcmUvc3JjL29wcy9wcmVsdS50cyJdLCJuYW1lcyI6W10sIm1hcHBpbmdzIjoiQUFBQTs7Ozs7Ozs7Ozs7Ozs7O0dBZUc7QUFFSCxPQUFPLEVBQUMsTUFBTSxFQUFDLE1BQU0sV0FBVyxDQUFDO0FBQ2pDLE9BQU8sRUFBQyxLQUFLLEVBQWMsTUFBTSxpQkFBaUIsQ0FBQztBQUduRCxPQUFPLEVBQUMsZUFBZSxFQUFDLE1BQU0sb0JBQW9CLENBQUM7QUFHbkQsT0FBTyxFQUFDLEVBQUUsRUFBQyxNQUFNLGFBQWEsQ0FBQztBQUUvQjs7Ozs7Ozs7Ozs7Ozs7O0dBZUc7QUFDSCxTQUFTLE1BQU0sQ0FBbUIsQ0FBZSxFQUFFLEtBQW1CO0lBQ3BFLE1BQU0sRUFBRSxHQUFHLGVBQWUsQ0FBQyxDQUFDLEVBQUUsR0FBRyxFQUFFLE9BQU8sQ0FBQyxDQUFDO0lBQzVDLE1BQU0sTUFBTSxHQUFHLGVBQWUsQ0FBQyxLQUFLLEVBQUUsT0FBTyxFQUFFLE9BQU8sQ0FBQyxDQUFDO0lBRXhELE1BQU0sTUFBTSxHQUFnQixFQUFDLENBQUMsRUFBRSxFQUFFLEVBQUUsS0FBSyxFQUFFLE1BQU0sRUFBQyxDQUFDO0lBQ25ELE9BQU8sTUFBTSxDQUFDLFNBQVMsQ0FBQyxLQUFLLEVBQUUsTUFBOEIsQ0FBQyxDQUFDO0FBQ2pFLENBQUM7QUFFRCxNQUFNLENBQUMsTUFBTSxLQUFLLEdBQUcsRUFBRSxDQUFDLEVBQUMsTUFBTSxFQUFDLENBQUMsQ0FBQyIsInNvdXJjZXNDb250ZW50IjpbIi8qKlxuICogQGxpY2Vuc2VcbiAqIENvcHlyaWdodCAyMDIwIEdvb2dsZSBMTEMuIEFsbCBSaWdodHMgUmVzZXJ2ZWQuXG4gKiBMaWNlbnNlZCB1bmRlciB0aGUgQXBhY2hlIExpY2Vuc2UsIFZlcnNpb24gMi4wICh0aGUgXCJMaWNlbnNlXCIpO1xuICogeW91IG1heSBub3QgdXNlIHRoaXMgZmlsZSBleGNlcHQgaW4gY29tcGxpYW5jZSB3aXRoIHRoZSBMaWNlbnNlLlxuICogWW91IG1heSBvYnRhaW4gYSBjb3B5IG9mIHRoZSBMaWNlbnNlIGF0XG4gKlxuICogaHR0cDovL3d3dy5hcGFjaGUub3JnL2xpY2Vuc2VzL0xJQ0VOU0UtMi4wXG4gKlxuICogVW5sZXNzIHJlcXVpcmVkIGJ5IGFwcGxpY2FibGUgbGF3IG9yIGFncmVlZCB0byBpbiB3cml0aW5nLCBzb2Z0d2FyZVxuICogZGlzdHJpYnV0ZWQgdW5kZXIgdGhlIExpY2Vuc2UgaXMgZGlzdHJpYnV0ZWQgb24gYW4gXCJBUyBJU1wiIEJBU0lTLFxuICogV0lUSE9VVCBXQVJSQU5USUVTIE9SIENPTkRJVElPTlMgT0YgQU5ZIEtJTkQsIGVpdGhlciBleHByZXNzIG9yIGltcGxpZWQuXG4gKiBTZWUgdGhlIExpY2Vuc2UgZm9yIHRoZSBzcGVjaWZpYyBsYW5ndWFnZSBnb3Zlcm5pbmcgcGVybWlzc2lvbnMgYW5kXG4gKiBsaW1pdGF0aW9ucyB1bmRlciB0aGUgTGljZW5zZS5cbiAqID09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09XG4gKi9cblxuaW1wb3J0IHtFTkdJTkV9IGZyb20gJy4uL2VuZ2luZSc7XG5pbXBvcnQge1ByZWx1LCBQcmVsdUlucHV0c30gZnJvbSAnLi4va2VybmVsX25hbWVzJztcbmltcG9ydCB7VGVuc29yfSBmcm9tICcuLi90ZW5zb3InO1xuaW1wb3J0IHtOYW1lZFRlbnNvck1hcH0gZnJvbSAnLi4vdGVuc29yX3R5cGVzJztcbmltcG9ydCB7Y29udmVydFRvVGVuc29yfSBmcm9tICcuLi90ZW5zb3JfdXRpbF9lbnYnO1xuaW1wb3J0IHtUZW5zb3JMaWtlfSBmcm9tICcuLi90eXBlcyc7XG5cbmltcG9ydCB7b3B9IGZyb20gJy4vb3BlcmF0aW9uJztcblxuLyoqXG4gKiBDb21wdXRlcyBsZWFreSByZWN0aWZpZWQgbGluZWFyIGVsZW1lbnQtd2lzZSB3aXRoIHBhcmFtZXRyaWMgYWxwaGFzLlxuICpcbiAqIGB4IDwgMCA/IGFscGhhICogeCA6IGYoeCkgPSB4YFxuICpcbiAqIGBgYGpzXG4gKiBjb25zdCB4ID0gdGYudGVuc29yMWQoWy0xLCAyLCAtMywgNF0pO1xuICogY29uc3QgYWxwaGEgPSB0Zi5zY2FsYXIoMC4xKTtcbiAqXG4gKiB4LnByZWx1KGFscGhhKS5wcmludCgpOyAgLy8gb3IgdGYucHJlbHUoeCwgYWxwaGEpXG4gKiBgYGBcbiAqIEBwYXJhbSB4IFRoZSBpbnB1dCB0ZW5zb3IuXG4gKiBAcGFyYW0gYWxwaGEgU2NhbGluZyBmYWN0b3IgZm9yIG5lZ2F0aXZlIHZhbHVlcy5cbiAqXG4gKiBAZG9jIHtoZWFkaW5nOiAnT3BlcmF0aW9ucycsIHN1YmhlYWRpbmc6ICdCYXNpYyBtYXRoJ31cbiAqL1xuZnVuY3Rpb24gcHJlbHVfPFQgZXh0ZW5kcyBUZW5zb3I+KHg6IFR8VGVuc29yTGlrZSwgYWxwaGE6IFR8VGVuc29yTGlrZSk6IFQge1xuICBjb25zdCAkeCA9IGNvbnZlcnRUb1RlbnNvcih4LCAneCcsICdwcmVsdScpO1xuICBjb25zdCAkYWxwaGEgPSBjb252ZXJ0VG9UZW5zb3IoYWxwaGEsICdhbHBoYScsICdwcmVsdScpO1xuXG4gIGNvbnN0IGlucHV0czogUHJlbHVJbnB1dHMgPSB7eDogJHgsIGFscGhhOiAkYWxwaGF9O1xuICByZXR1cm4gRU5HSU5FLnJ1bktlcm5lbChQcmVsdSwgaW5wdXRzIGFzIHt9IGFzIE5hbWVkVGVuc29yTWFwKTtcbn1cblxuZXhwb3J0IGNvbnN0IHByZWx1ID0gb3Aoe3ByZWx1X30pO1xuIl19","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from './buffer';\nimport { op } from './operation';\nimport { UniformRandom } from './rand_util';\n/**\n * Creates a `tf.Tensor` with values sampled from a uniform distribution.\n *\n * The generated values follow a uniform distribution in the range [minval,\n * maxval). The lower bound minval is included in the range, while the upper\n * bound maxval is excluded.\n *\n * ```js\n * tf.randomUniform([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param minval The lower bound on the range of random values to generate.\n * Defaults to 0.\n * @param maxval The upper bound on the range of random values to generate.\n * Defaults to 1.\n * @param dtype The data type of the output tensor. Defaults to 'float32'.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction randomUniform_(shape, minval = 0, maxval = 1, dtype = 'float32', seed) {\n const res = buffer(shape, dtype);\n const random = new UniformRandom(minval, maxval, null, seed);\n for (let i = 0; i < res.values.length; i++) {\n res.values[i] = random.nextValue();\n }\n return res.toTensor();\n}\nexport const randomUniform = op({ randomUniform_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Relu6 } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes rectified linear 6 element-wise: `min(max(x, 0), 6)`.\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 8]);\n *\n * x.relu6().print(); // or tf.relu6(x)\n * ```\n * @param x The input tensor. If the dtype is `bool`, the output dtype will be\n * `int32'.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction relu6_(x) {\n const $x = convertToTensor(x, 'x', 'relu6');\n const inputs = { x: $x };\n return ENGINE.runKernel(Relu6, inputs);\n}\nexport const relu6 = op({ relu6_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Round } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes round of input `tf.Tensor` element-wise: `round(x)`.\n * It implements banker's rounding.\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3]);\n *\n * x.round().print(); // or tf.round(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction round_(x) {\n const $x = convertToTensor(x, 'x', 'round');\n const inputs = { x: $x };\n return ENGINE.runKernel(Round, inputs);\n}\nexport const round = op({ round_ });\n//# sourceMappingURL=data:application/json;base64,eyJ2ZXJzaW9uIjozLCJmaWxlIjoicm91bmQuanMiLCJzb3VyY2VSb290IjoiIiwic291cmNlcyI6WyIuLi8uLi8uLi8uLi8uLi8uLi90ZmpzLWNvcmUvc3JjL29wcy9yb3VuZC50cyJdLCJuYW1lcyI6W10sIm1hcHBpbmdzIjoiQUFBQTs7Ozs7Ozs7Ozs7Ozs7O0dBZUc7QUFFSCxPQUFPLEVBQUMsTUFBTSxFQUFDLE1BQU0sV0FBVyxDQUFDO0FBQ2pDLE9BQU8sRUFBQyxLQUFLLEVBQWMsTUFBTSxpQkFBaUIsQ0FBQztBQUduRCxPQUFPLEVBQUMsZUFBZSxFQUFDLE1BQU0sb0JBQW9CLENBQUM7QUFHbkQsT0FBTyxFQUFDLEVBQUUsRUFBQyxNQUFNLGFBQWEsQ0FBQztBQUUvQjs7Ozs7Ozs7Ozs7O0dBWUc7QUFDSCxTQUFTLE1BQU0sQ0FBbUIsQ0FBZTtJQUMvQyxNQUFNLEVBQUUsR0FBRyxlQUFlLENBQUMsQ0FBQyxFQUFFLEdBQUcsRUFBRSxPQUFPLENBQUMsQ0FBQztJQUM1QyxNQUFNLE1BQU0sR0FBZ0IsRUFBQyxDQUFDLEVBQUUsRUFBRSxFQUFDLENBQUM7SUFFcEMsT0FBTyxNQUFNLENBQUMsU0FBUyxDQUFDLEtBQUssRUFBRSxNQUE4QixDQUFDLENBQUM7QUFDakUsQ0FBQztBQUVELE1BQU0sQ0FBQyxNQUFNLEtBQUssR0FBRyxFQUFFLENBQUMsRUFBQyxNQUFNLEVBQUMsQ0FBQyxDQUFDIiwic291cmNlc0NvbnRlbnQiOlsiLyoqXG4gKiBAbGljZW5zZVxuICogQ29weXJpZ2h0IDIwMTggR29vZ2xlIExMQy4gQWxsIFJpZ2h0cyBSZXNlcnZlZC5cbiAqIExpY2Vuc2VkIHVuZGVyIHRoZSBBcGFjaGUgTGljZW5zZSwgVmVyc2lvbiAyLjAgKHRoZSBcIkxpY2Vuc2VcIik7XG4gKiB5b3UgbWF5IG5vdCB1c2UgdGhpcyBmaWxlIGV4Y2VwdCBpbiBjb21wbGlhbmNlIHdpdGggdGhlIExpY2Vuc2UuXG4gKiBZb3UgbWF5IG9idGFpbiBhIGNvcHkgb2YgdGhlIExpY2Vuc2UgYXRcbiAqXG4gKiBodHRwOi8vd3d3LmFwYWNoZS5vcmcvbGljZW5zZXMvTElDRU5TRS0yLjBcbiAqXG4gKiBVbmxlc3MgcmVxdWlyZWQgYnkgYXBwbGljYWJsZSBsYXcgb3IgYWdyZWVkIHRvIGluIHdyaXRpbmcsIHNvZnR3YXJlXG4gKiBkaXN0cmlidXRlZCB1bmRlciB0aGUgTGljZW5zZSBpcyBkaXN0cmlidXRlZCBvbiBhbiBcIkFTIElTXCIgQkFTSVMsXG4gKiBXSVRIT1VUIFdBUlJBTlRJRVMgT1IgQ09ORElUSU9OUyBPRiBBTlkgS0lORCwgZWl0aGVyIGV4cHJlc3Mgb3IgaW1wbGllZC5cbiAqIFNlZSB0aGUgTGljZW5zZSBmb3IgdGhlIHNwZWNpZmljIGxhbmd1YWdlIGdvdmVybmluZyBwZXJtaXNzaW9ucyBhbmRcbiAqIGxpbWl0YXRpb25zIHVuZGVyIHRoZSBMaWNlbnNlLlxuICogPT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT1cbiAqL1xuXG5pbXBvcnQge0VOR0lORX0gZnJvbSAnLi4vZW5naW5lJztcbmltcG9ydCB7Um91bmQsIFJvdW5kSW5wdXRzfSBmcm9tICcuLi9rZXJuZWxfbmFtZXMnO1xuaW1wb3J0IHtUZW5zb3J9IGZyb20gJy4uL3RlbnNvcic7XG5pbXBvcnQge05hbWVkVGVuc29yTWFwfSBmcm9tICcuLi90ZW5zb3JfdHlwZXMnO1xuaW1wb3J0IHtjb252ZXJ0VG9UZW5zb3J9IGZyb20gJy4uL3RlbnNvcl91dGlsX2Vudic7XG5pbXBvcnQge1RlbnNvckxpa2V9IGZyb20gJy4uL3R5cGVzJztcblxuaW1wb3J0IHtvcH0gZnJvbSAnLi9vcGVyYXRpb24nO1xuXG4vKipcbiAqIENvbXB1dGVzIHJvdW5kIG9mIGlucHV0IGB0Zi5UZW5zb3JgIGVsZW1lbnQtd2lzZTogYHJvdW5kKHgpYC5cbiAqIEl0IGltcGxlbWVudHMgYmFua2VyJ3Mgcm91bmRpbmcuXG4gKlxuICogYGBganNcbiAqIGNvbnN0IHggPSB0Zi50ZW5zb3IxZChbLjYsIDEuMSwgLTMuM10pO1xuICpcbiAqIHgucm91bmQoKS5wcmludCgpOyAgLy8gb3IgdGYucm91bmQoeClcbiAqIGBgYFxuICogQHBhcmFtIHggVGhlIGlucHV0IHRlbnNvci5cbiAqXG4gKiBAZG9jIHtoZWFkaW5nOiAnT3BlcmF0aW9ucycsIHN1YmhlYWRpbmc6ICdCYXNpYyBtYXRoJ31cbiAqL1xuZnVuY3Rpb24gcm91bmRfPFQgZXh0ZW5kcyBUZW5zb3I+KHg6IFR8VGVuc29yTGlrZSk6IFQge1xuICBjb25zdCAkeCA9IGNvbnZlcnRUb1RlbnNvcih4LCAneCcsICdyb3VuZCcpO1xuICBjb25zdCBpbnB1dHM6IFJvdW5kSW5wdXRzID0ge3g6ICR4fTtcblxuICByZXR1cm4gRU5HSU5FLnJ1bktlcm5lbChSb3VuZCwgaW5wdXRzIGFzIHt9IGFzIE5hbWVkVGVuc29yTWFwKTtcbn1cblxuZXhwb3J0IGNvbnN0IHJvdW5kID0gb3Aoe3JvdW5kX30pO1xuIl19","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Rsqrt } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes reciprocal of square root of the input `tf.Tensor` element-wise:\n * `y = 1 / sqrt(x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 4, -1]);\n *\n * x.rsqrt().print(); // or tf.rsqrt(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction rsqrt_(x) {\n const $x = convertToTensor(x, 'x', 'rsqrt', 'float32');\n const inputs = { x: $x };\n return ENGINE.runKernel(Rsqrt, inputs);\n}\nexport const rsqrt = op({ rsqrt_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sin } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes sin of the input Tensor element-wise: `sin(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);\n *\n * x.sin().print(); // or tf.sin(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sin_(x) {\n const $x = convertToTensor(x, 'x', 'sin', 'float32');\n const inputs = { x: $x };\n return ENGINE.runKernel(Sin, inputs);\n}\nexport const sin = op({ sin_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sinh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes hyperbolic sin of the input `tf.Tensor` element-wise: `sinh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.sinh().print(); // or tf.sinh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sinh_(x) {\n const $x = convertToTensor(x, 'x', 'sinh');\n const inputs = { x: $x };\n return ENGINE.runKernel(Sinh, inputs);\n}\nexport const sinh = op({ sinh_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { complex } from '../complex';\nimport { concat } from '../concat';\nimport { imag } from '../imag';\nimport { mul } from '../mul';\nimport { op } from '../operation';\nimport { real } from '../real';\nimport { reshape } from '../reshape';\nimport { reverse } from '../reverse';\nimport { scalar } from '../scalar';\nimport { slice } from '../slice';\nimport { ifft } from './ifft';\n/**\n * Inversed real value input fast Fourier transform.\n *\n * Computes the 1-dimensional inversed discrete Fourier transform over the\n * inner-most dimension of the real input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n * const imag = tf.tensor1d([0, 0, 0]);\n * const x = tf.complex(real, imag);\n *\n * x.irfft().print();\n * ```\n * @param input The real value input to compute an irfft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction irfft_(input) {\n const innerDimensionSize = input.shape[input.shape.length - 1];\n const batch = input.size / innerDimensionSize;\n let ret;\n if (innerDimensionSize <= 2) {\n const complexInput = reshape(input, [batch, innerDimensionSize]);\n ret = ifft(complexInput);\n }\n else {\n // The length of unique components of the DFT of a real-valued signal\n // is 2 * (input_len - 1)\n const outputShape = [batch, 2 * (innerDimensionSize - 1)];\n const realInput = reshape(real(input), [batch, innerDimensionSize]);\n const imagInput = reshape(imag(input), [batch, innerDimensionSize]);\n const realConjugate = reverse(slice(realInput, [0, 1], [batch, innerDimensionSize - 2]), 1);\n const imagConjugate = mul(reverse(slice(imagInput, [0, 1], [batch, innerDimensionSize - 2]), 1), scalar(-1));\n const r = concat([realInput, realConjugate], 1);\n const i = concat([imagInput, imagConjugate], 1);\n const complexInput = reshape(complex(r, i), [outputShape[0], outputShape[1]]);\n ret = ifft(complexInput);\n }\n ret = real(ret);\n // reshape the result if the input is 3D tensor.\n if (input.rank === 3 && input.shape[0] !== 0) {\n const temp = ret;\n const batch = input.shape[0];\n ret = reshape(ret, [batch, ret.shape[0] / batch, ret.shape[1]]);\n temp.dispose();\n }\n return ret;\n}\nexport const irfft = op({ irfft_ });\n//# sourceMappingURL=data:application/json;base64,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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { SquaredDifference } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns (a - b) * (a - b) element-wise.\n * Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.squaredDifference(b).print(); // or tf.squaredDifference(a, b)\n * ```\n *\n * ```js\n * // Broadcast squared difference a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.squaredDifference(b).print(); // or tf.squaredDifference(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction squaredDifference_(a, b) {\n let $a = convertToTensor(a, 'a', 'squaredDifference');\n let $b = convertToTensor(b, 'b', 'squaredDifference');\n [$a, $b] = makeTypesMatch($a, $b);\n assertAndGetBroadcastShape($a.shape, $b.shape);\n const inputs = { a: $a, b: $b };\n const attrs = {};\n return ENGINE.runKernel(SquaredDifference, inputs, attrs);\n}\nexport const squaredDifference = op({ squaredDifference_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-3 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor3d` as it makes the code more readable.\n *\n * ```js\n * // Pass a nested array.\n * tf.tensor3d([[[1], [2]], [[3], [4]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor3d([1, 2, 3, 4], [2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n * or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. If not provided, it is inferred from\n * `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor3d(values, shape, dtype) {\n assertNonNull(values);\n if (shape != null && shape.length !== 3) {\n throw new Error('tensor3d() requires shape to have three numbers');\n }\n const inferredShape = inferShape(values, dtype);\n if (inferredShape.length !== 3 && inferredShape.length !== 1) {\n throw new Error('tensor3d() requires values to be number[][][] or flat/TypedArray');\n }\n if (inferredShape.length === 1 && shape == null) {\n throw new Error('tensor3d() requires shape to be provided when `values` ' +\n 'are a flat array');\n }\n return makeTensor(values, shape, inferredShape, dtype);\n}\n//# 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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { UnsortedSegmentSum } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assert, isInt } from '../util';\nimport { op } from './operation';\n/**\n * Computes the sum along segments of a `tf.Tensor`.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * const segmentIds = tf.tensor1d([1, 2, 0, 1], 'int32');\n * const numSegments = 3;\n *\n * x.unsortedSegmentSum(segmentIds, numSegments).print()\n * //or tf.unsortedSegmentSum(x, segmentIds, numSegments)\n * ```\n * @param x The `tf.Tensor` that will be summed along its segments.\n * @param segmentIds A `tf.Tensor1D` whose rank is equal to the rank of `x`'s\n * dimension along the `axis`. Maps each element of `x` to a segment.\n * @param numSegments The number of distinct `segmentIds`.\n *\n * @doc {heading: 'Operations', subheading: 'Segment'}\n */\nfunction unsortedSegmentSum_(x, segmentIds, numSegments) {\n const $x = convertToTensor(x, 'x', 'unsortedSegmentSum');\n const $segmentIds = convertToTensor(segmentIds, 'segmentIds', 'unsortedSegmentSum', 'int32');\n assert(isInt(numSegments), () => 'numSegments must be of dtype int');\n const inputs = { x: $x, segmentIds: $segmentIds };\n const attrs = { numSegments };\n return ENGINE.runKernel(UnsortedSegmentSum, inputs, attrs);\n}\nexport const unsortedSegmentSum = op({ unsortedSegmentSum_ });\n//# 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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { whereImpl } from '../backends/where_impl';\nimport { convertToTensor } from '../tensor_util_env';\n/**\n * Returns the coordinates of true elements of condition.\n *\n * The coordinates are returned in a 2-D tensor where the first dimension (rows)\n * represents the number of true elements, and the second dimension (columns)\n * represents the coordinates of the true elements. Keep in mind, the shape of\n * the output tensor can vary depending on how many true values there are in\n * input. Indices are output in row-major order. The resulting tensor has the\n * shape `[numTrueElems, condition.rank]`.\n *\n * This is analogous to calling the python `tf.where(cond)` without an x or y.\n *\n * ```js\n * const cond = tf.tensor1d([false, false, true], 'bool');\n * const result = await tf.whereAsync(cond);\n * result.print();\n * ```\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nasync function whereAsync_(condition) {\n const $condition = convertToTensor(condition, 'condition', 'whereAsync', 'bool');\n const vals = await $condition.data();\n const res = whereImpl($condition.shape, vals);\n if (condition !== $condition) {\n $condition.dispose();\n }\n return res;\n}\nexport const whereAsync = whereAsync_;\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/** An implementation of the Where kernel shared between cpu and webgl */\nimport { buffer } from '../ops/buffer';\nexport function whereImpl(condShape, condVals) {\n const indices = [];\n for (let i = 0; i < condVals.length; i++) {\n if (condVals[i]) {\n indices.push(i);\n }\n }\n const inBuffer = buffer(condShape, 'int32');\n const out = buffer([indices.length, condShape.length], 'int32');\n for (let i = 0; i < indices.length; i++) {\n const loc = inBuffer.indexToLoc(indices[i]);\n const offset = i * condShape.length;\n out.values.set(loc, offset);\n }\n return out.toTensor();\n}\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Shared functionality among backends.\nexport { simpleAbsImpl } from './kernels/Abs';\nexport { addImpl } from './kernels/Add';\nexport { bincountImpl, bincountReduceImpl } from './kernels/Bincount_impl';\nexport { ceilImpl } from './kernels/Ceil';\nexport { concatImpl } from './kernels/Concat_impl';\nexport { equalImpl } from './kernels/Equal';\nexport { expImpl } from './kernels/Exp';\nexport { expm1Impl } from './kernels/Expm1';\nexport { floorImpl } from './kernels/Floor';\nexport { gatherNdImpl } from './kernels/GatherNd_Impl';\nexport { gatherV2Impl } from './kernels/GatherV2_impl';\nexport { greaterImpl } from './kernels/Greater';\nexport { greaterEqualImpl } from './kernels/GreaterEqual';\nexport { lessImpl } from './kernels/Less';\nexport { lessEqualImpl } from './kernels/LessEqual';\nexport { linSpaceImpl } from './kernels/LinSpace_impl';\nexport { logImpl } from './kernels/Log';\nexport { maxImpl } from './kernels/Max_impl';\nexport { maximumImpl } from './kernels/Maximum';\nexport { minimumImpl } from './kernels/Minimum';\nexport { multiplyImpl } from './kernels/Multiply';\nexport { negImpl } from './kernels/Neg';\nexport { notEqualImpl } from './kernels/NotEqual';\nexport { prodImpl } from './kernels/Prod';\nexport { rangeImpl } from './kernels/Range_impl';\nexport { rsqrtImpl } from './kernels/Rsqrt';\nexport { sigmoidImpl } from './kernels/Sigmoid';\nexport { sliceImpl } from './kernels/Slice';\nexport { sparseFillEmptyRowsImpl } from './kernels/SparseFillEmptyRows_impl';\nexport { sparseReshapeImpl } from './kernels/SparseReshape_impl';\nexport { sparseSegmentReductionImpl } from './kernels/SparseSegmentReduction_impl';\nexport { sqrtImpl } from './kernels/Sqrt';\nexport { squaredDifferenceImpl } from './kernels/SquaredDifference';\nexport { stridedSliceImpl } from './kernels/StridedSlice_impl';\nexport { stringNGramsImpl } from './kernels/StringNGrams_impl';\nexport { stringSplitImpl } from './kernels/StringSplit_impl';\nexport { stringToHashBucketFastImpl } from './kernels/StringToHashBucketFast_impl';\nexport { subImpl } from './kernels/Sub';\nexport { tileImpl } from './kernels/Tile_impl';\nexport { topKImpl } from './kernels/TopK_impl';\nexport { transposeImpl } from './kernels/Transpose_impl';\nexport { uniqueImpl } from './kernels/Unique_impl';\n//# 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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Abs, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function simpleAbsImpl(vals) {\n const resultValues = new Float32Array(vals.length);\n for (let i = 0; i < vals.length; ++i) {\n resultValues[i] = Math.abs(vals[i]);\n }\n return resultValues;\n}\nexport const abs = (args) => {\n const { x } = args.inputs;\n const cpuBackend = args.backend;\n assertNotComplex(x, 'abs');\n let resultValues = new Float32Array(util.sizeFromShape(x.shape));\n const values = cpuBackend.data.get(x.dataId).values;\n resultValues = simpleAbsImpl(values);\n return cpuBackend.makeOutput(resultValues, x.shape, x.dtype);\n};\nexport const absConfig = {\n kernelName: Abs,\n backendName: 'cpu',\n kernelFunc: abs,\n};\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Ceil } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const ceilImpl = createSimpleUnaryImpl((xi) => Math.ceil(xi));\nexport const ceil = unaryKernelFuncFromImpl(Ceil, ceilImpl);\nexport const ceilConfig = {\n kernelName: Ceil,\n backendName: 'cpu',\n kernelFunc: ceil,\n};\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nexport function concatImpl(inputs, outShape, dtype, simplyConcat) {\n const outVals = util.getArrayFromDType(dtype, util.sizeFromShape(outShape));\n if (simplyConcat && dtype !== 'string') {\n // Use built-in TypedArray.set() method for speed.\n let offset = 0;\n inputs.forEach(input => {\n const size = util.sizeFromShape(input.shape);\n outVals.set(input.vals, offset);\n offset += size;\n });\n }\n else {\n let colOffset = 0;\n inputs.forEach(input => {\n const decodedData = dtype === 'string' ?\n backend_util.fromUint8ToStringArray(input.vals) :\n input.vals;\n let tIdx = 0;\n for (let row = 0; row < input.shape[0]; ++row) {\n const resIdx = row * outShape[1] + colOffset;\n for (let col = 0; col < input.shape[1]; ++col) {\n outVals[resIdx + col] = decodedData[tIdx++];\n }\n }\n colOffset += input.shape[1];\n });\n }\n return outVals;\n}\n//# sourceMappingURL=data:application/json;base64,eyJ2ZXJzaW9uIjozLCJmaWxlIjoiQ29uY2F0X2ltcGwuanMiLCJzb3VyY2VSb290IjoiIiwic291cmNlcyI6WyIuLi8uLi8uLi8uLi8uLi8uLi90ZmpzLWJhY2tlbmQtY3B1L3NyYy9rZXJuZWxzL0NvbmNhdF9pbXBsLnRzIl0sIm5hbWVzIjpbXSwibWFwcGluZ3MiOiJBQUFBOzs7Ozs7Ozs7Ozs7Ozs7R0FlRztBQUVILE9BQU8sRUFBQyxZQUFZLEVBQXVDLElBQUksRUFBQyxNQUFNLHVCQUF1QixDQUFDO0FBRTlGLE1BQU0sVUFBVSxVQUFVLENBQ3RCLE1BQXFELEVBQUUsUUFBa0IsRUFDekUsS0FBZSxFQUFFLFlBQXFCO0lBQ3hDLE1BQU0sT0FBTyxHQUFHLElBQUksQ0FBQyxpQkFBaUIsQ0FBQyxLQUFLLEVBQUUsSUFBSSxDQUFDLGFBQWEsQ0FBQyxRQUFRLENBQUMsQ0FBQyxDQUFDO0lBRTVFLElBQUksWUFBWSxJQUFJLEtBQUssS0FBSyxRQUFRLEVBQUU7UUFDdEMsa0RBQWtEO1FBQ2xELElBQUksTUFBTSxHQUFHLENBQUMsQ0FBQztRQUNmLE1BQU0sQ0FBQyxPQUFPLENBQUMsS0FBSyxDQUFDLEVBQUU7WUFDckIsTUFBTSxJQUFJLEdBQUcsSUFBSSxDQUFDLGFBQWEsQ0FBQyxLQUFLLENBQUMsS0FBSyxDQUFDLENBQUM7WUFFNUMsT0FBc0IsQ0FBQyxHQUFHLENBQUMsS0FBSyxDQUFDLElBQWtCLEVBQUUsTUFBTSxDQUFDLENBQUM7WUFDOUQsTUFBTSxJQUFJLElBQUksQ0FBQztRQUNqQixDQUFDLENBQUMsQ0FBQztLQUNKO1NBQU07UUFDTCxJQUFJLFNBQVMsR0FBRyxDQUFDLENBQUM7UUFFbEIsTUFBTSxDQUFDLE9BQU8sQ0FBQyxLQUFLLENBQUMsRUFBRTtZQUNyQixNQUFNLFdBQVcsR0FBRyxLQUFLLEtBQUssUUFBUSxDQUFDLENBQUM7Z0JBQ3BDLFlBQVksQ0FBQyxzQkFBc0IsQ0FBQyxLQUFLLENBQUMsSUFBb0IsQ0FBQyxDQUFDLENBQUM7Z0JBQ2pFLEtBQUssQ0FBQyxJQUFrQixDQUFDO1lBRTdCLElBQUksSUFBSSxHQUFHLENBQUMsQ0FBQztZQUViLEtBQUssSUFBSSxHQUFHLEdBQUcsQ0FBQyxFQUFFLEdBQUcsR0FBRyxLQUFLLENBQUMsS0FBSyxDQUFDLENBQUMsQ0FBQyxFQUFFLEVBQUUsR0FBRyxFQUFFO2dCQUM3QyxNQUFNLE1BQU0sR0FBRyxHQUFHLEdBQUcsUUFBUSxDQUFDLENBQUMsQ0FBQyxHQUFHLFNBQVMsQ0FBQztnQkFDN0MsS0FBSyxJQUFJLEdBQUcsR0FBRyxDQUFDLEVBQUUsR0FBRyxHQUFHLEtBQUssQ0FBQyxLQUFLLENBQUMsQ0FBQyxDQUFDLEVBQUUsRUFBRSxHQUFHLEVBQUU7b0JBQzdDLE9BQU8sQ0FBQyxNQUFNLEdBQUcsR0FBRyxDQUFDLEdBQUcsV0FBVyxDQUFDLElBQUksRUFBRSxDQUFDLENBQUM7aUJBQzdDO2FBQ0Y7WUFFRCxTQUFTLElBQUksS0FBSyxDQUFDLEtBQUssQ0FBQyxDQUFDLENBQUMsQ0FBQztRQUM5QixDQUFDLENBQUMsQ0FBQztLQUNKO0lBRUQsT0FBTyxPQUFPLENBQUM7QUFDakIsQ0FBQyIsInNvdXJjZXNDb250ZW50IjpbIi8qKlxuICogQGxpY2Vuc2VcbiAqIENvcHlyaWdodCAyMDIwIEdvb2dsZSBMTEMuIEFsbCBSaWdodHMgUmVzZXJ2ZWQuXG4gKiBMaWNlbnNlZCB1bmRlciB0aGUgQXBhY2hlIExpY2Vuc2UsIFZlcnNpb24gMi4wICh0aGUgXCJMaWNlbnNlXCIpO1xuICogeW91IG1heSBub3QgdXNlIHRoaXMgZmlsZSBleGNlcHQgaW4gY29tcGxpYW5jZSB3aXRoIHRoZSBMaWNlbnNlLlxuICogWW91IG1heSBvYnRhaW4gYSBjb3B5IG9mIHRoZSBMaWNlbnNlIGF0XG4gKlxuICogaHR0cDovL3d3dy5hcGFjaGUub3JnL2xpY2Vuc2VzL0xJQ0VOU0UtMi4wXG4gKlxuICogVW5sZXNzIHJlcXVpcmVkIGJ5IGFwcGxpY2FibGUgbGF3IG9yIGFncmVlZCB0byBpbiB3cml0aW5nLCBzb2Z0d2FyZVxuICogZGlzdHJpYnV0ZWQgdW5kZXIgdGhlIExpY2Vuc2UgaXMgZGlzdHJpYnV0ZWQgb24gYW4gXCJBUyBJU1wiIEJBU0lTLFxuICogV0lUSE9VVCBXQVJSQU5USUVTIE9SIENPTkRJVElPTlMgT0YgQU5ZIEtJTkQsIGVpdGhlciBleHByZXNzIG9yIGltcGxpZWQuXG4gKiBTZWUgdGhlIExpY2Vuc2UgZm9yIHRoZSBzcGVjaWZpYyBsYW5ndWFnZSBnb3Zlcm5pbmcgcGVybWlzc2lvbnMgYW5kXG4gKiBsaW1pdGF0aW9ucyB1bmRlciB0aGUgTGljZW5zZS5cbiAqID09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09XG4gKi9cblxuaW1wb3J0IHtiYWNrZW5kX3V0aWwsIEJhY2tlbmRWYWx1ZXMsIERhdGFUeXBlLCBUeXBlZEFycmF5LCB1dGlsfSBmcm9tICdAdGVuc29yZmxvdy90ZmpzLWNvcmUnO1xuXG5leHBvcnQgZnVuY3Rpb24gY29uY2F0SW1wbChcbiAgICBpbnB1dHM6IEFycmF5PHt2YWxzOiBCYWNrZW5kVmFsdWVzLCBzaGFwZTogbnVtYmVyW119Piwgb3V0U2hhcGU6IG51bWJlcltdLFxuICAgIGR0eXBlOiBEYXRhVHlwZSwgc2ltcGx5Q29uY2F0OiBib29sZWFuKTogVHlwZWRBcnJheXxzdHJpbmdbXSB7XG4gIGNvbnN0IG91dFZhbHMgPSB1dGlsLmdldEFycmF5RnJvbURUeXBlKGR0eXBlLCB1dGlsLnNpemVGcm9tU2hhcGUob3V0U2hhcGUpKTtcblxuICBpZiAoc2ltcGx5Q29uY2F0ICYmIGR0eXBlICE9PSAnc3RyaW5nJykge1xuICAgIC8vIFVzZSBidWlsdC1pbiBUeXBlZEFycmF5LnNldCgpIG1ldGhvZCBmb3Igc3BlZWQuXG4gICAgbGV0IG9mZnNldCA9IDA7XG4gICAgaW5wdXRzLmZvckVhY2goaW5wdXQgPT4ge1xuICAgICAgY29uc3Qgc2l6ZSA9IHV0aWwuc2l6ZUZyb21TaGFwZShpbnB1dC5zaGFwZSk7XG5cbiAgICAgIChvdXRWYWxzIGFzIFR5cGVkQXJyYXkpLnNldChpbnB1dC52YWxzIGFzIFR5cGVkQXJyYXksIG9mZnNldCk7XG4gICAgICBvZmZzZXQgKz0gc2l6ZTtcbiAgICB9KTtcbiAgfSBlbHNlIHtcbiAgICBsZXQgY29sT2Zmc2V0ID0gMDtcblxuICAgIGlucHV0cy5mb3JFYWNoKGlucHV0ID0+IHtcbiAgICAgIGNvbnN0IGRlY29kZWREYXRhID0gZHR5cGUgPT09ICdzdHJpbmcnID9cbiAgICAgICAgICBiYWNrZW5kX3V0aWwuZnJvbVVpbnQ4VG9TdHJpbmdBcnJheShpbnB1dC52YWxzIGFzIFVpbnQ4QXJyYXlbXSkgOlxuICAgICAgICAgIGlucHV0LnZhbHMgYXMgVHlwZWRBcnJheTtcblxuICAgICAgbGV0IHRJZHggPSAwO1xuXG4gICAgICBmb3IgKGxldCByb3cgPSAwOyByb3cgPCBpbnB1dC5zaGFwZVswXTsgKytyb3cpIHtcbiAgICAgICAgY29uc3QgcmVzSWR4ID0gcm93ICogb3V0U2hhcGVbMV0gKyBjb2xPZmZzZXQ7XG4gICAgICAgIGZvciAobGV0IGNvbCA9IDA7IGNvbCA8IGlucHV0LnNoYXBlWzFdOyArK2NvbCkge1xuICAgICAgICAgIG91dFZhbHNbcmVzSWR4ICsgY29sXSA9IGRlY29kZWREYXRhW3RJZHgrK107XG4gICAgICAgIH1cbiAgICAgIH1cblxuICAgICAgY29sT2Zmc2V0ICs9IGlucHV0LnNoYXBlWzFdO1xuICAgIH0pO1xuICB9XG5cbiAgcmV0dXJuIG91dFZhbHM7XG59XG4iXX0=","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Expm1 } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const expm1Impl = createSimpleUnaryImpl((xi) => Math.expm1(xi));\nexport const expm1 = unaryKernelFuncFromImpl(Expm1, expm1Impl);\nexport const expm1Config = {\n kernelName: Expm1,\n backendName: 'cpu',\n kernelFunc: expm1,\n};\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Floor } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const floorImpl = createSimpleUnaryImpl((xi) => Math.floor(xi));\nexport const floor = unaryKernelFuncFromImpl(Floor, floorImpl);\nexport const floorConfig = {\n kernelName: Floor,\n backendName: 'cpu',\n kernelFunc: floor,\n};\n//# sourceMappingURL=data:application/json;base64,eyJ2ZXJzaW9uIjozLCJmaWxlIjoiRmxvb3IuanMiLCJzb3VyY2VSb290IjoiIiwic291cmNlcyI6WyIuLi8uLi8uLi8uLi8uLi8uLi90ZmpzLWJhY2tlbmQtY3B1L3NyYy9rZXJuZWxzL0Zsb29yLnRzIl0sIm5hbWVzIjpbXSwibWFwcGluZ3MiOiJBQUFBOzs7Ozs7Ozs7Ozs7Ozs7R0FlRztBQUVILE9BQU8sRUFBQyxLQUFLLEVBQWUsTUFBTSx1QkFBdUIsQ0FBQztBQUUxRCxPQUFPLEVBQUMscUJBQXFCLEVBQUMsTUFBTSxxQkFBcUIsQ0FBQztBQUMxRCxPQUFPLEVBQUMsdUJBQXVCLEVBQUMsTUFBTSxzQkFBc0IsQ0FBQztBQUU3RCxNQUFNLENBQUMsTUFBTSxTQUFTLEdBQUcscUJBQXFCLENBQUMsQ0FBQyxFQUFFLEVBQUUsRUFBRSxDQUFDLElBQUksQ0FBQyxLQUFLLENBQUMsRUFBRSxDQUFDLENBQUMsQ0FBQztBQUN2RSxNQUFNLENBQUMsTUFBTSxLQUFLLEdBQUcsdUJBQXVCLENBQUMsS0FBSyxFQUFFLFNBQVMsQ0FBQyxDQUFDO0FBRS9ELE1BQU0sQ0FBQyxNQUFNLFdBQVcsR0FBaUI7SUFDdkMsVUFBVSxFQUFFLEtBQUs7SUFDakIsV0FBVyxFQUFFLEtBQUs7SUFDbEIsVUFBVSxFQUFFLEtBQUs7Q0FDbEIsQ0FBQyIsInNvdXJjZXNDb250ZW50IjpbIi8qKlxuICogQGxpY2Vuc2VcbiAqIENvcHlyaWdodCAyMDIwIEdvb2dsZSBMTEMuIEFsbCBSaWdodHMgUmVzZXJ2ZWQuXG4gKiBMaWNlbnNlZCB1bmRlciB0aGUgQXBhY2hlIExpY2Vuc2UsIFZlcnNpb24gMi4wICh0aGUgTGljZW5zZSk7XG4gKiB5b3UgbWF5IG5vdCB1c2UgdGhpcyBmaWxlIGV4Y2VwdCBpbiBjb21wbGlhbmNlIHdpdGggdGhlIExpY2Vuc2UuXG4gKiBZb3UgbWF5IG9idGFpbiBhIGNvcHkgb2YgdGhlIExpY2Vuc2UgYXRcbiAqXG4gKiBodHRwOi8vd3d3LmFwYWNoZS5vcmcvbGljZW5zZXMvTElDRU5TRS0yLjBcbiAqXG4gKiBVbmxlc3MgcmVxdWlyZWQgYnkgYXBwbGljYWJsZSBsYXcgb3IgYWdyZWVkIHRvIGluIHdyaXRpbmcsIHNvZnR3YXJlXG4gKiBkaXN0cmlidXRlZCB1bmRlciB0aGUgTGljZW5zZSBpcyBkaXN0cmlidXRlZCBvbiBhbiBBUyBJUyBCQVNJUyxcbiAqIFdJVEhPVVQgV0FSUkFOVElFUyBPUiBDT05ESVRJT05TIE9GIEFOWSBLSU5ELCBlaXRoZXIgZXhwcmVzcyBvciBpbXBsaWVkLlxuICogU2VlIHRoZSBMaWNlbnNlIGZvciB0aGUgc3BlY2lmaWMgbGFuZ3VhZ2UgZ292ZXJuaW5nIHBlcm1pc3Npb25zIGFuZFxuICogbGltaXRhdGlvbnMgdW5kZXIgdGhlIExpY2Vuc2UuXG4gKiA9PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PVxuICovXG5cbmltcG9ydCB7Rmxvb3IsIEtlcm5lbENvbmZpZ30gZnJvbSAnQHRlbnNvcmZsb3cvdGZqcy1jb3JlJztcblxuaW1wb3J0IHtjcmVhdGVTaW1wbGVVbmFyeUltcGx9IGZyb20gJy4uL3V0aWxzL3VuYXJ5X2ltcGwnO1xuaW1wb3J0IHt1bmFyeUtlcm5lbEZ1bmNGcm9tSW1wbH0gZnJvbSAnLi4vdXRpbHMvdW5hcnlfdXRpbHMnO1xuXG5leHBvcnQgY29uc3QgZmxvb3JJbXBsID0gY3JlYXRlU2ltcGxlVW5hcnlJbXBsKCh4aSkgPT4gTWF0aC5mbG9vcih4aSkpO1xuZXhwb3J0IGNvbnN0IGZsb29yID0gdW5hcnlLZXJuZWxGdW5jRnJvbUltcGwoRmxvb3IsIGZsb29ySW1wbCk7XG5cbmV4cG9ydCBjb25zdCBmbG9vckNvbmZpZzogS2VybmVsQ29uZmlnID0ge1xuICBrZXJuZWxOYW1lOiBGbG9vcixcbiAgYmFja2VuZE5hbWU6ICdjcHUnLFxuICBrZXJuZWxGdW5jOiBmbG9vcixcbn07XG4iXX0=","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from '@tensorflow/tfjs-core';\nexport function gatherNdImpl(indicesData, paramsBuf, dtype, numSlices, sliceRank, sliceSize, strides, paramsShape, paramsSize) {\n const outBuf = buffer([numSlices, sliceSize], dtype);\n for (let i = 0; i < numSlices; i++) {\n const index = [];\n let flattenIndex = 0;\n for (let j = 0; j < sliceRank; j++) {\n const dim = indicesData[i * sliceRank + j];\n flattenIndex += dim * strides[j];\n index.push(dim);\n }\n if (flattenIndex < 0 || flattenIndex >= paramsSize / sliceSize) {\n throw new Error(`Invalid indices: ${index} does not index into ${paramsShape}`);\n }\n for (let k = 0; k < sliceSize; k++) {\n outBuf.values[i * sliceSize + k] =\n paramsBuf.get(...paramsBuf.indexToLoc(flattenIndex * sliceSize + k));\n }\n }\n return outBuf;\n}\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from '@tensorflow/tfjs-core';\nexport function gatherV2Impl(xBuf, indicesBuf, flattenOutputShape) {\n const outBuf = buffer(flattenOutputShape, xBuf.dtype);\n for (let i = 0; i < outBuf.size; ++i) {\n const newLoc = outBuf.indexToLoc(i);\n const originalLoc = newLoc.slice();\n const batchIdx = originalLoc[0];\n const indicesIdx = originalLoc[2];\n const indicesIndex = indicesBuf.locToIndex([batchIdx, indicesIdx]);\n originalLoc[2] = indicesBuf.values[indicesIndex];\n const originalIndex = xBuf.locToIndex(originalLoc);\n if (0 <= originalIndex && originalIndex < xBuf.values.length) {\n outBuf.values[i] = xBuf.values[originalIndex];\n } // Else, index is out of bounds, so leave the default zero val in outBuf.\n }\n return outBuf;\n}\n//# 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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Greater } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc } from '../utils/binary_utils';\nexport const greaterImpl = createSimpleBinaryKernelImpl((a, b) => (a > b) ? 1 : 0);\nexport const greater = binaryKernelFunc(Greater, greaterImpl, null /* complexImpl */, 'bool');\nexport const greaterConfig = {\n kernelName: Greater,\n backendName: 'cpu',\n kernelFunc: greater\n};\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { GreaterEqual } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc } from '../utils/binary_utils';\nexport const greaterEqualImpl = createSimpleBinaryKernelImpl((a, b) => (a >= b) ? 1 : 0);\nexport const greaterEqual = binaryKernelFunc(GreaterEqual, greaterEqualImpl, null /* complexImpl */, 'bool');\nexport const greaterEqualConfig = {\n kernelName: GreaterEqual,\n backendName: 'cpu',\n kernelFunc: greaterEqual\n};\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Less } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc } from '../utils/binary_utils';\nexport const lessImpl = createSimpleBinaryKernelImpl((a, b) => (a < b) ? 1 : 0);\nexport const less = binaryKernelFunc(Less, lessImpl, null /* complexImpl */, 'bool');\nexport const lessConfig = {\n kernelName: Less,\n backendName: 'cpu',\n kernelFunc: less\n};\n//# sourceMappingURL=data:application/json;base64,eyJ2ZXJzaW9uIjozLCJmaWxlIjoiTGVzcy5qcyIsInNvdXJjZVJvb3QiOiIiLCJzb3VyY2VzIjpbIi4uLy4uLy4uLy4uLy4uLy4uL3RmanMtYmFja2VuZC1jcHUvc3JjL2tlcm5lbHMvTGVzcy50cyJdLCJuYW1lcyI6W10sIm1hcHBpbmdzIjoiQUFBQTs7Ozs7Ozs7Ozs7Ozs7O0dBZUc7QUFFSCxPQUFPLEVBQWUsSUFBSSxFQUFDLE1BQU0sdUJBQXVCLENBQUM7QUFFekQsT0FBTyxFQUFDLDRCQUE0QixFQUFDLE1BQU0sc0JBQXNCLENBQUM7QUFDbEUsT0FBTyxFQUFDLGdCQUFnQixFQUFDLE1BQU0sdUJBQXVCLENBQUM7QUFFdkQsTUFBTSxDQUFDLE1BQU0sUUFBUSxHQUNqQiw0QkFBNEIsQ0FBQyxDQUFDLENBQVMsRUFBRSxDQUFTLEVBQUUsRUFBRSxDQUFDLENBQUMsQ0FBQyxHQUFHLENBQUMsQ0FBQyxDQUFDLENBQUMsQ0FBQyxDQUFDLENBQUMsQ0FBQyxDQUFDLENBQUMsQ0FBQyxDQUFDO0FBQzVFLE1BQU0sQ0FBQyxNQUFNLElBQUksR0FDYixnQkFBZ0IsQ0FBQyxJQUFJLEVBQUUsUUFBUSxFQUFFLElBQUksQ0FBQyxpQkFBaUIsRUFBRSxNQUFNLENBQUMsQ0FBQztBQUVyRSxNQUFNLENBQUMsTUFBTSxVQUFVLEdBQWlCO0lBQ3RDLFVBQVUsRUFBRSxJQUFJO0lBQ2hCLFdBQVcsRUFBRSxLQUFLO0lBQ2xCLFVBQVUsRUFBRSxJQUFJO0NBQ2pCLENBQUMiLCJzb3VyY2VzQ29udGVudCI6WyIvKipcbiAqIEBsaWNlbnNlXG4gKiBDb3B5cmlnaHQgMjAyMCBHb29nbGUgTExDLiBBbGwgUmlnaHRzIFJlc2VydmVkLlxuICogTGljZW5zZWQgdW5kZXIgdGhlIEFwYWNoZSBMaWNlbnNlLCBWZXJzaW9uIDIuMCAodGhlIFwiTGljZW5zZVwiKTtcbiAqIHlvdSBtYXkgbm90IHVzZSB0aGlzIGZpbGUgZXhjZXB0IGluIGNvbXBsaWFuY2Ugd2l0aCB0aGUgTGljZW5zZS5cbiAqIFlvdSBtYXkgb2J0YWluIGEgY29weSBvZiB0aGUgTGljZW5zZSBhdFxuICpcbiAqIGh0dHA6Ly93d3cuYXBhY2hlLm9yZy9saWNlbnNlcy9MSUNFTlNFLTIuMFxuICpcbiAqIFVubGVzcyByZXF1aXJlZCBieSBhcHBsaWNhYmxlIGxhdyBvciBhZ3JlZWQgdG8gaW4gd3JpdGluZywgc29mdHdhcmVcbiAqIGRpc3RyaWJ1dGVkIHVuZGVyIHRoZSBMaWNlbnNlIGlzIGRpc3RyaWJ1dGVkIG9uIGFuIFwiQVMgSVNcIiBCQVNJUyxcbiAqIFdJVEhPVVQgV0FSUkFOVElFUyBPUiBDT05ESVRJT05TIE9GIEFOWSBLSU5ELCBlaXRoZXIgZXhwcmVzcyBvciBpbXBsaWVkLlxuICogU2VlIHRoZSBMaWNlbnNlIGZvciB0aGUgc3BlY2lmaWMgbGFuZ3VhZ2UgZ292ZXJuaW5nIHBlcm1pc3Npb25zIGFuZFxuICogbGltaXRhdGlvbnMgdW5kZXIgdGhlIExpY2Vuc2UuXG4gKiA9PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PVxuICovXG5cbmltcG9ydCB7S2VybmVsQ29uZmlnLCBMZXNzfSBmcm9tICdAdGVuc29yZmxvdy90ZmpzLWNvcmUnO1xuXG5pbXBvcnQge2NyZWF0ZVNpbXBsZUJpbmFyeUtlcm5lbEltcGx9IGZyb20gJy4uL3V0aWxzL2JpbmFyeV9pbXBsJztcbmltcG9ydCB7YmluYXJ5S2VybmVsRnVuY30gZnJvbSAnLi4vdXRpbHMvYmluYXJ5X3V0aWxzJztcblxuZXhwb3J0IGNvbnN0IGxlc3NJbXBsID1cbiAgICBjcmVhdGVTaW1wbGVCaW5hcnlLZXJuZWxJbXBsKChhOiBudW1iZXIsIGI6IG51bWJlcikgPT4gKGEgPCBiKSA/IDEgOiAwKTtcbmV4cG9ydCBjb25zdCBsZXNzID1cbiAgICBiaW5hcnlLZXJuZWxGdW5jKExlc3MsIGxlc3NJbXBsLCBudWxsIC8qIGNvbXBsZXhJbXBsICovLCAnYm9vbCcpO1xuXG5leHBvcnQgY29uc3QgbGVzc0NvbmZpZzogS2VybmVsQ29uZmlnID0ge1xuICBrZXJuZWxOYW1lOiBMZXNzLFxuICBiYWNrZW5kTmFtZTogJ2NwdScsXG4gIGtlcm5lbEZ1bmM6IGxlc3Ncbn07XG4iXX0=","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { LessEqual } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc } from '../utils/binary_utils';\nexport const lessEqualImpl = createSimpleBinaryKernelImpl((a, b) => (a <= b) ? 1 : 0);\nexport const lessEqual = binaryKernelFunc(LessEqual, lessEqualImpl, null /* complexImpl */, 'bool');\nexport const lessEqualConfig = {\n kernelName: LessEqual,\n backendName: 'cpu',\n kernelFunc: lessEqual\n};\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport function linSpaceImpl(start, stop, num) {\n const step = (stop - start) / (num - 1);\n const values = util.makeZerosTypedArray(num, 'float32');\n values[0] = start;\n for (let i = 1; i < values.length; i++) {\n values[i] = values[i - 1] + step;\n }\n return values;\n}\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Log } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const logImpl = createSimpleUnaryImpl((xi) => Math.log(xi));\nexport const log = unaryKernelFuncFromImpl(Log, logImpl);\nexport const logConfig = {\n kernelName: Log,\n backendName: 'cpu',\n kernelFunc: log,\n};\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport function maxImpl(aVals, reduceSize, outShape, dtype) {\n const vals = util.getTypedArrayFromDType(dtype, util.sizeFromShape(outShape));\n for (let i = 0; i < vals.length; ++i) {\n const offset = i * reduceSize;\n let max = aVals[offset];\n for (let j = 0; j < reduceSize; ++j) {\n const value = aVals[offset + j];\n if (Number.isNaN(value) ||\n value > max) { // comparison with NaN always return false\n max = value;\n }\n }\n vals[i] = max;\n }\n return vals;\n}\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Maximum } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc } from '../utils/binary_utils';\nexport const maximumImpl = createSimpleBinaryKernelImpl(((aValue, bValue) => Math.max(aValue, bValue)));\nexport const maximum = binaryKernelFunc(Maximum, maximumImpl);\nexport const maximumConfig = {\n kernelName: Maximum,\n backendName: 'cpu',\n kernelFunc: maximum\n};\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Minimum } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc } from '../utils/binary_utils';\nexport const minimumImpl = createSimpleBinaryKernelImpl(((aValue, bValue) => Math.min(aValue, bValue)));\nexport const minimum = binaryKernelFunc(Minimum, minimumImpl);\nexport const minimumConfig = {\n kernelName: Minimum,\n backendName: 'cpu',\n kernelFunc: minimum\n};\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Neg, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { multiplyImpl } from './Multiply';\nexport function negImpl(xVals, xShape, xDtype) {\n const minusOne = util.createScalarValue(-1, xDtype);\n return multiplyImpl([], xShape, minusOne, xVals, xDtype);\n}\nexport function neg(args) {\n const { inputs, backend } = args;\n const { x } = inputs;\n assertNotComplex(x, 'neg');\n const xVals = backend.data.get(x.dataId).values;\n const [res, newShape] = negImpl(xVals, x.shape, x.dtype);\n return backend.makeTensorInfo(newShape, x.dtype, res);\n}\nexport const negConfig = {\n kernelName: Neg,\n backendName: 'cpu',\n kernelFunc: neg\n};\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { NotEqual } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc } from '../utils/binary_utils';\nexport const notEqualImpl = createSimpleBinaryKernelImpl(((a, b) => (a !== b) ? 1 : 0));\nexport const notEqual = binaryKernelFunc(NotEqual, notEqualImpl, null /* complexOp */, 'bool');\nexport const notEqualConfig = {\n kernelName: NotEqual,\n backendName: 'cpu',\n kernelFunc: notEqual\n};\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Prod, upcastType, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { transpose } from './Transpose';\nexport function prodImpl(xShape, xDtype, xVals, reductionAxes) {\n const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(xShape, reductionAxes);\n const outDtype = upcastType(xDtype, 'int32');\n const outVals = util.makeZerosTypedArray(util.sizeFromShape(outShape), outDtype);\n const reduceSize = util.sizeFromShape(reduceShape);\n for (let i = 0; i < outVals.length; ++i) {\n const offset = i * reduceSize;\n let prod = 1;\n for (let j = 0; j < reduceSize; ++j) {\n prod *= xVals[offset + j];\n }\n outVals[i] = prod;\n }\n return { outVals, outShape, outDtype };\n}\nexport function prod(args) {\n const { inputs, backend, attrs } = args;\n const { x } = inputs;\n const { axis, keepDims } = attrs;\n assertNotComplex(x, 'prod');\n const xRank = x.shape.length;\n const axes = util.parseAxisParam(axis, x.shape);\n const permutation = backend_util.getAxesPermutation(axes, xRank);\n let reductionAxes = axes;\n let permutedX = x;\n const intermediateTensorInfos = [];\n if (permutation != null) {\n permutedX = transpose({ inputs: { x }, backend, attrs: { perm: permutation } });\n intermediateTensorInfos.push(permutedX);\n reductionAxes = backend_util.getInnerMostAxes(reductionAxes.length, xRank);\n }\n const xVals = backend.data.get(permutedX.dataId).values;\n const { outVals, outShape, outDtype } = prodImpl(permutedX.shape, permutedX.dtype, xVals, reductionAxes);\n let resultShape = outShape;\n if (keepDims) {\n resultShape = backend_util.expandShapeToKeepDim(outShape, axes);\n }\n intermediateTensorInfos.forEach(t => backend.disposeIntermediateTensorInfo(t));\n return backend.makeTensorInfo(resultShape, outDtype, outVals);\n}\nexport const prodConfig = {\n kernelName: Prod,\n backendName: 'cpu',\n kernelFunc: prod\n};\n//# 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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport function rangeImpl(start, stop, step, dtype) {\n const sameStartStop = start === stop;\n const increasingRangeNegativeStep = start < stop && step < 0;\n const decreasingRangePositiveStep = stop < start && step > 1;\n if (sameStartStop || increasingRangeNegativeStep ||\n decreasingRangePositiveStep) {\n return util.makeZerosTypedArray(0, dtype);\n }\n const numElements = Math.abs(Math.ceil((stop - start) / step));\n const values = util.makeZerosTypedArray(numElements, dtype);\n if (stop < start && step === 1) {\n // Auto adjust the step's sign if it hasn't been set\n // (or was set to 1)\n step = -1;\n }\n values[0] = start;\n for (let i = 1; i < values.length; i++) {\n values[i] = values[i - 1] + step;\n }\n return values;\n}\n//# sourceMappingURL=data:application/json;base64,eyJ2ZXJzaW9uIjozLCJmaWxlIjoiUmFuZ2VfaW1wbC5qcyIsInNvdXJjZVJvb3QiOiIiLCJzb3VyY2VzIjpbIi4uLy4uLy4uLy4uLy4uLy4uL3RmanMtYmFja2VuZC1jcHUvc3JjL2tlcm5lbHMvUmFuZ2VfaW1wbC50cyJdLCJuYW1lcyI6W10sIm1hcHBpbmdzIjoiQUFBQTs7Ozs7Ozs7Ozs7Ozs7O0dBZUc7QUFFSCxPQUFPLEVBQWMsSUFBSSxFQUFDLE1BQU0sdUJBQXVCLENBQUM7QUFFeEQsTUFBTSxVQUFVLFNBQVMsQ0FDckIsS0FBYSxFQUFFLElBQVksRUFBRSxJQUFZLEVBQ3pDLEtBQXdCO0lBQzFCLE1BQU0sYUFBYSxHQUFHLEtBQUssS0FBSyxJQUFJLENBQUM7SUFDckMsTUFBTSwyQkFBMkIsR0FBRyxLQUFLLEdBQUcsSUFBSSxJQUFJLElBQUksR0FBRyxDQUFDLENBQUM7SUFDN0QsTUFBTSwyQkFBMkIsR0FBRyxJQUFJLEdBQUcsS0FBSyxJQUFJLElBQUksR0FBRyxDQUFDLENBQUM7SUFFN0QsSUFBSSxhQUFhLElBQUksMkJBQTJCO1FBQzVDLDJCQUEyQixFQUFFO1FBQy9CLE9BQU8sSUFBSSxDQUFDLG1CQUFtQixDQUFDLENBQUMsRUFBRSxLQUFLLENBQUMsQ0FBQztLQUMzQztJQUVELE1BQU0sV0FBVyxHQUFHLElBQUksQ0FBQyxHQUFHLENBQUMsSUFBSSxDQUFDLElBQUksQ0FBQyxDQUFDLElBQUksR0FBRyxLQUFLLENBQUMsR0FBRyxJQUFJLENBQUMsQ0FBQyxDQUFDO0lBQy9ELE1BQU0sTUFBTSxHQUFHLElBQUksQ0FBQyxtQkFBbUIsQ0FBQyxXQUFXLEVBQUUsS0FBSyxDQUFDLENBQUM7SUFFNUQsSUFBSSxJQUFJLEdBQUcsS0FBSyxJQUFJLElBQUksS0FBSyxDQUFDLEVBQUU7UUFDOUIsb0RBQW9EO1FBQ3BELG9CQUFvQjtRQUNwQixJQUFJLEdBQUcsQ0FBQyxDQUFDLENBQUM7S0FDWDtJQUVELE1BQU0sQ0FBQyxDQUFDLENBQUMsR0FBRyxLQUFLLENBQUM7SUFDbEIsS0FBSyxJQUFJLENBQUMsR0FBRyxDQUFDLEVBQUUsQ0FBQyxHQUFHLE1BQU0sQ0FBQyxNQUFNLEVBQUUsQ0FBQyxFQUFFLEVBQUU7UUFDdEMsTUFBTSxDQUFDLENBQUMsQ0FBQyxHQUFHLE1BQU0sQ0FBQyxDQUFDLEdBQUcsQ0FBQyxDQUFDLEdBQUcsSUFBSSxDQUFDO0tBQ2xDO0lBQ0QsT0FBTyxNQUFNLENBQUM7QUFDaEIsQ0FBQyIsInNvdXJjZXNDb250ZW50IjpbIi8qKlxuICogQGxpY2Vuc2VcbiAqIENvcHlyaWdodCAyMDIwIEdvb2dsZSBMTEMuIEFsbCBSaWdodHMgUmVzZXJ2ZWQuXG4gKiBMaWNlbnNlZCB1bmRlciB0aGUgQXBhY2hlIExpY2Vuc2UsIFZlcnNpb24gMi4wICh0aGUgXCJMaWNlbnNlXCIpO1xuICogeW91IG1heSBub3QgdXNlIHRoaXMgZmlsZSBleGNlcHQgaW4gY29tcGxpYW5jZSB3aXRoIHRoZSBMaWNlbnNlLlxuICogWW91IG1heSBvYnRhaW4gYSBjb3B5IG9mIHRoZSBMaWNlbnNlIGF0XG4gKlxuICogaHR0cDovL3d3dy5hcGFjaGUub3JnL2xpY2Vuc2VzL0xJQ0VOU0UtMi4wXG4gKlxuICogVW5sZXNzIHJlcXVpcmVkIGJ5IGFwcGxpY2FibGUgbGF3IG9yIGFncmVlZCB0byBpbiB3cml0aW5nLCBzb2Z0d2FyZVxuICogZGlzdHJpYnV0ZWQgdW5kZXIgdGhlIExpY2Vuc2UgaXMgZGlzdHJpYnV0ZWQgb24gYW4gXCJBUyBJU1wiIEJBU0lTLFxuICogV0lUSE9VVCBXQVJSQU5USUVTIE9SIENPTkRJVElPTlMgT0YgQU5ZIEtJTkQsIGVpdGhlciBleHByZXNzIG9yIGltcGxpZWQuXG4gKiBTZWUgdGhlIExpY2Vuc2UgZm9yIHRoZSBzcGVjaWZpYyBsYW5ndWFnZSBnb3Zlcm5pbmcgcGVybWlzc2lvbnMgYW5kXG4gKiBsaW1pdGF0aW9ucyB1bmRlciB0aGUgTGljZW5zZS5cbiAqID09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09XG4gKi9cblxuaW1wb3J0IHtEYXRhVHlwZU1hcCwgdXRpbH0gZnJvbSAnQHRlbnNvcmZsb3cvdGZqcy1jb3JlJztcblxuZXhwb3J0IGZ1bmN0aW9uIHJhbmdlSW1wbChcbiAgICBzdGFydDogbnVtYmVyLCBzdG9wOiBudW1iZXIsIHN0ZXA6IG51bWJlcixcbiAgICBkdHlwZTogJ2Zsb2F0MzInfCdpbnQzMicpOiBEYXRhVHlwZU1hcFsnZmxvYXQzMicgfCAnaW50MzInXSB7XG4gIGNvbnN0IHNhbWVTdGFydFN0b3AgPSBzdGFydCA9PT0gc3RvcDtcbiAgY29uc3QgaW5jcmVhc2luZ1JhbmdlTmVnYXRpdmVTdGVwID0gc3RhcnQgPCBzdG9wICYmIHN0ZXAgPCAwO1xuICBjb25zdCBkZWNyZWFzaW5nUmFuZ2VQb3NpdGl2ZVN0ZXAgPSBzdG9wIDwgc3RhcnQgJiYgc3RlcCA+IDE7XG5cbiAgaWYgKHNhbWVTdGFydFN0b3AgfHwgaW5jcmVhc2luZ1JhbmdlTmVnYXRpdmVTdGVwIHx8XG4gICAgICBkZWNyZWFzaW5nUmFuZ2VQb3NpdGl2ZVN0ZXApIHtcbiAgICByZXR1cm4gdXRpbC5tYWtlWmVyb3NUeXBlZEFycmF5KDAsIGR0eXBlKTtcbiAgfVxuXG4gIGNvbnN0IG51bUVsZW1lbnRzID0gTWF0aC5hYnMoTWF0aC5jZWlsKChzdG9wIC0gc3RhcnQpIC8gc3RlcCkpO1xuICBjb25zdCB2YWx1ZXMgPSB1dGlsLm1ha2VaZXJvc1R5cGVkQXJyYXkobnVtRWxlbWVudHMsIGR0eXBlKTtcblxuICBpZiAoc3RvcCA8IHN0YXJ0ICYmIHN0ZXAgPT09IDEpIHtcbiAgICAvLyBBdXRvIGFkanVzdCB0aGUgc3RlcCdzIHNpZ24gaWYgaXQgaGFzbid0IGJlZW4gc2V0XG4gICAgLy8gKG9yIHdhcyBzZXQgdG8gMSlcbiAgICBzdGVwID0gLTE7XG4gIH1cblxuICB2YWx1ZXNbMF0gPSBzdGFydDtcbiAgZm9yIChsZXQgaSA9IDE7IGkgPCB2YWx1ZXMubGVuZ3RoOyBpKyspIHtcbiAgICB2YWx1ZXNbaV0gPSB2YWx1ZXNbaSAtIDFdICsgc3RlcDtcbiAgfVxuICByZXR1cm4gdmFsdWVzO1xufVxuIl19","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Rsqrt } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const rsqrtImpl = createSimpleUnaryImpl((xi) => 1 / Math.sqrt(xi));\nexport const rsqrt = unaryKernelFuncFromImpl(Rsqrt, rsqrtImpl);\nexport const rsqrtConfig = {\n kernelName: Rsqrt,\n backendName: 'cpu',\n kernelFunc: rsqrt,\n};\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nexport function sparseFillEmptyRowsImpl(indices, indicesShape, indicesDType, values, valuesDType, denseShape, defaultValue) {\n const indicesCount = indicesShape[0];\n const denseRows = denseShape[0];\n const emptyRowIndicator = new Array(denseRows);\n const reverseIndexMap = new Array(indicesCount);\n const rank = indicesShape[1];\n if (denseRows === 0) {\n if (indicesCount !== 0) {\n throw new Error(backend_util.getSparseFillEmptyRowsIndicesDenseShapeMismatch(indicesCount));\n }\n const outputIndices = util.getArrayFromDType(indicesDType, 0);\n const outputValues = util.getArrayFromDType(valuesDType, 0);\n return [\n outputIndices, [0, rank], outputValues, emptyRowIndicator, reverseIndexMap\n ];\n }\n let rowsAreOrdered = true;\n let lastIndicesRow = 0;\n const csrOffset = new Array(denseRows).fill(0);\n for (let i = 0; i < indicesCount; ++i) {\n // indices is a 2d tensor with shape of [N, rank]\n const row = indices[i * rank];\n if (row < 0) {\n throw new Error(backend_util.getSparseFillEmptyRowsNegativeIndexErrorMessage(i, row));\n }\n if (row >= denseRows) {\n throw new Error(backend_util.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(i, row, denseRows));\n }\n ++csrOffset[row];\n rowsAreOrdered = rowsAreOrdered && (row >= lastIndicesRow);\n lastIndicesRow = row;\n }\n let allRowsFull = true;\n for (let row = 0; row < denseRows; ++row) {\n // csrOffset here describes the number of elements in this dense row\n const rowEmpty = (csrOffset[row] === 0);\n emptyRowIndicator[row] = rowEmpty;\n allRowsFull = allRowsFull && !rowEmpty;\n // In filled version, each row has at least one element.\n csrOffset[row] = Math.max(csrOffset[row], 1);\n // Update csrOffset to represent the number of elements up to and\n // including denseRows + 1:\n // csrOffset[0] == #{elements of row 0}\n // csrOffset[1] == #{elements of row 1} + #{elements of row 0}\n // ..\n // csrOffset[i] == starting index for elements in row i + 1.\n if (row > 0) {\n csrOffset[row] += csrOffset[row - 1];\n }\n }\n if (allRowsFull && rowsAreOrdered) {\n const outputIndices = indices;\n const outputValues = values;\n for (let i = 0; i < indicesCount; ++i) {\n reverseIndexMap[i] = i;\n }\n return [\n outputIndices, [indicesCount, rank], outputValues, emptyRowIndicator,\n reverseIndexMap\n ];\n }\n else {\n const fullIndicesCount = csrOffset[denseRows - 1];\n const outputIndices = util.getArrayFromDType(indicesDType, fullIndicesCount * rank);\n const outputValues = util.getArrayFromDType(valuesDType, fullIndicesCount);\n const filledCount = new Array(denseRows).fill(0);\n // Fill in values for rows that are not missing\n for (let i = 0; i < indicesCount; ++i) {\n // indices is a 2d tensor with shape of [N, rank]\n const row = indices[i * rank];\n const offset = filledCount[row];\n const outputI = ((row === 0) ? 0 : csrOffset[row - 1]) + offset;\n filledCount[row]++; // Increment the filled count for this row.\n for (let j = 0; j < rank; ++j) {\n // indices and outputIndices are 2d tensors with shape of [N, rank]\n outputIndices[outputI * rank + j] = indices[i * rank + j];\n }\n outputValues[outputI] = values[i];\n // We'll need this reverse index map to backprop correctly.\n reverseIndexMap[i] = outputI;\n }\n // Fill in values for rows that are missing\n for (let row = 0; 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All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, DataType, TypedArray, util} from '@tensorflow/tfjs-core';\n\nexport function sparseFillEmptyRowsImpl(\n    indices: TypedArray, indicesShape: number[], indicesDType: DataType,\n    values: TypedArray, valuesDType: DataType, denseShape: TypedArray,\n    defaultValue: number):\n    [TypedArray, number[], TypedArray, boolean[], number[]] {\n  const indicesCount = indicesShape[0];\n  const denseRows = denseShape[0];\n\n  const emptyRowIndicator: boolean[] = new Array(denseRows);\n  const reverseIndexMap: number[] = new Array(indicesCount);\n\n  const rank = indicesShape[1];\n\n  if (denseRows === 0) {\n    if (indicesCount !== 0) {\n      throw new Error(\n          backend_util.getSparseFillEmptyRowsIndicesDenseShapeMismatch(\n              indicesCount));\n    }\n    const outputIndices = util.getArrayFromDType(indicesDType, 0) as TypedArray;\n    const outputValues = util.getArrayFromDType(valuesDType, 0) as TypedArray;\n    return [\n      outputIndices, [0, rank], outputValues, emptyRowIndicator, reverseIndexMap\n    ];\n  }\n\n  let rowsAreOrdered = true;\n  let lastIndicesRow = 0;\n  const csrOffset: number[] = new Array(denseRows).fill(0);\n\n  for (let i = 0; i < indicesCount; ++i) {\n    // indices is a 2d tensor with shape of [N, rank]\n    const row = indices[i * rank];\n    if (row < 0) {\n      throw new Error(\n          backend_util.getSparseFillEmptyRowsNegativeIndexErrorMessage(i, row));\n    }\n    if (row >= denseRows) {\n      throw new Error(\n          backend_util.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(\n              i, row, denseRows));\n    }\n    ++csrOffset[row];\n    rowsAreOrdered = rowsAreOrdered && (row >= lastIndicesRow);\n    lastIndicesRow = row;\n  }\n\n  let allRowsFull = true;\n  for (let row = 0; row < denseRows; ++row) {\n    // csrOffset here describes the number of elements in this dense row\n    const rowEmpty = (csrOffset[row] === 0);\n    emptyRowIndicator[row] = rowEmpty;\n    allRowsFull = allRowsFull && !rowEmpty;\n    // In filled version, each row has at least one element.\n    csrOffset[row] = Math.max(csrOffset[row], 1);\n    // Update csrOffset to represent the number of elements up to and\n    // including denseRows + 1:\n    //  csrOffset[0] == #{elements of row 0}\n    //  csrOffset[1] == #{elements of row 1} + #{elements of row 0}\n    //  ..\n    //  csrOffset[i] == starting index for elements in row i + 1.\n    if (row > 0) {\n      csrOffset[row] += csrOffset[row - 1];\n    }\n  }\n\n  if (allRowsFull && rowsAreOrdered) {\n    const outputIndices: TypedArray = indices;\n    const outputValues: TypedArray = values;\n    for (let i = 0; i < indicesCount; ++i) {\n      reverseIndexMap[i] = i;\n    }\n    return [\n      outputIndices, [indicesCount, rank], outputValues, emptyRowIndicator,\n      reverseIndexMap\n    ];\n  } else {\n    const fullIndicesCount = csrOffset[denseRows - 1];\n    const outputIndices =\n        util.getArrayFromDType(indicesDType, fullIndicesCount * rank) as\n        TypedArray;\n    const outputValues =\n        util.getArrayFromDType(valuesDType, fullIndicesCount) as TypedArray;\n    const filledCount: number[] = new Array(denseRows).fill(0);\n\n    // Fill in values for rows that are not missing\n    for (let i = 0; i < indicesCount; ++i) {\n      // indices is a 2d tensor with shape of [N, rank]\n      const row = indices[i * rank];\n      const offset = filledCount[row];\n      const outputI = ((row === 0) ? 0 : csrOffset[row - 1]) + offset;\n      filledCount[row]++;  // Increment the filled count for this row.\n      for (let j = 0; j < rank; ++j) {\n        // indices and outputIndices are 2d tensors with shape of [N, rank]\n        outputIndices[outputI * rank + j] = indices[i * rank + j];\n      }\n      outputValues[outputI] = values[i];\n      // We'll need this reverse index map to backprop correctly.\n      reverseIndexMap[i] = outputI;\n    }\n\n    // Fill in values for rows that are missing\n    for (let row = 0; row < denseRows; ++row) {\n      const rowCount = filledCount[row];\n      if (rowCount === 0) {  // We haven't filled this row\n        const startingIndex = (row === 0) ? 0 : csrOffset[row - 1];\n        // Remaining index values were set to zero already.\n        // Just need to set the row index in the right location.\n        // outputIndices is a 2d tensor with shape of [N, rank]\n        outputIndices[startingIndex * rank + 0] = row;\n        for (let col = 1; col < rank; ++col) {\n          outputIndices[startingIndex * rank + col] = 0;\n        }\n        outputValues[startingIndex] = defaultValue;\n      }\n    }\n    return [\n      outputIndices, [fullIndicesCount, rank], outputValues, emptyRowIndicator,\n      reverseIndexMap\n    ];\n  }\n}\n"]}","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nexport function sparseReshapeImpl(inputIndices, inputIndicesShape, inputDType, inputShape, targetShape) {\n const denseSize = util.sizeFromShape(inputShape);\n const nnz = inputIndicesShape[0];\n const outputRank = targetShape.length;\n // Compute the output shape. 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All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, DataType, TypedArray, util} from '@tensorflow/tfjs-core';\n\nexport function sparseReshapeImpl(\n    inputIndices: TypedArray, inputIndicesShape: number[], inputDType: DataType,\n    inputShape: number[],\n    targetShape: number[]): [TypedArray, number[], number[]] {\n  const denseSize = util.sizeFromShape(inputShape);\n  const nnz = inputIndicesShape[0];\n  const outputRank = targetShape.length;\n\n  // Compute the output shape. Determine product of specified dimensions, and\n  // find the index of the unspecified one.\n  const outputShape: number[] = [];\n  let product = 1;\n  let unknownIndex = -1;\n  for (let d = 0; d < outputRank; ++d) {\n    const size = targetShape[d];\n    if (size === -1) {\n      if (unknownIndex !== -1) {\n        throw new Error(\n            backend_util\n                .getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(\n                    unknownIndex, d));\n      }\n      unknownIndex = d;\n      outputShape.push(1);\n    } else {\n      if (size < 0) {\n        throw new Error(\n            backend_util.getSparseReshapeNegativeOutputDimErrorMessage(\n                d, size));\n      }\n      product *= size;\n      outputShape.push(size);\n    }\n  }\n  if (unknownIndex !== -1) {\n    if (product <= 0) {\n      throw new Error(\n          backend_util.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage());\n    }\n    const missing = Math.trunc(denseSize / product);\n    if (product * missing !== denseSize) {\n      throw new Error(\n          backend_util.getSparseReshapeInputOutputMultipleErrorMessage(\n              inputShape, outputShape));\n    }\n\n    outputShape[unknownIndex] = missing;\n  }\n  const outputSize = util.sizeFromShape(outputShape);\n  if (outputSize !== denseSize) {\n    throw new Error(\n        backend_util.getSparseReshapeInputOutputMismatchErrorMessage(\n            inputShape, outputShape));\n  }\n\n  const inputRank = inputShape.length;\n  const inputStrides: number[] = [];\n  if (inputRank > 0) {\n    inputStrides[inputRank - 1] = 1;\n    for (let d = inputRank - 2; d >= 0; --d) {\n      inputStrides[d] = inputStrides[d + 1] * inputShape[d + 1];\n    }\n  }\n\n  const outputStrides: number[] = [];\n  if (outputRank > 0) {\n    outputStrides[outputRank - 1] = 1;\n    for (let d = outputRank - 2; d >= 0; --d) {\n      outputStrides[d] = outputStrides[d + 1] * outputShape[d + 1];\n    }\n  }\n\n  const newIndices =\n      util.getArrayFromDType(inputDType, nnz * outputRank) as TypedArray;\n  for (let i = 0; i < nnz; ++i) {\n    let id = 0;\n    for (let j = 0; j < inputRank; ++j) {\n      // inputIndices is a 2d tensor with shape of [nnz, inputRank]\n      id += inputIndices[i * inputRank + j] * inputStrides[j];\n    }\n    for (let j = 0; j < outputRank; ++j) {\n      // newIndices is a 2d tensor with shape of [nnz, outputRank]\n      newIndices[i * outputRank + j] = Math.trunc(id / outputStrides[j]);\n      id %= outputStrides[j];\n    }\n  }\n  return [newIndices, [nnz, outputRank], outputShape];\n}\n"]}","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sqrt } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const sqrtImpl = createSimpleUnaryImpl((xi) => Math.sqrt(xi));\nexport const sqrt = unaryKernelFunc(Sqrt, (xi) => Math.sqrt(xi));\nexport const sqrtConfig = {\n kernelName: Sqrt,\n backendName: 'cpu',\n kernelFunc: sqrt,\n};\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SquaredDifference } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc } from '../utils/binary_utils';\nexport const squaredDifferenceImpl = createSimpleBinaryKernelImpl(((a, b) => {\n const diff = a - b;\n return diff * diff;\n}));\nexport const squaredDifference = binaryKernelFunc(SquaredDifference, squaredDifferenceImpl);\nexport const squaredDifferenceConfig = {\n kernelName: SquaredDifference,\n backendName: 'cpu',\n kernelFunc: squaredDifference\n};\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from '@tensorflow/tfjs-core';\nexport function stridedSliceImpl(outShape, xBuf, strides, begin) {\n const outBuf = buffer(outShape, xBuf.dtype);\n for (let i = 0; i < outBuf.size; i++) {\n const loc = outBuf.indexToLoc(i);\n const newLoc = new Array(loc.length);\n for (let j = 0; j < newLoc.length; j++) {\n newLoc[j] = loc[j] * strides[j] + begin[j];\n }\n outBuf.set(xBuf.get(...newLoc), ...loc);\n }\n return outBuf;\n}\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\n/**\n * The StringNGramsOp class creates ngrams from ragged string data.\n * The constructor contains all attributes related to the operation such as\n * padding widths and strings, and the compute function can be used to\n * compute the ngrams for different ragged tensor inputs.\n */\nclass StringNGramsOp {\n constructor(separator, nGramWidths, leftPad, rightPad, padWidth, preserveShortSequences) {\n this.separator = util.encodeString(separator);\n this.nGramWidths = nGramWidths;\n this.leftPad = util.encodeString(leftPad);\n this.rightPad = util.encodeString(rightPad);\n this.padWidth = padWidth;\n this.preserveShort = preserveShortSequences;\n }\n getPadWidth(nGramWidth) {\n // Ngrams can be padded with either a fixed pad width or a dynamic pad\n // width depending on the 'padWidth' arg, but in no case should the padding\n // ever be wider than 'nGramWidth' - 1.\n return Math.min(this.padWidth < 0 ? nGramWidth - 1 : this.padWidth, nGramWidth - 1);\n }\n getNumNGrams(length, nGramWidth) {\n const padWidth = this.getPadWidth(nGramWidth);\n return Math.max(0, ((length + 2 * padWidth) - nGramWidth) + 1);\n }\n createNGrams(data, splitIndex, output, outputStartIndex, numNGrams, nGramWidth) {\n for (let nGramIndex = 0; nGramIndex < numNGrams; ++nGramIndex) {\n const padWidth = this.getPadWidth(nGramWidth);\n const leftPadding = Math.max(0, padWidth - nGramIndex);\n const rightPadding = Math.max(0, padWidth - (numNGrams - (nGramIndex + 1)));\n const numTokens = nGramWidth - (leftPadding + rightPadding);\n const dataStartIndex = splitIndex + (leftPadding > 0 ? 0 : nGramIndex - padWidth);\n // Calculate the total expected size of the nGram so we can reserve the\n // correct amount of space in the string.\n let nGramSize = 0;\n // Size of the left padding.\n nGramSize += leftPadding * this.leftPad.length;\n // Size of the tokens.\n for (let n = 0; n < numTokens; ++n) {\n nGramSize += data[dataStartIndex + n].length;\n }\n // Size of the right padding.\n nGramSize += rightPadding * this.rightPad.length;\n // Size of the separators.\n const numSeparators = leftPadding + rightPadding + numTokens - 1;\n nGramSize += numSeparators * this.separator.length;\n // Build the nGram.\n output[outputStartIndex + nGramIndex] = new Uint8Array(nGramSize);\n const nGram = output[outputStartIndex + nGramIndex];\n let nextNGramIndex = 0;\n const appendToNGram = (str) => str.forEach((value) => nGram[nextNGramIndex++] = value);\n for (let n = 0; n < leftPadding; ++n) {\n appendToNGram(this.leftPad);\n appendToNGram(this.separator);\n }\n // Only output first numTokens - 1 pairs of data and separator\n for (let n = 0; n < numTokens - 1; ++n) {\n appendToNGram(data[dataStartIndex + n]);\n appendToNGram(this.separator);\n }\n // Handle case when there are no tokens or no right padding as these\n // can result in consecutive separators.\n if (numTokens > 0) {\n // If we have tokens, then output last and then pair each separator\n // with the right padding that follows, to ensure nGram ends either with\n // the token or with the right pad.\n appendToNGram(data[dataStartIndex + numTokens - 1]);\n for (let n = 0; n < rightPadding; ++n) {\n appendToNGram(this.separator);\n appendToNGram(this.rightPad);\n }\n }\n else {\n // If we don't have tokens, then the last item inserted into the nGram\n // has been the separator from the left padding loop above. Hence,\n // output right pad and separator and make sure to finish with a\n // padding, not a separator.\n for (let n = 0; n < rightPadding - 1; ++n) {\n appendToNGram(this.rightPad);\n appendToNGram(this.separator);\n }\n appendToNGram(this.rightPad);\n }\n }\n }\n // Data and splits together form the definition of the ragged tensor,\n // where data is 1 dimensional and contains the values of the tensor\n // and splits denotes the indices at which each row starts.\n compute(data, splits) {\n // Validate that the splits are valid indices into data, only if there are\n // splits specified.\n const inputDataSize = data.length;\n const splitsSize = splits.length;\n if (splitsSize > 0) {\n let prevSplit = splits[0];\n if (prevSplit !== 0) {\n throw new Error(`First split value must be 0, got ${prevSplit}`);\n }\n for (let i = 1; i < splitsSize; ++i) {\n let validSplits = splits[i] >= prevSplit;\n validSplits = validSplits && (splits[i] <= inputDataSize);\n if (!validSplits) {\n throw new Error(`Invalid split value ${splits[i]}, must be in [${prevSplit}, ${inputDataSize}]`);\n }\n prevSplit = splits[i];\n }\n if (prevSplit !== inputDataSize) {\n throw new Error(`Last split value must be data size. Expected ${inputDataSize}, got ${prevSplit}`);\n }\n }\n const numBatchItems = splitsSize - 1;\n const nGramsSplits = util.getArrayFromDType('int32', splitsSize);\n // If there is no data or size, return an empty ragged tensor.\n if (inputDataSize === 0 || splitsSize === 0) {\n const empty = new Array(inputDataSize);\n for (let i = 0; i <= numBatchItems; ++i) {\n nGramsSplits[i] = 0;\n }\n return [empty, nGramsSplits];\n }\n nGramsSplits[0] = 0;\n for (let i = 1; i <= numBatchItems; ++i) {\n const length = splits[i] - splits[i - 1];\n let numNGrams = 0;\n this.nGramWidths.forEach((nGramWidth) => {\n numNGrams += this.getNumNGrams(length, nGramWidth);\n });\n if (this.preserveShort && length > 0 && numNGrams === 0) {\n numNGrams = 1;\n }\n nGramsSplits[i] = nGramsSplits[i - 1] + numNGrams;\n }\n const nGrams = new Array(nGramsSplits[numBatchItems]);\n for (let i = 0; i < numBatchItems; ++i) {\n const splitIndex = splits[i];\n let outputStartIdx = nGramsSplits[i];\n this.nGramWidths.forEach((nGramWidth) => {\n const length = splits[i + 1] - splits[i];\n const numNGrams = this.getNumNGrams(length, nGramWidth);\n this.createNGrams(data, splitIndex, nGrams, outputStartIdx, numNGrams, nGramWidth);\n outputStartIdx += numNGrams;\n });\n // If we're preserving short sequences, check to see if no sequence was\n // generated by comparing the current output start idx to the original\n // one (nGramSplitsdata). If no ngrams were generated, then they will\n // be equal (since we increment outputStartIdx by numNGrams every\n // time we create a set of ngrams.)\n if (this.preserveShort && outputStartIdx === nGramsSplits[i]) {\n const dataLength = splits[i + 1] - splits[i];\n // One legitimate reason to not have any ngrams when this.preserveShort\n // is true is if the sequence itself is empty. In that case, move on.\n if (dataLength === 0) {\n continue;\n }\n // We don't have to worry about dynamic padding sizes here: if padding\n // was dynamic, every sequence would have had sufficient padding to\n // generate at least one nGram.\n const nGramWidth = dataLength + 2 * this.padWidth;\n const numNGrams = 1;\n this.createNGrams(data, splitIndex, nGrams, outputStartIdx, numNGrams, nGramWidth);\n }\n }\n return [nGrams, nGramsSplits];\n }\n}\nexport function stringNGramsImpl(data, dataSplits, separator, nGramWidths, leftPad, rightPad, padWidth, preserveShortSequences) {\n return new StringNGramsOp(separator, nGramWidths, leftPad, rightPad, padWidth, preserveShortSequences)\n .compute(data, dataSplits);\n}\n//# 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* @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {util} from '@tensorflow/tfjs-core';\n\n/**\n * The StringNGramsOp class creates ngrams from ragged string data.\n * The constructor contains all attributes related to the operation such as\n * padding widths and strings, and the compute function can be used to\n * compute the ngrams for different ragged tensor inputs.\n */\nclass StringNGramsOp {\n  private separator: Uint8Array;\n  private nGramWidths: number[];\n  private padWidth: number;\n  private leftPad: Uint8Array;\n  private rightPad: Uint8Array;\n  private preserveShort: boolean;\n\n  constructor(\n      separator: string, nGramWidths: number[], leftPad: string,\n      rightPad: string, padWidth: number, preserveShortSequences: boolean) {\n    this.separator = util.encodeString(separator);\n    this.nGramWidths = nGramWidths;\n    this.leftPad = util.encodeString(leftPad);\n    this.rightPad = util.encodeString(rightPad);\n    this.padWidth = padWidth;\n    this.preserveShort = preserveShortSequences;\n  }\n\n  private getPadWidth(nGramWidth: number) {\n    // Ngrams can be padded with either a fixed pad width or a dynamic pad\n    // width depending on the 'padWidth' arg, but in no case should the padding\n    // ever be wider than 'nGramWidth' - 1.\n    return Math.min(\n        this.padWidth < 0 ? nGramWidth - 1 : this.padWidth, nGramWidth - 1);\n  }\n\n  private getNumNGrams(length: number, nGramWidth: number) {\n    const padWidth = this.getPadWidth(nGramWidth);\n    return Math.max(0, ((length + 2 * padWidth) - nGramWidth) + 1);\n  }\n\n  private createNGrams(\n      data: Uint8Array[], splitIndex: number, output: Uint8Array[],\n      outputStartIndex: number, numNGrams: number, nGramWidth: number) {\n    for (let nGramIndex = 0; nGramIndex < numNGrams; ++nGramIndex) {\n      const padWidth = this.getPadWidth(nGramWidth);\n      const leftPadding = Math.max(0, padWidth - nGramIndex);\n      const rightPadding =\n          Math.max(0, padWidth - (numNGrams - (nGramIndex + 1)));\n      const numTokens = nGramWidth - (leftPadding + rightPadding);\n      const dataStartIndex =\n          splitIndex + (leftPadding > 0 ? 0 : nGramIndex - padWidth);\n\n      // Calculate the total expected size of the nGram so we can reserve the\n      // correct amount of space in the string.\n      let nGramSize = 0;\n      // Size of the left padding.\n      nGramSize += leftPadding * this.leftPad.length;\n      // Size of the tokens.\n      for (let n = 0; n < numTokens; ++n) {\n        nGramSize += data[dataStartIndex + n].length;\n      }\n      // Size of the right padding.\n      nGramSize += rightPadding * this.rightPad.length;\n      // Size of the separators.\n      const numSeparators = leftPadding + rightPadding + numTokens - 1;\n      nGramSize += numSeparators * this.separator.length;\n\n      // Build the nGram.\n      output[outputStartIndex + nGramIndex] = new Uint8Array(nGramSize);\n      const nGram = output[outputStartIndex + nGramIndex];\n\n      let nextNGramIndex = 0;\n      const appendToNGram = (str: Uint8Array) =>\n          str.forEach((value) => nGram[nextNGramIndex++] = value);\n\n      for (let n = 0; n < leftPadding; ++n) {\n        appendToNGram(this.leftPad);\n        appendToNGram(this.separator);\n      }\n      // Only output first numTokens - 1 pairs of data and separator\n      for (let n = 0; n < numTokens - 1; ++n) {\n        appendToNGram(data[dataStartIndex + n]);\n        appendToNGram(this.separator);\n      }\n      // Handle case when there are no tokens or no right padding as these\n      // can result in consecutive separators.\n      if (numTokens > 0) {\n        // If we have tokens, then output last and then pair each separator\n        // with the right padding that follows, to ensure nGram ends either with\n        // the token or with the right pad.\n        appendToNGram(data[dataStartIndex + numTokens - 1]);\n        for (let n = 0; n < rightPadding; ++n) {\n          appendToNGram(this.separator);\n          appendToNGram(this.rightPad);\n        }\n      } else {\n        // If we don't have tokens, then the last item inserted into the nGram\n        // has been the separator from the left padding loop above. Hence,\n        // output right pad and separator and make sure to finish with a\n        // padding, not a separator.\n        for (let n = 0; n < rightPadding - 1; ++n) {\n          appendToNGram(this.rightPad);\n          appendToNGram(this.separator);\n        }\n        appendToNGram(this.rightPad);\n      }\n    }\n  }\n\n  // Data and splits together form the definition of the ragged tensor,\n  // where data is 1 dimensional and contains the values of the tensor\n  // and splits denotes the indices at which each row starts.\n  public compute(data: Uint8Array[], splits: Int32Array):\n      [Uint8Array[], Int32Array] {\n    // Validate that the splits are valid indices into data, only if there are\n    // splits specified.\n    const inputDataSize = data.length;\n    const splitsSize = splits.length;\n    if (splitsSize > 0) {\n      let prevSplit = splits[0];\n      if (prevSplit !== 0) {\n        throw new Error(`First split value must be 0, got ${prevSplit}`);\n      }\n      for (let i = 1; i < splitsSize; ++i) {\n        let validSplits = splits[i] >= prevSplit;\n        validSplits = validSplits && (splits[i] <= inputDataSize);\n        if (!validSplits) {\n          throw new Error(`Invalid split value ${splits[i]}, must be in [${\n              prevSplit}, ${inputDataSize}]`);\n        }\n        prevSplit = splits[i];\n      }\n      if (prevSplit !== inputDataSize) {\n        throw new Error(`Last split value must be data size. Expected ${\n            inputDataSize}, got ${prevSplit}`);\n      }\n    }\n\n    const numBatchItems = splitsSize - 1;\n    const nGramsSplits = util.getArrayFromDType('int32', splitsSize);\n    // If there is no data or size, return an empty ragged tensor.\n    if (inputDataSize === 0 || splitsSize === 0) {\n      const empty: Uint8Array[] = new Array(inputDataSize);\n      for (let i = 0; i <= numBatchItems; ++i) {\n        nGramsSplits[i] = 0;\n      }\n      return [empty, nGramsSplits];\n    }\n\n    nGramsSplits[0] = 0;\n    for (let i = 1; i <= numBatchItems; ++i) {\n      const length = splits[i] - splits[i - 1];\n      let numNGrams = 0;\n      this.nGramWidths.forEach((nGramWidth) => {\n        numNGrams += this.getNumNGrams(length, nGramWidth);\n      });\n      if (this.preserveShort && length > 0 && numNGrams === 0) {\n        numNGrams = 1;\n      }\n      nGramsSplits[i] = nGramsSplits[i - 1] + numNGrams;\n    }\n\n    const nGrams: Uint8Array[] = new Array(nGramsSplits[numBatchItems]);\n\n    for (let i = 0; i < numBatchItems; ++i) {\n      const splitIndex = splits[i];\n      let outputStartIdx = nGramsSplits[i];\n      this.nGramWidths.forEach((nGramWidth) => {\n        const length = splits[i + 1] - splits[i];\n        const numNGrams = this.getNumNGrams(length, nGramWidth);\n        this.createNGrams(\n            data, splitIndex, nGrams, outputStartIdx, numNGrams, nGramWidth);\n        outputStartIdx += numNGrams;\n      });\n      // If we're preserving short sequences, check to see if no sequence was\n      // generated by comparing the current output start idx to the original\n      // one (nGramSplitsdata). If no ngrams were generated, then they will\n      // be equal (since we increment outputStartIdx by numNGrams every\n      // time we create a set of ngrams.)\n      if (this.preserveShort && outputStartIdx === nGramsSplits[i]) {\n        const dataLength = splits[i + 1] - splits[i];\n        // One legitimate reason to not have any ngrams when this.preserveShort\n        // is true is if the sequence itself is empty. In that case, move on.\n        if (dataLength === 0) {\n          continue;\n        }\n        // We don't have to worry about dynamic padding sizes here: if padding\n        // was dynamic, every sequence would have had sufficient padding to\n        // generate at least one nGram.\n        const nGramWidth = dataLength + 2 * this.padWidth;\n        const numNGrams = 1;\n        this.createNGrams(\n            data, splitIndex, nGrams, outputStartIdx, numNGrams, nGramWidth);\n      }\n    }\n    return [nGrams, nGramsSplits];\n  }\n}\n\nexport function stringNGramsImpl(\n    data: Uint8Array[], dataSplits: Int32Array, separator: string,\n    nGramWidths: number[], leftPad: string, rightPad: string, padWidth: number,\n    preserveShortSequences: boolean): [Uint8Array[], Int32Array] {\n  return new StringNGramsOp(\n             separator, nGramWidths, leftPad, rightPad, padWidth,\n             preserveShortSequences)\n      .compute(data, dataSplits);\n}\n"]}","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nfunction split(str, delimiters, skipEmpty, result) {\n if (!str.length) {\n return;\n }\n // When the delimiter is empty, the input is split into individual characters.\n if (delimiters.length === 0) {\n for (let i = 0; i < str.length; ++i) {\n result.push(str.subarray(i, i + 1));\n }\n return;\n }\n // When there is one delimiter, the input is split only at that delimiter.\n if (delimiters.length === 1) {\n const delimiter = delimiters[0];\n let f = str.indexOf(delimiter);\n while (f !== -1) {\n const token = str.subarray(0, f);\n if (!skipEmpty || token.length !== 0) {\n result.push(token);\n }\n str = str.subarray(f + 1);\n f = str.indexOf(delimiter);\n }\n if (!skipEmpty || str.length !== 0) {\n result.push(str);\n }\n return;\n }\n // When there are multiple delimiters, the input is split at every instance\n // one of the delimiters appears.\n let tokenStart = 0;\n for (let i = 0; i < str.length + 1; i++) {\n if ((i === str.length) || (delimiters.indexOf(str[i]) !== -1)) {\n const token = str.subarray(tokenStart, i);\n if (!skipEmpty || token.length !== 0) {\n result.push(token);\n }\n tokenStart = i + 1;\n }\n }\n}\nexport function stringSplitImpl(input, delimiter, skipEmpty) {\n const batchSize = input.length;\n // Empty delimiter means split the input character by character.\n const tokens = [];\n let outputSize = 0;\n let maxNumEntries = 0;\n const numIndices = new Array(batchSize);\n for (let i = 0; i < batchSize; ++i) {\n const prevTokensLength = tokens.length;\n split(input[i], delimiter, skipEmpty, tokens);\n const nEntries = tokens.length - prevTokensLength;\n numIndices[i] = nEntries;\n outputSize += nEntries;\n maxNumEntries = Math.max(maxNumEntries, nEntries);\n }\n const indices = util.getArrayFromDType('int32', outputSize * 2);\n const values = new Array(outputSize);\n const shape = [batchSize, maxNumEntries];\n let c = 0;\n for (let i = 0; i < batchSize; 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All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {TypedArray, util} from '@tensorflow/tfjs-core';\n\nfunction split(\n    str: Uint8Array, delimiters: Uint8Array, skipEmpty: boolean,\n    result: Uint8Array[]): void {\n  if (!str.length) {\n    return;\n  }\n  // When the delimiter is empty, the input is split into individual characters.\n  if (delimiters.length === 0) {\n    for (let i = 0; i < str.length; ++i) {\n      result.push(str.subarray(i, i + 1));\n    }\n    return;\n  }\n  // When there is one delimiter, the input is split only at that delimiter.\n  if (delimiters.length === 1) {\n    const delimiter = delimiters[0];\n    let f = str.indexOf(delimiter);\n    while (f !== -1) {\n      const token = str.subarray(0, f);\n      if (!skipEmpty || token.length !== 0) {\n        result.push(token);\n      }\n      str = str.subarray(f + 1);\n      f = str.indexOf(delimiter);\n    }\n    if (!skipEmpty || str.length !== 0) {\n      result.push(str);\n    }\n    return;\n  }\n  // When there are multiple delimiters, the input is split at every instance\n  // one of the delimiters appears.\n  let tokenStart = 0;\n  for (let i = 0; i < str.length + 1; i++) {\n    if ((i === str.length) || (delimiters.indexOf(str[i]) !== -1)) {\n      const token = str.subarray(tokenStart, i);\n      if (!skipEmpty || token.length !== 0) {\n        result.push(token);\n      }\n      tokenStart = i + 1;\n    }\n  }\n}\n\nexport function stringSplitImpl(\n    input: Uint8Array[], delimiter: Uint8Array,\n    skipEmpty: boolean): [TypedArray, Uint8Array[], [number, number]] {\n  const batchSize = input.length;\n\n  // Empty delimiter means split the input character by character.\n  const tokens: Uint8Array[] = [];\n\n  let outputSize = 0;\n  let maxNumEntries = 0;\n  const numIndices: number[] = new Array(batchSize);\n  for (let i = 0; i < batchSize; ++i) {\n    const prevTokensLength = tokens.length;\n    split(input[i], delimiter, skipEmpty, tokens);\n    const nEntries = tokens.length - prevTokensLength;\n    numIndices[i] = nEntries;\n    outputSize += nEntries;\n    maxNumEntries = Math.max(maxNumEntries, nEntries);\n  }\n\n  const indices = util.getArrayFromDType('int32', outputSize * 2) as TypedArray;\n  const values: Uint8Array[] = new Array(outputSize);\n  const shape: [number, number] = [batchSize, maxNumEntries];\n\n  let c = 0;\n  for (let i = 0; i < batchSize; ++i) {\n    for (let j = 0; j < numIndices[i]; ++j) {\n      // indices is a 2d tensor with shape of [outputSize, 2]\n      indices[c * 2] = i;\n      indices[c * 2 + 1] = j;\n      values[c] = tokens[c];\n      ++c;\n    }\n  }\n\n  return [indices, values, shape];\n}\n"]}","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport function stringToHashBucketFastImpl(input, numBuckets) {\n const output = util.getArrayFromDType('int32', input.length);\n for (let i = 0; i < input.length; ++i) {\n output[i] =\n util.fingerPrint64(input[i]).modulo(numBuckets).getLowBitsUnsigned();\n }\n return output;\n}\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from '@tensorflow/tfjs-core';\n/**\n * An implementation of the tile kernel shared between webgl and cpu for string\n * tensors only.\n */\nexport function tileImpl(xBuf, reps) {\n const newShape = new Array(xBuf.rank);\n for (let i = 0; i < newShape.length; i++) {\n newShape[i] = xBuf.shape[i] * reps[i];\n }\n const result = buffer(newShape, xBuf.dtype);\n for (let i = 0; i < result.values.length; ++i) {\n const newLoc = result.indexToLoc(i);\n const originalLoc = new Array(xBuf.rank);\n for (let j = 0; j < originalLoc.length; j++) {\n originalLoc[j] = newLoc[j] % xBuf.shape[j];\n }\n const originalIndex = xBuf.locToIndex(originalLoc);\n result.values[i] = xBuf.values[originalIndex];\n }\n return result;\n}\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/** An implementation of the TopK kernel shared between webgl and cpu. */\nimport { buffer, util } from '@tensorflow/tfjs-core';\nconst comparePair = (a, b) => {\n const valueDiff = b.value - a.value;\n return valueDiff === 0 ? a.index - b.index : valueDiff;\n};\n/**\n * Partitions array where all elements smaller than the (k+1) smallest element\n * are found to the left of it, and all larger to the right of it.\n * Based on the Floyd-Rivest Algorithm, ref:\n * https://en.wikipedia.org/wiki/Floyd%E2%80%93Rivest_algorithm\n * @param array: Array to partition\n * @param left: Left index for the interval\n * @param right: Right index for the interval\n * @param k: Desired index value, where array[k] is the (k+1)th smallest element\n * when left = 0\n */\nfunction select(array, k, left = 0, right = array.length - 1) {\n while (right > left) {\n // Use select recursively to sample a smaller set of size s\n // the arbitrary constants 600 and 0.5 are used in the original\n // version to minimize execution time.\n if (right - left > 600) {\n const n = right - left + 1;\n const i = k - left + 1;\n const z = Math.log(n);\n const s = 0.5 * Math.exp(2 * z / 3);\n const sd = 0.5 * Math.sqrt(z * s * (n - s) / n) * Math.sign(i - n / 2);\n const newLeft = Math.max(left, Math.floor(k - i * s / n + sd));\n const newRight = Math.min(right, Math.floor(k + (n - i) * s / n + sd));\n select(array, k, newLeft, newRight);\n }\n // partition the elements between left and right around t\n const t = array[k];\n let i = left;\n let j = right;\n util.swap(array, left, k);\n if (comparePair(array[right], t) > 0) {\n util.swap(array, left, right);\n }\n while (i < j) {\n util.swap(array, i, j);\n i++;\n j--;\n while (comparePair(array[i], t) < 0) {\n i = i + 1;\n }\n while (comparePair(array[j], t) > 0) {\n j = j - 1;\n }\n }\n if (comparePair(array[left], t) === 0) {\n util.swap(array, left, j);\n }\n else {\n j = j + 1;\n util.swap(array, j, right);\n }\n // Adjust left and right towards the boundaries of the subset\n // containing the (k - left + 1)th smallest element.\n if (j <= k) {\n left = j + 1;\n }\n if (k <= j) {\n right = j - 1;\n }\n }\n}\nexport function topKImpl(x, xShape, xDtype, k, sorted) {\n // Reshape into a 2d tensor [batch, lastDim] and compute topk along lastDim.\n const lastDim = xShape[xShape.length - 1];\n const [batch, size] = [x.length / lastDim, lastDim];\n const allTopKVals = util.getTypedArrayFromDType(xDtype, batch * k);\n const allTopKIndices = util.getTypedArrayFromDType('int32', batch * k);\n for (let b = 0; b < batch; b++) {\n const offset = b * size;\n const vals = x.subarray(offset, offset + size);\n let valAndInd = new Array(vals.length);\n vals.forEach((value, index) => valAndInd[index] = { value, index });\n if (k < valAndInd.length) {\n select(valAndInd, k);\n valAndInd = valAndInd.slice(0, k);\n }\n if (sorted) {\n valAndInd.sort(comparePair);\n }\n const outOffset = b * k;\n const topKVals = allTopKVals.subarray(outOffset, outOffset + k);\n const topKIndices = allTopKIndices.subarray(outOffset, outOffset + k);\n for (let i = 0; i < k; i++) {\n topKVals[i] = valAndInd[i].value;\n topKIndices[i] = valAndInd[i].index;\n }\n }\n // Reshape back to the original input shape, except that the last\n // dimension is k.\n const outputShape = xShape.slice();\n outputShape[outputShape.length - 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All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/** An implementation of the TopK kernel shared between webgl and cpu. */\n\nimport {buffer, NumericDataType, Rank, ShapeMap, Tensor, TensorBuffer, TypedArray, util} from '@tensorflow/tfjs-core';\n\ntype Pair = {\n  value: number,\n  index: number\n};\n\nconst comparePair = (a: Pair, b: Pair) => {\n  const valueDiff = b.value - a.value;\n  return valueDiff === 0 ? a.index - b.index : valueDiff;\n};\n\n/**\n * Partitions array where all elements smaller than the (k+1) smallest element\n * are found to the left of it, and all larger to the right of it.\n * Based on the Floyd-Rivest Algorithm, ref:\n * https://en.wikipedia.org/wiki/Floyd%E2%80%93Rivest_algorithm\n * @param array: Array to partition\n * @param left: Left index for the interval\n * @param right: Right index for the interval\n * @param k: Desired index value, where array[k] is the (k+1)th smallest element\n *           when left = 0\n */\nfunction select(array: Pair[], k: number, left = 0, right = array.length - 1) {\n  while (right > left) {\n    // Use select recursively to sample a smaller set of size s\n    // the arbitrary constants 600 and 0.5 are used in the original\n    // version to minimize execution time.\n    if (right - left > 600) {\n      const n = right - left + 1;\n      const i = k - left + 1;\n      const z = Math.log(n);\n      const s = 0.5 * Math.exp(2 * z / 3);\n      const sd = 0.5 * Math.sqrt(z * s * (n - s) / n) * Math.sign(i - n / 2);\n      const newLeft = Math.max(left, Math.floor(k - i * s / n + sd));\n      const newRight = Math.min(right, Math.floor(k + (n - i) * s / n + sd));\n      select(array, k, newLeft, newRight);\n    }\n    // partition the elements between left and right around t\n    const t = array[k];\n    let i = left;\n    let j = right;\n\n    util.swap(array, left, k);\n\n    if (comparePair(array[right], t) > 0) {\n      util.swap(array, left, right);\n    }\n    while (i < j) {\n      util.swap(array, i, j);\n      i++;\n      j--;\n      while (comparePair(array[i], t) < 0) {\n        i = i + 1;\n      }\n      while (comparePair(array[j], t) > 0) {\n        j = j - 1;\n      }\n    }\n    if (comparePair(array[left], t) === 0) {\n      util.swap(array, left, j);\n    } else {\n      j = j + 1;\n      util.swap(array, j, right);\n    }\n    // Adjust left and right towards the boundaries of the subset\n    // containing the (k - left + 1)th smallest element.\n    if (j <= k) {\n      left = j + 1;\n    }\n    if (k <= j) {\n      right = j - 1;\n    }\n  }\n}\n\nexport function topKImpl<T extends Tensor, R extends Rank>(\n    x: TypedArray, xShape: number[], xDtype: NumericDataType, k: number,\n    sorted: boolean):\n    [TensorBuffer<R, NumericDataType>, TensorBuffer<R, 'int32'>] {\n  // Reshape into a 2d tensor [batch, lastDim] and compute topk along lastDim.\n  const lastDim = xShape[xShape.length - 1];\n  const [batch, size] = [x.length / lastDim, lastDim];\n  const allTopKVals = util.getTypedArrayFromDType(xDtype, batch * k);\n  const allTopKIndices = util.getTypedArrayFromDType('int32', batch * k);\n\n  for (let b = 0; b < batch; b++) {\n    const offset = b * size;\n    const vals = x.subarray(offset, offset + size);\n\n    let valAndInd: Pair[] = new Array(vals.length);\n    vals.forEach(\n        (value: number, index: number) => valAndInd[index] = {value, index});\n\n    if (k < valAndInd.length) {\n      select(valAndInd, k);\n      valAndInd = valAndInd.slice(0, k);\n    }\n\n    if (sorted) {\n      valAndInd.sort(comparePair);\n    }\n    \n    const outOffset = b * k;\n    const topKVals = allTopKVals.subarray(outOffset, outOffset + k);\n    const topKIndices = allTopKIndices.subarray(outOffset, outOffset + k);\n    for (let i = 0; i < k; i++) {\n      topKVals[i] = valAndInd[i].value;\n      topKIndices[i] = valAndInd[i].index;\n    }\n  }\n  // Reshape back to the original input shape, except that the last\n  // dimension is k.\n  const outputShape = xShape.slice();\n  outputShape[outputShape.length - 1] = k;\n\n  return [\n    buffer(outputShape as ShapeMap[R], xDtype, allTopKVals),\n    buffer(outputShape as ShapeMap[R], 'int32', allTopKIndices)\n  ];\n}\n"]}","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { TensorBuffer, util } from '@tensorflow/tfjs-core';\nexport function uniqueImpl(values, axis, shape, dtype) {\n // Normalize and validate axis.\n const $axis = util.parseAxisParam(axis, shape)[0];\n // Calculate the new shape that is suitable for extracting data along the\n // given axis.\n //\n // The rank is 3.\n // The size of the 1st dimension is the size of all the axes < the given axis.\n // The size of the 2nd dimension is the same as the size of the given axis.\n // The size of the 3rd dimension is the size of all the axes > the given axis.\n //\n // For example, for a 4D tensor with shape=[2, 3, 5, 4] and axis=2, the\n // newShape would be: [2*3, 5, 4].\n //\n // Note that this is not the final output shape. This will be the shape for an\n // intermediate TensorBuffer (see inputBuffer below) to allow us to extract\n // values along the given axis. To demonstrate how it works, consider the\n // following example:\n //\n // Input: a 3D tensor, with shape [1, 2, 3]\n // [\n // [\n // [1,2,3],\n // [4,5,6]\n // ]\n // ]\n // Axis: 2 (the last axis).\n // Along axis 2, we expect to extract 3 tensors: [1,4], [2,5], [3,6].\n //\n // For this example, newShape would be: [2, 3, 1], where 2 is calculated from\n // 1*2. The re-shaped data would look like:\n //\n // [\n // [\n // [1], [2], [3]\n // ],\n // [\n // [4], [5], [6]\n // ]\n // ]\n //\n // Then, we can construct a 3-level nested loop by the following dimension\n // order to extract the values along the axis (dimension1):\n // i: dimension1 // 0,1,2 (newShape[1])\n // m: dimension0 // 0,1 (newShape[0])\n // n: dimension2 // 0 (newShape[2])\n //\n // m, i, n\n // ---------\n // Iteration 0: data at [0, 0, 0] => \"1\"\n // Iteration 1: data at [1, 0, 0] => \"4\"\n // We got [1,4].\n // Iteration 2: data at [0, 1, 0] => \"2\"\n // Iteration 3: data at [1, 1, 0] => \"5\"\n // We got [2,5].\n // Iteration 4: data at [0, 2, 0] => \"3\"\n // Iteration 5: data at [1, 2, 0] => \"6\"\n // We got [3,6].\n const newShape = [1, shape[0], 1];\n for (let i = 0; i < $axis; i++) {\n newShape[0] *= shape[i];\n }\n newShape[1] = shape[$axis];\n for (let i = $axis + 1; i < shape.length; i++) {\n newShape[2] *= shape[i];\n }\n // A map from unique elements (their string representations) to their values\n // in \"indices\" (below).\n const uniqueElements = {};\n // The indices of each unique element in the original tensor along the given\n // axis. It is 1D and has the same size as the given axis.\n const indices = new Int32Array(shape[$axis]);\n // Create a buffer so we can easily extract value at a given location.\n const inputBuffer = new TensorBuffer(newShape, dtype, values);\n // The indices along the given axis that have unique elements. This is a\n // de-duped version of \"indices\" above.\n const uniqueIndices = [];\n const is1DTensor = newShape[0] === 1 && newShape[2] === 1;\n for (let i = 0; i < shape[$axis]; i++) {\n // Extract values along the axis.\n let element;\n if (is1DTensor) {\n // Fast path for 1D tensor input.\n element = values[i].toString();\n }\n else {\n const axisValues = [];\n for (let m = 0; m < newShape[0]; m++) {\n for (let n = 0; n < newShape[2]; n++) {\n axisValues.push(inputBuffer.get(m, i, n));\n }\n }\n element = axisValues.join(',');\n }\n // Dedup and update various indices.\n if (uniqueElements[element] !== undefined) {\n indices[i] = uniqueElements[element];\n }\n else {\n const uniqueIndex = Object.keys(uniqueElements).length;\n uniqueElements[element] = uniqueIndex;\n indices[i] = uniqueIndex;\n uniqueIndices.push(i);\n }\n }\n // Now we know where each of the unique elements are located along the axis\n // (uniqueIndices). 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All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {BackendValues, DataType, TensorBuffer, TypedArray, util} from '@tensorflow/tfjs-core';\n\nexport function uniqueImpl(\n    values: BackendValues, axis: number, shape: number[], dtype: DataType): {\n  outputValues: BackendValues,\n  outputShape: number[],\n  indices: BackendValues\n} {\n  // Normalize and validate axis.\n  const $axis = util.parseAxisParam(axis, shape)[0];\n\n  // Calculate the new shape that is suitable for extracting data along the\n  // given axis.\n  //\n  // The rank is 3.\n  // The size of the 1st dimension is the size of all the axes < the given axis.\n  // The size of the 2nd dimension is the same as the size of the given axis.\n  // The size of the 3rd dimension is the size of all the axes > the given axis.\n  //\n  // For example, for a 4D tensor with shape=[2, 3, 5, 4] and axis=2, the\n  // newShape would be: [2*3, 5, 4].\n  //\n  // Note that this is not the final output shape. This will be the shape for an\n  // intermediate TensorBuffer (see inputBuffer below) to allow us to extract\n  // values along the given axis. To demonstrate how it works, consider the\n  // following example:\n  //\n  // Input: a 3D tensor, with shape [1, 2, 3]\n  // [\n  //   [\n  //      [1,2,3],\n  //      [4,5,6]\n  //   ]\n  // ]\n  // Axis: 2 (the last axis).\n  // Along axis 2, we expect to extract 3 tensors: [1,4], [2,5], [3,6].\n  //\n  // For this example, newShape would be: [2, 3, 1], where 2 is calculated from\n  // 1*2. The re-shaped data would look like:\n  //\n  // [\n  //   [\n  //     [1], [2], [3]\n  //   ],\n  //   [\n  //     [4], [5], [6]\n  //   ]\n  // ]\n  //\n  // Then, we can construct a 3-level nested loop by the following dimension\n  // order to extract the values along the axis (dimension1):\n  // i: dimension1       // 0,1,2 (newShape[1])\n  //   m: dimension0     // 0,1   (newShape[0])\n  //     n: dimension2   // 0     (newShape[2])\n  //\n  //                       m, i, n\n  //                      ---------\n  // Iteration 0: data at [0, 0, 0] => \"1\"\n  // Iteration 1: data at [1, 0, 0] => \"4\"\n  // We got [1,4].\n  // Iteration 2: data at [0, 1, 0] => \"2\"\n  // Iteration 3: data at [1, 1, 0] => \"5\"\n  // We got [2,5].\n  // Iteration 4: data at [0, 2, 0] => \"3\"\n  // Iteration 5: data at [1, 2, 0] => \"6\"\n  // We got [3,6].\n  const newShape = [1, shape[0], 1];\n  for (let i = 0; i < $axis; i++) {\n    newShape[0] *= shape[i];\n  }\n  newShape[1] = shape[$axis];\n  for (let i = $axis + 1; i < shape.length; i++) {\n    newShape[2] *= shape[i];\n  }\n\n  // A map from unique elements (their string representations) to their values\n  // in \"indices\" (below).\n  const uniqueElements: {[key: string]: number} = {};\n  // The indices of each unique element in the original tensor along the given\n  // axis. It is 1D and has the same size as the given axis.\n  const indices = new Int32Array(shape[$axis]);\n  // Create a buffer so we can easily extract value at a given location.\n  const inputBuffer = new TensorBuffer(newShape, dtype, values as TypedArray);\n  // The indices along the given axis that have unique elements. This is a\n  // de-duped version of \"indices\" above.\n  const uniqueIndices: number[] = [];\n  const is1DTensor = newShape[0] === 1 && newShape[2] === 1;\n  for (let i = 0; i < shape[$axis]; i++) {\n    // Extract values along the axis.\n    let element: string;\n    if (is1DTensor) {\n      // Fast path for 1D tensor input.\n      element = values[i].toString();\n    } else {\n      const axisValues = [];\n      for (let m = 0; m < newShape[0]; m++) {\n        for (let n = 0; n < newShape[2]; n++) {\n          axisValues.push(inputBuffer.get(m, i, n));\n        }\n      }\n      element = axisValues.join(',');\n    }\n\n    // Dedup and update various indices.\n    if (uniqueElements[element] !== undefined) {\n      indices[i] = uniqueElements[element];\n    } else {\n      const uniqueIndex = Object.keys(uniqueElements).length;\n      uniqueElements[element] = uniqueIndex;\n      indices[i] = uniqueIndex;\n      uniqueIndices.push(i);\n    }\n  }\n\n  // Now we know where each of the unique elements are located along the axis\n  // (uniqueIndices). Extract them from input buffer and store them in the\n  // output buffer.\n  const outputTmpShape = newShape.slice();\n  outputTmpShape[1] = Object.keys(uniqueElements).length;\n  const outputBuffer = new TensorBuffer(outputTmpShape, dtype);\n  uniqueIndices.forEach((uniqueElementIndex, i) => {\n    for (let m = 0; m < newShape[0]; m++) {\n      for (let n = 0; n < newShape[2]; n++) {\n        outputBuffer.set(inputBuffer.get(m, uniqueElementIndex, n), m, i, n);\n      }\n    }\n  });\n\n  // The output shape can be calculated from the input shape with the size of\n  // the given axis replaced by the number of unique elements along that axis.\n  const outputShape = shape.slice();\n  outputShape[$axis] = outputTmpShape[1];\n\n  return {\n    outputValues: outputBuffer.values as BackendValues,\n    outputShape,\n    indices,\n  };\n}\n"]}","module.exports = Long;\r\n\r\n/**\r\n * wasm optimizations, to do native i64 multiplication and divide\r\n */\r\nvar wasm = null;\r\n\r\ntry {\r\n wasm = new WebAssembly.Instance(new WebAssembly.Module(new Uint8Array([\r\n 0, 97, 115, 109, 1, 0, 0, 0, 1, 13, 2, 96, 0, 1, 127, 96, 4, 127, 127, 127, 127, 1, 127, 3, 7, 6, 0, 1, 1, 1, 1, 1, 6, 6, 1, 127, 1, 65, 0, 11, 7, 50, 6, 3, 109, 117, 108, 0, 1, 5, 100, 105, 118, 95, 115, 0, 2, 5, 100, 105, 118, 95, 117, 0, 3, 5, 114, 101, 109, 95, 115, 0, 4, 5, 114, 101, 109, 95, 117, 0, 5, 8, 103, 101, 116, 95, 104, 105, 103, 104, 0, 0, 10, 191, 1, 6, 4, 0, 35, 0, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 126, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 127, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 128, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 129, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 130, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11\r\n ])), {}).exports;\r\n} catch (e) {\r\n // no wasm support :(\r\n}\r\n\r\n/**\r\n * Constructs a 64 bit two's-complement integer, given its low and high 32 bit values as *signed* integers.\r\n * See the from* functions below for more convenient ways of constructing Longs.\r\n * @exports Long\r\n * @class A Long class for representing a 64 bit two's-complement integer value.\r\n * @param {number} low The low (signed) 32 bits of the long\r\n * @param {number} high The high (signed) 32 bits of the long\r\n * @param {boolean=} unsigned Whether unsigned or not, defaults to signed\r\n * @constructor\r\n */\r\nfunction Long(low, high, unsigned) {\r\n\r\n /**\r\n * The low 32 bits as a signed value.\r\n * @type {number}\r\n */\r\n this.low = low | 0;\r\n\r\n /**\r\n * The high 32 bits as a signed value.\r\n * @type {number}\r\n */\r\n this.high = high | 0;\r\n\r\n /**\r\n * Whether unsigned or not.\r\n * @type {boolean}\r\n */\r\n this.unsigned = !!unsigned;\r\n}\r\n\r\n// The internal representation of a long is the two given signed, 32-bit values.\r\n// We use 32-bit pieces because these are the size of integers on which\r\n// Javascript performs bit-operations. For operations like addition and\r\n// multiplication, we split each number into 16 bit pieces, which can easily be\r\n// multiplied within Javascript's floating-point representation without overflow\r\n// or change in sign.\r\n//\r\n// In the algorithms below, we frequently reduce the negative case to the\r\n// positive case by negating the input(s) and then post-processing the result.\r\n// Note that we must ALWAYS check specially whether those values are MIN_VALUE\r\n// (-2^63) because -MIN_VALUE == MIN_VALUE (since 2^63 cannot be represented as\r\n// a positive number, it overflows back into a negative). Not handling this\r\n// case would often result in infinite recursion.\r\n//\r\n// Common constant values ZERO, ONE, NEG_ONE, etc. are defined below the from*\r\n// methods on which they depend.\r\n\r\n/**\r\n * An indicator used to reliably determine if an object is a Long or not.\r\n * @type {boolean}\r\n * @const\r\n * @private\r\n */\r\nLong.prototype.__isLong__;\r\n\r\nObject.defineProperty(Long.prototype, \"__isLong__\", { value: true });\r\n\r\n/**\r\n * @function\r\n * @param {*} obj Object\r\n * @returns {boolean}\r\n * @inner\r\n */\r\nfunction isLong(obj) {\r\n return (obj && obj[\"__isLong__\"]) === true;\r\n}\r\n\r\n/**\r\n * Tests if the specified object is a Long.\r\n * @function\r\n * @param {*} obj Object\r\n * @returns {boolean}\r\n */\r\nLong.isLong = isLong;\r\n\r\n/**\r\n * A cache of the Long representations of small integer values.\r\n * @type {!Object}\r\n * @inner\r\n */\r\nvar INT_CACHE = {};\r\n\r\n/**\r\n * A cache of the Long representations of small unsigned integer values.\r\n * @type {!Object}\r\n * @inner\r\n */\r\nvar UINT_CACHE = {};\r\n\r\n/**\r\n * @param {number} value\r\n * @param {boolean=} unsigned\r\n * @returns {!Long}\r\n * @inner\r\n */\r\nfunction fromInt(value, unsigned) {\r\n var obj, cachedObj, cache;\r\n if (unsigned) {\r\n value >>>= 0;\r\n if (cache = (0 <= value && value < 256)) {\r\n cachedObj = UINT_CACHE[value];\r\n if (cachedObj)\r\n return cachedObj;\r\n }\r\n obj = fromBits(value, (value | 0) < 0 ? -1 : 0, true);\r\n if (cache)\r\n UINT_CACHE[value] = obj;\r\n return obj;\r\n } else {\r\n value |= 0;\r\n if (cache = (-128 <= value && value < 128)) {\r\n cachedObj = INT_CACHE[value];\r\n if (cachedObj)\r\n return cachedObj;\r\n }\r\n obj = fromBits(value, value < 0 ? -1 : 0, false);\r\n if (cache)\r\n INT_CACHE[value] = obj;\r\n return obj;\r\n }\r\n}\r\n\r\n/**\r\n * Returns a Long representing the given 32 bit integer value.\r\n * @function\r\n * @param {number} value The 32 bit integer in question\r\n * @param {boolean=} unsigned Whether unsigned or not, defaults to signed\r\n * @returns {!Long} The corresponding Long value\r\n */\r\nLong.fromInt = fromInt;\r\n\r\n/**\r\n * @param {number} value\r\n * @param {boolean=} unsigned\r\n * @returns {!Long}\r\n * @inner\r\n */\r\nfunction fromNumber(value, unsigned) {\r\n if (isNaN(value))\r\n return unsigned ? UZERO : ZERO;\r\n if (unsigned) {\r\n if (value < 0)\r\n return UZERO;\r\n if (value >= TWO_PWR_64_DBL)\r\n return MAX_UNSIGNED_VALUE;\r\n } else {\r\n if (value <= -TWO_PWR_63_DBL)\r\n return MIN_VALUE;\r\n if (value + 1 >= TWO_PWR_63_DBL)\r\n return MAX_VALUE;\r\n }\r\n if (value < 0)\r\n return fromNumber(-value, unsigned).neg();\r\n return fromBits((value % TWO_PWR_32_DBL) | 0, (value / TWO_PWR_32_DBL) | 0, unsigned);\r\n}\r\n\r\n/**\r\n * Returns a Long representing the given value, provided that it is a finite number. Otherwise, zero is returned.\r\n * @function\r\n * @param {number} value The number in question\r\n * @param {boolean=} unsigned Whether unsigned or not, defaults to signed\r\n * @returns {!Long} The corresponding Long value\r\n */\r\nLong.fromNumber = fromNumber;\r\n\r\n/**\r\n * @param {number} lowBits\r\n * @param {number} highBits\r\n * @param {boolean=} unsigned\r\n * @returns {!Long}\r\n * @inner\r\n */\r\nfunction fromBits(lowBits, highBits, unsigned) {\r\n return new Long(lowBits, highBits, unsigned);\r\n}\r\n\r\n/**\r\n * Returns a Long representing the 64 bit integer that comes by concatenating the given low and high bits. Each is\r\n * assumed to use 32 bits.\r\n * @function\r\n * @param {number} lowBits The low 32 bits\r\n * @param {number} highBits The high 32 bits\r\n * @param {boolean=} unsigned Whether unsigned or not, defaults to signed\r\n * @returns {!Long} The corresponding Long value\r\n */\r\nLong.fromBits = fromBits;\r\n\r\n/**\r\n * @function\r\n * @param {number} base\r\n * @param {number} exponent\r\n * @returns {number}\r\n * @inner\r\n */\r\nvar pow_dbl = Math.pow; // Used 4 times (4*8 to 15+4)\r\n\r\n/**\r\n * @param {string} str\r\n * @param {(boolean|number)=} unsigned\r\n * @param {number=} radix\r\n * @returns {!Long}\r\n * @inner\r\n */\r\nfunction fromString(str, unsigned, radix) {\r\n if (str.length === 0)\r\n throw Error('empty string');\r\n if (str === \"NaN\" || str === \"Infinity\" || str === \"+Infinity\" || str === \"-Infinity\")\r\n return ZERO;\r\n if (typeof unsigned === 'number') {\r\n // For goog.math.long compatibility\r\n radix = unsigned,\r\n unsigned = false;\r\n } else {\r\n unsigned = !! unsigned;\r\n }\r\n radix = radix || 10;\r\n if (radix < 2 || 36 < radix)\r\n throw RangeError('radix');\r\n\r\n var p;\r\n if ((p = str.indexOf('-')) > 0)\r\n throw Error('interior hyphen');\r\n else if (p === 0) {\r\n return fromString(str.substring(1), unsigned, radix).neg();\r\n }\r\n\r\n // Do several (8) digits each time through the loop, so as to\r\n // minimize the calls to the very expensive emulated div.\r\n var radixToPower = fromNumber(pow_dbl(radix, 8));\r\n\r\n var result = ZERO;\r\n for (var i = 0; i < str.length; i += 8) {\r\n var size = Math.min(8, str.length - i),\r\n value = parseInt(str.substring(i, i + size), radix);\r\n if (size < 8) {\r\n var power = fromNumber(pow_dbl(radix, size));\r\n result = result.mul(power).add(fromNumber(value));\r\n } else {\r\n result = result.mul(radixToPower);\r\n result = result.add(fromNumber(value));\r\n }\r\n }\r\n result.unsigned = unsigned;\r\n return result;\r\n}\r\n\r\n/**\r\n * Returns a Long representation of the given string, written using the specified radix.\r\n * @function\r\n * @param {string} str The textual representation of the Long\r\n * @param {(boolean|number)=} unsigned Whether unsigned or not, defaults to signed\r\n * @param {number=} radix The radix in which the text is written (2-36), defaults to 10\r\n * @returns {!Long} The corresponding Long value\r\n */\r\nLong.fromString = fromString;\r\n\r\n/**\r\n * @function\r\n * @param {!Long|number|string|!{low: number, high: number, unsigned: boolean}} val\r\n * @param {boolean=} unsigned\r\n * @returns {!Long}\r\n * @inner\r\n */\r\nfunction fromValue(val, unsigned) {\r\n if (typeof val === 'number')\r\n return fromNumber(val, unsigned);\r\n if (typeof val === 'string')\r\n return fromString(val, unsigned);\r\n // Throws for non-objects, converts non-instanceof Long:\r\n return fromBits(val.low, val.high, typeof unsigned === 'boolean' ? unsigned : val.unsigned);\r\n}\r\n\r\n/**\r\n * Converts the specified value to a Long using the appropriate from* function for its type.\r\n * @function\r\n * @param {!Long|number|string|!{low: number, high: number, unsigned: boolean}} val Value\r\n * @param {boolean=} unsigned Whether unsigned or not, defaults to signed\r\n * @returns {!Long}\r\n */\r\nLong.fromValue = fromValue;\r\n\r\n// NOTE: the compiler should inline these constant values below and then remove these variables, so there should be\r\n// no runtime penalty for these.\r\n\r\n/**\r\n * @type {number}\r\n * @const\r\n * @inner\r\n */\r\nvar TWO_PWR_16_DBL = 1 << 16;\r\n\r\n/**\r\n * @type {number}\r\n * @const\r\n * @inner\r\n */\r\nvar TWO_PWR_24_DBL = 1 << 24;\r\n\r\n/**\r\n * @type {number}\r\n * @const\r\n * @inner\r\n */\r\nvar TWO_PWR_32_DBL = TWO_PWR_16_DBL * TWO_PWR_16_DBL;\r\n\r\n/**\r\n * @type {number}\r\n * @const\r\n * @inner\r\n */\r\nvar TWO_PWR_64_DBL = TWO_PWR_32_DBL * TWO_PWR_32_DBL;\r\n\r\n/**\r\n * @type {number}\r\n * @const\r\n * @inner\r\n */\r\nvar TWO_PWR_63_DBL = TWO_PWR_64_DBL / 2;\r\n\r\n/**\r\n * @type {!Long}\r\n * @const\r\n * @inner\r\n */\r\nvar TWO_PWR_24 = fromInt(TWO_PWR_24_DBL);\r\n\r\n/**\r\n * @type {!Long}\r\n * @inner\r\n */\r\nvar ZERO = fromInt(0);\r\n\r\n/**\r\n * Signed zero.\r\n * @type {!Long}\r\n */\r\nLong.ZERO = ZERO;\r\n\r\n/**\r\n * @type {!Long}\r\n * @inner\r\n */\r\nvar UZERO = fromInt(0, true);\r\n\r\n/**\r\n * Unsigned zero.\r\n * @type {!Long}\r\n */\r\nLong.UZERO = UZERO;\r\n\r\n/**\r\n * @type {!Long}\r\n * @inner\r\n */\r\nvar ONE = fromInt(1);\r\n\r\n/**\r\n * Signed one.\r\n * @type {!Long}\r\n */\r\nLong.ONE = ONE;\r\n\r\n/**\r\n * @type {!Long}\r\n * @inner\r\n */\r\nvar UONE = fromInt(1, true);\r\n\r\n/**\r\n * Unsigned one.\r\n * @type {!Long}\r\n */\r\nLong.UONE = UONE;\r\n\r\n/**\r\n * @type {!Long}\r\n * @inner\r\n */\r\nvar NEG_ONE = fromInt(-1);\r\n\r\n/**\r\n * Signed negative one.\r\n * @type {!Long}\r\n */\r\nLong.NEG_ONE = NEG_ONE;\r\n\r\n/**\r\n * @type {!Long}\r\n * @inner\r\n */\r\nvar MAX_VALUE = fromBits(0xFFFFFFFF|0, 0x7FFFFFFF|0, false);\r\n\r\n/**\r\n * Maximum signed value.\r\n * @type {!Long}\r\n */\r\nLong.MAX_VALUE = MAX_VALUE;\r\n\r\n/**\r\n * @type {!Long}\r\n * @inner\r\n */\r\nvar MAX_UNSIGNED_VALUE = fromBits(0xFFFFFFFF|0, 0xFFFFFFFF|0, true);\r\n\r\n/**\r\n * Maximum unsigned value.\r\n * @type {!Long}\r\n */\r\nLong.MAX_UNSIGNED_VALUE = MAX_UNSIGNED_VALUE;\r\n\r\n/**\r\n * @type {!Long}\r\n * @inner\r\n */\r\nvar MIN_VALUE = fromBits(0, 0x80000000|0, false);\r\n\r\n/**\r\n * Minimum signed value.\r\n * @type {!Long}\r\n */\r\nLong.MIN_VALUE = MIN_VALUE;\r\n\r\n/**\r\n * @alias Long.prototype\r\n * @inner\r\n */\r\nvar LongPrototype = Long.prototype;\r\n\r\n/**\r\n * Converts the Long to a 32 bit integer, assuming it is a 32 bit integer.\r\n * @returns {number}\r\n */\r\nLongPrototype.toInt = function toInt() {\r\n return this.unsigned ? this.low >>> 0 : this.low;\r\n};\r\n\r\n/**\r\n * Converts the Long to a the nearest floating-point representation of this value (double, 53 bit mantissa).\r\n * @returns {number}\r\n */\r\nLongPrototype.toNumber = function toNumber() {\r\n if (this.unsigned)\r\n return ((this.high >>> 0) * TWO_PWR_32_DBL) + (this.low >>> 0);\r\n return this.high * TWO_PWR_32_DBL + (this.low >>> 0);\r\n};\r\n\r\n/**\r\n * Converts the Long to a string written in the specified radix.\r\n * @param {number=} radix Radix (2-36), defaults to 10\r\n * @returns {string}\r\n * @override\r\n * @throws {RangeError} If `radix` is out of range\r\n */\r\nLongPrototype.toString = function toString(radix) {\r\n radix = radix || 10;\r\n if (radix < 2 || 36 < radix)\r\n throw RangeError('radix');\r\n if (this.isZero())\r\n return '0';\r\n if (this.isNegative()) { // Unsigned Longs are never negative\r\n if (this.eq(MIN_VALUE)) {\r\n // We need to change the Long value before it can be negated, so we remove\r\n // the bottom-most digit in this base and then recurse to do the rest.\r\n var radixLong = fromNumber(radix),\r\n div = this.div(radixLong),\r\n rem1 = div.mul(radixLong).sub(this);\r\n return div.toString(radix) + rem1.toInt().toString(radix);\r\n } else\r\n return '-' + this.neg().toString(radix);\r\n }\r\n\r\n // Do several (6) digits each time through the loop, so as to\r\n // minimize the calls to the very expensive emulated div.\r\n var radixToPower = fromNumber(pow_dbl(radix, 6), this.unsigned),\r\n rem = this;\r\n var result = '';\r\n while (true) {\r\n var remDiv = rem.div(radixToPower),\r\n intval = rem.sub(remDiv.mul(radixToPower)).toInt() >>> 0,\r\n digits = intval.toString(radix);\r\n rem = remDiv;\r\n if (rem.isZero())\r\n return digits + result;\r\n else {\r\n while (digits.length < 6)\r\n digits = '0' + digits;\r\n result = '' + digits + result;\r\n }\r\n }\r\n};\r\n\r\n/**\r\n * Gets the high 32 bits as a signed integer.\r\n * @returns {number} Signed high bits\r\n */\r\nLongPrototype.getHighBits = function getHighBits() {\r\n return this.high;\r\n};\r\n\r\n/**\r\n * Gets the high 32 bits as an unsigned integer.\r\n * @returns {number} Unsigned high bits\r\n */\r\nLongPrototype.getHighBitsUnsigned = function getHighBitsUnsigned() {\r\n return this.high >>> 0;\r\n};\r\n\r\n/**\r\n * Gets the low 32 bits as a signed integer.\r\n * @returns {number} Signed low bits\r\n */\r\nLongPrototype.getLowBits = function getLowBits() {\r\n return this.low;\r\n};\r\n\r\n/**\r\n * Gets the low 32 bits as an unsigned integer.\r\n * @returns {number} Unsigned low bits\r\n */\r\nLongPrototype.getLowBitsUnsigned = function getLowBitsUnsigned() {\r\n return this.low >>> 0;\r\n};\r\n\r\n/**\r\n * Gets the number of bits needed to represent the absolute value of this Long.\r\n * @returns {number}\r\n */\r\nLongPrototype.getNumBitsAbs = function getNumBitsAbs() {\r\n if (this.isNegative()) // Unsigned Longs are never negative\r\n return this.eq(MIN_VALUE) ? 64 : this.neg().getNumBitsAbs();\r\n var val = this.high != 0 ? this.high : this.low;\r\n for (var bit = 31; bit > 0; bit--)\r\n if ((val & (1 << bit)) != 0)\r\n break;\r\n return this.high != 0 ? bit + 33 : bit + 1;\r\n};\r\n\r\n/**\r\n * Tests if this Long's value equals zero.\r\n * @returns {boolean}\r\n */\r\nLongPrototype.isZero = function isZero() {\r\n return this.high === 0 && this.low === 0;\r\n};\r\n\r\n/**\r\n * Tests if this Long's value equals zero. This is an alias of {@link Long#isZero}.\r\n * @returns {boolean}\r\n */\r\nLongPrototype.eqz = LongPrototype.isZero;\r\n\r\n/**\r\n * Tests if this Long's value is negative.\r\n * @returns {boolean}\r\n */\r\nLongPrototype.isNegative = function isNegative() {\r\n return !this.unsigned && this.high < 0;\r\n};\r\n\r\n/**\r\n * Tests if this Long's value is positive.\r\n * @returns {boolean}\r\n */\r\nLongPrototype.isPositive = function isPositive() {\r\n return this.unsigned || this.high >= 0;\r\n};\r\n\r\n/**\r\n * Tests if this Long's value is odd.\r\n * @returns {boolean}\r\n */\r\nLongPrototype.isOdd = function isOdd() {\r\n return (this.low & 1) === 1;\r\n};\r\n\r\n/**\r\n * Tests if this Long's value is even.\r\n * @returns {boolean}\r\n */\r\nLongPrototype.isEven = function isEven() {\r\n return (this.low & 1) === 0;\r\n};\r\n\r\n/**\r\n * Tests if this Long's value equals the specified's.\r\n * @param {!Long|number|string} other Other value\r\n * @returns {boolean}\r\n */\r\nLongPrototype.equals = function equals(other) {\r\n if (!isLong(other))\r\n other = fromValue(other);\r\n if (this.unsigned !== other.unsigned && (this.high >>> 31) === 1 && (other.high >>> 31) === 1)\r\n return false;\r\n return this.high === other.high && this.low === other.low;\r\n};\r\n\r\n/**\r\n * Tests if this Long's value equals the specified's. This is an alias of {@link Long#equals}.\r\n * @function\r\n * @param {!Long|number|string} other Other value\r\n * @returns {boolean}\r\n */\r\nLongPrototype.eq = LongPrototype.equals;\r\n\r\n/**\r\n * Tests if this Long's value differs from the specified's.\r\n * @param {!Long|number|string} other Other value\r\n * @returns {boolean}\r\n */\r\nLongPrototype.notEquals = function notEquals(other) {\r\n return !this.eq(/* validates */ other);\r\n};\r\n\r\n/**\r\n * Tests if this Long's value differs from the specified's. This is an alias of {@link Long#notEquals}.\r\n * @function\r\n * @param {!Long|number|string} other Other value\r\n * @returns {boolean}\r\n */\r\nLongPrototype.neq = LongPrototype.notEquals;\r\n\r\n/**\r\n * Tests if this Long's value differs from the specified's. This is an alias of {@link Long#notEquals}.\r\n * @function\r\n * @param {!Long|number|string} other Other value\r\n * @returns {boolean}\r\n */\r\nLongPrototype.ne = LongPrototype.notEquals;\r\n\r\n/**\r\n * Tests if this Long's value is less than the specified's.\r\n * @param {!Long|number|string} other Other value\r\n * @returns {boolean}\r\n */\r\nLongPrototype.lessThan = function lessThan(other) {\r\n return this.comp(/* validates */ other) < 0;\r\n};\r\n\r\n/**\r\n * Tests if this Long's value is less than the specified's. This is an alias of {@link Long#lessThan}.\r\n * @function\r\n * @param {!Long|number|string} other Other value\r\n * @returns {boolean}\r\n */\r\nLongPrototype.lt = LongPrototype.lessThan;\r\n\r\n/**\r\n * Tests if this Long's value is less than or equal the specified's.\r\n * @param {!Long|number|string} other Other value\r\n * @returns {boolean}\r\n */\r\nLongPrototype.lessThanOrEqual = function lessThanOrEqual(other) {\r\n return this.comp(/* validates */ other) <= 0;\r\n};\r\n\r\n/**\r\n * Tests if this Long's value is less than or equal the specified's. This is an alias of {@link Long#lessThanOrEqual}.\r\n * @function\r\n * @param {!Long|number|string} other Other value\r\n * @returns {boolean}\r\n */\r\nLongPrototype.lte = LongPrototype.lessThanOrEqual;\r\n\r\n/**\r\n * Tests if this Long's value is less than or equal the specified's. This is an alias of {@link Long#lessThanOrEqual}.\r\n * @function\r\n * @param {!Long|number|string} other Other value\r\n * @returns {boolean}\r\n */\r\nLongPrototype.le = LongPrototype.lessThanOrEqual;\r\n\r\n/**\r\n * Tests if this Long's value is greater than the specified's.\r\n * @param {!Long|number|string} other Other value\r\n * @returns {boolean}\r\n */\r\nLongPrototype.greaterThan = function greaterThan(other) {\r\n return this.comp(/* validates */ other) > 0;\r\n};\r\n\r\n/**\r\n * Tests if this Long's value is greater than the specified's. This is an alias of {@link Long#greaterThan}.\r\n * @function\r\n * @param {!Long|number|string} other Other value\r\n * @returns {boolean}\r\n */\r\nLongPrototype.gt = LongPrototype.greaterThan;\r\n\r\n/**\r\n * Tests if this Long's value is greater than or equal the specified's.\r\n * @param {!Long|number|string} other Other value\r\n * @returns {boolean}\r\n */\r\nLongPrototype.greaterThanOrEqual = function greaterThanOrEqual(other) {\r\n return this.comp(/* validates */ other) >= 0;\r\n};\r\n\r\n/**\r\n * Tests if this Long's value is greater than or equal the specified's. This is an alias of {@link Long#greaterThanOrEqual}.\r\n * @function\r\n * @param {!Long|number|string} other Other value\r\n * @returns {boolean}\r\n */\r\nLongPrototype.gte = LongPrototype.greaterThanOrEqual;\r\n\r\n/**\r\n * Tests if this Long's value is greater than or equal the specified's. This is an alias of {@link Long#greaterThanOrEqual}.\r\n * @function\r\n * @param {!Long|number|string} other Other value\r\n * @returns {boolean}\r\n */\r\nLongPrototype.ge = LongPrototype.greaterThanOrEqual;\r\n\r\n/**\r\n * Compares this Long's value with the specified's.\r\n * @param {!Long|number|string} other Other value\r\n * @returns {number} 0 if they are the same, 1 if the this is greater and -1\r\n * if the given one is greater\r\n */\r\nLongPrototype.compare = function compare(other) {\r\n if (!isLong(other))\r\n other = fromValue(other);\r\n if (this.eq(other))\r\n return 0;\r\n var thisNeg = this.isNegative(),\r\n otherNeg = other.isNegative();\r\n if (thisNeg && !otherNeg)\r\n return -1;\r\n if (!thisNeg && otherNeg)\r\n return 1;\r\n // At this point the sign bits are the same\r\n if (!this.unsigned)\r\n return this.sub(other).isNegative() ? -1 : 1;\r\n // Both are positive if at least one is unsigned\r\n return (other.high >>> 0) > (this.high >>> 0) || (other.high === this.high && (other.low >>> 0) > (this.low >>> 0)) ? -1 : 1;\r\n};\r\n\r\n/**\r\n * Compares this Long's value with the specified's. This is an alias of {@link Long#compare}.\r\n * @function\r\n * @param {!Long|number|string} other Other value\r\n * @returns {number} 0 if they are the same, 1 if the this is greater and -1\r\n * if the given one is greater\r\n */\r\nLongPrototype.comp = LongPrototype.compare;\r\n\r\n/**\r\n * Negates this Long's value.\r\n * @returns {!Long} Negated Long\r\n */\r\nLongPrototype.negate = function negate() {\r\n if (!this.unsigned && this.eq(MIN_VALUE))\r\n return MIN_VALUE;\r\n return this.not().add(ONE);\r\n};\r\n\r\n/**\r\n * Negates this Long's value. This is an alias of {@link Long#negate}.\r\n * @function\r\n * @returns {!Long} Negated Long\r\n */\r\nLongPrototype.neg = LongPrototype.negate;\r\n\r\n/**\r\n * Returns the sum of this and the specified Long.\r\n * @param {!Long|number|string} addend Addend\r\n * @returns {!Long} Sum\r\n */\r\nLongPrototype.add = function add(addend) {\r\n if (!isLong(addend))\r\n addend = fromValue(addend);\r\n\r\n // Divide each number into 4 chunks of 16 bits, and then sum the chunks.\r\n\r\n var a48 = this.high >>> 16;\r\n var a32 = this.high & 0xFFFF;\r\n var a16 = this.low >>> 16;\r\n var a00 = this.low & 0xFFFF;\r\n\r\n var b48 = addend.high >>> 16;\r\n var b32 = addend.high & 0xFFFF;\r\n var b16 = addend.low >>> 16;\r\n var b00 = addend.low & 0xFFFF;\r\n\r\n var c48 = 0, c32 = 0, c16 = 0, c00 = 0;\r\n c00 += a00 + b00;\r\n c16 += c00 >>> 16;\r\n c00 &= 0xFFFF;\r\n c16 += a16 + b16;\r\n c32 += c16 >>> 16;\r\n c16 &= 0xFFFF;\r\n c32 += a32 + b32;\r\n c48 += c32 >>> 16;\r\n c32 &= 0xFFFF;\r\n c48 += a48 + b48;\r\n c48 &= 0xFFFF;\r\n return fromBits((c16 << 16) | c00, (c48 << 16) | c32, this.unsigned);\r\n};\r\n\r\n/**\r\n * Returns the difference of this and the specified Long.\r\n * @param {!Long|number|string} subtrahend Subtrahend\r\n * @returns {!Long} Difference\r\n */\r\nLongPrototype.subtract = function subtract(subtrahend) {\r\n if (!isLong(subtrahend))\r\n subtrahend = fromValue(subtrahend);\r\n return this.add(subtrahend.neg());\r\n};\r\n\r\n/**\r\n * Returns the difference of this and the specified Long. This is an alias of {@link Long#subtract}.\r\n * @function\r\n * @param {!Long|number|string} subtrahend Subtrahend\r\n * @returns {!Long} Difference\r\n */\r\nLongPrototype.sub = LongPrototype.subtract;\r\n\r\n/**\r\n * Returns the product of this and the specified Long.\r\n * @param {!Long|number|string} multiplier Multiplier\r\n * @returns {!Long} Product\r\n */\r\nLongPrototype.multiply = function multiply(multiplier) {\r\n if (this.isZero())\r\n return ZERO;\r\n if (!isLong(multiplier))\r\n multiplier = fromValue(multiplier);\r\n\r\n // use wasm support if present\r\n if (wasm) {\r\n var low = wasm.mul(this.low,\r\n this.high,\r\n multiplier.low,\r\n multiplier.high);\r\n return fromBits(low, wasm.get_high(), this.unsigned);\r\n }\r\n\r\n if (multiplier.isZero())\r\n return ZERO;\r\n if (this.eq(MIN_VALUE))\r\n return multiplier.isOdd() ? MIN_VALUE : ZERO;\r\n if (multiplier.eq(MIN_VALUE))\r\n return this.isOdd() ? MIN_VALUE : ZERO;\r\n\r\n if (this.isNegative()) {\r\n if (multiplier.isNegative())\r\n return this.neg().mul(multiplier.neg());\r\n else\r\n return this.neg().mul(multiplier).neg();\r\n } else if (multiplier.isNegative())\r\n return this.mul(multiplier.neg()).neg();\r\n\r\n // If both longs are small, use float multiplication\r\n if (this.lt(TWO_PWR_24) && multiplier.lt(TWO_PWR_24))\r\n return fromNumber(this.toNumber() * multiplier.toNumber(), this.unsigned);\r\n\r\n // Divide each long into 4 chunks of 16 bits, and then add up 4x4 products.\r\n // We can skip products that would overflow.\r\n\r\n var a48 = this.high >>> 16;\r\n var a32 = this.high & 0xFFFF;\r\n var a16 = this.low >>> 16;\r\n var a00 = this.low & 0xFFFF;\r\n\r\n var b48 = multiplier.high >>> 16;\r\n var b32 = multiplier.high & 0xFFFF;\r\n var b16 = multiplier.low >>> 16;\r\n var b00 = multiplier.low & 0xFFFF;\r\n\r\n var c48 = 0, c32 = 0, c16 = 0, c00 = 0;\r\n c00 += a00 * b00;\r\n c16 += c00 >>> 16;\r\n c00 &= 0xFFFF;\r\n c16 += a16 * b00;\r\n c32 += c16 >>> 16;\r\n c16 &= 0xFFFF;\r\n c16 += a00 * b16;\r\n c32 += c16 >>> 16;\r\n c16 &= 0xFFFF;\r\n c32 += a32 * b00;\r\n c48 += c32 >>> 16;\r\n c32 &= 0xFFFF;\r\n c32 += a16 * b16;\r\n c48 += c32 >>> 16;\r\n c32 &= 0xFFFF;\r\n c32 += a00 * b32;\r\n c48 += c32 >>> 16;\r\n c32 &= 0xFFFF;\r\n c48 += a48 * b00 + a32 * b16 + a16 * b32 + a00 * b48;\r\n c48 &= 0xFFFF;\r\n return fromBits((c16 << 16) | c00, (c48 << 16) | c32, this.unsigned);\r\n};\r\n\r\n/**\r\n * Returns the product of this and the specified Long. This is an alias of {@link Long#multiply}.\r\n * @function\r\n * @param {!Long|number|string} multiplier Multiplier\r\n * @returns {!Long} Product\r\n */\r\nLongPrototype.mul = LongPrototype.multiply;\r\n\r\n/**\r\n * Returns this Long divided by the specified. The result is signed if this Long is signed or\r\n * unsigned if this Long is unsigned.\r\n * @param {!Long|number|string} divisor Divisor\r\n * @returns {!Long} Quotient\r\n */\r\nLongPrototype.divide = function divide(divisor) {\r\n if (!isLong(divisor))\r\n divisor = fromValue(divisor);\r\n if (divisor.isZero())\r\n throw Error('division by zero');\r\n\r\n // use wasm support if present\r\n if (wasm) {\r\n // guard against signed division overflow: the largest\r\n // negative number / -1 would be 1 larger than the largest\r\n // positive number, due to two's complement.\r\n if (!this.unsigned &&\r\n this.high === -0x80000000 &&\r\n divisor.low === -1 && divisor.high === -1) {\r\n // be consistent with non-wasm code path\r\n return this;\r\n }\r\n var low = (this.unsigned ? wasm.div_u : wasm.div_s)(\r\n this.low,\r\n this.high,\r\n divisor.low,\r\n divisor.high\r\n );\r\n return fromBits(low, wasm.get_high(), this.unsigned);\r\n }\r\n\r\n if (this.isZero())\r\n return this.unsigned ? UZERO : ZERO;\r\n var approx, rem, res;\r\n if (!this.unsigned) {\r\n // This section is only relevant for signed longs and is derived from the\r\n // closure library as a whole.\r\n if (this.eq(MIN_VALUE)) {\r\n if (divisor.eq(ONE) || divisor.eq(NEG_ONE))\r\n return MIN_VALUE; // recall that -MIN_VALUE == MIN_VALUE\r\n else if (divisor.eq(MIN_VALUE))\r\n return ONE;\r\n else {\r\n // At this point, we have |other| >= 2, so |this/other| < |MIN_VALUE|.\r\n var halfThis = this.shr(1);\r\n approx = halfThis.div(divisor).shl(1);\r\n if (approx.eq(ZERO)) {\r\n return divisor.isNegative() ? ONE : NEG_ONE;\r\n } else {\r\n rem = this.sub(divisor.mul(approx));\r\n res = approx.add(rem.div(divisor));\r\n return res;\r\n }\r\n }\r\n } else if (divisor.eq(MIN_VALUE))\r\n return this.unsigned ? UZERO : ZERO;\r\n if (this.isNegative()) {\r\n if (divisor.isNegative())\r\n return this.neg().div(divisor.neg());\r\n return this.neg().div(divisor).neg();\r\n } else if (divisor.isNegative())\r\n return this.div(divisor.neg()).neg();\r\n res = ZERO;\r\n } else {\r\n // The algorithm below has not been made for unsigned longs. It's therefore\r\n // required to take special care of the MSB prior to running it.\r\n if (!divisor.unsigned)\r\n divisor = divisor.toUnsigned();\r\n if (divisor.gt(this))\r\n return UZERO;\r\n if (divisor.gt(this.shru(1))) // 15 >>> 1 = 7 ; with divisor = 8 ; true\r\n return UONE;\r\n res = UZERO;\r\n }\r\n\r\n // Repeat the following until the remainder is less than other: find a\r\n // floating-point that approximates remainder / other *from below*, add this\r\n // into the result, and subtract it from the remainder. It is critical that\r\n // the approximate value is less than or equal to the real value so that the\r\n // remainder never becomes negative.\r\n rem = this;\r\n while (rem.gte(divisor)) {\r\n // Approximate the result of division. This may be a little greater or\r\n // smaller than the actual value.\r\n approx = Math.max(1, Math.floor(rem.toNumber() / divisor.toNumber()));\r\n\r\n // We will tweak the approximate result by changing it in the 48-th digit or\r\n // the smallest non-fractional digit, whichever is larger.\r\n var log2 = Math.ceil(Math.log(approx) / Math.LN2),\r\n delta = (log2 <= 48) ? 1 : pow_dbl(2, log2 - 48),\r\n\r\n // Decrease the approximation until it is smaller than the remainder. Note\r\n // that if it is too large, the product overflows and is negative.\r\n approxRes = fromNumber(approx),\r\n approxRem = approxRes.mul(divisor);\r\n while (approxRem.isNegative() || approxRem.gt(rem)) {\r\n approx -= delta;\r\n approxRes = fromNumber(approx, this.unsigned);\r\n approxRem = approxRes.mul(divisor);\r\n }\r\n\r\n // We know the answer can't be zero... and actually, zero would cause\r\n // infinite recursion since we would make no progress.\r\n if (approxRes.isZero())\r\n approxRes = ONE;\r\n\r\n res = res.add(approxRes);\r\n rem = rem.sub(approxRem);\r\n }\r\n return res;\r\n};\r\n\r\n/**\r\n * Returns this Long divided by the specified. This is an alias of {@link Long#divide}.\r\n * @function\r\n * @param {!Long|number|string} divisor Divisor\r\n * @returns {!Long} Quotient\r\n */\r\nLongPrototype.div = LongPrototype.divide;\r\n\r\n/**\r\n * Returns this Long modulo the specified.\r\n * @param {!Long|number|string} divisor Divisor\r\n * @returns {!Long} Remainder\r\n */\r\nLongPrototype.modulo = function modulo(divisor) {\r\n if (!isLong(divisor))\r\n divisor = fromValue(divisor);\r\n\r\n // use wasm support if present\r\n if (wasm) {\r\n var low = (this.unsigned ? wasm.rem_u : wasm.rem_s)(\r\n this.low,\r\n this.high,\r\n divisor.low,\r\n divisor.high\r\n );\r\n return fromBits(low, wasm.get_high(), this.unsigned);\r\n }\r\n\r\n return this.sub(this.div(divisor).mul(divisor));\r\n};\r\n\r\n/**\r\n * Returns this Long modulo the specified. This is an alias of {@link Long#modulo}.\r\n * @function\r\n * @param {!Long|number|string} divisor Divisor\r\n * @returns {!Long} Remainder\r\n */\r\nLongPrototype.mod = LongPrototype.modulo;\r\n\r\n/**\r\n * Returns this Long modulo the specified. This is an alias of {@link Long#modulo}.\r\n * @function\r\n * @param {!Long|number|string} divisor Divisor\r\n * @returns {!Long} Remainder\r\n */\r\nLongPrototype.rem = LongPrototype.modulo;\r\n\r\n/**\r\n * Returns the bitwise NOT of this Long.\r\n * @returns {!Long}\r\n */\r\nLongPrototype.not = function not() {\r\n return fromBits(~this.low, ~this.high, this.unsigned);\r\n};\r\n\r\n/**\r\n * Returns the bitwise AND of this Long and the specified.\r\n * @param {!Long|number|string} other Other Long\r\n * @returns {!Long}\r\n */\r\nLongPrototype.and = function and(other) {\r\n if (!isLong(other))\r\n other = fromValue(other);\r\n return fromBits(this.low & other.low, this.high & other.high, this.unsigned);\r\n};\r\n\r\n/**\r\n * Returns the bitwise OR of this Long and the specified.\r\n * @param {!Long|number|string} other Other Long\r\n * @returns {!Long}\r\n */\r\nLongPrototype.or = function or(other) {\r\n if (!isLong(other))\r\n other = fromValue(other);\r\n return fromBits(this.low | other.low, this.high | other.high, this.unsigned);\r\n};\r\n\r\n/**\r\n * Returns the bitwise XOR of this Long and the given one.\r\n * @param {!Long|number|string} other Other Long\r\n * @returns {!Long}\r\n */\r\nLongPrototype.xor = function xor(other) {\r\n if (!isLong(other))\r\n other = fromValue(other);\r\n return fromBits(this.low ^ other.low, this.high ^ other.high, this.unsigned);\r\n};\r\n\r\n/**\r\n * Returns this Long with bits shifted to the left by the given amount.\r\n * @param {number|!Long} numBits Number of bits\r\n * @returns {!Long} Shifted Long\r\n */\r\nLongPrototype.shiftLeft = function shiftLeft(numBits) {\r\n if (isLong(numBits))\r\n numBits = numBits.toInt();\r\n if ((numBits &= 63) === 0)\r\n return this;\r\n else if (numBits < 32)\r\n return fromBits(this.low << numBits, (this.high << numBits) | (this.low >>> (32 - numBits)), this.unsigned);\r\n else\r\n return fromBits(0, this.low << (numBits - 32), this.unsigned);\r\n};\r\n\r\n/**\r\n * Returns this Long with bits shifted to the left by the given amount. This is an alias of {@link Long#shiftLeft}.\r\n * @function\r\n * @param {number|!Long} numBits Number of bits\r\n * @returns {!Long} Shifted Long\r\n */\r\nLongPrototype.shl = LongPrototype.shiftLeft;\r\n\r\n/**\r\n * Returns this Long with bits arithmetically shifted to the right by the given amount.\r\n * @param {number|!Long} numBits Number of bits\r\n * @returns {!Long} Shifted Long\r\n */\r\nLongPrototype.shiftRight = function shiftRight(numBits) {\r\n if (isLong(numBits))\r\n numBits = numBits.toInt();\r\n if ((numBits &= 63) === 0)\r\n return this;\r\n else if (numBits < 32)\r\n return fromBits((this.low >>> numBits) | (this.high << (32 - numBits)), this.high >> numBits, this.unsigned);\r\n else\r\n return fromBits(this.high >> (numBits - 32), this.high >= 0 ? 0 : -1, this.unsigned);\r\n};\r\n\r\n/**\r\n * Returns this Long with bits arithmetically shifted to the right by the given amount. This is an alias of {@link Long#shiftRight}.\r\n * @function\r\n * @param {number|!Long} numBits Number of bits\r\n * @returns {!Long} Shifted Long\r\n */\r\nLongPrototype.shr = LongPrototype.shiftRight;\r\n\r\n/**\r\n * Returns this Long with bits logically shifted to the right by the given amount.\r\n * @param {number|!Long} numBits Number of bits\r\n * @returns {!Long} Shifted Long\r\n */\r\nLongPrototype.shiftRightUnsigned = function shiftRightUnsigned(numBits) {\r\n if (isLong(numBits))\r\n numBits = numBits.toInt();\r\n numBits &= 63;\r\n if (numBits === 0)\r\n return this;\r\n else {\r\n var high = this.high;\r\n if (numBits < 32) {\r\n var low = this.low;\r\n return fromBits((low >>> numBits) | (high << (32 - numBits)), high >>> numBits, this.unsigned);\r\n } else if (numBits === 32)\r\n return fromBits(high, 0, this.unsigned);\r\n else\r\n return fromBits(high >>> (numBits - 32), 0, this.unsigned);\r\n }\r\n};\r\n\r\n/**\r\n * Returns this Long with bits logically shifted to the right by the given amount. This is an alias of {@link Long#shiftRightUnsigned}.\r\n * @function\r\n * @param {number|!Long} numBits Number of bits\r\n * @returns {!Long} Shifted Long\r\n */\r\nLongPrototype.shru = LongPrototype.shiftRightUnsigned;\r\n\r\n/**\r\n * Returns this Long with bits logically shifted to the right by the given amount. This is an alias of {@link Long#shiftRightUnsigned}.\r\n * @function\r\n * @param {number|!Long} numBits Number of bits\r\n * @returns {!Long} Shifted Long\r\n */\r\nLongPrototype.shr_u = LongPrototype.shiftRightUnsigned;\r\n\r\n/**\r\n * Converts this Long to signed.\r\n * @returns {!Long} Signed long\r\n */\r\nLongPrototype.toSigned = function toSigned() {\r\n if (!this.unsigned)\r\n return this;\r\n return fromBits(this.low, this.high, false);\r\n};\r\n\r\n/**\r\n * Converts this Long to unsigned.\r\n * @returns {!Long} Unsigned long\r\n */\r\nLongPrototype.toUnsigned = function toUnsigned() {\r\n if (this.unsigned)\r\n return this;\r\n return fromBits(this.low, this.high, true);\r\n};\r\n\r\n/**\r\n * Converts this Long to its byte representation.\r\n * @param {boolean=} le Whether little or big endian, defaults to big endian\r\n * @returns {!Array.} Byte representation\r\n */\r\nLongPrototype.toBytes = function toBytes(le) {\r\n return le ? this.toBytesLE() : this.toBytesBE();\r\n};\r\n\r\n/**\r\n * Converts this Long to its little endian byte representation.\r\n * @returns {!Array.} Little endian byte representation\r\n */\r\nLongPrototype.toBytesLE = function toBytesLE() {\r\n var hi = this.high,\r\n lo = this.low;\r\n return [\r\n lo & 0xff,\r\n lo >>> 8 & 0xff,\r\n lo >>> 16 & 0xff,\r\n lo >>> 24 ,\r\n hi & 0xff,\r\n hi >>> 8 & 0xff,\r\n hi >>> 16 & 0xff,\r\n hi >>> 24\r\n ];\r\n};\r\n\r\n/**\r\n * Converts this Long to its big endian byte representation.\r\n * @returns {!Array.} Big endian byte representation\r\n */\r\nLongPrototype.toBytesBE = function toBytesBE() {\r\n var hi = this.high,\r\n lo = this.low;\r\n return [\r\n hi >>> 24 ,\r\n hi >>> 16 & 0xff,\r\n hi >>> 8 & 0xff,\r\n hi & 0xff,\r\n lo >>> 24 ,\r\n lo >>> 16 & 0xff,\r\n lo >>> 8 & 0xff,\r\n lo & 0xff\r\n ];\r\n};\r\n\r\n/**\r\n * Creates a Long from its byte representation.\r\n * @param {!Array.} bytes Byte representation\r\n * @param {boolean=} unsigned Whether unsigned or not, defaults to signed\r\n * @param {boolean=} le Whether little or big endian, defaults to big endian\r\n * @returns {Long} The corresponding Long value\r\n */\r\nLong.fromBytes = function fromBytes(bytes, unsigned, le) {\r\n return le ? Long.fromBytesLE(bytes, unsigned) : Long.fromBytesBE(bytes, unsigned);\r\n};\r\n\r\n/**\r\n * Creates a Long from its little endian byte representation.\r\n * @param {!Array.} bytes Little endian byte representation\r\n * @param {boolean=} unsigned Whether unsigned or not, defaults to signed\r\n * @returns {Long} The corresponding Long value\r\n */\r\nLong.fromBytesLE = function fromBytesLE(bytes, unsigned) {\r\n return new Long(\r\n bytes[0] |\r\n bytes[1] << 8 |\r\n bytes[2] << 16 |\r\n bytes[3] << 24,\r\n bytes[4] |\r\n bytes[5] << 8 |\r\n bytes[6] << 16 |\r\n bytes[7] << 24,\r\n unsigned\r\n );\r\n};\r\n\r\n/**\r\n * Creates a Long from its big endian byte representation.\r\n * @param {!Array.} bytes Big endian byte representation\r\n * @param {boolean=} unsigned Whether unsigned or not, defaults to signed\r\n * @returns {Long} The corresponding Long value\r\n */\r\nLong.fromBytesBE = function fromBytesBE(bytes, unsigned) {\r\n return new Long(\r\n bytes[4] << 24 |\r\n bytes[5] << 16 |\r\n bytes[6] << 8 |\r\n bytes[7],\r\n bytes[0] << 24 |\r\n bytes[1] << 16 |\r\n bytes[2] << 8 |\r\n bytes[3],\r\n unsigned\r\n );\r\n};\r\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv3DBackpropInputV2 } from '../kernel_names';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the derivative of the input of a 3D convolution.\n *\n * @param xShape The shape of the input: [batch, depth, height, width,\n * in_channels]. If length of 4, batch of 1 is assumed.\n * @param dy The derivative of the output, of rank 5 or rank 4 of shape\n * `[batch, outDepth, outHeight, outWidth, in_channels]`.\n * If rank 4, batch of 1 is assumed.\n * @param filter The filter, rank 5, of shape\n * `[filterDepth, filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideDepth, strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm used:\n * - `same` and stride 1: output will be of same size as input,\n * regardless of filter size.\n * - `valid`: output will be smaller than input if filter is larger\n * than 1x1.\n */\nfunction conv3DBackpropInput_(xShape, dy, filter, strides, pad) {\n util.assert(xShape.length === dy.rank, () => `Length of inShape ` +\n `(${xShape.length}) and rank of dy (${dy.rank}) must match`);\n let xShape5D = xShape;\n let dy5D = dy;\n let reshapedTo5D = false;\n if (dy.rank === 4) {\n reshapedTo5D = true;\n dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);\n xShape5D = [1, xShape[0], xShape[1], xShape[2], xShape[3]];\n }\n const inDepth = xShape5D[4];\n const outDepth = dy5D.shape[4];\n util.assert(xShape5D.length === 5, () => `Error in conv3dDerInput: inShape must be length 5, but got length ` +\n `${xShape5D.length}.`);\n util.assert(dy5D.rank === 5, () => `Error in conv3dDerInput: dy must be rank 5, but got ` +\n `rank ${dy5D.rank}`);\n util.assert(filter.rank === 5, () => `Error in conv3dDerInput: filter must be rank 5, but got ` +\n `rank ${filter.rank}`);\n util.assert(inDepth === filter.shape[3], () => `Error in conv3dDerInput: depth of input (${inDepth}) must ` +\n `match input depth for filter ${filter.shape[3]}.`);\n util.assert(outDepth === filter.shape[4], () => `Error in conv3dDerInput: depth of output (${outDepth}) must ` +\n `match output depth for filter ${filter.shape[4]}.`);\n const inputs = { dy: dy5D, filter };\n const attrs = { pad, strides, inputShape: xShape5D };\n // tslint:disable-next-line: no-unnecessary-type-assertion\n const res = ENGINE.runKernel(Conv3DBackpropInputV2, inputs, attrs);\n if (reshapedTo5D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n }\n return res;\n}\nexport const conv3DBackpropInput = op({ conv3DBackpropInput_ });\n//# sourceMappingURL=data:application/json;base64,{"version":3,"file":"conv3d_backprop_input.js","sourceRoot":"","sources":["../../../../../../tfjs-core/src/ops/conv3d_backprop_input.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AACH,OAAO,EAAC,MAAM,EAAC,MAAM,WAAW,CAAC;AACjC,OAAO,EAAC,qBAAqB,EAA0D,MAAM,iBAAiB,CAAC;AAI/G,OAAO,KAAK,IAAI,MAAM,SAAS,CAAC;AAEhC,OAAO,EAAC,EAAE,EAAC,MAAM,aAAa,CAAC;AAC/B,OAAO,EAAC,OAAO,EAAC,MAAM,WAAW,CAAC;AAElC;;;;;;;;;;;;;;;;;GAiBG;AACH,SAAS,oBAAoB,CACzB,MAE6C,EAC7C,EAAK,EAAE,MAAgB,EAAE,OAAwC,EACjE,GAAmB;IACrB,IAAI,CAAC,MAAM,CACP,MAAM,CAAC,MAAM,KAAK,EAAE,CAAC,IAAI,EACzB,GAAG,EAAE,CAAC,oBAAoB;QACtB,IAAI,MAAM,CAAC,MAAM,qBAAqB,EAAE,CAAC,IAAI,cAAc,CAAC,CAAC;IAErE,IAAI,QAAQ,GAAG,MAAkD,CAAC;IAClE,IAAI,IAAI,GAAG,EAAc,CAAC;IAC1B,IAAI,YAAY,GAAG,KAAK,CAAC;IACzB,IAAI,EAAE,CAAC,IAAI,KAAK,CAAC,EAAE;QACjB,YAAY,GAAG,IAAI,CAAC;QACpB,IAAI,GAAG,OAAO,CAAC,EAAE,EAAE,CAAC,CAAC,EAAE,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;QAC5E,QAAQ,GAAG,CAAC,CAAC,EAAE,MAAM,CAAC,CAAC,CAAC,EAAE,MAAM,CAAC,CAAC,CAAC,EAAE,MAAM,CAAC,CAAC,CAAC,EAAE,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC;KAC5D;IAED,MAAM,OAAO,GAAG,QAAQ,CAAC,CAAC,CAAC,CAAC;IAC5B,MAAM,QAAQ,GAAG,IAAI,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC;IAC/B,IAAI,CAAC,MAAM,CACP,QAAQ,CAAC,MAAM,KAAK,CAAC,EACrB,GAAG,EAAE,CACD,oEAAoE;QACpE,GAAG,QAAQ,CAAC,MAAM,GAAG,CAAC,CAAC;IAC/B,IAAI,CAAC,MAAM,CACP,IAAI,CAAC,IAAI,KAAK,CAAC,EACf,GAAG,EAAE,CAAC,sDAAsD;QACxD,QAAQ,IAAI,CAAC,IAAI,EAAE,CAAC,CAAC;IAC7B,IAAI,CAAC,MAAM,CACP,MAAM,CAAC,IAAI,KAAK,CAAC,EACjB,GAAG,EAAE,CAAC,0DAA0D;QAC5D,QAAQ,MAAM,CAAC,IAAI,EAAE,CAAC,CAAC;IAC/B,IAAI,CAAC,MAAM,CACP,OAAO,KAAK,MAAM,CAAC,KAAK,CAAC,CAAC,CAAC,EAC3B,GAAG,EAAE,CAAC,4CAA4C,OAAO,SAAS;QAC9D,gCAAgC,MAAM,CAAC,KAAK,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC;IAC5D,IAAI,CAAC,MAAM,CACP,QAAQ,KAAK,MAAM,CAAC,KAAK,CAAC,CAAC,CAAC,EAC5B,GAAG,EAAE,CAAC,6CAA6C,QAAQ,SAAS;QAChE,iCAAiC,MAAM,CAAC,KAAK,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC;IAE7D,MAAM,MAAM,GAAgC,EAAC,EAAE,EAAE,IAAI,EAAE,MAAM,EAAC,CAAC;IAE/D,MAAM,KAAK,GACsB,EAAC,GAAG,EAAE,OAAO,EAAE,UAAU,EAAE,QAAQ,EAAC,CAAC;IAEtE,0DAA0D;IAC1D,MAAM,GAAG,GAAG,MAAM,CAAC,SAAS,CACZ,qBAAqB,EAAE,MAA8B,EACrD,KAA2B,CAAM,CAAC;IAElD,IAAI,YAAY,EAAE;QAChB,OAAO,OAAO,CACH,GAAG,EAAE,CAAC,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CACnE,CAAC;KACP;IACD,OAAO,GAAG,CAAC;AACb,CAAC;AAED,MAAM,CAAC,MAAM,mBAAmB,GAAG,EAAE,CAAC,EAAC,oBAAoB,EAAC,CAAC,CAAC","sourcesContent":["/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\nimport {Conv3DBackpropInputV2, Conv3DBackpropInputV2Attrs, Conv3DBackpropInputV2Inputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor4D, Tensor5D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport * as util from '../util';\n\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Computes the derivative of the input of a 3D convolution.\n *\n * @param xShape The shape of the input: [batch, depth, height, width,\n * in_channels]. If length of 4, batch of 1 is assumed.\n * @param dy The derivative of the output, of rank 5 or rank 4 of shape\n *   `[batch, outDepth, outHeight, outWidth, in_channels]`.\n * If rank 4, batch of 1 is assumed.\n * @param filter The filter, rank 5, of shape\n *     `[filterDepth, filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideDepth, strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm used:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n */\nfunction conv3DBackpropInput_<T extends Tensor4D|Tensor5D>(\n    xShape:\n        [number, number, number, number,\n         number]|[number, number, number, number],\n    dy: T, filter: Tensor5D, strides: [number, number, number]|number,\n    pad: 'valid'|'same'): T {\n  util.assert(\n      xShape.length === dy.rank,\n      () => `Length of inShape ` +\n          `(${xShape.length}) and rank of dy (${dy.rank}) must match`);\n\n  let xShape5D = xShape as [number, number, number, number, number];\n  let dy5D = dy as Tensor5D;\n  let reshapedTo5D = false;\n  if (dy.rank === 4) {\n    reshapedTo5D = true;\n    dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);\n    xShape5D = [1, xShape[0], xShape[1], xShape[2], xShape[3]];\n  }\n\n  const inDepth = xShape5D[4];\n  const outDepth = dy5D.shape[4];\n  util.assert(\n      xShape5D.length === 5,\n      () =>\n          `Error in conv3dDerInput: inShape must be length 5, but got length ` +\n          `${xShape5D.length}.`);\n  util.assert(\n      dy5D.rank === 5,\n      () => `Error in conv3dDerInput: dy must be rank 5, but got ` +\n          `rank ${dy5D.rank}`);\n  util.assert(\n      filter.rank === 5,\n      () => `Error in conv3dDerInput: filter must be rank 5, but got ` +\n          `rank ${filter.rank}`);\n  util.assert(\n      inDepth === filter.shape[3],\n      () => `Error in conv3dDerInput: depth of input (${inDepth}) must ` +\n          `match input depth for filter ${filter.shape[3]}.`);\n  util.assert(\n      outDepth === filter.shape[4],\n      () => `Error in conv3dDerInput: depth of output (${outDepth}) must ` +\n          `match output depth for filter ${filter.shape[4]}.`);\n\n  const inputs: Conv3DBackpropInputV2Inputs = {dy: dy5D, filter};\n\n  const attrs:\n      Conv3DBackpropInputV2Attrs = {pad, strides, inputShape: xShape5D};\n\n  // tslint:disable-next-line: no-unnecessary-type-assertion\n  const res = ENGINE.runKernel(\n                  Conv3DBackpropInputV2, inputs as {} as NamedTensorMap,\n                  attrs as {} as NamedAttrMap) as T;\n\n  if (reshapedTo5D) {\n    return reshape(\n               res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]) as\n        T;\n  }\n  return res;\n}\n\nexport const conv3DBackpropInput = op({conv3DBackpropInput_});\n"]}","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { DepthwiseConv2dNativeBackpropInput } from '../kernel_names';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nfunction depthwiseConv2dNativeBackpropInput_(xShape, dy, filter, strides, pad, dilations = [1, 1], dimRoundingMode) {\n let dy4D = dy;\n let reshapedTo4D = false;\n if (dy.rank === 3) {\n reshapedTo4D = true;\n dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n }\n const inputs = { dy: dy4D, filter };\n const attrs = { strides, pad, dimRoundingMode, dilations, inputShape: xShape };\n const res = \n // tslint:disable-next-line: no-unnecessary-type-assertion\n ENGINE.runKernel(DepthwiseConv2dNativeBackpropInput, inputs, attrs);\n if (reshapedTo4D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n }\n return res;\n}\nexport const depthwiseConv2dNativeBackpropInput = op({ depthwiseConv2dNativeBackpropInput_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { DepthwiseConv2dNativeBackpropFilter } from '../kernel_names';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nfunction depthwiseConv2dNativeBackpropFilter_(x, dy, filterShape, strides, pad, dilations = [1, 1], dimRoundingMode) {\n let x4D = x;\n if (x.rank === 3) {\n x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n }\n let dy4D = dy;\n if (dy4D.rank === 3) {\n dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n }\n const inputs = { x: x4D, dy: dy4D };\n const attrs = { strides, pad, dimRoundingMode, dilations, filterShape };\n // tslint:disable-next-line: no-unnecessary-type-assertion\n return ENGINE.runKernel(DepthwiseConv2dNativeBackpropFilter, inputs, attrs);\n}\nexport const depthwiseConv2dNativeBackpropFilter = op({ depthwiseConv2dNativeBackpropFilter_ });\n//# sourceMappingURL=data:application/json;base64,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","// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random(); // 0 <= x < 1. Every bit is random.\n// var x = random.quick(); // 0 <= x < 1. 32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baagøe.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by François Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three. It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures. Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { complex } from '../kernels/Complex';\n/**\n * Generates a tensorInfo with all zeros value.\n * @param backend cpu backend.\n * @param shape Shape for the zeros tensor.\n * @param dtype Optional. If set, the result has this dtype.\n */\nexport function zeros(backend, shape, dtype = 'float32') {\n if (dtype === 'complex64') {\n const real = zeros(backend, shape, 'float32');\n const imag = zeros(backend, shape, 'float32');\n return complex({ inputs: { real, imag }, backend });\n }\n const values = util.makeZerosTypedArray(util.sizeFromShape(shape), dtype);\n return backend.makeTensorInfo(shape, dtype, values);\n}\n//# sourceMappingURL=data:application/json;base64,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","// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random(); // 0 <= x < 1. Every bit is random.\n// var x = random.quick(); // 0 <= x < 1. 32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baagøe.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by François Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three. It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures. Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n","/*!\n * The buffer module from node.js, for the browser.\n *\n * @author Feross Aboukhadijeh \n * @license MIT\n */\n/* eslint-disable no-proto */\n\n'use strict'\n\nvar base64 = require('base64-js')\nvar ieee754 = require('ieee754')\nvar isArray = require('isarray')\n\nexports.Buffer = Buffer\nexports.SlowBuffer = SlowBuffer\nexports.INSPECT_MAX_BYTES = 50\n\n/**\n * If `Buffer.TYPED_ARRAY_SUPPORT`:\n * === true Use Uint8Array implementation (fastest)\n * === false Use Object implementation (most compatible, even IE6)\n *\n * Browsers that support typed arrays are IE 10+, Firefox 4+, Chrome 7+, Safari 5.1+,\n * Opera 11.6+, iOS 4.2+.\n *\n * Due to various browser bugs, sometimes the Object implementation will be used even\n * when the browser supports typed arrays.\n *\n * Note:\n *\n * - Firefox 4-29 lacks support for adding new properties to `Uint8Array` instances,\n * See: https://bugzilla.mozilla.org/show_bug.cgi?id=695438.\n *\n * - Chrome 9-10 is missing the `TypedArray.prototype.subarray` function.\n *\n * - IE10 has a broken `TypedArray.prototype.subarray` function which returns arrays of\n * incorrect length in some situations.\n\n * We detect these buggy browsers and set `Buffer.TYPED_ARRAY_SUPPORT` to `false` so they\n * get the Object implementation, which is slower but behaves correctly.\n */\nBuffer.TYPED_ARRAY_SUPPORT = global.TYPED_ARRAY_SUPPORT !== undefined\n ? global.TYPED_ARRAY_SUPPORT\n : typedArraySupport()\n\n/*\n * Export kMaxLength after typed array support is determined.\n */\nexports.kMaxLength = kMaxLength()\n\nfunction typedArraySupport () {\n try {\n var arr = new Uint8Array(1)\n arr.__proto__ = {__proto__: Uint8Array.prototype, foo: function () { return 42 }}\n return arr.foo() === 42 && // typed array instances can be augmented\n typeof arr.subarray === 'function' && // chrome 9-10 lack `subarray`\n arr.subarray(1, 1).byteLength === 0 // ie10 has broken `subarray`\n } catch (e) {\n return false\n }\n}\n\nfunction kMaxLength () {\n return Buffer.TYPED_ARRAY_SUPPORT\n ? 0x7fffffff\n : 0x3fffffff\n}\n\nfunction createBuffer (that, length) {\n if (kMaxLength() < length) {\n throw new RangeError('Invalid typed array length')\n }\n if (Buffer.TYPED_ARRAY_SUPPORT) {\n // Return an augmented `Uint8Array` instance, for best performance\n that = new Uint8Array(length)\n that.__proto__ = Buffer.prototype\n } else {\n // Fallback: Return an object instance of the Buffer class\n if (that === null) {\n that = new Buffer(length)\n }\n that.length = length\n }\n\n return that\n}\n\n/**\n * The Buffer constructor returns instances of `Uint8Array` that have their\n * prototype changed to `Buffer.prototype`. Furthermore, `Buffer` is a subclass of\n * `Uint8Array`, so the returned instances will have all the node `Buffer` methods\n * and the `Uint8Array` methods. Square bracket notation works as expected -- it\n * returns a single octet.\n *\n * The `Uint8Array` prototype remains unmodified.\n */\n\nfunction Buffer (arg, encodingOrOffset, length) {\n if (!Buffer.TYPED_ARRAY_SUPPORT && !(this instanceof Buffer)) {\n return new Buffer(arg, encodingOrOffset, length)\n }\n\n // Common case.\n if (typeof arg === 'number') {\n if (typeof encodingOrOffset === 'string') {\n throw new Error(\n 'If encoding is specified then the first argument must be a string'\n )\n }\n return allocUnsafe(this, arg)\n }\n return from(this, arg, encodingOrOffset, length)\n}\n\nBuffer.poolSize = 8192 // not used by this implementation\n\n// TODO: Legacy, not needed anymore. Remove in next major version.\nBuffer._augment = function (arr) {\n arr.__proto__ = Buffer.prototype\n return arr\n}\n\nfunction from (that, value, encodingOrOffset, length) {\n if (typeof value === 'number') {\n throw new TypeError('\"value\" argument must not be a number')\n }\n\n if (typeof ArrayBuffer !== 'undefined' && value instanceof ArrayBuffer) {\n return fromArrayBuffer(that, value, encodingOrOffset, length)\n }\n\n if (typeof value === 'string') {\n return fromString(that, value, encodingOrOffset)\n }\n\n return fromObject(that, value)\n}\n\n/**\n * Functionally equivalent to Buffer(arg, encoding) but throws a TypeError\n * if value is a number.\n * Buffer.from(str[, encoding])\n * Buffer.from(array)\n * Buffer.from(buffer)\n * Buffer.from(arrayBuffer[, byteOffset[, length]])\n **/\nBuffer.from = function (value, encodingOrOffset, length) {\n return from(null, value, encodingOrOffset, length)\n}\n\nif (Buffer.TYPED_ARRAY_SUPPORT) {\n Buffer.prototype.__proto__ = Uint8Array.prototype\n Buffer.__proto__ = Uint8Array\n if (typeof Symbol !== 'undefined' && Symbol.species &&\n Buffer[Symbol.species] === Buffer) {\n // Fix subarray() in ES2016. See: https://github.com/feross/buffer/pull/97\n Object.defineProperty(Buffer, Symbol.species, {\n value: null,\n configurable: true\n })\n }\n}\n\nfunction assertSize (size) {\n if (typeof size !== 'number') {\n throw new TypeError('\"size\" argument must be a number')\n } else if (size < 0) {\n throw new RangeError('\"size\" argument must not be negative')\n }\n}\n\nfunction alloc (that, size, fill, encoding) {\n assertSize(size)\n if (size <= 0) {\n return createBuffer(that, size)\n }\n if (fill !== undefined) {\n // Only pay attention to encoding if it's a string. This\n // prevents accidentally sending in a number that would\n // be interpretted as a start offset.\n return typeof encoding === 'string'\n ? createBuffer(that, size).fill(fill, encoding)\n : createBuffer(that, size).fill(fill)\n }\n return createBuffer(that, size)\n}\n\n/**\n * Creates a new filled Buffer instance.\n * alloc(size[, fill[, encoding]])\n **/\nBuffer.alloc = function (size, fill, encoding) {\n return alloc(null, size, fill, encoding)\n}\n\nfunction allocUnsafe (that, size) {\n assertSize(size)\n that = createBuffer(that, size < 0 ? 0 : checked(size) | 0)\n if (!Buffer.TYPED_ARRAY_SUPPORT) {\n for (var i = 0; i < size; ++i) {\n that[i] = 0\n }\n }\n return that\n}\n\n/**\n * Equivalent to Buffer(num), by default creates a non-zero-filled Buffer instance.\n * */\nBuffer.allocUnsafe = function (size) {\n return allocUnsafe(null, size)\n}\n/**\n * Equivalent to SlowBuffer(num), by default creates a non-zero-filled Buffer instance.\n */\nBuffer.allocUnsafeSlow = function (size) {\n return allocUnsafe(null, size)\n}\n\nfunction fromString (that, string, encoding) {\n if (typeof encoding !== 'string' || encoding === '') {\n encoding = 'utf8'\n }\n\n if (!Buffer.isEncoding(encoding)) {\n throw new TypeError('\"encoding\" must be a valid string encoding')\n }\n\n var length = byteLength(string, encoding) | 0\n that = createBuffer(that, length)\n\n var actual = that.write(string, encoding)\n\n if (actual !== length) {\n // Writing a hex string, for example, that contains invalid characters will\n // cause everything after the first invalid character to be ignored. (e.g.\n // 'abxxcd' will be treated as 'ab')\n that = that.slice(0, actual)\n }\n\n return that\n}\n\nfunction fromArrayLike (that, array) {\n var length = array.length < 0 ? 0 : checked(array.length) | 0\n that = createBuffer(that, length)\n for (var i = 0; i < length; i += 1) {\n that[i] = array[i] & 255\n }\n return that\n}\n\nfunction fromArrayBuffer (that, array, byteOffset, length) {\n array.byteLength // this throws if `array` is not a valid ArrayBuffer\n\n if (byteOffset < 0 || array.byteLength < byteOffset) {\n throw new RangeError('\\'offset\\' is out of bounds')\n }\n\n if (array.byteLength < byteOffset + (length || 0)) {\n throw new RangeError('\\'length\\' is out of bounds')\n }\n\n if (byteOffset === undefined && length === undefined) {\n array = new Uint8Array(array)\n } else if (length === undefined) {\n array = new Uint8Array(array, byteOffset)\n } else {\n array = new Uint8Array(array, byteOffset, length)\n }\n\n if (Buffer.TYPED_ARRAY_SUPPORT) {\n // Return an augmented `Uint8Array` instance, for best performance\n that = array\n that.__proto__ = Buffer.prototype\n } else {\n // Fallback: Return an object instance of the Buffer class\n that = fromArrayLike(that, array)\n }\n return that\n}\n\nfunction fromObject (that, obj) {\n if (Buffer.isBuffer(obj)) {\n var len = checked(obj.length) | 0\n that = createBuffer(that, len)\n\n if (that.length === 0) {\n return that\n }\n\n obj.copy(that, 0, 0, len)\n return that\n }\n\n if (obj) {\n if ((typeof ArrayBuffer !== 'undefined' &&\n obj.buffer instanceof ArrayBuffer) || 'length' in obj) {\n if (typeof obj.length !== 'number' || isnan(obj.length)) {\n return createBuffer(that, 0)\n }\n return fromArrayLike(that, obj)\n }\n\n if (obj.type === 'Buffer' && isArray(obj.data)) {\n return fromArrayLike(that, obj.data)\n }\n }\n\n throw new TypeError('First argument must be a string, Buffer, ArrayBuffer, Array, or array-like object.')\n}\n\nfunction checked (length) {\n // Note: cannot use `length < kMaxLength()` here because that fails when\n // length is NaN (which is otherwise coerced to zero.)\n if (length >= kMaxLength()) {\n throw new RangeError('Attempt to allocate Buffer larger than maximum ' +\n 'size: 0x' + kMaxLength().toString(16) + ' bytes')\n }\n return length | 0\n}\n\nfunction SlowBuffer (length) {\n if (+length != length) { // eslint-disable-line eqeqeq\n length = 0\n }\n return Buffer.alloc(+length)\n}\n\nBuffer.isBuffer = function isBuffer (b) {\n return !!(b != null && b._isBuffer)\n}\n\nBuffer.compare = function compare (a, b) {\n if (!Buffer.isBuffer(a) || !Buffer.isBuffer(b)) {\n throw new TypeError('Arguments must be Buffers')\n }\n\n if (a === b) return 0\n\n var x = a.length\n var y = b.length\n\n for (var i = 0, len = Math.min(x, y); i < len; ++i) {\n if (a[i] !== b[i]) {\n x = a[i]\n y = b[i]\n break\n }\n }\n\n if (x < y) return -1\n if (y < x) return 1\n return 0\n}\n\nBuffer.isEncoding = function isEncoding (encoding) {\n switch (String(encoding).toLowerCase()) {\n case 'hex':\n case 'utf8':\n case 'utf-8':\n case 'ascii':\n case 'latin1':\n case 'binary':\n case 'base64':\n case 'ucs2':\n case 'ucs-2':\n case 'utf16le':\n case 'utf-16le':\n return true\n default:\n return false\n }\n}\n\nBuffer.concat = function concat (list, length) {\n if (!isArray(list)) {\n throw new TypeError('\"list\" argument must be an Array of Buffers')\n }\n\n if (list.length === 0) {\n return Buffer.alloc(0)\n }\n\n var i\n if (length === undefined) {\n length = 0\n for (i = 0; i < list.length; ++i) {\n length += list[i].length\n }\n }\n\n var buffer = Buffer.allocUnsafe(length)\n var pos = 0\n for (i = 0; i < list.length; ++i) {\n var buf = list[i]\n if (!Buffer.isBuffer(buf)) {\n throw new TypeError('\"list\" argument must be an Array of Buffers')\n }\n buf.copy(buffer, pos)\n pos += buf.length\n }\n return buffer\n}\n\nfunction byteLength (string, encoding) {\n if (Buffer.isBuffer(string)) {\n return string.length\n }\n if (typeof ArrayBuffer !== 'undefined' && typeof ArrayBuffer.isView === 'function' &&\n (ArrayBuffer.isView(string) || string instanceof ArrayBuffer)) {\n return string.byteLength\n }\n if (typeof string !== 'string') {\n string = '' + string\n }\n\n var len = string.length\n if (len === 0) return 0\n\n // Use a for loop to avoid recursion\n var loweredCase = false\n for (;;) {\n switch (encoding) {\n case 'ascii':\n case 'latin1':\n case 'binary':\n return len\n case 'utf8':\n case 'utf-8':\n case undefined:\n return utf8ToBytes(string).length\n case 'ucs2':\n case 'ucs-2':\n case 'utf16le':\n case 'utf-16le':\n return len * 2\n case 'hex':\n return len >>> 1\n case 'base64':\n return base64ToBytes(string).length\n default:\n if (loweredCase) return utf8ToBytes(string).length // assume utf8\n encoding = ('' + encoding).toLowerCase()\n loweredCase = true\n }\n }\n}\nBuffer.byteLength = byteLength\n\nfunction slowToString (encoding, start, end) {\n var loweredCase = false\n\n // No need to verify that \"this.length <= MAX_UINT32\" since it's a read-only\n // property of a typed array.\n\n // This behaves neither like String nor Uint8Array in that we set start/end\n // to their upper/lower bounds if the value passed is out of range.\n // undefined is handled specially as per ECMA-262 6th Edition,\n // Section 13.3.3.7 Runtime Semantics: KeyedBindingInitialization.\n if (start === undefined || start < 0) {\n start = 0\n }\n // Return early if start > this.length. Done here to prevent potential uint32\n // coercion fail below.\n if (start > this.length) {\n return ''\n }\n\n if (end === undefined || end > this.length) {\n end = this.length\n }\n\n if (end <= 0) {\n return ''\n }\n\n // Force coersion to uint32. This will also coerce falsey/NaN values to 0.\n end >>>= 0\n start >>>= 0\n\n if (end <= start) {\n return ''\n }\n\n if (!encoding) encoding = 'utf8'\n\n while (true) {\n switch (encoding) {\n case 'hex':\n return hexSlice(this, start, end)\n\n case 'utf8':\n case 'utf-8':\n return utf8Slice(this, start, end)\n\n case 'ascii':\n return asciiSlice(this, start, end)\n\n case 'latin1':\n case 'binary':\n return latin1Slice(this, start, end)\n\n case 'base64':\n return base64Slice(this, start, end)\n\n case 'ucs2':\n case 'ucs-2':\n case 'utf16le':\n case 'utf-16le':\n return utf16leSlice(this, start, end)\n\n default:\n if (loweredCase) throw new TypeError('Unknown encoding: ' + encoding)\n encoding = (encoding + '').toLowerCase()\n loweredCase = true\n }\n }\n}\n\n// The property is used by `Buffer.isBuffer` and `is-buffer` (in Safari 5-7) to detect\n// Buffer instances.\nBuffer.prototype._isBuffer = true\n\nfunction swap (b, n, m) {\n var i = b[n]\n b[n] = b[m]\n b[m] = i\n}\n\nBuffer.prototype.swap16 = function swap16 () {\n var len = this.length\n if (len % 2 !== 0) {\n throw new RangeError('Buffer size must be a multiple of 16-bits')\n }\n for (var i = 0; i < len; i += 2) {\n swap(this, i, i + 1)\n }\n return this\n}\n\nBuffer.prototype.swap32 = function swap32 () {\n var len = this.length\n if (len % 4 !== 0) {\n throw new RangeError('Buffer size must be a multiple of 32-bits')\n }\n for (var i = 0; i < len; i += 4) {\n swap(this, i, i + 3)\n swap(this, i + 1, i + 2)\n }\n return this\n}\n\nBuffer.prototype.swap64 = function swap64 () {\n var len = this.length\n if (len % 8 !== 0) {\n throw new RangeError('Buffer size must be a multiple of 64-bits')\n }\n for (var i = 0; i < len; i += 8) {\n swap(this, i, i + 7)\n swap(this, i + 1, i + 6)\n swap(this, i + 2, i + 5)\n swap(this, i + 3, i + 4)\n }\n return this\n}\n\nBuffer.prototype.toString = function toString () {\n var length = this.length | 0\n if (length === 0) return ''\n if (arguments.length === 0) return utf8Slice(this, 0, length)\n return slowToString.apply(this, arguments)\n}\n\nBuffer.prototype.equals = function equals (b) {\n if (!Buffer.isBuffer(b)) throw new TypeError('Argument must be a Buffer')\n if (this === b) return true\n return Buffer.compare(this, b) === 0\n}\n\nBuffer.prototype.inspect = function inspect () {\n var str = ''\n var max = exports.INSPECT_MAX_BYTES\n if (this.length > 0) {\n str = this.toString('hex', 0, max).match(/.{2}/g).join(' ')\n if (this.length > max) str += ' ... '\n }\n return ''\n}\n\nBuffer.prototype.compare = function compare (target, start, end, thisStart, thisEnd) {\n if (!Buffer.isBuffer(target)) {\n throw new TypeError('Argument must be a Buffer')\n }\n\n if (start === undefined) {\n start = 0\n }\n if (end === undefined) {\n end = target ? target.length : 0\n }\n if (thisStart === undefined) {\n thisStart = 0\n }\n if (thisEnd === undefined) {\n thisEnd = this.length\n }\n\n if (start < 0 || end > target.length || thisStart < 0 || thisEnd > this.length) {\n throw new RangeError('out of range index')\n }\n\n if (thisStart >= thisEnd && start >= end) {\n return 0\n }\n if (thisStart >= thisEnd) {\n return -1\n }\n if (start >= end) {\n return 1\n }\n\n start >>>= 0\n end >>>= 0\n thisStart >>>= 0\n thisEnd >>>= 0\n\n if (this === target) return 0\n\n var x = thisEnd - thisStart\n var y = end - start\n var len = Math.min(x, y)\n\n var thisCopy = this.slice(thisStart, thisEnd)\n var targetCopy = target.slice(start, end)\n\n for (var i = 0; i < len; ++i) {\n if (thisCopy[i] !== targetCopy[i]) {\n x = thisCopy[i]\n y = targetCopy[i]\n break\n }\n }\n\n if (x < y) return -1\n if (y < x) return 1\n return 0\n}\n\n// Finds either the first index of `val` in `buffer` at offset >= `byteOffset`,\n// OR the last index of `val` in `buffer` at offset <= `byteOffset`.\n//\n// Arguments:\n// - buffer - a Buffer to search\n// - val - a string, Buffer, or number\n// - byteOffset - an index into `buffer`; will be clamped to an int32\n// - encoding - an optional encoding, relevant is val is a string\n// - dir - true for indexOf, false for lastIndexOf\nfunction bidirectionalIndexOf (buffer, val, byteOffset, encoding, dir) {\n // Empty buffer means no match\n if (buffer.length === 0) return -1\n\n // Normalize byteOffset\n if (typeof byteOffset === 'string') {\n encoding = byteOffset\n byteOffset = 0\n } else if (byteOffset > 0x7fffffff) {\n byteOffset = 0x7fffffff\n } else if (byteOffset < -0x80000000) {\n byteOffset = -0x80000000\n }\n byteOffset = +byteOffset // Coerce to Number.\n if (isNaN(byteOffset)) {\n // byteOffset: it it's undefined, null, NaN, \"foo\", etc, search whole buffer\n byteOffset = dir ? 0 : (buffer.length - 1)\n }\n\n // Normalize byteOffset: negative offsets start from the end of the buffer\n if (byteOffset < 0) byteOffset = buffer.length + byteOffset\n if (byteOffset >= buffer.length) {\n if (dir) return -1\n else byteOffset = buffer.length - 1\n } else if (byteOffset < 0) {\n if (dir) byteOffset = 0\n else return -1\n }\n\n // Normalize val\n if (typeof val === 'string') {\n val = Buffer.from(val, encoding)\n }\n\n // Finally, search either indexOf (if dir is true) or lastIndexOf\n if (Buffer.isBuffer(val)) {\n // Special case: looking for empty string/buffer always fails\n if (val.length === 0) {\n return -1\n }\n return arrayIndexOf(buffer, val, byteOffset, encoding, dir)\n } else if (typeof val === 'number') {\n val = val & 0xFF // Search for a byte value [0-255]\n if (Buffer.TYPED_ARRAY_SUPPORT &&\n typeof Uint8Array.prototype.indexOf === 'function') {\n if (dir) {\n return Uint8Array.prototype.indexOf.call(buffer, val, byteOffset)\n } else {\n return Uint8Array.prototype.lastIndexOf.call(buffer, val, byteOffset)\n }\n }\n return arrayIndexOf(buffer, [ val ], byteOffset, encoding, dir)\n }\n\n throw new TypeError('val must be string, number or Buffer')\n}\n\nfunction arrayIndexOf (arr, val, byteOffset, encoding, dir) {\n var indexSize = 1\n var arrLength = arr.length\n var valLength = val.length\n\n if (encoding !== undefined) {\n encoding = String(encoding).toLowerCase()\n if (encoding === 'ucs2' || encoding === 'ucs-2' ||\n encoding === 'utf16le' || encoding === 'utf-16le') {\n if (arr.length < 2 || val.length < 2) {\n return -1\n }\n indexSize = 2\n arrLength /= 2\n valLength /= 2\n byteOffset /= 2\n }\n }\n\n function read (buf, i) {\n if (indexSize === 1) {\n return buf[i]\n } else {\n return buf.readUInt16BE(i * indexSize)\n }\n }\n\n var i\n if (dir) {\n var foundIndex = -1\n for (i = byteOffset; i < arrLength; i++) {\n if (read(arr, i) === read(val, foundIndex === -1 ? 0 : i - foundIndex)) {\n if (foundIndex === -1) foundIndex = i\n if (i - foundIndex + 1 === valLength) return foundIndex * indexSize\n } else {\n if (foundIndex !== -1) i -= i - foundIndex\n foundIndex = -1\n }\n }\n } else {\n if (byteOffset + valLength > arrLength) byteOffset = arrLength - valLength\n for (i = byteOffset; i >= 0; i--) {\n var found = true\n for (var j = 0; j < valLength; j++) {\n if (read(arr, i + j) !== read(val, j)) {\n found = false\n break\n }\n }\n if (found) return i\n }\n }\n\n return -1\n}\n\nBuffer.prototype.includes = function includes (val, byteOffset, encoding) {\n return this.indexOf(val, byteOffset, encoding) !== -1\n}\n\nBuffer.prototype.indexOf = function indexOf (val, byteOffset, encoding) {\n return bidirectionalIndexOf(this, val, byteOffset, encoding, true)\n}\n\nBuffer.prototype.lastIndexOf = function lastIndexOf (val, byteOffset, encoding) {\n return bidirectionalIndexOf(this, val, byteOffset, encoding, false)\n}\n\nfunction hexWrite (buf, string, offset, length) {\n offset = Number(offset) || 0\n var remaining = buf.length - offset\n if (!length) {\n length = remaining\n } else {\n length = Number(length)\n if (length > remaining) {\n length = remaining\n }\n }\n\n // must be an even number of digits\n var strLen = string.length\n if (strLen % 2 !== 0) throw new TypeError('Invalid hex string')\n\n if (length > strLen / 2) {\n length = strLen / 2\n }\n for (var i = 0; i < length; ++i) {\n var parsed = parseInt(string.substr(i * 2, 2), 16)\n if (isNaN(parsed)) return i\n buf[offset + i] = parsed\n }\n return i\n}\n\nfunction utf8Write (buf, string, offset, length) {\n return blitBuffer(utf8ToBytes(string, buf.length - offset), buf, offset, length)\n}\n\nfunction asciiWrite (buf, string, offset, length) {\n return blitBuffer(asciiToBytes(string), buf, offset, length)\n}\n\nfunction latin1Write (buf, string, offset, length) {\n return asciiWrite(buf, string, offset, length)\n}\n\nfunction base64Write (buf, string, offset, length) {\n return blitBuffer(base64ToBytes(string), buf, offset, length)\n}\n\nfunction ucs2Write (buf, string, offset, length) {\n return blitBuffer(utf16leToBytes(string, buf.length - offset), buf, offset, length)\n}\n\nBuffer.prototype.write = function write (string, offset, length, encoding) {\n // Buffer#write(string)\n if (offset === undefined) {\n encoding = 'utf8'\n length = this.length\n offset = 0\n // Buffer#write(string, encoding)\n } else if (length === undefined && typeof offset === 'string') {\n encoding = offset\n length = this.length\n offset = 0\n // Buffer#write(string, offset[, length][, encoding])\n } else if (isFinite(offset)) {\n offset = offset | 0\n if (isFinite(length)) {\n length = length | 0\n if (encoding === undefined) encoding = 'utf8'\n } else {\n encoding = length\n length = undefined\n }\n // legacy write(string, encoding, offset, length) - remove in v0.13\n } else {\n throw new Error(\n 'Buffer.write(string, encoding, offset[, length]) is no longer supported'\n )\n }\n\n var remaining = this.length - offset\n if (length === undefined || length > remaining) length = remaining\n\n if ((string.length > 0 && (length < 0 || offset < 0)) || offset > this.length) {\n throw new RangeError('Attempt to write outside buffer bounds')\n }\n\n if (!encoding) encoding = 'utf8'\n\n var loweredCase = false\n for (;;) {\n switch (encoding) {\n case 'hex':\n return hexWrite(this, string, offset, length)\n\n case 'utf8':\n case 'utf-8':\n return utf8Write(this, string, offset, length)\n\n case 'ascii':\n return asciiWrite(this, string, offset, length)\n\n case 'latin1':\n case 'binary':\n return latin1Write(this, string, offset, length)\n\n case 'base64':\n // Warning: maxLength not taken into account in base64Write\n return base64Write(this, string, offset, length)\n\n case 'ucs2':\n case 'ucs-2':\n case 'utf16le':\n case 'utf-16le':\n return ucs2Write(this, string, offset, length)\n\n default:\n if (loweredCase) throw new TypeError('Unknown encoding: ' + encoding)\n encoding = ('' + encoding).toLowerCase()\n loweredCase = true\n }\n }\n}\n\nBuffer.prototype.toJSON = function toJSON () {\n return {\n type: 'Buffer',\n data: Array.prototype.slice.call(this._arr || this, 0)\n }\n}\n\nfunction base64Slice (buf, start, end) {\n if (start === 0 && end === buf.length) {\n return base64.fromByteArray(buf)\n } else {\n return base64.fromByteArray(buf.slice(start, end))\n }\n}\n\nfunction utf8Slice (buf, start, end) {\n end = Math.min(buf.length, end)\n var res = []\n\n var i = start\n while (i < end) {\n var firstByte = buf[i]\n var codePoint = null\n var bytesPerSequence = (firstByte > 0xEF) ? 4\n : (firstByte > 0xDF) ? 3\n : (firstByte > 0xBF) ? 2\n : 1\n\n if (i + bytesPerSequence <= end) {\n var secondByte, thirdByte, fourthByte, tempCodePoint\n\n switch (bytesPerSequence) {\n case 1:\n if (firstByte < 0x80) {\n codePoint = firstByte\n }\n break\n case 2:\n secondByte = buf[i + 1]\n if ((secondByte & 0xC0) === 0x80) {\n tempCodePoint = (firstByte & 0x1F) << 0x6 | (secondByte & 0x3F)\n if (tempCodePoint > 0x7F) {\n codePoint = tempCodePoint\n }\n }\n break\n case 3:\n secondByte = buf[i + 1]\n thirdByte = buf[i + 2]\n if ((secondByte & 0xC0) === 0x80 && (thirdByte & 0xC0) === 0x80) {\n tempCodePoint = (firstByte & 0xF) << 0xC | (secondByte & 0x3F) << 0x6 | (thirdByte & 0x3F)\n if (tempCodePoint > 0x7FF && (tempCodePoint < 0xD800 || tempCodePoint > 0xDFFF)) {\n codePoint = tempCodePoint\n }\n }\n break\n case 4:\n secondByte = buf[i + 1]\n thirdByte = buf[i + 2]\n fourthByte = buf[i + 3]\n if ((secondByte & 0xC0) === 0x80 && (thirdByte & 0xC0) === 0x80 && (fourthByte & 0xC0) === 0x80) {\n tempCodePoint = (firstByte & 0xF) << 0x12 | (secondByte & 0x3F) << 0xC | (thirdByte & 0x3F) << 0x6 | (fourthByte & 0x3F)\n if (tempCodePoint > 0xFFFF && tempCodePoint < 0x110000) {\n codePoint = tempCodePoint\n }\n }\n }\n }\n\n if (codePoint === null) {\n // we did not generate a valid codePoint so insert a\n // replacement char (U+FFFD) and advance only 1 byte\n codePoint = 0xFFFD\n bytesPerSequence = 1\n } else if (codePoint > 0xFFFF) {\n // encode to utf16 (surrogate pair dance)\n codePoint -= 0x10000\n res.push(codePoint >>> 10 & 0x3FF | 0xD800)\n codePoint = 0xDC00 | codePoint & 0x3FF\n }\n\n res.push(codePoint)\n i += bytesPerSequence\n }\n\n return decodeCodePointsArray(res)\n}\n\n// Based on http://stackoverflow.com/a/22747272/680742, the browser with\n// the lowest limit is Chrome, with 0x10000 args.\n// We go 1 magnitude less, for safety\nvar MAX_ARGUMENTS_LENGTH = 0x1000\n\nfunction decodeCodePointsArray (codePoints) {\n var len = codePoints.length\n if (len <= MAX_ARGUMENTS_LENGTH) {\n return String.fromCharCode.apply(String, codePoints) // avoid extra slice()\n }\n\n // Decode in chunks to avoid \"call stack size exceeded\".\n var res = ''\n var i = 0\n while (i < len) {\n res += String.fromCharCode.apply(\n String,\n codePoints.slice(i, i += MAX_ARGUMENTS_LENGTH)\n )\n }\n return res\n}\n\nfunction asciiSlice (buf, start, end) {\n var ret = ''\n end = Math.min(buf.length, end)\n\n for (var i = start; i < end; ++i) {\n ret += String.fromCharCode(buf[i] & 0x7F)\n }\n return ret\n}\n\nfunction latin1Slice (buf, start, end) {\n var ret = ''\n end = Math.min(buf.length, end)\n\n for (var i = start; i < end; ++i) {\n ret += String.fromCharCode(buf[i])\n }\n return ret\n}\n\nfunction hexSlice (buf, start, end) {\n var len = buf.length\n\n if (!start || start < 0) start = 0\n if (!end || end < 0 || end > len) end = len\n\n var out = ''\n for (var i = start; i < end; ++i) {\n out += toHex(buf[i])\n }\n return out\n}\n\nfunction utf16leSlice (buf, start, end) {\n var bytes = buf.slice(start, end)\n var res = ''\n for (var i = 0; i < bytes.length; i += 2) {\n res += String.fromCharCode(bytes[i] + bytes[i + 1] * 256)\n }\n return res\n}\n\nBuffer.prototype.slice = function slice (start, end) {\n var len = this.length\n start = ~~start\n end = end === undefined ? len : ~~end\n\n if (start < 0) {\n start += len\n if (start < 0) start = 0\n } else if (start > len) {\n start = len\n }\n\n if (end < 0) {\n end += len\n if (end < 0) end = 0\n } else if (end > len) {\n end = len\n }\n\n if (end < start) end = start\n\n var newBuf\n if (Buffer.TYPED_ARRAY_SUPPORT) {\n newBuf = this.subarray(start, end)\n newBuf.__proto__ = Buffer.prototype\n } else {\n var sliceLen = end - start\n newBuf = new Buffer(sliceLen, undefined)\n for (var i = 0; i < sliceLen; ++i) {\n newBuf[i] = this[i + start]\n }\n }\n\n return newBuf\n}\n\n/*\n * Need to make sure that buffer isn't trying to write out of bounds.\n */\nfunction checkOffset (offset, ext, length) {\n if ((offset % 1) !== 0 || offset < 0) throw new RangeError('offset is not uint')\n if (offset + ext > length) throw new RangeError('Trying to access beyond buffer length')\n}\n\nBuffer.prototype.readUIntLE = function readUIntLE (offset, byteLength, noAssert) {\n offset = offset | 0\n byteLength = byteLength | 0\n if (!noAssert) checkOffset(offset, byteLength, this.length)\n\n var val = this[offset]\n var mul = 1\n var i = 0\n while (++i < byteLength && (mul *= 0x100)) {\n val += this[offset + i] * mul\n }\n\n return val\n}\n\nBuffer.prototype.readUIntBE = function readUIntBE (offset, byteLength, noAssert) {\n offset = offset | 0\n byteLength = byteLength | 0\n if (!noAssert) {\n checkOffset(offset, byteLength, this.length)\n }\n\n var val = this[offset + --byteLength]\n var mul = 1\n while (byteLength > 0 && (mul *= 0x100)) {\n val += this[offset + --byteLength] * mul\n }\n\n return val\n}\n\nBuffer.prototype.readUInt8 = function readUInt8 (offset, noAssert) {\n if (!noAssert) checkOffset(offset, 1, this.length)\n return this[offset]\n}\n\nBuffer.prototype.readUInt16LE = function readUInt16LE (offset, noAssert) {\n if (!noAssert) checkOffset(offset, 2, this.length)\n return this[offset] | (this[offset + 1] << 8)\n}\n\nBuffer.prototype.readUInt16BE = function readUInt16BE (offset, noAssert) {\n if (!noAssert) checkOffset(offset, 2, this.length)\n return (this[offset] << 8) | this[offset + 1]\n}\n\nBuffer.prototype.readUInt32LE = function readUInt32LE (offset, noAssert) {\n if (!noAssert) checkOffset(offset, 4, this.length)\n\n return ((this[offset]) |\n (this[offset + 1] << 8) |\n (this[offset + 2] << 16)) +\n (this[offset + 3] * 0x1000000)\n}\n\nBuffer.prototype.readUInt32BE = function readUInt32BE (offset, noAssert) {\n if (!noAssert) checkOffset(offset, 4, this.length)\n\n return (this[offset] * 0x1000000) +\n ((this[offset + 1] << 16) |\n (this[offset + 2] << 8) |\n this[offset + 3])\n}\n\nBuffer.prototype.readIntLE = function readIntLE (offset, byteLength, noAssert) {\n offset = offset | 0\n byteLength = byteLength | 0\n if (!noAssert) checkOffset(offset, byteLength, this.length)\n\n var val = this[offset]\n var mul = 1\n var i = 0\n while (++i < byteLength && (mul *= 0x100)) {\n val += this[offset + i] * mul\n }\n mul *= 0x80\n\n if (val >= mul) val -= Math.pow(2, 8 * byteLength)\n\n return val\n}\n\nBuffer.prototype.readIntBE = function readIntBE (offset, byteLength, noAssert) {\n offset = offset | 0\n byteLength = byteLength | 0\n if (!noAssert) checkOffset(offset, byteLength, this.length)\n\n var i = byteLength\n var mul = 1\n var val = this[offset + --i]\n while (i > 0 && (mul *= 0x100)) {\n val += this[offset + --i] * mul\n }\n mul *= 0x80\n\n if (val >= mul) val -= Math.pow(2, 8 * byteLength)\n\n return val\n}\n\nBuffer.prototype.readInt8 = function readInt8 (offset, noAssert) {\n if (!noAssert) checkOffset(offset, 1, this.length)\n if (!(this[offset] & 0x80)) return (this[offset])\n return ((0xff - this[offset] + 1) * -1)\n}\n\nBuffer.prototype.readInt16LE = function readInt16LE (offset, noAssert) {\n if (!noAssert) checkOffset(offset, 2, this.length)\n var val = this[offset] | (this[offset + 1] << 8)\n return (val & 0x8000) ? val | 0xFFFF0000 : val\n}\n\nBuffer.prototype.readInt16BE = function readInt16BE (offset, noAssert) {\n if (!noAssert) checkOffset(offset, 2, this.length)\n var val = this[offset + 1] | (this[offset] << 8)\n return (val & 0x8000) ? val | 0xFFFF0000 : val\n}\n\nBuffer.prototype.readInt32LE = function readInt32LE (offset, noAssert) {\n if (!noAssert) checkOffset(offset, 4, this.length)\n\n return (this[offset]) |\n (this[offset + 1] << 8) |\n (this[offset + 2] << 16) |\n (this[offset + 3] << 24)\n}\n\nBuffer.prototype.readInt32BE = function readInt32BE (offset, noAssert) {\n if (!noAssert) checkOffset(offset, 4, this.length)\n\n return (this[offset] << 24) |\n (this[offset + 1] << 16) |\n (this[offset + 2] << 8) |\n (this[offset + 3])\n}\n\nBuffer.prototype.readFloatLE = function readFloatLE (offset, noAssert) {\n if (!noAssert) checkOffset(offset, 4, this.length)\n return ieee754.read(this, offset, true, 23, 4)\n}\n\nBuffer.prototype.readFloatBE = function readFloatBE (offset, noAssert) {\n if (!noAssert) checkOffset(offset, 4, this.length)\n return ieee754.read(this, offset, false, 23, 4)\n}\n\nBuffer.prototype.readDoubleLE = function readDoubleLE (offset, noAssert) {\n if (!noAssert) checkOffset(offset, 8, this.length)\n return ieee754.read(this, offset, true, 52, 8)\n}\n\nBuffer.prototype.readDoubleBE = function readDoubleBE (offset, noAssert) {\n if (!noAssert) checkOffset(offset, 8, this.length)\n return ieee754.read(this, offset, false, 52, 8)\n}\n\nfunction checkInt (buf, value, offset, ext, max, min) {\n if (!Buffer.isBuffer(buf)) throw new TypeError('\"buffer\" argument must be a Buffer instance')\n if (value > max || value < min) throw new RangeError('\"value\" argument is out of bounds')\n if (offset + ext > buf.length) throw new RangeError('Index out of range')\n}\n\nBuffer.prototype.writeUIntLE = function writeUIntLE (value, offset, byteLength, noAssert) {\n value = +value\n offset = offset | 0\n byteLength = byteLength | 0\n if (!noAssert) {\n var maxBytes = Math.pow(2, 8 * byteLength) - 1\n checkInt(this, value, offset, byteLength, maxBytes, 0)\n }\n\n var mul = 1\n var i = 0\n this[offset] = value & 0xFF\n while (++i < byteLength && (mul *= 0x100)) {\n this[offset + i] = (value / mul) & 0xFF\n }\n\n return offset + byteLength\n}\n\nBuffer.prototype.writeUIntBE = function writeUIntBE (value, offset, byteLength, noAssert) {\n value = +value\n offset = offset | 0\n byteLength = byteLength | 0\n if (!noAssert) {\n var maxBytes = Math.pow(2, 8 * byteLength) - 1\n checkInt(this, value, offset, byteLength, maxBytes, 0)\n }\n\n var i = byteLength - 1\n var mul = 1\n this[offset + i] = value & 0xFF\n while (--i >= 0 && (mul *= 0x100)) {\n this[offset + i] = (value / mul) & 0xFF\n }\n\n return offset + byteLength\n}\n\nBuffer.prototype.writeUInt8 = function writeUInt8 (value, offset, noAssert) {\n value = +value\n offset = offset | 0\n if (!noAssert) checkInt(this, value, offset, 1, 0xff, 0)\n if (!Buffer.TYPED_ARRAY_SUPPORT) value = Math.floor(value)\n this[offset] = (value & 0xff)\n return offset + 1\n}\n\nfunction objectWriteUInt16 (buf, value, offset, littleEndian) {\n if (value < 0) value = 0xffff + value + 1\n for (var i = 0, j = Math.min(buf.length - offset, 2); i < j; ++i) {\n buf[offset + i] = (value & (0xff << (8 * (littleEndian ? i : 1 - i)))) >>>\n (littleEndian ? i : 1 - i) * 8\n }\n}\n\nBuffer.prototype.writeUInt16LE = function writeUInt16LE (value, offset, noAssert) {\n value = +value\n offset = offset | 0\n if (!noAssert) checkInt(this, value, offset, 2, 0xffff, 0)\n if (Buffer.TYPED_ARRAY_SUPPORT) {\n this[offset] = (value & 0xff)\n this[offset + 1] = (value >>> 8)\n } else {\n objectWriteUInt16(this, value, offset, true)\n }\n return offset + 2\n}\n\nBuffer.prototype.writeUInt16BE = function writeUInt16BE (value, offset, noAssert) {\n value = +value\n offset = offset | 0\n if (!noAssert) checkInt(this, value, offset, 2, 0xffff, 0)\n if (Buffer.TYPED_ARRAY_SUPPORT) {\n this[offset] = (value >>> 8)\n this[offset + 1] = (value & 0xff)\n } else {\n objectWriteUInt16(this, value, offset, false)\n }\n return offset + 2\n}\n\nfunction objectWriteUInt32 (buf, value, offset, littleEndian) {\n if (value < 0) value = 0xffffffff + value + 1\n for (var i = 0, j = Math.min(buf.length - offset, 4); i < j; ++i) {\n buf[offset + i] = (value >>> (littleEndian ? i : 3 - i) * 8) & 0xff\n }\n}\n\nBuffer.prototype.writeUInt32LE = function writeUInt32LE (value, offset, noAssert) {\n value = +value\n offset = offset | 0\n if (!noAssert) checkInt(this, value, offset, 4, 0xffffffff, 0)\n if (Buffer.TYPED_ARRAY_SUPPORT) {\n this[offset + 3] = (value >>> 24)\n this[offset + 2] = (value >>> 16)\n this[offset + 1] = (value >>> 8)\n this[offset] = (value & 0xff)\n } else {\n objectWriteUInt32(this, value, offset, true)\n }\n return offset + 4\n}\n\nBuffer.prototype.writeUInt32BE = function writeUInt32BE (value, offset, noAssert) {\n value = +value\n offset = offset | 0\n if (!noAssert) checkInt(this, value, offset, 4, 0xffffffff, 0)\n if (Buffer.TYPED_ARRAY_SUPPORT) {\n this[offset] = (value >>> 24)\n this[offset + 1] = (value >>> 16)\n this[offset + 2] = (value >>> 8)\n this[offset + 3] = (value & 0xff)\n } else {\n objectWriteUInt32(this, value, offset, false)\n }\n return offset + 4\n}\n\nBuffer.prototype.writeIntLE = function writeIntLE (value, offset, byteLength, noAssert) {\n value = +value\n offset = offset | 0\n if (!noAssert) {\n var limit = Math.pow(2, 8 * byteLength - 1)\n\n checkInt(this, value, offset, byteLength, limit - 1, -limit)\n }\n\n var i = 0\n var mul = 1\n var sub = 0\n this[offset] = value & 0xFF\n while (++i < byteLength && (mul *= 0x100)) {\n if (value < 0 && sub === 0 && this[offset + i - 1] !== 0) {\n sub = 1\n }\n this[offset + i] = ((value / mul) >> 0) - sub & 0xFF\n }\n\n return offset + byteLength\n}\n\nBuffer.prototype.writeIntBE = function writeIntBE (value, offset, byteLength, noAssert) {\n value = +value\n offset = offset | 0\n if (!noAssert) {\n var limit = Math.pow(2, 8 * byteLength - 1)\n\n checkInt(this, value, offset, byteLength, limit - 1, -limit)\n }\n\n var i = byteLength - 1\n var mul = 1\n var sub = 0\n this[offset + i] = value & 0xFF\n while (--i >= 0 && (mul *= 0x100)) {\n if (value < 0 && sub === 0 && this[offset + i + 1] !== 0) {\n sub = 1\n }\n this[offset + i] = ((value / mul) >> 0) - sub & 0xFF\n }\n\n return offset + byteLength\n}\n\nBuffer.prototype.writeInt8 = function writeInt8 (value, offset, noAssert) {\n value = +value\n offset = offset | 0\n if (!noAssert) checkInt(this, value, offset, 1, 0x7f, -0x80)\n if (!Buffer.TYPED_ARRAY_SUPPORT) value = Math.floor(value)\n if (value < 0) value = 0xff + value + 1\n this[offset] = (value & 0xff)\n return offset + 1\n}\n\nBuffer.prototype.writeInt16LE = function writeInt16LE (value, offset, noAssert) {\n value = +value\n offset = offset | 0\n if (!noAssert) checkInt(this, value, offset, 2, 0x7fff, -0x8000)\n if (Buffer.TYPED_ARRAY_SUPPORT) {\n this[offset] = (value & 0xff)\n this[offset + 1] = (value >>> 8)\n } else {\n objectWriteUInt16(this, value, offset, true)\n }\n return offset + 2\n}\n\nBuffer.prototype.writeInt16BE = function writeInt16BE (value, offset, noAssert) {\n value = +value\n offset = offset | 0\n if (!noAssert) checkInt(this, value, offset, 2, 0x7fff, -0x8000)\n if (Buffer.TYPED_ARRAY_SUPPORT) {\n this[offset] = (value >>> 8)\n this[offset + 1] = (value & 0xff)\n } else {\n objectWriteUInt16(this, value, offset, false)\n }\n return offset + 2\n}\n\nBuffer.prototype.writeInt32LE = function writeInt32LE (value, offset, noAssert) {\n value = +value\n offset = offset | 0\n if (!noAssert) checkInt(this, value, offset, 4, 0x7fffffff, -0x80000000)\n if (Buffer.TYPED_ARRAY_SUPPORT) {\n this[offset] = (value & 0xff)\n this[offset + 1] = (value >>> 8)\n this[offset + 2] = (value >>> 16)\n this[offset + 3] = (value >>> 24)\n } else {\n objectWriteUInt32(this, value, offset, true)\n }\n return offset + 4\n}\n\nBuffer.prototype.writeInt32BE = function writeInt32BE (value, offset, noAssert) {\n value = +value\n offset = offset | 0\n if (!noAssert) checkInt(this, value, offset, 4, 0x7fffffff, -0x80000000)\n if (value < 0) value = 0xffffffff + value + 1\n if (Buffer.TYPED_ARRAY_SUPPORT) {\n this[offset] = (value >>> 24)\n this[offset + 1] = (value >>> 16)\n this[offset + 2] = (value >>> 8)\n this[offset + 3] = (value & 0xff)\n } else {\n objectWriteUInt32(this, value, offset, false)\n }\n return offset + 4\n}\n\nfunction checkIEEE754 (buf, value, offset, ext, max, min) {\n if (offset + ext > buf.length) throw new RangeError('Index out of range')\n if (offset < 0) throw new RangeError('Index out of range')\n}\n\nfunction writeFloat (buf, value, offset, littleEndian, noAssert) {\n if (!noAssert) {\n checkIEEE754(buf, value, offset, 4, 3.4028234663852886e+38, -3.4028234663852886e+38)\n }\n ieee754.write(buf, value, offset, littleEndian, 23, 4)\n return offset + 4\n}\n\nBuffer.prototype.writeFloatLE = function writeFloatLE (value, offset, noAssert) {\n return writeFloat(this, value, offset, true, noAssert)\n}\n\nBuffer.prototype.writeFloatBE = function writeFloatBE (value, offset, noAssert) {\n return writeFloat(this, value, offset, false, noAssert)\n}\n\nfunction writeDouble (buf, value, offset, littleEndian, noAssert) {\n if (!noAssert) {\n checkIEEE754(buf, value, offset, 8, 1.7976931348623157E+308, -1.7976931348623157E+308)\n }\n ieee754.write(buf, value, offset, littleEndian, 52, 8)\n return offset + 8\n}\n\nBuffer.prototype.writeDoubleLE = function writeDoubleLE (value, offset, noAssert) {\n return writeDouble(this, value, offset, true, noAssert)\n}\n\nBuffer.prototype.writeDoubleBE = function writeDoubleBE (value, offset, noAssert) {\n return writeDouble(this, value, offset, false, noAssert)\n}\n\n// copy(targetBuffer, targetStart=0, sourceStart=0, sourceEnd=buffer.length)\nBuffer.prototype.copy = function copy (target, targetStart, start, end) {\n if (!start) start = 0\n if (!end && end !== 0) end = this.length\n if (targetStart >= target.length) targetStart = target.length\n if (!targetStart) targetStart = 0\n if (end > 0 && end < start) end = start\n\n // Copy 0 bytes; we're done\n if (end === start) return 0\n if (target.length === 0 || this.length === 0) return 0\n\n // Fatal error conditions\n if (targetStart < 0) {\n throw new RangeError('targetStart out of bounds')\n }\n if (start < 0 || start >= this.length) throw new RangeError('sourceStart out of bounds')\n if (end < 0) throw new RangeError('sourceEnd out of bounds')\n\n // Are we oob?\n if (end > this.length) end = this.length\n if (target.length - targetStart < end - start) {\n end = target.length - targetStart + start\n }\n\n var len = end - start\n var i\n\n if (this === target && start < targetStart && targetStart < end) {\n // descending copy from end\n for (i = len - 1; i >= 0; --i) {\n target[i + targetStart] = this[i + start]\n }\n } else if (len < 1000 || !Buffer.TYPED_ARRAY_SUPPORT) {\n // ascending copy from start\n for (i = 0; i < len; ++i) {\n target[i + targetStart] = this[i + start]\n }\n } else {\n Uint8Array.prototype.set.call(\n target,\n this.subarray(start, start + len),\n targetStart\n )\n }\n\n return len\n}\n\n// Usage:\n// buffer.fill(number[, offset[, end]])\n// buffer.fill(buffer[, offset[, end]])\n// buffer.fill(string[, offset[, end]][, encoding])\nBuffer.prototype.fill = function fill (val, start, end, encoding) {\n // Handle string cases:\n if (typeof val === 'string') {\n if (typeof start === 'string') {\n encoding = start\n start = 0\n end = this.length\n } else if (typeof end === 'string') {\n encoding = end\n end = this.length\n }\n if (val.length === 1) {\n var code = val.charCodeAt(0)\n if (code < 256) {\n val = code\n }\n }\n if (encoding !== undefined && typeof encoding !== 'string') {\n throw new TypeError('encoding must be a string')\n }\n if (typeof encoding === 'string' && !Buffer.isEncoding(encoding)) {\n throw new TypeError('Unknown encoding: ' + encoding)\n }\n } else if (typeof val === 'number') {\n val = val & 255\n }\n\n // Invalid ranges are not set to a default, so can range check early.\n if (start < 0 || this.length < start || this.length < end) {\n throw new RangeError('Out of range index')\n }\n\n if (end <= start) {\n return this\n }\n\n start = start >>> 0\n end = end === undefined ? this.length : end >>> 0\n\n if (!val) val = 0\n\n var i\n if (typeof val === 'number') {\n for (i = start; i < end; ++i) {\n this[i] = val\n }\n } else {\n var bytes = Buffer.isBuffer(val)\n ? val\n : utf8ToBytes(new Buffer(val, encoding).toString())\n var len = bytes.length\n for (i = 0; i < end - start; ++i) {\n this[i + start] = bytes[i % len]\n }\n }\n\n return this\n}\n\n// HELPER FUNCTIONS\n// ================\n\nvar INVALID_BASE64_RE = /[^+\\/0-9A-Za-z-_]/g\n\nfunction base64clean (str) {\n // Node strips out invalid characters like \\n and \\t from the string, base64-js does not\n str = stringtrim(str).replace(INVALID_BASE64_RE, '')\n // Node converts strings with length < 2 to ''\n if (str.length < 2) return ''\n // Node allows for non-padded base64 strings (missing trailing ===), base64-js does not\n while (str.length % 4 !== 0) {\n str = str + '='\n }\n return str\n}\n\nfunction stringtrim (str) {\n if (str.trim) return str.trim()\n return str.replace(/^\\s+|\\s+$/g, '')\n}\n\nfunction toHex (n) {\n if (n < 16) return '0' + n.toString(16)\n return n.toString(16)\n}\n\nfunction utf8ToBytes (string, units) {\n units = units || Infinity\n var codePoint\n var length = string.length\n var leadSurrogate = null\n var bytes = []\n\n for (var i = 0; i < length; ++i) {\n codePoint = string.charCodeAt(i)\n\n // is surrogate component\n if (codePoint > 0xD7FF && codePoint < 0xE000) {\n // last char was a lead\n if (!leadSurrogate) {\n // no lead yet\n if (codePoint > 0xDBFF) {\n // unexpected trail\n if ((units -= 3) > -1) bytes.push(0xEF, 0xBF, 0xBD)\n continue\n } else if (i + 1 === length) {\n // unpaired lead\n if ((units -= 3) > -1) bytes.push(0xEF, 0xBF, 0xBD)\n continue\n }\n\n // valid lead\n leadSurrogate = codePoint\n\n continue\n }\n\n // 2 leads in a row\n if (codePoint < 0xDC00) {\n if ((units -= 3) > -1) bytes.push(0xEF, 0xBF, 0xBD)\n leadSurrogate = codePoint\n continue\n }\n\n // valid surrogate pair\n codePoint = (leadSurrogate - 0xD800 << 10 | codePoint - 0xDC00) + 0x10000\n } else if (leadSurrogate) {\n // valid bmp char, but last char was a lead\n if ((units -= 3) > -1) bytes.push(0xEF, 0xBF, 0xBD)\n }\n\n leadSurrogate = null\n\n // encode utf8\n if (codePoint < 0x80) {\n if ((units -= 1) < 0) break\n bytes.push(codePoint)\n } else if (codePoint < 0x800) {\n if ((units -= 2) < 0) break\n bytes.push(\n codePoint >> 0x6 | 0xC0,\n codePoint & 0x3F | 0x80\n )\n } else if (codePoint < 0x10000) {\n if ((units -= 3) < 0) break\n bytes.push(\n codePoint >> 0xC | 0xE0,\n codePoint >> 0x6 & 0x3F | 0x80,\n codePoint & 0x3F | 0x80\n )\n } else if (codePoint < 0x110000) {\n if ((units -= 4) < 0) break\n bytes.push(\n codePoint >> 0x12 | 0xF0,\n codePoint >> 0xC & 0x3F | 0x80,\n codePoint >> 0x6 & 0x3F | 0x80,\n codePoint & 0x3F | 0x80\n )\n } else {\n throw new Error('Invalid code point')\n }\n }\n\n return bytes\n}\n\nfunction asciiToBytes (str) {\n var byteArray = []\n for (var i = 0; i < str.length; ++i) {\n // Node's code seems to be doing this and not & 0x7F..\n byteArray.push(str.charCodeAt(i) & 0xFF)\n }\n return byteArray\n}\n\nfunction utf16leToBytes (str, units) {\n var c, hi, lo\n var byteArray = []\n for (var i = 0; i < str.length; ++i) {\n if ((units -= 2) < 0) break\n\n c = str.charCodeAt(i)\n hi = c >> 8\n lo = c % 256\n byteArray.push(lo)\n byteArray.push(hi)\n }\n\n return byteArray\n}\n\nfunction base64ToBytes (str) {\n return base64.toByteArray(base64clean(str))\n}\n\nfunction blitBuffer (src, dst, offset, length) {\n for (var i = 0; i < length; ++i) {\n if ((i + offset >= dst.length) || (i >= src.length)) break\n dst[i + offset] = src[i]\n }\n return i\n}\n\nfunction isnan (val) {\n return val !== val // eslint-disable-line no-self-compare\n}\n","\"use strict\";\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * https://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nvar __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {\n return new (P || (P = Promise))(function (resolve, reject) {\n function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }\n function rejected(value) { try { step(generator[\"throw\"](value)); } catch (e) { reject(e); } }\n function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }\n step((generator = generator.apply(thisArg, _arguments || [])).next());\n });\n};\nvar __generator = (this && this.__generator) || function (thisArg, body) {\n var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;\n return g = { next: verb(0), \"throw\": verb(1), \"return\": verb(2) }, typeof Symbol === \"function\" && (g[Symbol.iterator] = function() { return this; }), g;\n function verb(n) { return function (v) { return step([n, v]); }; }\n function step(op) {\n if (f) throw new TypeError(\"Generator is already executing.\");\n while (_) try {\n if (f = 1, y && (t = op[0] & 2 ? y[\"return\"] : op[0] ? y[\"throw\"] || ((t = y[\"return\"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;\n if (y = 0, t) op = [op[0] & 2, t.value];\n switch (op[0]) {\n case 0: case 1: t = op; break;\n case 4: _.label++; return { value: op[1], done: false };\n case 5: _.label++; y = op[1]; op = [0]; continue;\n case 7: op = _.ops.pop(); _.trys.pop(); continue;\n default:\n if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }\n if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }\n if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }\n if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }\n if (t[2]) _.ops.pop();\n _.trys.pop(); continue;\n }\n op = body.call(thisArg, _);\n } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }\n if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };\n }\n};\nObject.defineProperty(exports, \"__esModule\", { value: true });\nvar mediapipe_facemesh_1 = require(\"./mediapipe-facemesh\");\nvar SupportedPackages;\n(function (SupportedPackages) {\n SupportedPackages[\"mediapipeFacemesh\"] = \"mediapipe-facemesh\";\n})(SupportedPackages = exports.SupportedPackages || (exports.SupportedPackages = {}));\n/**\n * Load face-landmarks-detection.\n *\n * @param pkg - The name of the package to load, e.g. 'mediapipe-facemesh'.\n * @param config - a configuration object with the following properties:\n * - `maxContinuousChecks` How many frames to go without running the bounding\n * box detector. Only relevant if maxFaces > 1. Defaults to 5.\n * - `detectionConfidence` Threshold for discarding a prediction. Defaults to\n * 0.9.\n * - `maxFaces` The maximum number of faces detected in the input. Should be\n * set to the minimum number for performance. Defaults to 10.\n * - `iouThreshold` A float representing the threshold for deciding whether\n * boxes overlap too much in non-maximum suppression. Must be between [0, 1].\n * Defaults to 0.3.\n * - `scoreThreshold` A threshold for deciding when to remove boxes based\n * on score in non-maximum suppression. Defaults to 0.75.\n * - `shouldLoadIrisModel` Whether to also load the iris detection model.\n * Defaults to true.\n */\nfunction load(pkg, config) {\n if (pkg === void 0) { pkg = SupportedPackages.mediapipeFacemesh; }\n if (config === void 0) { config = {}; }\n return __awaiter(this, void 0, void 0, function () {\n return __generator(this, function (_a) {\n if (pkg === SupportedPackages.mediapipeFacemesh) {\n return [2 /*return*/, mediapipe_facemesh_1.load(config)];\n }\n else {\n throw new Error(pkg + \" is not a valid package name.\");\n }\n return [2 /*return*/];\n });\n });\n}\nexports.load = load;\n//# sourceMappingURL=index.js.map","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Acos } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes acos of the input `tf.Tensor` element-wise: `acos(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.acos().print(); // or tf.acos(x)\n * ```\n * @param x The input tensor.\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction acos_(x) {\n const $x = convertToTensor(x, 'x', 'acos');\n const inputs = { x: $x };\n return ENGINE.runKernel(Acos, inputs);\n}\nexport const acos = op({ acos_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Acosh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the inverse hyperbolic cos of the input `tf.Tensor` element-wise:\n * `acosh(x)`\n *\n * ```js\n * const x = tf.tensor1d([10, 1, 3, 5.7]);\n *\n * x.acosh().print(); // or tf.acosh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction acosh_(x) {\n const $x = convertToTensor(x, 'x', 'acosh');\n const inputs = { x: $x };\n return ENGINE.runKernel(Acosh, inputs);\n}\nexport const acosh = op({ acosh_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { AddN } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Adds a list of `tf.Tensor`s element-wise, each with the same shape and dtype.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * const c = tf.tensor1d([5, 6]);\n *\n * tf.addN([a, b, c]).print();\n * ```\n * @param tensors A list of tensors with the same shape and dtype.\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction addN_(tensors) {\n util.assert(Array.isArray(tensors), () => 'The argument passed to tf.addN() must be a list of tensors');\n util.assert(tensors.length >= 1, () => `Must pass at least one tensor to tf.addN(), but got ` +\n `${tensors.length}`);\n const $tensors = tensors.map((t, i) => convertToTensor(t, `tensors${i}`, 'addN'));\n const firstTensor = $tensors[0];\n $tensors.forEach(t => {\n if (t.dtype !== firstTensor.dtype) {\n throw new Error('All tensors passed to tf.addN() must have the same dtype');\n }\n });\n $tensors.forEach(t => {\n if (!util.arraysEqual(t.shape, firstTensor.shape)) {\n throw new Error('All tensors passed to tf.addN() must have the same shape');\n }\n });\n const inputs = $tensors;\n return ENGINE.runKernel(AddN, inputs);\n}\nexport const addN = op({ addN_ });\n//# sourceMappingURL=data:application/json;base64,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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { All } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the logical and of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and an\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 1, 1], 'bool');\n *\n * x.all().print(); // or tf.all(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');\n *\n * const axis = 1;\n * x.all(axis).print(); // or tf.all(x, axis)\n * ```\n *\n * @param x The input tensor. Must be of dtype bool.\n * @param axis The dimension(s) to reduce. By default it reduces\n * all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction all_(x, axis = null, keepDims = false) {\n const $x = convertToTensor(x, 'x', 'all', 'bool');\n const inputs = { x: $x };\n const attrs = { axis, keepDims };\n return ENGINE.runKernel(All, inputs, attrs);\n}\nexport const all = op({ all_ });\n//# sourceMappingURL=data:application/json;base64,eyJ2ZXJzaW9uIjozLCJmaWxlIjoiYWxsLmpzIiwic291cmNlUm9vdCI6IiIsInNvdXJjZXMiOlsiLi4vLi4vLi4vLi4vLi4vLi4vdGZqcy1jb3JlL3NyYy9vcHMvYWxsLnRzIl0sIm5hbWVzIjpbXSwibWFwcGluZ3MiOiJBQUFBOzs7Ozs7Ozs7Ozs7Ozs7R0FlRztBQUVILE9BQU8sRUFBQyxNQUFNLEVBQUMsTUFBTSxXQUFXLENBQUM7QUFDakMsT0FBTyxFQUFDLEdBQUcsRUFBc0IsTUFBTSxpQkFBaUIsQ0FBQztBQUl6RCxPQUFPLEVBQUMsZUFBZSxFQUFDLE1BQU0sb0JBQW9CLENBQUM7QUFHbkQsT0FBTyxFQUFDLEVBQUUsRUFBQyxNQUFNLGFBQWEsQ0FBQztBQUUvQjs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7OztHQTRCRztBQUNILFNBQVMsSUFBSSxDQUNULENBQW9CLEVBQUUsT0FBd0IsSUFBSSxFQUFFLFFBQVEsR0FBRyxLQUFLO0lBQ3RFLE1BQU0sRUFBRSxHQUFHLGVBQWUsQ0FBQyxDQUFDLEVBQUUsR0FBRyxFQUFFLEtBQUssRUFBRSxNQUFNLENBQUMsQ0FBQztJQUVsRCxNQUFNLE1BQU0sR0FBYyxFQUFDLENBQUMsRUFBRSxFQUFFLEVBQUMsQ0FBQztJQUNsQyxNQUFNLEtBQUssR0FBYSxFQUFDLElBQUksRUFBRSxRQUFRLEVBQUMsQ0FBQztJQUV6QyxPQUFPLE1BQU0sQ0FBQyxTQUFTLENBQ25CLEdBQUcsRUFBRSxNQUE4QixFQUFFLEtBQTJCLENBQUMsQ0FBQztBQUN4RSxDQUFDO0FBRUQsTUFBTSxDQUFDLE1BQU0sR0FBRyxHQUFHLEVBQUUsQ0FBQyxFQUFDLElBQUksRUFBQyxDQUFDLENBQUMiLCJzb3VyY2VzQ29udGVudCI6WyIvKipcbiAqIEBsaWNlbnNlXG4gKiBDb3B5cmlnaHQgMjAyMCBHb29nbGUgTExDLiBBbGwgUmlnaHRzIFJlc2VydmVkLlxuICogTGljZW5zZWQgdW5kZXIgdGhlIEFwYWNoZSBMaWNlbnNlLCBWZXJzaW9uIDIuMCAodGhlIFwiTGljZW5zZVwiKTtcbiAqIHlvdSBtYXkgbm90IHVzZSB0aGlzIGZpbGUgZXhjZXB0IGluIGNvbXBsaWFuY2Ugd2l0aCB0aGUgTGljZW5zZS5cbiAqIFlvdSBtYXkgb2J0YWluIGEgY29weSBvZiB0aGUgTGljZW5zZSBhdFxuICpcbiAqIGh0dHA6Ly93d3cuYXBhY2hlLm9yZy9saWNlbnNlcy9MSUNFTlNFLTIuMFxuICpcbiAqIFVubGVzcyByZXF1aXJlZCBieSBhcHBsaWNhYmxlIGxhdyBvciBhZ3JlZWQgdG8gaW4gd3JpdGluZywgc29mdHdhcmVcbiAqIGRpc3RyaWJ1dGVkIHVuZGVyIHRoZSBMaWNlbnNlIGlzIGRpc3RyaWJ1dGVkIG9uIGFuIFwiQVMgSVNcIiBCQVNJUyxcbiAqIFdJVEhPVVQgV0FSUkFOVElFUyBPUiBDT05ESVRJT05TIE9GIEFOWSBLSU5ELCBlaXRoZXIgZXhwcmVzcyBvciBpbXBsaWVkLlxuICogU2VlIHRoZSBMaWNlbnNlIGZvciB0aGUgc3BlY2lmaWMgbGFuZ3VhZ2UgZ292ZXJuaW5nIHBlcm1pc3Npb25zIGFuZFxuICogbGltaXRhdGlvbnMgdW5kZXIgdGhlIExpY2Vuc2UuXG4gKiA9PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PVxuICovXG5cbmltcG9ydCB7RU5HSU5FfSBmcm9tICcuLi9lbmdpbmUnO1xuaW1wb3J0IHtBbGwsIEFsbEF0dHJzLCBBbGxJbnB1dHN9IGZyb20gJy4uL2tlcm5lbF9uYW1lcyc7XG5pbXBvcnQge05hbWVkQXR0ck1hcH0gZnJvbSAnLi4va2VybmVsX3JlZ2lzdHJ5JztcbmltcG9ydCB7VGVuc29yfSBmcm9tICcuLi90ZW5zb3InO1xuaW1wb3J0IHtOYW1lZFRlbnNvck1hcH0gZnJvbSAnLi4vdGVuc29yX3R5cGVzJztcbmltcG9ydCB7Y29udmVydFRvVGVuc29yfSBmcm9tICcuLi90ZW5zb3JfdXRpbF9lbnYnO1xuaW1wb3J0IHtUZW5zb3JMaWtlfSBmcm9tICcuLi90eXBlcyc7XG5cbmltcG9ydCB7b3B9IGZyb20gJy4vb3BlcmF0aW9uJztcblxuLyoqXG4gKiBDb21wdXRlcyB0aGUgbG9naWNhbCBhbmQgb2YgZWxlbWVudHMgYWNyb3NzIGRpbWVuc2lvbnMgb2YgYSBgdGYuVGVuc29yYC5cbiAqXG4gKiBSZWR1Y2VzIHRoZSBpbnB1dCBhbG9uZyB0aGUgZGltZW5zaW9ucyBnaXZlbiBpbiBgYXhlc2AuIFVubGVzcyBga2VlcERpbXNgXG4gKiBpcyB0cnVlLCB0aGUgcmFuayBvZiB0aGUgYHRmLlRlbnNvcmAgaXMgcmVkdWNlZCBieSAxIGZvciBlYWNoIGVudHJ5IGluXG4gKiBgYXhlc2AuIElmIGBrZWVwRGltc2AgaXMgdHJ1ZSwgdGhlIHJlZHVjZWQgZGltZW5zaW9ucyBhcmUgcmV0YWluZWQgd2l0aFxuICogbGVuZ3RoIDEuIElmIGBheGVzYCBoYXMgbm8gZW50cmllcywgYWxsIGRpbWVuc2lvbnMgYXJlIHJlZHVjZWQsIGFuZCBhblxuICogYHRmLlRlbnNvcmAgd2l0aCBhIHNpbmdsZSBlbGVtZW50IGlzIHJldHVybmVkLlxuICpcbiAqIGBgYGpzXG4gKiBjb25zdCB4ID0gdGYudGVuc29yMWQoWzEsIDEsIDFdLCAnYm9vbCcpO1xuICpcbiAqIHguYWxsKCkucHJpbnQoKTsgIC8vIG9yIHRmLmFsbCh4KVxuICogYGBgXG4gKlxuICogYGBganNcbiAqIGNvbnN0IHggPSB0Zi50ZW5zb3IyZChbMSwgMSwgMCwgMF0sIFsyLCAyXSwgJ2Jvb2wnKTtcbiAqXG4gKiBjb25zdCBheGlzID0gMTtcbiAqIHguYWxsKGF4aXMpLnByaW50KCk7ICAvLyBvciB0Zi5hbGwoeCwgYXhpcylcbiAqIGBgYFxuICpcbiAqIEBwYXJhbSB4IFRoZSBpbnB1dCB0ZW5zb3IuIE11c3QgYmUgb2YgZHR5cGUgYm9vbC5cbiAqIEBwYXJhbSBheGlzIFRoZSBkaW1lbnNpb24ocykgdG8gcmVkdWNlLiBCeSBkZWZhdWx0IGl0IHJlZHVjZXNcbiAqICAgICBhbGwgZGltZW5zaW9ucy5cbiAqIEBwYXJhbSBrZWVwRGltcyBJZiB0cnVlLCByZXRhaW5zIHJlZHVjZWQgZGltZW5zaW9ucyB3aXRoIHNpemUgMS5cbiAqXG4gKiBAZG9jIHtoZWFkaW5nOiAnT3BlcmF0aW9ucycsIHN1YmhlYWRpbmc6ICdSZWR1Y3Rpb24nfVxuICovXG5mdW5jdGlvbiBhbGxfPFQgZXh0ZW5kcyBUZW5zb3I+KFxuICAgIHg6IFRlbnNvcnxUZW5zb3JMaWtlLCBheGlzOiBudW1iZXJ8bnVtYmVyW10gPSBudWxsLCBrZWVwRGltcyA9IGZhbHNlKTogVCB7XG4gIGNvbnN0ICR4ID0gY29udmVydFRvVGVuc29yKHgsICd4JywgJ2FsbCcsICdib29sJyk7XG5cbiAgY29uc3QgaW5wdXRzOiBBbGxJbnB1dHMgPSB7eDogJHh9O1xuICBjb25zdCBhdHRyczogQWxsQXR0cnMgPSB7YXhpcywga2VlcERpbXN9O1xuXG4gIHJldHVybiBFTkdJTkUucnVuS2VybmVsKFxuICAgICAgQWxsLCBpbnB1dHMgYXMge30gYXMgTmFtZWRUZW5zb3JNYXAsIGF0dHJzIGFzIHt9IGFzIE5hbWVkQXR0ck1hcCk7XG59XG5cbmV4cG9ydCBjb25zdCBhbGwgPSBvcCh7YWxsX30pO1xuIl19","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Any } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the logical or of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and an\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 1, 1], 'bool');\n *\n * x.any().print(); // or tf.any(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');\n *\n * const axis = 1;\n * x.any(axis).print(); // or tf.any(x, axis)\n * ```\n *\n * @param x The input tensor. Must be of dtype bool.\n * @param axis The dimension(s) to reduce. By default it reduces\n * all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction any_(x, axis = null, keepDims = false) {\n const $x = convertToTensor(x, 'x', 'any', 'bool');\n const inputs = { x: $x };\n const attrs = { axis, keepDims };\n return ENGINE.runKernel(Any, inputs, attrs);\n}\n// tslint:disable-next-line:variable-name\nexport const any = op({ any_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ArgMax } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns the indices of the maximum values along an `axis`.\n *\n * The result has the same shape as `input` with the dimension along `axis`\n * removed.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.argMax().print(); // or tf.argMax(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 4, 3], [2, 2]);\n *\n * const axis = 1;\n * x.argMax(axis).print(); // or tf.argMax(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension to reduce. Defaults to 0 (outer-most dimension).\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction argMax_(x, axis = 0) {\n const $x = convertToTensor(x, 'x', 'argMax');\n const inputs = { x: $x };\n const attrs = { axis };\n return ENGINE.runKernel(ArgMax, inputs, attrs);\n}\nexport const argMax = op({ argMax_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ArgMin } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns the indices of the minimum values along an `axis`.\n *\n * The result has the same shape as `input` with the dimension along `axis`\n * removed.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.argMin().print(); // or tf.argMin(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 4, 3], [2, 2]);\n *\n * const axis = 1;\n * x.argMin(axis).print(); // or tf.argMin(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension to reduce. Defaults to 0 (outer-most dimension).\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction argMin_(x, axis = 0) {\n const $x = convertToTensor(x, 'x', 'argMin');\n const inputs = { x: $x };\n const attrs = { axis };\n return ENGINE.runKernel(ArgMin, inputs, attrs);\n}\nexport const argMin = op({ argMin_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Asin } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes asin of the input `tf.Tensor` element-wise: `asin(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.asin().print(); // or tf.asin(x)\n * ```\n * @param x The input tensor.\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction asin_(x) {\n const $x = convertToTensor(x, 'x', 'asin');\n const inputs = { x: $x };\n return ENGINE.runKernel(Asin, inputs);\n}\nexport const asin = op({ asin_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Asinh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes inverse hyperbolic sin of the input `tf.Tensor` element-wise:\n * `asinh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.asinh().print(); // or tf.asinh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction asinh_(x) {\n const $x = convertToTensor(x, 'x', 'asinh');\n const inputs = { x: $x };\n return ENGINE.runKernel(Asinh, inputs);\n}\nexport const asinh = op({ asinh_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Atan } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes atan of the input `tf.Tensor` element-wise: `atan(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.atan().print(); // or tf.atan(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction atan_(x) {\n const $x = convertToTensor(x, 'x', 'atan');\n const inputs = { x: $x };\n return ENGINE.runKernel(Atan, inputs);\n}\nexport const atan = op({ atan_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Atan2 } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes arctangent of `tf.Tensor`s a / b element-wise: `atan2(a, b)`.\n * Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1.0, 1.0, -1.0, .7]);\n * const b = tf.tensor1d([2.0, 13.0, 3.5, .21]);\n *\n * tf.atan2(a, b).print()\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction atan2_(a, b) {\n let $a = convertToTensor(a, 'a', 'atan2');\n let $b = convertToTensor(b, 'b', 'atan2');\n [$a, $b] = makeTypesMatch($a, $b);\n const inputs = { a: $a, b: $b };\n return ENGINE.runKernel(Atan2, inputs);\n}\nexport const atan2 = op({ atan2_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Atanh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes inverse hyperbolic tan of the input `tf.Tensor` element-wise:\n * `atanh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, .1, -.1, .7]);\n *\n * x.atanh().print(); // or tf.atanh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction atanh_(x) {\n const $x = convertToTensor(x, 'x', 'atanh');\n const inputs = { x: $x };\n return ENGINE.runKernel(Atanh, inputs);\n}\nexport const atanh = op({ atanh_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { AvgPool3D } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { checkPadOnDimRoundingMode } from './conv_util';\nimport { cast } from './cast';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the 3D average pooling.\n *\n * ```js\n * const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);\n * const result = tf.avgPool3d(x, 2, 1, 'valid');\n * result.print();\n * ```\n *\n * @param x The input tensor, of rank 5 or rank 4 of shape\n * `[batch, depth, height, width, inChannels]`.\n * @param filterSize The filter size:\n * `[filterDepth, filterHeight, filterWidth]`.\n * If `filterSize` is a single number,\n * then `filterDepth == filterHeight == filterWidth`.\n * @param strides The strides of the pooling:\n * `[strideDepth, strideHeight, strideWidth]`.\n * If `strides` is a single number,\n * then `strideDepth == strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n * - `same` and stride 1: output will be of same size as input,\n * regardless of filter size.\n * - `valid`: output will be smaller than input if filter is larger\n * than 1*1x1.\n * - For more info, see this guide:\n * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](\n * https://www.tensorflow.org/api_docs/python/tf/nn/convolution)\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n * provided, it will default to truncate.\n * @param dataFormat An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n * \"NDHWC\". Specify the data format of the input and output data. With the\n * default format \"NDHWC\", the data is stored in the order of: [batch,\n * depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction avgPool3d_(x, filterSize, strides, pad, dimRoundingMode, dataFormat = 'NDHWC') {\n const $x = convertToTensor(x, 'x', 'avgPool3d', 'float32');\n let x5D = $x;\n let reshapedTo5D = false;\n if ($x.rank === 4) {\n reshapedTo5D = true;\n x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n }\n util.assert(x5D.rank === 5, () => `Error in avgPool3d: x must be rank 5 but got rank ${x5D.rank}.`);\n util.assert(dataFormat === 'NDHWC', () => `Error in avgPool3d: Only NDHWC is currently supported, ` +\n `but got dataFormat of ${dataFormat}`);\n checkPadOnDimRoundingMode('avgPool3d', pad, dimRoundingMode);\n const inputs = { x: x5D };\n const attrs = { filterSize, strides, pad, dimRoundingMode, dataFormat };\n // tslint:disable-next-line: no-unnecessary-type-assertion\n let res = ENGINE.runKernel(AvgPool3D, inputs, attrs);\n res = cast(res, x5D.dtype);\n if (reshapedTo5D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n }\n return res;\n}\nexport const avgPool3d = op({ avgPool3d_ });\n//# sourceMappingURL=data:application/json;base64,{"version":3,"file":"avg_pool_3d.js","sourceRoot":"","sources":["../../../../../../tfjs-core/src/ops/avg_pool_3d.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAEH,OAAO,EAAC,MAAM,EAAC,MAAM,WAAW,CAAC;AACjC,OAAO,EAAC,SAAS,EAAkC,MAAM,iBAAiB,CAAC;AAI3E,OAAO,EAAC,eAAe,EAAC,MAAM,oBAAoB,CAAC;AAEnD,OAAO,KAAK,IAAI,MAAM,SAAS,CAAC;AAEhC,OAAO,EAAC,yBAAyB,EAAC,MAAM,aAAa,CAAC;AACtD,OAAO,EAAC,IAAI,EAAC,MAAM,QAAQ,CAAC;AAC5B,OAAO,EAAC,EAAE,EAAC,MAAM,aAAa,CAAC;AAC/B,OAAO,EAAC,OAAO,EAAC,MAAM,WAAW,CAAC;AAElC;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;GAmCG;AACH,SAAS,UAAU,CACf,CAAe,EAAE,UAA2C,EAC5D,OAAwC,EAAE,GAA0B,EACpE,eAAwC,EACxC,aAA8B,OAAO;IACvC,MAAM,EAAE,GAAG,eAAe,CAAC,CAAC,EAAE,GAAG,EAAE,WAAW,EAAE,SAAS,CAAC,CAAC;IAE3D,IAAI,GAAG,GAAG,EAAc,CAAC;IACzB,IAAI,YAAY,GAAG,KAAK,CAAC;IACzB,IAAI,EAAE,CAAC,IAAI,KAAK,CAAC,EAAE;QACjB,YAAY,GAAG,IAAI,CAAC;QACpB,GAAG,GAAG,OAAO,CAAC,EAAE,EAAE,CAAC,CAAC,EAAE,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;KAC5E;IAED,IAAI,CAAC,MAAM,CACP,GAAG,CAAC,IAAI,KAAK,CAAC,EACd,GAAG,EAAE,CAAC,qDAAqD,GAAG,CAAC,IAAI,GAAG,CAAC,CAAC;IAC5E,IAAI,CAAC,MAAM,CACP,UAAU,KAAK,OAAO,EACtB,GAAG,EAAE,CAAC,yDAAyD;QAC3D,yBAAyB,UAAU,EAAE,CAAC,CAAC;IAC/C,yBAAyB,CAAC,WAAW,EAAE,GAAG,EAAE,eAAe,CAAC,CAAC;IAC7D,MAAM,MAAM,GAAoB,EAAC,CAAC,EAAE,GAAG,EAAC,CAAC;IACzC,MAAM,KAAK,GACU,EAAC,UAAU,EAAE,OAAO,EAAE,GAAG,EAAE,eAAe,EAAE,UAAU,EAAC,CAAC;IAE7E,0DAA0D;IAC1D,IAAI,GAAG,GAAG,MAAM,CAAC,SAAS,CACZ,SAAS,EAAE,MAA8B,EACzC,KAA2B,CAAM,CAAC;IAEhD,GAAG,GAAG,IAAI,CAAC,GAAG,EAAE,GAAG,CAAC,KAAK,CAAC,CAAC;IAE3B,IAAI,YAAY,EAAE;QAChB,OAAO,OAAO,CACH,GAAG,EAAE,CAAC,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CACnE,CAAC;KACP;IAED,OAAO,GAAG,CAAC;AACb,CAAC;AAED,MAAM,CAAC,MAAM,SAAS,GAAG,EAAE,CAAC,EAAC,UAAU,EAAC,CAAC,CAAC","sourcesContent":["/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {AvgPool3D, AvgPool3DAttrs, AvgPool3DInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor4D, Tensor5D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {checkPadOnDimRoundingMode} from './conv_util';\nimport {cast} from './cast';\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Computes the 3D average pooling.\n *\n * ```js\n * const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);\n * const result = tf.avgPool3d(x, 2, 1, 'valid');\n * result.print();\n * ```\n *\n * @param x The input tensor, of rank 5 or rank 4 of shape\n *     `[batch, depth, height, width, inChannels]`.\n * @param filterSize The filter size:\n *     `[filterDepth, filterHeight, filterWidth]`.\n *     If `filterSize` is a single number,\n *     then `filterDepth == filterHeight == filterWidth`.\n * @param strides The strides of the pooling:\n *     `[strideDepth, strideHeight, strideWidth]`.\n *     If `strides` is a single number,\n *     then `strideDepth == strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1*1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](\n *          https://www.tensorflow.org/api_docs/python/tf/nn/convolution)\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n *     provided, it will default to truncate.\n * @param dataFormat An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n *     \"NDHWC\". Specify the data format of the input and output data. With the\n *     default format \"NDHWC\", the data is stored in the order of: [batch,\n *     depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction avgPool3d_<T extends Tensor4D|Tensor5D>(\n    x: T|TensorLike, filterSize: [number, number, number]|number,\n    strides: [number, number, number]|number, pad: 'valid'|'same'|number,\n    dimRoundingMode?: 'floor'|'round'|'ceil',\n    dataFormat: 'NDHWC'|'NCDHW' = 'NDHWC'): T {\n  const $x = convertToTensor(x, 'x', 'avgPool3d', 'float32');\n\n  let x5D = $x as Tensor5D;\n  let reshapedTo5D = false;\n  if ($x.rank === 4) {\n    reshapedTo5D = true;\n    x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n  }\n\n  util.assert(\n      x5D.rank === 5,\n      () => `Error in avgPool3d: x must be rank 5 but got rank ${x5D.rank}.`);\n  util.assert(\n      dataFormat === 'NDHWC',\n      () => `Error in avgPool3d: Only NDHWC is currently supported, ` +\n          `but got dataFormat of ${dataFormat}`);\n  checkPadOnDimRoundingMode('avgPool3d', pad, dimRoundingMode);\n  const inputs: AvgPool3DInputs = {x: x5D};\n  const attrs:\n      AvgPool3DAttrs = {filterSize, strides, pad, dimRoundingMode, dataFormat};\n\n  // tslint:disable-next-line: no-unnecessary-type-assertion\n  let res = ENGINE.runKernel(\n                AvgPool3D, inputs as {} as NamedTensorMap,\n                attrs as {} as NamedAttrMap) as T;\n\n  res = cast(res, x5D.dtype);\n\n  if (reshapedTo5D) {\n    return reshape(\n               res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]) as\n        T;\n  }\n\n  return res;\n}\n\nexport const avgPool3d = op({avgPool3d_});\n"]}","/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { BroadcastArgs } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Return the shape of s0 op s1 with broadcast.\n *\n * compute r0, the broadcasted shape as a tensor.\n * s0, s1 and r0 are all integer vectors.\n *\n * This function returns the shape of the result of an operation between\n * two tensors of size s0 and s1 performed with broadcast.\n *\n * @param s0 A tensor representing a shape\n * @param s1 A tensor representing a shape\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction broadcastArgs_(s0, s1) {\n const shape1Input = convertToTensor(s0, 's0', 'broadcastArgs', 'int32');\n const shape2Input = convertToTensor(s1, 's1', 'broadcastArgs', 'int32');\n if (shape1Input.rank !== 1) {\n throw new Error('broadcastArgs(): first input must be a vector (rank=1). ' +\n `Has rank ${shape1Input.rank}`);\n }\n if (shape2Input.rank !== 1) {\n throw new Error('broadcastArgs(): second input must be a vector (rank=1). ' +\n `Has rank ${shape2Input.rank}`);\n }\n const inputs = { s0: shape1Input, s1: shape2Input };\n return ENGINE.runKernel(BroadcastArgs, inputs);\n}\nexport const broadcastArgs = op({ broadcastArgs_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Ceil } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes ceiling of input `tf.Tensor` element-wise: `ceil(x)`\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3]);\n *\n * x.ceil().print(); // or tf.ceil(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction ceil_(x) {\n const $x = convertToTensor(x, 'x', 'ceil', 'float32');\n const inputs = { x: $x };\n return ENGINE.runKernel(Ceil, inputs);\n}\nexport const ceil = op({ ceil_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ClipByValue } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Clips values element-wise. `max(min(x, clipValueMax), clipValueMin)`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.clipByValue(-2, 3).print(); // or tf.clipByValue(x, -2, 3)\n * ```\n * @param x The input tensor.\n * @param clipValueMin Lower-bound of range to be clipped to.\n * @param clipValueMax Upper-bound of range to be clipped to.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction clipByValue_(x, clipValueMin, clipValueMax) {\n const $x = convertToTensor(x, 'x', 'clipByValue');\n util.assert((clipValueMin <= clipValueMax), () => `Error in clip: min (${clipValueMin}) must be ` +\n `less than or equal to max (${clipValueMax}).`);\n const inputs = { x: $x };\n const attrs = { clipValueMin, clipValueMax };\n return ENGINE.runKernel(ClipByValue, inputs, attrs);\n}\nexport const clipByValue = op({ clipByValue_ });\n//# 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{ convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { conv2d } from './conv2d';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes a 1D convolution over the input x.\n *\n * @param x The input tensor, of rank 3 or rank 2, of shape\n * `[batch, width, inChannels]`. If rank 2, batch of 1 is assumed.\n * @param filter The filter, rank 3, of shape\n * `[filterWidth, inDepth, outDepth]`.\n * @param stride The number of entries by which the filter is moved right at\n * each step.\n * @param pad The type of padding algorithm.\n * - `same` and stride 1: output will be of same size as input,\n * regardless of filter size.\n * - `valid`: output will be smaller than input if filter is larger\n * than 1x1.\n * - For more info, see this guide:\n * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](\n * https://www.tensorflow.org/api_docs/python/tf/nn/convolution)\n * @param dataFormat An optional string from \"NWC\", \"NCW\". Defaults to \"NWC\",\n * the data is stored in the order of [batch, in_width, in_channels]. Only\n * \"NWC\" is currently supported.\n * @param dilation The dilation rate in which we sample input values in\n * atrous convolution. Defaults to `1`. If it is greater than 1, then\n * stride must be `1`.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n * provided, it will default to truncate.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv1d_(x, filter, stride, pad, dataFormat = 'NWC', dilation = 1, dimRoundingMode) {\n const $x = convertToTensor(x, 'x', 'conv1d');\n const $filter = convertToTensor(filter, 'filter', 'conv1d');\n let x3D = $x;\n let reshapedTo3D = false;\n if ($x.rank === 2) {\n reshapedTo3D = true;\n x3D = reshape($x, [1, $x.shape[0], $x.shape[1]]);\n }\n util.assert(x3D.rank === 3, () => `Error in conv1d: input must be rank 3, but got rank ${x3D.rank}.`);\n util.assert($filter.rank === 3, () => `Error in conv1d: filter must be rank 3, but got rank ` +\n `${$filter.rank}.`);\n conv_util.checkPadOnDimRoundingMode('conv1d', pad, dimRoundingMode);\n util.assert(x3D.shape[2] === $filter.shape[1], () => `Error in conv1d: depth of input (${x3D.shape[2]}) must match ` +\n `input depth for filter ${$filter.shape[1]}.`);\n util.assert(conv_util.eitherStridesOrDilationsAreOne(stride, dilation), () => 'Error in conv1D: Either stride or dilation must be 1. ' +\n `Got stride ${stride} and dilation '${dilation}'`);\n util.assert(dataFormat === 'NWC', () => `Error in conv1d: got dataFormat of ${dataFormat} but only NWC is currently supported.`);\n const filter4D = reshape($filter, [1, $filter.shape[0], $filter.shape[1], $filter.shape[2]]);\n const input4D = reshape(x3D, [x3D.shape[0], 1, x3D.shape[1], x3D.shape[2]]);\n const strides = [1, stride];\n const dilations = [1, dilation];\n const conv2dDataFormat = 'NHWC';\n const res = conv2d(input4D, filter4D, strides, pad, conv2dDataFormat, dilations, dimRoundingMode);\n if (reshapedTo3D) {\n return reshape(res, [res.shape[2], res.shape[3]]);\n }\n return reshape(res, [res.shape[0], res.shape[2], res.shape[3]]);\n}\nexport const conv1d = op({ conv1d_ });\n//# sourceMappingURL=data:application/json;base64,{"version":3,"file":"conv1d.js","sourceRoot":"","sources":["../../../../../../tfjs-core/src/ops/conv1d.ts"],"names":[],"mappings":"AAiBA,OAAO,EAAC,eAAe,EAAC,MAAM,oBAAoB,CAAC;AAEnD,OAAO,KAAK,IAAI,MAAM,SAAS,CAAC;AAEhC,OAAO,EAAC,MAAM,EAAC,MAAM,UAAU,CAAC;AAChC,OAAO,KAAK,SAAS,MAAM,aAAa,CAAC;AACzC,OAAO,EAAC,EAAE,EAAC,MAAM,aAAa,CAAC;AAC/B,OAAO,EAAC,OAAO,EAAC,MAAM,WAAW,CAAC;AAElC;;;;;;;;;;;;;;;;;;;;;;;;;;;GA2BG;AACH,SAAS,OAAO,CACZ,CAAe,EAAE,MAA2B,EAAE,MAAc,EAC5D,GAAoD,EACpD,aAA0B,KAAK,EAAE,QAAQ,GAAG,CAAC,EAC7C,eAAwC;IAC1C,MAAM,EAAE,GAAG,eAAe,CAAC,CAAC,EAAE,GAAG,EAAE,QAAQ,CAAC,CAAC;IAC7C,MAAM,OAAO,GAAG,eAAe,CAAC,MAAM,EAAE,QAAQ,EAAE,QAAQ,CAAC,CAAC;IAE5D,IAAI,GAAG,GAAG,EAAc,CAAC;IACzB,IAAI,YAAY,GAAG,KAAK,CAAC;IACzB,IAAI,EAAE,CAAC,IAAI,KAAK,CAAC,EAAE;QACjB,YAAY,GAAG,IAAI,CAAC;QACpB,GAAG,GAAG,OAAO,CAAC,EAAE,EAAE,CAAC,CAAC,EAAE,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;KAClD;IAED,IAAI,CAAC,MAAM,CACP,GAAG,CAAC,IAAI,KAAK,CAAC,EACd,GAAG,EAAE,CAAC,uDAAuD,GAAG,CAAC,IAAI,GAAG,CAAC,CAAC;IAC9E,IAAI,CAAC,MAAM,CACP,OAAO,CAAC,IAAI,KAAK,CAAC,EAClB,GAAG,EAAE,CAAC,uDAAuD;QACzD,GAAG,OAAO,CAAC,IAAI,GAAG,CAAC,CAAC;IAC5B,SAAS,CAAC,yBAAyB,CAAC,QAAQ,EAAE,GAAG,EAAE,eAAe,CAAC,CAAC;IACpE,IAAI,CAAC,MAAM,CACP,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,KAAK,OAAO,CAAC,KAAK,CAAC,CAAC,CAAC,EACjC,GAAG,EAAE,CAAC,oCAAoC,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,eAAe;QACjE,0BAA0B,OAAO,CAAC,KAAK,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC;IACvD,IAAI,CAAC,MAAM,CACP,SAAS,CAAC,8BAA8B,CAAC,MAAM,EAAE,QAAQ,CAAC,EAC1D,GAAG,EAAE,CAAC,wDAAwD;QAC1D,cAAc,MAAM,kBAAkB,QAAQ,GAAG,CAAC,CAAC;IAC3D,IAAI,CAAC,MAAM,CACP,UAAU,KAAK,KAAK,EACpB,GAAG,EAAE,CAAC,sCACF,UAAU,uCAAuC,CAAC,CAAC;IAE3D,MAAM,QAAQ,GAAG,OAAO,CACpB,OAAO,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,OAAO,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,OAAO,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;IACxE,MAAM,OAAO,GAAG,OAAO,CAAC,GAAG,EAAE,CAAC,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;IAC5E,MAAM,OAAO,GAAqB,CAAC,CAAC,EAAE,MAAM,CAAC,CAAC;IAC9C,MAAM,SAAS,GAAqB,CAAC,CAAC,EAAE,QAAQ,CAAC,CAAC;IAElD,MAAM,gBAAgB,GAAG,MAAM,CAAC;IAEhC,MAAM,GAAG,GAAG,MAAM,CACb,OAAoB,EAAG,QAAqB,EAAE,OAAO,EAAE,GAAG,EAC3D,gBAAgB,EAAE,SAAS,EAAE,eAAe,CAAC,CAAC;IAElD,IAAI,YAAY,EAAE;QAChB,OAAO,OAAO,CAAC,GAAG,EAAE,CAAC,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAM,CAAC;KACxD;IAED,OAAO,OAAO,CAAC,GAAG,EAAE,CAAC,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAM,CAAC;AACvE,CAAC;AAED,MAAM,CAAC,MAAM,MAAM,GAAG,EAAE,CAAC,EAAC,OAAO,EAAC,CAAC,CAAC","sourcesContent":["/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor2D, Tensor3D, Tensor4D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {conv2d} from './conv2d';\nimport * as conv_util from './conv_util';\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Computes a 1D convolution over the input x.\n *\n * @param x The input tensor, of rank 3 or rank 2, of shape\n *     `[batch, width, inChannels]`. If rank 2, batch of 1 is assumed.\n * @param filter The filter, rank 3, of shape\n *     `[filterWidth, inDepth, outDepth]`.\n * @param stride The number of entries by which the filter is moved right at\n *     each step.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](\n *          https://www.tensorflow.org/api_docs/python/tf/nn/convolution)\n * @param dataFormat An optional string from \"NWC\", \"NCW\". Defaults to \"NWC\",\n *     the data is stored in the order of [batch, in_width, in_channels]. Only\n *     \"NWC\" is currently supported.\n * @param dilation The dilation rate in which we sample input values in\n *     atrous convolution. Defaults to `1`. If it is greater than 1, then\n *     stride must be `1`.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n *     provided, it will default to truncate.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv1d_<T extends Tensor2D|Tensor3D>(\n    x: T|TensorLike, filter: Tensor3D|TensorLike, stride: number,\n    pad: 'valid'|'same'|number|conv_util.ExplicitPadding,\n    dataFormat: 'NWC'|'NCW' = 'NWC', dilation = 1,\n    dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  const $x = convertToTensor(x, 'x', 'conv1d');\n  const $filter = convertToTensor(filter, 'filter', 'conv1d');\n\n  let x3D = $x as Tensor3D;\n  let reshapedTo3D = false;\n  if ($x.rank === 2) {\n    reshapedTo3D = true;\n    x3D = reshape($x, [1, $x.shape[0], $x.shape[1]]);\n  }\n\n  util.assert(\n      x3D.rank === 3,\n      () => `Error in conv1d: input must be rank 3, but got rank ${x3D.rank}.`);\n  util.assert(\n      $filter.rank === 3,\n      () => `Error in conv1d: filter must be rank 3, but got rank ` +\n          `${$filter.rank}.`);\n  conv_util.checkPadOnDimRoundingMode('conv1d', pad, dimRoundingMode);\n  util.assert(\n      x3D.shape[2] === $filter.shape[1],\n      () => `Error in conv1d: depth of input (${x3D.shape[2]}) must match ` +\n          `input depth for filter ${$filter.shape[1]}.`);\n  util.assert(\n      conv_util.eitherStridesOrDilationsAreOne(stride, dilation),\n      () => 'Error in conv1D: Either stride or dilation must be 1. ' +\n          `Got stride ${stride} and dilation '${dilation}'`);\n  util.assert(\n      dataFormat === 'NWC',\n      () => `Error in conv1d: got dataFormat of ${\n          dataFormat} but only NWC is currently supported.`);\n\n  const filter4D = reshape(\n      $filter, [1, $filter.shape[0], $filter.shape[1], $filter.shape[2]]);\n  const input4D = reshape(x3D, [x3D.shape[0], 1, x3D.shape[1], x3D.shape[2]]);\n  const strides: [number, number] = [1, stride];\n  const dilations: [number, number] = [1, dilation];\n\n  const conv2dDataFormat = 'NHWC';\n\n  const res = conv2d(\n      (input4D as Tensor4D), (filter4D as Tensor4D), strides, pad,\n      conv2dDataFormat, dilations, dimRoundingMode);\n\n  if (reshapedTo3D) {\n    return reshape(res, [res.shape[2], res.shape[3]]) as T;\n  }\n\n  return reshape(res, [res.shape[0], res.shape[2], res.shape[3]]) as T;\n}\n\nexport const conv1d = op({conv1d_});\n"]}","import { convertToTensor } from '../tensor_util_env';\nimport { conv2DBackpropInput } from './conv2d_backprop_input';\nimport { op } from './operation';\n/**\n * Computes the transposed 2D convolution of an image, also known as a\n * deconvolution.\n *\n * @param x The input image, of rank 4 or rank 3, of shape\n * `[batch, height, width, inDepth]`. If rank 3, batch of 1 is assumed.\n * @param filter The filter, rank 4, of shape\n * `[filterHeight, filterWidth, outDepth, inDepth]`.\n * `inDepth` must match `inDepth` in `x`.\n * @param outputShape Output shape, of rank 4 or rank 3:\n * `[batch, height, width, outDepth]`. If rank 3, batch of 1 is assumed.\n * @param strides The strides of the original convolution:\n * `[strideHeight, strideWidth]`.\n * @param pad The type of padding algorithm used in the non-transpose version\n * of the op.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n * provided, it will default to truncate.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv2dTranspose_(x, filter, outputShape, strides, pad, dimRoundingMode) {\n const $x = convertToTensor(x, 'x', 'conv2dTranspose');\n const $filter = convertToTensor(filter, 'filter', 'conv2dTranspose');\n return conv2DBackpropInput(outputShape, $x, $filter, strides, pad, 'NHWC', dimRoundingMode);\n}\nexport const conv2dTranspose = op({ conv2dTranspose_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv3D } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { eitherStridesOrDilationsAreOne } from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes a 3D convolution over the input x.\n *\n * @param x The input tensor, of rank 5 or rank 4, of shape\n * `[batch, depth, height, width, channels]`. If rank 4,\n * batch of 1 is assumed.\n * @param filter The filter, rank 5, of shape\n * `[filterDepth, filterHeight, filterWidth, inChannels, outChannels]`.\n * inChannels must match between input and filter.\n * @param strides The strides of the convolution: `[strideDepth, strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n * - `same` and stride 1: output will be of same size as input,\n * regardless of filter size.\n * - `valid`: output will be smaller than input if filter is larger\n * than 1x1.\n * - For more info, see this guide:\n * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](\n * https://www.tensorflow.org/api_docs/python/tf/nn/convolution)\n * @param dataFormat: An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n * \"NDHWC\". Specify the data format of the input and output data. With the\n * default format \"NDHWC\", the data is stored in the order of: [batch,\n * depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @param dilations The dilation rates: `[dilationDepth, dilationHeight,\n * dilationWidth]` in which we sample input values across the height\n * and width dimensions in atrous convolution. Defaults to `[1, 1, 1]`.\n * If `dilations` is a single number, then\n * `dilationDepth == dilationHeight == dilationWidth`. If it is greater\n * than 1, then all values of `strides` must be 1.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv3d_(x, filter, strides, pad, dataFormat = 'NDHWC', dilations = [1, 1, 1]) {\n const $x = convertToTensor(x, 'x', 'conv3d');\n const $filter = convertToTensor(filter, 'filter', 'conv3d');\n let x5D = $x;\n let reshapedTo5D = false;\n if ($x.rank === 4) {\n reshapedTo5D = true;\n x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n }\n util.assert(x5D.rank === 5, () => `Error in conv3d: input must be rank 5, but got rank ${x5D.rank}.`);\n util.assert($filter.rank === 5, () => `Error in conv3d: filter must be rank 5, but got rank ` +\n `${$filter.rank}.`);\n util.assert(x5D.shape[4] === $filter.shape[3], () => `Error in conv3d: depth of input (${x5D.shape[4]}) must match ` +\n `input depth for filter ${$filter.shape[3]}.`);\n util.assert(eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in conv3D: Either strides or dilations must be 1. ' +\n `Got strides ${strides} and dilations '${dilations}'`);\n util.assert(dataFormat === 'NDHWC', () => `Error in conv3d: got dataFormat of ${dataFormat} but only NDHWC is currently supported.`);\n const inputs = { x: x5D, filter: $filter };\n const attrs = { strides, pad, dataFormat, dilations };\n // tslint:disable-next-line: no-unnecessary-type-assertion\n const res = ENGINE.runKernel(Conv3D, inputs, attrs);\n if (reshapedTo5D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n }\n return res;\n}\nexport const conv3d = op({ conv3d_ });\n//# sourceMappingURL=data:application/json;base64,{"version":3,"file":"conv3d.js","sourceRoot":"","sources":["../../../../../../tfjs-core/src/ops/conv3d.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AACH,OAAO,EAAC,MAAM,EAAC,MAAM,WAAW,CAAC;AACjC,OAAO,EAAC,MAAM,EAA4B,MAAM,iBAAiB,CAAC;AAIlE,OAAO,EAAC,eAAe,EAAC,MAAM,oBAAoB,CAAC;AAEnD,OAAO,KAAK,IAAI,MAAM,SAAS,CAAC;AAEhC,OAAO,EAAC,8BAA8B,EAAC,MAAM,aAAa,CAAC;AAC3D,OAAO,EAAC,EAAE,EAAC,MAAM,aAAa,CAAC;AAC/B,OAAO,EAAC,OAAO,EAAC,MAAM,WAAW,CAAC;AAElC;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;GA+BG;AACH,SAAS,OAAO,CACZ,CAAe,EAAE,MAA2B,EAC5C,OAAwC,EAAE,GAAmB,EAC7D,aAA8B,OAAO,EACrC,YAA6C,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC;IACxD,MAAM,EAAE,GAAG,eAAe,CAAC,CAAC,EAAE,GAAG,EAAE,QAAQ,CAAC,CAAC;IAC7C,MAAM,OAAO,GAAG,eAAe,CAAC,MAAM,EAAE,QAAQ,EAAE,QAAQ,CAAC,CAAC;IAE5D,IAAI,GAAG,GAAG,EAAc,CAAC;IACzB,IAAI,YAAY,GAAG,KAAK,CAAC;IAEzB,IAAI,EAAE,CAAC,IAAI,KAAK,CAAC,EAAE;QACjB,YAAY,GAAG,IAAI,CAAC;QACpB,GAAG,GAAG,OAAO,CAAC,EAAE,EAAE,CAAC,CAAC,EAAE,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;KAC5E;IACD,IAAI,CAAC,MAAM,CACP,GAAG,CAAC,IAAI,KAAK,CAAC,EACd,GAAG,EAAE,CAAC,uDAAuD,GAAG,CAAC,IAAI,GAAG,CAAC,CAAC;IAC9E,IAAI,CAAC,MAAM,CACP,OAAO,CAAC,IAAI,KAAK,CAAC,EAClB,GAAG,EAAE,CAAC,uDAAuD;QACzD,GAAG,OAAO,CAAC,IAAI,GAAG,CAAC,CAAC;IAC5B,IAAI,CAAC,MAAM,CACP,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,KAAK,OAAO,CAAC,KAAK,CAAC,CAAC,CAAC,EACjC,GAAG,EAAE,CAAC,oCAAoC,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,eAAe;QACjE,0BAA0B,OAAO,CAAC,KAAK,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC;IACvD,IAAI,CAAC,MAAM,CACP,8BAA8B,CAAC,OAAO,EAAE,SAAS,CAAC,EAClD,GAAG,EAAE,CAAC,0DAA0D;QAC5D,eAAe,OAAO,mBAAmB,SAAS,GAAG,CAAC,CAAC;IAC/D,IAAI,CAAC,MAAM,CACP,UAAU,KAAK,OAAO,EACtB,GAAG,EAAE,CAAC,sCACF,UAAU,yCAAyC,CAAC,CAAC;IAE7D,MAAM,MAAM,GAAiB,EAAC,CAAC,EAAE,GAAG,EAAE,MAAM,EAAE,OAAO,EAAC,CAAC;IAEvD,MAAM,KAAK,GAAgB,EAAC,OAAO,EAAE,GAAG,EAAE,UAAU,EAAE,SAAS,EAAC,CAAC;IAEjE,0DAA0D;IAC1D,MAAM,GAAG,GAAG,MAAM,CAAC,SAAS,CACZ,MAAM,EAAE,MAA8B,EACtC,KAA2B,CAAM,CAAC;IAElD,IAAI,YAAY,EAAE;QAChB,OAAO,OAAO,CACH,GAAG,EAAE,CAAC,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CACnE,CAAC;KACP;IACD,OAAO,GAAG,CAAC;AACb,CAAC;AAED,MAAM,CAAC,MAAM,MAAM,GAAG,EAAE,CAAC,EAAC,OAAO,EAAC,CAAC,CAAC","sourcesContent":["/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\nimport {Conv3D, Conv3DAttrs, Conv3DInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor4D, Tensor5D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {eitherStridesOrDilationsAreOne} from './conv_util';\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Computes a 3D convolution over the input x.\n *\n * @param x The input tensor, of rank 5 or rank 4, of shape\n *     `[batch, depth, height, width, channels]`. If rank 4,\n * batch of 1 is assumed.\n * @param filter The filter, rank 5, of shape\n *     `[filterDepth, filterHeight, filterWidth, inChannels, outChannels]`.\n *      inChannels must match between input and filter.\n * @param strides The strides of the convolution: `[strideDepth, strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](\n *          https://www.tensorflow.org/api_docs/python/tf/nn/convolution)\n * @param dataFormat: An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n *     \"NDHWC\". Specify the data format of the input and output data. With the\n *     default format \"NDHWC\", the data is stored in the order of: [batch,\n *     depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @param dilations The dilation rates: `[dilationDepth, dilationHeight,\n *     dilationWidth]` in which we sample input values across the height\n *     and width dimensions in atrous convolution. Defaults to `[1, 1, 1]`.\n *     If `dilations` is a single number, then\n *     `dilationDepth == dilationHeight == dilationWidth`. If it is greater\n *     than 1, then all values of `strides` must be 1.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv3d_<T extends Tensor4D|Tensor5D>(\n    x: T|TensorLike, filter: Tensor5D|TensorLike,\n    strides: [number, number, number]|number, pad: 'valid'|'same',\n    dataFormat: 'NDHWC'|'NCDHW' = 'NDHWC',\n    dilations: [number, number, number]|number = [1, 1, 1]): T {\n  const $x = convertToTensor(x, 'x', 'conv3d');\n  const $filter = convertToTensor(filter, 'filter', 'conv3d');\n\n  let x5D = $x as Tensor5D;\n  let reshapedTo5D = false;\n\n  if ($x.rank === 4) {\n    reshapedTo5D = true;\n    x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n  }\n  util.assert(\n      x5D.rank === 5,\n      () => `Error in conv3d: input must be rank 5, but got rank ${x5D.rank}.`);\n  util.assert(\n      $filter.rank === 5,\n      () => `Error in conv3d: filter must be rank 5, but got rank ` +\n          `${$filter.rank}.`);\n  util.assert(\n      x5D.shape[4] === $filter.shape[3],\n      () => `Error in conv3d: depth of input (${x5D.shape[4]}) must match ` +\n          `input depth for filter ${$filter.shape[3]}.`);\n  util.assert(\n      eitherStridesOrDilationsAreOne(strides, dilations),\n      () => 'Error in conv3D: Either strides or dilations must be 1. ' +\n          `Got strides ${strides} and dilations '${dilations}'`);\n  util.assert(\n      dataFormat === 'NDHWC',\n      () => `Error in conv3d: got dataFormat of ${\n          dataFormat} but only NDHWC is currently supported.`);\n\n  const inputs: Conv3DInputs = {x: x5D, filter: $filter};\n\n  const attrs: Conv3DAttrs = {strides, pad, dataFormat, dilations};\n\n  // tslint:disable-next-line: no-unnecessary-type-assertion\n  const res = ENGINE.runKernel(\n                  Conv3D, inputs as {} as NamedTensorMap,\n                  attrs as {} as NamedAttrMap) as T;\n\n  if (reshapedTo5D) {\n    return reshape(\n               res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]) as\n        T;\n  }\n  return res;\n}\n\nexport const conv3d = op({conv3d_});\n"]}","/**\n * @license\n * Copyright 2022 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the 'License');\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an 'AS IS' BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Cumprod } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the cumulative product of a `tf.Tensor` along `axis`.\n *\n * ```js\n * const x = tf.tensor([1, 2, 3, 4]);\n * x.cumprod().print();\n * ```\n * ```js\n * const x = tf.tensor([[1, 2], [3, 4]]);\n * x.cumprod().print();\n * ```\n *\n * @param x The input tensor to cumulatively multiply.\n * @param axis The axis along which to multiply. Optional. Defaults to 0.\n * @param exclusive Whether to perform exclusive cumulative product. Optional.\n * Defaults to false. If set to true then the product of each tensor entry\n * does not include its own value, but only the values previous to it\n * along the specified axis.\n * @param reverse Whether to multiply in the opposite direction. Optional.\n * Defaults to false.\n *\n * @doc {heading: 'Operations', subheading: 'Scan'}\n */\nfunction cumprod_(x, axis = 0, exclusive = false, reverse = false) {\n const $x = convertToTensor(x, 'x', 'cumprod');\n const inputs = { x: $x };\n const attrs = { axis, exclusive, reverse };\n return ENGINE.runKernel(Cumprod, inputs, attrs);\n}\nexport const cumprod = op({ cumprod_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { DenseBincount } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Outputs a vector with length `size` and the same dtype as `weights`.\n *\n * If `weights` are empty, then index `i` stores the number of times the value\n * `i` is counted in `x`. If `weights` are non-empty, then index `i` stores the\n * sum of the value in `weights` at each index where the corresponding value in\n * `x` is `i`.\n *\n * Values in `x` outside of the range [0, size) are ignored.\n *\n * @param x The input int tensor, rank 1 or rank 2.\n * @param weights The weights tensor, must have the same shape as x, or a\n * length-0 Tensor, in which case it acts as all weights equal to 1.\n * @param size Non-negative integer.\n * @param binaryOutput Optional. Whether the kernel should count the appearance\n * or number of occurrences. Defaults to False.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction denseBincount_(x, weights, size, binaryOutput = false) {\n const $x = convertToTensor(x, 'x', 'denseBincount');\n const $weights = convertToTensor(weights, 'weights', 'denseBincount');\n util.assert($x.dtype === 'int32', () => `Error in denseBincount: input ` +\n `dtype must be int32, but got ${$x.dtype}`);\n util.assert($x.rank <= 2, () => `Error in denseBincount: input must be at most rank 2, but got ` +\n `rank ${$x.rank}.`);\n util.assert(size >= 0, () => `size must be non-negative, but got ${size}.`);\n util.assert($weights.size === $x.size || $weights.size === 0, () => `Error in denseBincount: weights must have the same shape as x or ` +\n `0-length, but got x shape: ${$x.shape}, weights shape: ` +\n `${$weights.shape}.`);\n const inputs = { x: $x, weights: $weights };\n const attrs = { size, binaryOutput };\n return ENGINE.runKernel(DenseBincount, inputs, attrs);\n}\nexport const denseBincount = op({ denseBincount_ });\n//# 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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { DepthToSpace } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Rearranges data from depth into blocks of spatial data. More specifically,\n * this op outputs a copy of the input tensor where values from the `depth`\n * dimension are moved in spatial blocks to the `height` and `width` dimensions.\n * The attr `blockSize` indicates the input block size and how the data is\n * moved.\n *\n * - Chunks of data of size `blockSize * blockSize` from depth are rearranged\n * into non-overlapping blocks of size `blockSize x blockSize`\n *\n * - The width the output tensor is `inputWidth * blockSize`, whereas the\n * height is `inputHeight * blockSize`\n *\n * - The Y, X coordinates within each block of the output image are determined\n * by the high order component of the input channel index\n *\n * - The depth of the input tensor must be divisible by `blockSize *\n * blockSize`\n *\n * The `dataFormat` attr specifies the layout of the input and output tensors\n * with the following options: \"NHWC\": [ `batch, height, width, channels` ]\n * \"NCHW\": [ `batch, channels, height, width` ]\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [1, 1, 1, 4]);\n * const blockSize = 2;\n * const dataFormat = \"NHWC\";\n *\n * tf.depthToSpace(x, blockSize, dataFormat).print();\n * ```\n *\n * @param x The input tensor of rank 4\n * @param blockSIze An `int` that is `>= 2`. The size of the spatial block\n * @param dataFormat An optional string from: \"NHWC\", \"NCHW\". Defaults to \"NHWC\"\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction depthToSpace_(x, blockSize, dataFormat = 'NHWC') {\n const $x = convertToTensor(x, 'x', 'depthToSpace', 'float32');\n const inputHeight = (dataFormat === 'NHWC') ? $x.shape[1] : $x.shape[2];\n const inputWidth = (dataFormat === 'NHWC') ? $x.shape[2] : $x.shape[3];\n const inputDepth = (dataFormat === 'NHWC') ? $x.shape[3] : $x.shape[1];\n util.assert(blockSize > 1, () => `blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);\n util.assert(inputHeight * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying\n ${inputHeight} and ${blockSize} for depthToSpace with input shape\n ${$x.shape}`);\n util.assert(inputWidth * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying\n ${inputWidth} and ${blockSize} for depthToSpace with input shape\n ${$x.shape}`);\n util.assert((inputDepth % (blockSize * blockSize) === 0), () => `Dimension size must be evenly divisible by ${blockSize * blockSize} but is ${inputDepth} for depthToSpace with input shape ${$x.shape}`);\n const inputs = { x: $x };\n const attrs = { blockSize, dataFormat };\n return ENGINE.runKernel(DepthToSpace, inputs, attrs);\n}\nexport const depthToSpace = op({ depthToSpace_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Dilation2D } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the grayscale dilation over the input `x`.\n *\n * @param x The input tensor, rank 3 or rank 4 of shape\n * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filter The filter tensor, rank 3, of shape\n * `[filterHeight, filterWidth, depth]`.\n * @param strides The strides of the sliding window for each dimension of the\n * input tensor: `[strideHeight, strideWidth]`.\n * If `strides` is a single number,\n * then `strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n * - `same` and stride 1: output will be of same size as input,\n * regardless of filter size.\n * - `valid`: output will be smaller than input if filter is larger\n * than 1*1x1.\n * - For more info, see this guide:\n * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](\n * https://www.tensorflow.org/api_docs/python/tf/nn/convolution)\n * @param dataFormat Specify the data format of the input and output data.\n * Defaults to 'NHWC'. Only 'NHWC' is currently supported. With the\n * default format \"NHWC\", the data is stored in the order of: [batch,\n * height, width, channels].\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n * in which we sample input values across the height and width dimensions\n * for atrous morphological dilation. Defaults to `[1, 1]`. If `dilations`\n * is a single number, then `dilationHeight == dilationWidth`. If it is\n * greater than 1, then all values of `strides` must be 1.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction dilation2d_(x, filter, strides, pad, dilations = [1, 1], dataFormat = 'NHWC') {\n const $x = convertToTensor(x, 'x', 'dilation2d');\n const $filter = convertToTensor(filter, 'filter', 'dilation2d');\n util.assert($x.rank === 3 || $x.rank === 4, () => `Error in dilation2d: input must be rank 3 or 4, but got rank ` +\n `${$x.rank}.`);\n util.assert($filter.rank === 3, () => `Error in dilation2d: filter must be rank 3, but got rank ` +\n `${$filter.rank}.`);\n util.assert(dataFormat === 'NHWC', () => `Error in dilation2d: Only NHWC is currently supported, ` +\n `but got dataFormat of ${dataFormat}`);\n let x4D = $x;\n let reshapedTo4D = false;\n if ($x.rank === 3) {\n x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n reshapedTo4D = true;\n }\n const inputs = { x: x4D, filter: $filter };\n const attrs = { strides, pad, dilations };\n // tslint:disable-next-line: no-unnecessary-type-assertion\n const res = ENGINE.runKernel(Dilation2D, inputs, attrs);\n if (reshapedTo4D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n }\n return res;\n}\nexport const dilation2d = op({ dilation2d_ });\n//# sourceMappingURL=data:application/json;base64,{"version":3,"file":"dilation2d.js","sourceRoot":"","sources":["../../../../../../tfjs-core/src/ops/dilation2d.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAEH,OAAO,EAAC,MAAM,EAAC,MAAM,WAAW,CAAC;AACjC,OAAO,EAAC,UAAU,EAAoC,MAAM,iBAAiB,CAAC;AAI9E,OAAO,EAAC,eAAe,EAAC,MAAM,oBAAoB,CAAC;AAEnD,OAAO,KAAK,IAAI,MAAM,SAAS,CAAC;AAEhC,OAAO,EAAC,EAAE,EAAC,MAAM,aAAa,CAAC;AAC/B,OAAO,EAAC,OAAO,EAAC,MAAM,WAAW,CAAC;AAElC;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;GA8BG;AACH,SAAS,WAAW,CAChB,CAAe,EAAE,MAA2B,EAC5C,OAAgC,EAAE,GAAmB,EACrD,YAAqC,CAAC,CAAC,EAAE,CAAC,CAAC,EAC3C,aAAqB,MAAM;IAC7B,MAAM,EAAE,GAAG,eAAe,CAAC,CAAC,EAAE,GAAG,EAAE,YAAY,CAAC,CAAC;IACjD,MAAM,OAAO,GAAG,eAAe,CAAC,MAAM,EAAE,QAAQ,EAAE,YAAY,CAAC,CAAC;IAEhE,IAAI,CAAC,MAAM,CACP,EAAE,CAAC,IAAI,KAAK,CAAC,IAAI,EAAE,CAAC,IAAI,KAAK,CAAC,EAC9B,GAAG,EAAE,CAAC,+DAA+D;QACjE,GAAG,EAAE,CAAC,IAAI,GAAG,CAAC,CAAC;IACvB,IAAI,CAAC,MAAM,CACP,OAAO,CAAC,IAAI,KAAK,CAAC,EAClB,GAAG,EAAE,CAAC,2DAA2D;QAC7D,GAAG,OAAO,CAAC,IAAI,GAAG,CAAC,CAAC;IAC5B,IAAI,CAAC,MAAM,CACP,UAAU,KAAK,MAAM,EACrB,GAAG,EAAE,CAAC,yDAAyD;QAC3D,yBAAyB,UAAU,EAAE,CAAC,CAAC;IAE/C,IAAI,GAAG,GAAG,EAAc,CAAC;IACzB,IAAI,YAAY,GAAG,KAAK,CAAC;IAEzB,IAAI,EAAE,CAAC,IAAI,KAAK,CAAC,EAAE;QACjB,GAAG,GAAG,OAAO,CAAC,EAAE,EAAE,CAAC,CAAC,EAAE,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;QAC9D,YAAY,GAAG,IAAI,CAAC;KACrB;IAED,MAAM,MAAM,GAAqB,EAAC,CAAC,EAAE,GAAG,EAAE,MAAM,EAAE,OAAO,EAAC,CAAC;IAC3D,MAAM,KAAK,GAAoB,EAAC,OAAO,EAAE,GAAG,EAAE,SAAS,EAAC,CAAC;IAEzD,0DAA0D;IAC1D,MAAM,GAAG,GAAG,MAAM,CAAC,SAAS,CACZ,UAAU,EAAE,MAA8B,EAC1C,KAA2B,CAAM,CAAC;IAElD,IAAI,YAAY,EAAE;QAChB,OAAO,OAAO,CAAC,GAAG,EAAE,CAAC,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAM,CAAC;KACtE;IAED,OAAO,GAAG,CAAC;AACb,CAAC;AAED,MAAM,CAAC,MAAM,UAAU,GAAG,EAAE,CAAC,EAAC,WAAW,EAAC,CAAC,CAAC","sourcesContent":["/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Dilation2D, Dilation2DAttrs, Dilation2DInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor3D, Tensor4D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Computes the grayscale dilation over the input `x`.\n *\n * @param x The input tensor, rank 3 or rank 4 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filter The filter tensor, rank 3, of shape\n *     `[filterHeight, filterWidth, depth]`.\n * @param strides The strides of the sliding window for each dimension of the\n *     input tensor: `[strideHeight, strideWidth]`.\n *     If `strides` is a single number,\n *     then `strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1*1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](\n *          https://www.tensorflow.org/api_docs/python/tf/nn/convolution)\n * @param dataFormat Specify the data format of the input and output data.\n *      Defaults to 'NHWC'. Only 'NHWC' is currently supported. With the\n *      default format \"NHWC\", the data is stored in the order of: [batch,\n *      height, width, channels].\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     for atrous morphological dilation. Defaults to `[1, 1]`. If `dilations`\n *     is a single number, then `dilationHeight == dilationWidth`. If it is\n *     greater than 1, then all values of `strides` must be 1.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction dilation2d_<T extends Tensor3D|Tensor4D>(\n    x: T|TensorLike, filter: Tensor3D|TensorLike,\n    strides: [number, number]|number, pad: 'valid'|'same',\n    dilations: [number, number]|number = [1, 1],\n    dataFormat: 'NHWC' = 'NHWC'): T {\n  const $x = convertToTensor(x, 'x', 'dilation2d');\n  const $filter = convertToTensor(filter, 'filter', 'dilation2d');\n\n  util.assert(\n      $x.rank === 3 || $x.rank === 4,\n      () => `Error in dilation2d: input must be rank 3 or 4, but got rank ` +\n          `${$x.rank}.`);\n  util.assert(\n      $filter.rank === 3,\n      () => `Error in dilation2d: filter must be rank 3, but got rank ` +\n          `${$filter.rank}.`);\n  util.assert(\n      dataFormat === 'NHWC',\n      () => `Error in dilation2d: Only NHWC is currently supported, ` +\n          `but got dataFormat of ${dataFormat}`);\n\n  let x4D = $x as Tensor4D;\n  let reshapedTo4D = false;\n\n  if ($x.rank === 3) {\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    reshapedTo4D = true;\n  }\n\n  const inputs: Dilation2DInputs = {x: x4D, filter: $filter};\n  const attrs: Dilation2DAttrs = {strides, pad, dilations};\n\n  // tslint:disable-next-line: no-unnecessary-type-assertion\n  const res = ENGINE.runKernel(\n                  Dilation2D, inputs as {} as NamedTensorMap,\n                  attrs as {} as NamedAttrMap) as T;\n\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as T;\n  }\n\n  return res;\n}\n\nexport const dilation2d = op({dilation2d_});\n"]}","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { div } from './div';\nimport { equal } from './equal';\nimport { op } from './operation';\nimport { where } from './where';\nimport { zerosLike } from './zeros_like';\n/**\n * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting. Return 0\n * if denominator is 0.\n *\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 9, 16]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n * const c = tf.tensor1d([0, 0, 0, 0]);\n *\n * a.divNoNan(b).print(); // or tf.divNoNan(a, b)\n * a.divNoNan(c).print(); // or tf.divNoNan(a, c)\n * ```\n *\n * ```js\n * // Broadcast div a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(2);\n * const c = tf.scalar(0);\n *\n * a.divNoNan(b).print(); // or tf.divNoNan(a, b)\n * a.divNoNan(c).print(); // or tf.divNoNan(a, c)\n * ```\n *\n * @param a The first tensor as the numerator.\n * @param b The second tensor as the denominator. Must have the same dtype as\n * `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction divNoNan_(a, b) {\n // TODO: Make this into its own kernel.\n let $a = convertToTensor(a, 'a', 'div');\n let $b = convertToTensor(b, 'b', 'div');\n [$a, $b] = makeTypesMatch($a, $b);\n const divResult = div($a, $b);\n const zeros = zerosLike(divResult);\n const bEqualsZero = equal($b, zeros);\n return where(bEqualsZero, zeros, divResult);\n}\nexport const divNoNan = op({ divNoNan_ });\n//# 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* @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Einsum } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Tensor contraction over specified indices and outer product.\n *\n * `einsum` allows defining Tensors by defining their element-wise computation.\n * This computation is based on\n * [Einstein summation](https://en.wikipedia.org/wiki/Einstein_notation).\n *\n * Some special cases include:\n *\n * Matrix multiplication:\n * ```js\n * const x = tf.tensor2d([[1, 2, 3], [4, 5, 6]]);\n * const y = tf.tensor2d([[0, 1], [2, 3], [4, 5]]);\n * x.print();\n * y.print();\n * tf.einsum('ij,jk->ik', x, y).print();\n * ```\n *\n * Dot product:\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n * const y = tf.tensor1d([0, 1, 2]);\n * x.print();\n * y.print();\n * tf.einsum('i,i->', x, y).print();\n * ```\n *\n * Batch dot product:\n * ```js\n * const x = tf.tensor2d([[1, 2, 3], [4, 5, 6]]);\n * const y = tf.tensor2d([[0, 1, 2], [3, 4, 5]]);\n * x.print();\n * y.print();\n * tf.einsum('bi,bi->b', x, y).print();\n * ```\n *\n * Outer prouduct:\n * ```js\n * const x = tf.tensor1d([1, 3, 5]);\n * const y = tf.tensor1d([2, 4, 6]);\n * x.print();\n * y.print();\n * tf.einsum('i,j->ij', x, y).print();\n * ```\n *\n * Matrix transpose:\n * ```js\n * const x = tf.tensor2d([[1, 2], [3, 4]]);\n * x.print();\n * tf.einsum('ij->ji', x).print();\n * ```\n *\n * Batch matrix transpose:\n * ```js\n * const x = tf.tensor3d([[[1, 2], [3, 4]], [[-1, -2], [-3, -4]]]);\n * x.print();\n * tf.einsum('bij->bji', x).print();\n * ```\n *\n * Limitations:\n *\n * This implementation of einsum has the following limitations:\n *\n * - Does not support >2 input tensors.\n * - Does not support duplicate axes for any given input tensor. E.g., equation\n * 'ii->' is not suppoted.\n * - The `...` notation is not supported.\n *\n * @param equation a string describing the contraction, in the same format as\n * [numpy.einsum](https://numpy.org/doc/stable/reference/generated/numpy.einsum.html).\n * @param tensors the input(s) to contract (each one a Tensor), whose shapes\n * should be consistent with equation.\n * @returns The output tensor.\n *\n * @doc {heading: 'Tensors', subheading: 'Matrices'}\n */\nexport function einsum_(equation, ...tensors) {\n const $tensors = tensors.map((t, i) => convertToTensor(t, `tensors${i}`, 'einsum'));\n const attrs = { equation };\n return ENGINE.runKernel(Einsum, $tensors, attrs);\n}\nexport const einsum = op({ einsum_ });\n//# 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* @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Erf } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { cast } from './cast';\nimport { op } from './operation';\n/**\n * Computes gause error function of the input `tf.Tensor` element-wise:\n * `erf(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, .1, -.1, .7]);\n *\n * x.erf().print(); // or tf.erf(x);\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction erf_(x) {\n let $x = convertToTensor(x, 'x', 'erf');\n util.assert($x.dtype === 'int32' || $x.dtype === 'float32', () => 'Input dtype must be `int32` or `float32`.');\n if ($x.dtype === 'int32') {\n $x = cast($x, 'float32');\n }\n const inputs = { x: $x };\n return ENGINE.runKernel(Erf, inputs);\n}\nexport const erf = op({ erf_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Expm1 } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes exponential of the input `tf.Tensor` minus one element-wise.\n * `e ^ x - 1`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, -3]);\n *\n * x.expm1().print(); // or tf.expm1(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction expm1_(x) {\n const $x = convertToTensor(x, 'x', 'expm1');\n const inputs = { x: $x };\n return ENGINE.runKernel(Expm1, inputs);\n}\nexport const expm1 = op({ expm1_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { IsNan } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * RReturns which elements of x are NaN.\n *\n * ```js\n * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);\n *\n * x.isNaN().print(); // or tf.isNaN(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction isNaN_(x) {\n const $x = convertToTensor(x, 'x', 'isNaN');\n const inputs = { x: $x };\n return ENGINE.runKernel(IsNan, inputs);\n}\nexport const isNaN = op({ isNaN_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LinSpace } from '../kernel_names';\n/**\n * Return an evenly spaced sequence of numbers over the given interval.\n *\n * ```js\n * tf.linspace(0, 9, 10).print();\n * ```\n * @param start The start value of the sequence.\n * @param stop The end value of the sequence.\n * @param num The number of values to generate.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function linspace(start, stop, num) {\n if (num <= 0) {\n throw new Error('The number of values should be positive.');\n }\n const attrs = { start, stop, num };\n return ENGINE.runKernel(LinSpace, {}, attrs);\n}\n//# sourceMappingURL=data:application/json;base64,eyJ2ZXJzaW9uIjozLCJmaWxlIjoibGluc3BhY2UuanMiLCJzb3VyY2VSb290IjoiIiwic291cmNlcyI6WyIuLi8uLi8uLi8uLi8uLi8uLi90ZmpzLWNvcmUvc3JjL29wcy9saW5zcGFjZS50cyJdLCJuYW1lcyI6W10sIm1hcHBpbmdzIjoiQUFBQTs7Ozs7Ozs7Ozs7Ozs7O0dBZUc7QUFFSCxPQUFPLEVBQUMsTUFBTSxFQUFDLE1BQU0sV0FBVyxDQUFDO0FBQ2pDLE9BQU8sRUFBQyxRQUFRLEVBQWdCLE1BQU0saUJBQWlCLENBQUM7QUFJeEQ7Ozs7Ozs7Ozs7O0dBV0c7QUFDSCxNQUFNLFVBQVUsUUFBUSxDQUFDLEtBQWEsRUFBRSxJQUFZLEVBQUUsR0FBVztJQUMvRCxJQUFJLEdBQUcsSUFBSSxDQUFDLEVBQUU7UUFDWixNQUFNLElBQUksS0FBSyxDQUFDLDBDQUEwQyxDQUFDLENBQUM7S0FDN0Q7SUFFRCxNQUFNLEtBQUssR0FBa0IsRUFBQyxLQUFLLEVBQUUsSUFBSSxFQUFFLEdBQUcsRUFBQyxDQUFDO0lBQ2hELE9BQU8sTUFBTSxDQUFDLFNBQVMsQ0FBQyxRQUFRLEVBQUUsRUFBRSxFQUFFLEtBQTJCLENBQUMsQ0FBQztBQUNyRSxDQUFDIiwic291cmNlc0NvbnRlbnQiOlsiLyoqXG4gKiBAbGljZW5zZVxuICogQ29weXJpZ2h0IDIwMTggR29vZ2xlIExMQy4gQWxsIFJpZ2h0cyBSZXNlcnZlZC5cbiAqIExpY2Vuc2VkIHVuZGVyIHRoZSBBcGFjaGUgTGljZW5zZSwgVmVyc2lvbiAyLjAgKHRoZSBcIkxpY2Vuc2VcIik7XG4gKiB5b3UgbWF5IG5vdCB1c2UgdGhpcyBmaWxlIGV4Y2VwdCBpbiBjb21wbGlhbmNlIHdpdGggdGhlIExpY2Vuc2UuXG4gKiBZb3UgbWF5IG9idGFpbiBhIGNvcHkgb2YgdGhlIExpY2Vuc2UgYXRcbiAqXG4gKiBodHRwOi8vd3d3LmFwYWNoZS5vcmcvbGljZW5zZXMvTElDRU5TRS0yLjBcbiAqXG4gKiBVbmxlc3MgcmVxdWlyZWQgYnkgYXBwbGljYWJsZSBsYXcgb3IgYWdyZWVkIHRvIGluIHdyaXRpbmcsIHNvZnR3YXJlXG4gKiBkaXN0cmlidXRlZCB1bmRlciB0aGUgTGljZW5zZSBpcyBkaXN0cmlidXRlZCBvbiBhbiBcIkFTIElTXCIgQkFTSVMsXG4gKiBXSVRIT1VUIFdBUlJBTlRJRVMgT1IgQ09ORElUSU9OUyBPRiBBTlkgS0lORCwgZWl0aGVyIGV4cHJlc3Mgb3IgaW1wbGllZC5cbiAqIFNlZSB0aGUgTGljZW5zZSBmb3IgdGhlIHNwZWNpZmljIGxhbmd1YWdlIGdvdmVybmluZyBwZXJtaXNzaW9ucyBhbmRcbiAqIGxpbWl0YXRpb25zIHVuZGVyIHRoZSBMaWNlbnNlLlxuICogPT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT1cbiAqL1xuXG5pbXBvcnQge0VOR0lORX0gZnJvbSAnLi4vZW5naW5lJztcbmltcG9ydCB7TGluU3BhY2UsIExpblNwYWNlQXR0cnN9IGZyb20gJy4uL2tlcm5lbF9uYW1lcyc7XG5pbXBvcnQge05hbWVkQXR0ck1hcH0gZnJvbSAnLi4va2VybmVsX3JlZ2lzdHJ5JztcbmltcG9ydCB7VGVuc29yMUR9IGZyb20gJy4uL3RlbnNvcic7XG5cbi8qKlxuICogUmV0dXJuIGFuIGV2ZW5seSBzcGFjZWQgc2VxdWVuY2Ugb2YgbnVtYmVycyBvdmVyIHRoZSBnaXZlbiBpbnRlcnZhbC5cbiAqXG4gKiBgYGBqc1xuICogdGYubGluc3BhY2UoMCwgOSwgMTApLnByaW50KCk7XG4gKiBgYGBcbiAqIEBwYXJhbSBzdGFydCBUaGUgc3RhcnQgdmFsdWUgb2YgdGhlIHNlcXVlbmNlLlxuICogQHBhcmFtIHN0b3AgVGhlIGVuZCB2YWx1ZSBvZiB0aGUgc2VxdWVuY2UuXG4gKiBAcGFyYW0gbnVtIFRoZSBudW1iZXIgb2YgdmFsdWVzIHRvIGdlbmVyYXRlLlxuICpcbiAqIEBkb2Mge2hlYWRpbmc6ICdUZW5zb3JzJywgc3ViaGVhZGluZzogJ0NyZWF0aW9uJ31cbiAqL1xuZXhwb3J0IGZ1bmN0aW9uIGxpbnNwYWNlKHN0YXJ0OiBudW1iZXIsIHN0b3A6IG51bWJlciwgbnVtOiBudW1iZXIpOiBUZW5zb3IxRCB7XG4gIGlmIChudW0gPD0gMCkge1xuICAgIHRocm93IG5ldyBFcnJvcignVGhlIG51bWJlciBvZiB2YWx1ZXMgc2hvdWxkIGJlIHBvc2l0aXZlLicpO1xuICB9XG5cbiAgY29uc3QgYXR0cnM6IExpblNwYWNlQXR0cnMgPSB7c3RhcnQsIHN0b3AsIG51bX07XG4gIHJldHVybiBFTkdJTkUucnVuS2VybmVsKExpblNwYWNlLCB7fSwgYXR0cnMgYXMge30gYXMgTmFtZWRBdHRyTWFwKTtcbn1cbiJdfQ==","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LRN } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Normalizes the activation of a local neighborhood across or within\n * channels.\n *\n * @param x The input tensor. The 4-D input tensor is treated as a 3-D array\n * of 1D vectors (along the last dimension), and each vector is\n * normalized independently.\n * @param depthRadius The number of adjacent channels in the 1D normalization\n * window.\n * @param bias A constant bias term for the basis.\n * @param alpha A scale factor, usually positive.\n * @param beta An exponent.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction localResponseNormalization_(x, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5) {\n const $x = convertToTensor(x, 'x', 'localResponseNormalization');\n util.assert($x.rank === 4 || $x.rank === 3, () => `Error in localResponseNormalization: x must be rank 3 or 4 but got\n rank ${$x.rank}.`);\n util.assert(util.isInt(depthRadius), () => `Error in localResponseNormalization: depthRadius must be an ` +\n `integer but got depthRadius ${depthRadius}.`);\n let x4D = $x;\n let reshapedTo4D = false;\n if ($x.rank === 3) {\n reshapedTo4D = true;\n x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n }\n const inputs = { x: x4D };\n const attrs = { depthRadius, bias, alpha, beta };\n // tslint:disable-next-line: no-unnecessary-type-assertion\n const res = ENGINE.runKernel(LRN, inputs, attrs);\n if (reshapedTo4D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n }\n else {\n return res;\n }\n}\nexport const localResponseNormalization = op({ localResponseNormalization_ });\n//# 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* @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { customGrad } from '../gradients';\nimport { convertToTensor } from '../tensor_util_env';\nimport { cast } from './cast';\nimport { exp } from './exp';\nimport { log } from './log';\nimport { max } from './max';\nimport { mul } from './mul';\nimport { op } from './operation';\nimport { sub } from './sub';\nimport { sum } from './sum';\n/**\n * Computes the log softmax.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n *\n * a.logSoftmax().print(); // or tf.logSoftmax(a)\n * ```\n *\n * ```js\n * const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]);\n *\n * a.logSoftmax().print(); // or tf.logSoftmax(a)\n * ```\n *\n * @param logits The logits array.\n * @param axis The dimension softmax would be performed on. Defaults to `-1`\n * which indicates the last dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction logSoftmax_(logits, axis = -1) {\n const $logits = convertToTensor(logits, 'logits', 'logSoftmax');\n if (axis === -1) {\n axis = $logits.rank - 1;\n }\n if (axis !== $logits.rank - 1) {\n throw Error('Log Softmax along a non-last dimension is not yet supported. ' +\n `Logits was rank ${$logits.rank} and axis was ${axis}`);\n }\n // const forward: ForwardFunc = (backend, save) => {\n // const keepDims = true;\n // const xMax = max(logits, axis, true);\n // const shifted = sub(logits, xMax);\n // const value =\n // sub(cast(shifted, 'float32'), log(sum(exp(shifted), axis,\n // keepDims)));\n // save([value]);\n // return value;\n // };\n // Use a custom gradient for numerical stability.\n const customOp = customGrad((logits, save) => {\n const keepDims = true;\n const xMax = max(logits, axis, true);\n const shifted = sub(logits, xMax);\n const value = sub(cast(shifted, 'float32'), log(sum(exp(shifted), axis, keepDims)));\n save([value]);\n const gradFunc = (dy, saved) => {\n const [value] = saved;\n const keepDims = true;\n const softmax = exp(value);\n return sub(dy, mul(sum(dy, axis, keepDims), softmax));\n };\n return { value, gradFunc };\n });\n return customOp($logits);\n // TODO Use Engine.runKernel when CPU/WebGL/WASM backends implement this.\n // const inputs: LogSoftmaxInputs = {logits: $logits};\n // const attrs: LogSoftmaxAttrs = {axis};\n // return ENGINE.runKernel(\n // LogSoftmax, inputs as {} as NamedTensorMap,\n // attrs as {} as NamedAttrMap);\n}\nexport const logSoftmax = op({ logSoftmax_ });\n//# 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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { MaxPool3D } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { checkPadOnDimRoundingMode } from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the 3D max pooling.\n *\n * ```js\n * const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);\n * const result = tf.maxPool3d(x, 2, 1, 'valid');\n * result.print();\n * ```\n *\n * @param x The input tensor, of rank 5 or rank 4 of shape\n * `[batch, depth, height, width, inChannels]`.\n * @param filterSize The filter size:\n * `[filterDepth, filterHeight, filterWidth]`.\n * If `filterSize` is a single number,\n * then `filterDepth == filterHeight == filterWidth`.\n * @param strides The strides of the pooling:\n * `[strideDepth, strideHeight, strideWidth]`.\n * If `strides` is a single number,\n * then `strideDepth == strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n * - `same` and stride 1: output will be of same size as input,\n * regardless of filter size.\n * - `valid`: output will be smaller than input if filter is larger\n * than 1*1x1.\n * - For more info, see this guide:\n * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](\n * https://www.tensorflow.org/api_docs/python/tf/nn/convolution)\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n * provided, it will default to truncate.\n * @param dataFormat An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n * \"NDHWC\". Specify the data format of the input and output data. With the\n * default format \"NDHWC\", the data is stored in the order of: [batch,\n * depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction maxPool3d_(x, filterSize = [1, 1, 1], strides, pad, dimRoundingMode, dataFormat = 'NDHWC') {\n const $x = convertToTensor(x, 'x', 'maxPool3d');\n let x5D = $x;\n let reshapedTo5D = false;\n if ($x.rank === 4) {\n reshapedTo5D = true;\n x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n }\n util.assert(x5D.rank === 5, () => `Error in maxPool3d: x must be rank 5 but got rank ${x5D.rank}.`);\n util.assert(dataFormat === 'NDHWC', () => `Error in maxPool3d: Only NDHWC is currently supported, ` +\n `but got dataFormat of ${dataFormat}`);\n checkPadOnDimRoundingMode('maxPool3d', pad, dimRoundingMode);\n const inputs = { x: x5D };\n const attrs = { filterSize, strides, pad, dimRoundingMode, dataFormat };\n // tslint:disable-next-line: no-unnecessary-type-assertion\n const res = ENGINE.runKernel(MaxPool3D, inputs, attrs);\n if (reshapedTo5D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n }\n return res;\n}\nexport const maxPool3d = op({ maxPool3d_ });\n//# sourceMappingURL=data:application/json;base64,{"version":3,"file":"max_pool_3d.js","sourceRoot":"","sources":["../../../../../../tfjs-core/src/ops/max_pool_3d.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAEH,OAAO,EAAC,MAAM,EAAC,MAAM,WAAW,CAAC;AACjC,OAAO,EAAC,SAAS,EAAkC,MAAM,iBAAiB,CAAC;AAI3E,OAAO,EAAC,eAAe,EAAC,MAAM,oBAAoB,CAAC;AAEnD,OAAO,KAAK,IAAI,MAAM,SAAS,CAAC;AAEhC,OAAO,EAAC,yBAAyB,EAAC,MAAM,aAAa,CAAC;AACtD,OAAO,EAAC,EAAE,EAAC,MAAM,aAAa,CAAC;AAC/B,OAAO,EAAC,OAAO,EAAC,MAAM,WAAW,CAAC;AAElC;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;GAkCG;AACH,SAAS,UAAU,CACf,CAAe,EAAE,aAA8C,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EACxE,OAAwC,EAAE,GAA0B,EACpE,eAAwC,EACxC,aAA8B,OAAO;IACvC,MAAM,EAAE,GAAG,eAAe,CAAC,CAAC,EAAE,GAAG,EAAE,WAAW,CAAC,CAAC;IAEhD,IAAI,GAAG,GAAG,EAAc,CAAC;IACzB,IAAI,YAAY,GAAG,KAAK,CAAC;IACzB,IAAI,EAAE,CAAC,IAAI,KAAK,CAAC,EAAE;QACjB,YAAY,GAAG,IAAI,CAAC;QACpB,GAAG,GAAG,OAAO,CAAC,EAAE,EAAE,CAAC,CAAC,EAAE,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;KAC5E;IAED,IAAI,CAAC,MAAM,CACP,GAAG,CAAC,IAAI,KAAK,CAAC,EACd,GAAG,EAAE,CAAC,qDAAqD,GAAG,CAAC,IAAI,GAAG,CAAC,CAAC;IAC5E,IAAI,CAAC,MAAM,CACP,UAAU,KAAK,OAAO,EACtB,GAAG,EAAE,CAAC,yDAAyD;QAC3D,yBAAyB,UAAU,EAAE,CAAC,CAAC;IAC/C,yBAAyB,CAAC,WAAW,EAAE,GAAG,EAAE,eAAe,CAAC,CAAC;IAC7D,MAAM,MAAM,GAAoB,EAAC,CAAC,EAAE,GAAG,EAAC,CAAC;IACzC,MAAM,KAAK,GACU,EAAC,UAAU,EAAE,OAAO,EAAE,GAAG,EAAE,eAAe,EAAE,UAAU,EAAC,CAAC;IAE7E,0DAA0D;IAC1D,MAAM,GAAG,GAAG,MAAM,CAAC,SAAS,CACZ,SAAS,EAAE,MAA8B,EACzC,KAA2B,CAAM,CAAC;IAElD,IAAI,YAAY,EAAE;QAChB,OAAO,OAAO,CACH,GAAG,EAAE,CAAC,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CACnE,CAAC;KACP;IAED,OAAO,GAAG,CAAC;AACb,CAAC;AAED,MAAM,CAAC,MAAM,SAAS,GAAG,EAAE,CAAC,EAAC,UAAU,EAAC,CAAC,CAAC","sourcesContent":["/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {MaxPool3D, MaxPool3DAttrs, MaxPool3DInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor4D, Tensor5D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {checkPadOnDimRoundingMode} from './conv_util';\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Computes the 3D max pooling.\n *\n * ```js\n * const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);\n * const result = tf.maxPool3d(x, 2, 1, 'valid');\n * result.print();\n * ```\n *\n * @param x The input tensor, of rank 5 or rank 4 of shape\n *     `[batch, depth, height, width, inChannels]`.\n * @param filterSize The filter size:\n *     `[filterDepth, filterHeight, filterWidth]`.\n *     If `filterSize` is a single number,\n *     then `filterDepth == filterHeight == filterWidth`.\n * @param strides The strides of the pooling:\n *     `[strideDepth, strideHeight, strideWidth]`.\n *     If `strides` is a single number,\n *     then `strideDepth == strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1*1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](\n *          https://www.tensorflow.org/api_docs/python/tf/nn/convolution)\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n *     provided, it will default to truncate.\n * @param dataFormat An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n *     \"NDHWC\". Specify the data format of the input and output data. With the\n *     default format \"NDHWC\", the data is stored in the order of: [batch,\n *     depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction maxPool3d_<T extends Tensor4D|Tensor5D>(\n    x: T|TensorLike, filterSize: [number, number, number]|number = [1, 1, 1],\n    strides: [number, number, number]|number, pad: 'valid'|'same'|number,\n    dimRoundingMode?: 'floor'|'round'|'ceil',\n    dataFormat: 'NDHWC'|'NCDHW' = 'NDHWC'): T {\n  const $x = convertToTensor(x, 'x', 'maxPool3d');\n\n  let x5D = $x as Tensor5D;\n  let reshapedTo5D = false;\n  if ($x.rank === 4) {\n    reshapedTo5D = true;\n    x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n  }\n\n  util.assert(\n      x5D.rank === 5,\n      () => `Error in maxPool3d: x must be rank 5 but got rank ${x5D.rank}.`);\n  util.assert(\n      dataFormat === 'NDHWC',\n      () => `Error in maxPool3d: Only NDHWC is currently supported, ` +\n          `but got dataFormat of ${dataFormat}`);\n  checkPadOnDimRoundingMode('maxPool3d', pad, dimRoundingMode);\n  const inputs: MaxPool3DInputs = {x: x5D};\n  const attrs:\n      MaxPool3DAttrs = {filterSize, strides, pad, dimRoundingMode, dataFormat};\n\n  // tslint:disable-next-line: no-unnecessary-type-assertion\n  const res = ENGINE.runKernel(\n                  MaxPool3D, inputs as {} as NamedTensorMap,\n                  attrs as {} as NamedAttrMap) as T;\n\n  if (reshapedTo5D) {\n    return reshape(\n               res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]) as\n        T;\n  }\n\n  return res;\n}\n\nexport const maxPool3d = op({maxPool3d_});\n"]}","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { MaxPoolWithArgmax } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the 2D max pooling of an image with Argmax index.\n * The indices in argmax are flattened, so that a maximum value at position `[b,\n * y, x, c]` becomes flattened index: `(y * width + x) * channels + c` if\n * include_batch_in_index is False; `((b * height + y) * width + x) * channels\n * +c` if include_batch_in_index is True.\n *\n * The indices returned are always in `[0, height) x [0, width)` before\n * flattening.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n * `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n * `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dataFormat An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n * \"NDHWC\". Specify the data format of the input and output data. With the\n * default format \"NDHWC\", the data is stored in the order of: [batch,\n * depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @param pad The type of padding algorithm.\n * - `same` and stride 1: output will be of same size as input,\n * regardless of filter size.\n * - `valid`: output will be smaller than input if filter is larger\n * than 1x1.\n * - For more info, see this guide:\n * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](\n * https://www.tensorflow.org/api_docs/python/tf/nn/convolution)\n * @param includeBatchIndex Defaults to False. Whether to include batch\n * dimension in flattened index of argmax.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction maxPoolWithArgmax_(x, filterSize, strides, pad, includeBatchInIndex = false) {\n const $x = convertToTensor(x, 'x', 'maxPoolWithArgmax');\n const inputs = { x: $x };\n const attrs = { filterSize, strides, pad, includeBatchInIndex };\n // tslint:disable-next-line: no-unnecessary-type-assertion\n const result = ENGINE.runKernel(MaxPoolWithArgmax, inputs, attrs);\n return { result: result[0], indexes: result[1] };\n}\nexport const maxPoolWithArgmax = op({ maxPoolWithArgmax_ });\n//# 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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { MirrorPad } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Pads a `tf.Tensor` using mirror padding.\n *\n * This operation implements the `REFLECT` and `SYMMETRIC` modes of pad.\n *\n * ```js\n * const x = tf.range(0, 9).reshape([1, 1, 3, 3]);\n * x.mirrorPad([[0, 0], [0, 0], [2, 2], [2, 2]], 'reflect').print();\n * ```\n * @param x The tensor to pad.\n * @param paddings An array of length `R` (the rank of the tensor), where\n * each element is a length-2 tuple of ints `[padBefore, padAfter]`,\n * specifying how much to pad along each dimension of the tensor.\n * In \"reflect\" mode, the padded regions do not include the borders,\n * while in \"symmetric\" mode the padded regions do include the borders.\n * For example, if the input is `[1, 2, 3]` and paddings is `[0, 2]`,\n * then the output is `[1, 2, 3, 2, 1]` in \"reflect\" mode, and\n * `[1, 2, 3, 3, 2]` in \"symmetric\" mode.\n * If `mode` is \"reflect\" then both `paddings[D, 0]` and `paddings[D, 1]`\n * must be no greater than `x.shape[D] - 1`. If mode is \"symmetric\"\n * then both `paddings[D, 0]` and `paddings[D, 1]` must be no greater than\n * `x.shape[D]`\n * @param mode String to specify padding mode. Can be `'reflect' | 'symmetric'`\n */\n/** @doc {heading: 'Tensors', subheading: 'Transformations'} */\nfunction mirrorPad_(x, paddings, mode) {\n util.assert(mode === 'reflect' || mode === 'symmetric', () => `Invalid mode. Mode must be either reflect or symmetric. ` +\n `Got ${mode}.`);\n const $x = convertToTensor(x, 'x', 'mirrorPad');\n if ($x.rank === 0) {\n throw new Error('mirrorPad(scalar) is not defined. ' +\n 'Pass non-scalar to mirrorPad');\n }\n util.assert(paddings.length === $x.rank, () => `Padding doesn't match input. Must be ${$x.rank}. ` +\n `Got ${paddings.length}.`);\n const shapeOffset = mode === 'reflect' ? 1 : 0;\n for (let i = 0; i < $x.rank; i++) {\n util.assert(paddings[i].length === 2, () => `Invalid number of paddings. Must be length of 2 each.`);\n util.assert(paddings[i][0] >= 0 && paddings[i][0] <= $x.shape[i] - shapeOffset &&\n paddings[i][1] >= 0 && paddings[i][1] <= $x.shape[i] - shapeOffset, () => `Padding in dimension ${i} cannot be greater than or equal ` +\n `to ${$x.shape[i] - shapeOffset} or less than 0 for input of ` +\n `shape ${$x.shape}`);\n }\n const attrs = { paddings, mode };\n const inputs = { x: $x };\n return ENGINE.runKernel(MirrorPad, inputs, attrs);\n}\nexport const mirrorPad = op({ mirrorPad_ });\n//# sourceMappingURL=data:application/json;base64,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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Mod } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns the mod of a and b element-wise.\n * `floor(x / y) * y + mod(x, y) = x`\n * Supports broadcasting.\n *\n * We also expose `tf.modStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.mod(b).print(); // or tf.mod(a, b)\n * ```\n *\n * ```js\n * // Broadcast a mod b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.mod(b).print(); // or tf.mod(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction mod_(a, b) {\n let $a = convertToTensor(a, 'a', 'mod');\n let $b = convertToTensor(b, 'b', 'mod');\n [$a, $b] = makeTypesMatch($a, $b);\n const inputs = { a: $a, b: $b };\n return ENGINE.runKernel(Mod, inputs);\n}\nexport const mod = op({ mod_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Multinomial } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Creates a `tf.Tensor` with values drawn from a multinomial distribution.\n *\n * ```js\n * const probs = tf.tensor([.75, .25]);\n * tf.multinomial(probs, 3).print();\n * ```\n *\n * @param logits 1D array with unnormalized log-probabilities, or\n * 2D array of shape `[batchSize, numOutcomes]`. See the `normalized`\n * parameter.\n * @param numSamples Number of samples to draw for each row slice.\n * @param seed The seed number.\n * @param normalized Whether the provided `logits` are normalized true\n * probabilities (sum to 1). Defaults to false.\n * @return 1D array of shape `[numSamples]`, or 2D array of shape\n * `[batchSize, numSamples]`, depending on the rank of the input.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction multinomial_(logits, numSamples, seed, normalized = false) {\n const $logits = convertToTensor(logits, 'logits', 'multinomial');\n const numOutcomes = $logits.size;\n const origRank = $logits.rank;\n if (numOutcomes < 2) {\n throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ` +\n `${numOutcomes}.`);\n }\n if (origRank > 2) {\n throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`);\n }\n // TODO(lina128): Investigate correct seed behavior. The code seems not allow\n // setting see to 0.\n seed = seed || Math.random();\n // The kernel only accepts (and returns) rank 2 tensors.\n const logits2D = origRank === 1 ? reshape($logits, [1, -1]) : $logits;\n const inputs = { logits: logits2D };\n const attrs = { numSamples, seed, normalized };\n // tslint:disable-next-line: no-unnecessary-type-assertion\n const res = ENGINE.runKernel(Multinomial, inputs, attrs);\n // tslint:disable-next-line:no-unnecessary-type-assertion\n return origRank === 1 ? reshape(res, [res.size]) : res;\n}\nexport const multinomial = op({ multinomial_ });\n//# 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* @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { OnesLike } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Creates a `tf.Tensor` with all elements set to 1 with the same shape as the\n * given tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n * tf.onesLike(x).print();\n * ```\n * @param x A tensor.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction onesLike_(x) {\n const $x = convertToTensor(x, 'x', 'onesLike');\n const inputs = { x: $x };\n return ENGINE.runKernel(OnesLike, inputs);\n}\nexport const onesLike = op({ onesLike_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Prod } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { cast } from './cast';\nimport { op } from './operation';\n/**\n * Computes the product of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and a\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.prod().print(); // or tf.prod(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.prod(axis).print(); // or tf.prod(x, axis)\n * ```\n *\n * @param x The input tensor to compute the product over. If the dtype is `bool`\n * it will be converted to `int32` and the output dtype will be `int32`.\n * @param axis The dimension(s) to reduce. By default it reduces\n * all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction prod_(x, axis = null, keepDims = false) {\n let $x = convertToTensor(x, 'x', 'prod');\n if ($x.dtype === 'bool') {\n // bool is not an allowed type for the underlying kernel.\n $x = cast($x, 'int32');\n }\n const inputs = { x: $x };\n const attrs = { axis, keepDims };\n return ENGINE.runKernel(Prod, inputs, attrs);\n}\nexport const prod = op({ prod_ });\n//# 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* @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Reciprocal } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes reciprocal of x element-wise: `1 / x`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, 2]);\n *\n * x.reciprocal().print(); // or tf.reciprocal(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction reciprocal_(x) {\n const $x = convertToTensor(x, 'x', 'reciprocal');\n const inputs = { x: $x };\n return ENGINE.runKernel(Reciprocal, inputs);\n}\nexport const reciprocal = op({ reciprocal_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Selu } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes scaled exponential linear element-wise.\n *\n * `x < 0 ? scale * alpha * (exp(x) - 1) : x`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.selu().print(); // or tf.selu(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction selu_(x) {\n const $x = convertToTensor(x, 'x', 'selu');\n const inputs = { x: $x };\n return ENGINE.runKernel(Selu, inputs);\n}\nexport const selu = op({ selu_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { TensorBuffer } from '../tensor';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\n/**\n * Computes the difference between two lists of numbers.\n *\n * Given a Tensor `x` and a Tensor `y`, this operation returns a Tensor `out`\n * that represents all values that are in `x` but not in `y`. The returned\n * Tensor `out` is sorted in the same order that the numbers appear in `x`\n * (duplicates are preserved). This operation also returns a Tensor indices that\n * represents the position of each out element in `x`. In other words:\n *\n * `out[i] = x[idx[i]] for i in [0, 1, ..., out.length - 1]`\n *\n * ```js\n * const x = [1, 2, 3, 4, 5, 6];\n * const y = [1, 3, 5];\n *\n * const [out, indices] = await tf.setdiff1dAsync(x, y);\n * out.print(); // [2, 4, 6]\n * indices.print(); // [1, 3, 5]\n * ```\n *\n * @param x 1-D Tensor. Values to keep.\n * @param y 1-D Tensor. Must have the same type as x. Values to exclude in the\n * output.\n * @returns Promise of Tensor tuple [out, indices].\n * out: Tensor with the same type as x.\n * indices: A Tensor of type int32.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nasync function setdiff1dAsync_(x, y) {\n const $x = convertToTensor(x, 'x', 'setdiff1d');\n const $y = convertToTensor(y, 'y', 'setdiff1d');\n util.assert($x.dtype === $y.dtype, () => `x and y should have the same dtype, but got x (${$x.dtype}) and y (${$y.dtype}).`);\n util.assert($x.rank === 1, () => `x should be 1D tensor, but got x (${$x.shape}).`);\n util.assert($y.rank === 1, () => `y should be 1D tensor, but got y (${$y.shape}).`);\n const xVals = await $x.data();\n const yVals = await $y.data();\n const ySet = new Set(yVals);\n let outputSize = 0;\n for (let i = 0; i < xVals.length; i++) {\n if (!ySet.has(xVals[i])) {\n outputSize++;\n }\n }\n const buffer = new TensorBuffer([outputSize], $x.dtype);\n const indices = new TensorBuffer([outputSize], 'int32');\n for (let i = 0, p = 0; i < xVals.length; i++) {\n if (!ySet.has(xVals[i])) {\n buffer.values[p] = xVals[i];\n indices.values[p] = i;\n p++;\n }\n }\n return [buffer.toTensor(), indices.toTensor()];\n}\nexport const setdiff1dAsync = setdiff1dAsync_;\n//# sourceMappingURL=data:application/json;base64,eyJ2ZXJzaW9uIjozLCJmaWxlIjoic2V0ZGlmZjFkX2FzeW5jLmpzIiwic291cmNlUm9vdCI6IiIsInNvdXJjZXMiOlsiLi4vLi4vLi4vLi4vLi4vLi4vdGZqcy1jb3JlL3NyYy9vcHMvc2V0ZGlmZjFkX2FzeW5jLnRzIl0sIm5hbWVzIjpbXSwibWFwcGluZ3MiOiJBQUFBOzs7Ozs7Ozs7Ozs7Ozs7R0FlRztBQUNILE9BQU8sRUFBUyxZQUFZLEVBQUMsTUFBTSxXQUFXLENBQUM7QUFDL0MsT0FBTyxFQUFDLGVBQWUsRUFBQyxNQUFNLG9CQUFvQixDQUFDO0FBRW5ELE9BQU8sS0FBSyxJQUFJLE1BQU0sU0FBUyxDQUFDO0FBRWhDOzs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7O0dBNEJHO0FBQ0gsS0FBSyxVQUFVLGVBQWUsQ0FDMUIsQ0FBb0IsRUFBRSxDQUFvQjtJQUM1QyxNQUFNLEVBQUUsR0FBRyxlQUFlLENBQUMsQ0FBQyxFQUFFLEdBQUcsRUFBRSxXQUFXLENBQUMsQ0FBQztJQUNoRCxNQUFNLEVBQUUsR0FBRyxlQUFlLENBQUMsQ0FBQyxFQUFFLEdBQUcsRUFBRSxXQUFXLENBQUMsQ0FBQztJQUVoRCxJQUFJLENBQUMsTUFBTSxDQUNQLEVBQUUsQ0FBQyxLQUFLLEtBQUssRUFBRSxDQUFDLEtBQUssRUFDckIsR0FBRyxFQUFFLENBQUMsa0RBQ0YsRUFBRSxDQUFDLEtBQUssWUFBWSxFQUFFLENBQUMsS0FBSyxJQUFJLENBQUMsQ0FBQztJQUUxQyxJQUFJLENBQUMsTUFBTSxDQUNQLEVBQUUsQ0FBQyxJQUFJLEtBQUssQ0FBQyxFQUFFLEdBQUcsRUFBRSxDQUFDLHFDQUFxQyxFQUFFLENBQUMsS0FBSyxJQUFJLENBQUMsQ0FBQztJQUU1RSxJQUFJLENBQUMsTUFBTSxDQUNQLEVBQUUsQ0FBQyxJQUFJLEtBQUssQ0FBQyxFQUFFLEdBQUcsRUFBRSxDQUFDLHFDQUFxQyxFQUFFLENBQUMsS0FBSyxJQUFJLENBQUMsQ0FBQztJQUU1RSxNQUFNLEtBQUssR0FBRyxNQUFNLEVBQUUsQ0FBQyxJQUFJLEVBQUUsQ0FBQztJQUM5QixNQUFNLEtBQUssR0FBRyxNQUFNLEVBQUUsQ0FBQyxJQUFJLEVBQUUsQ0FBQztJQUM5QixNQUFNLElBQUksR0FBRyxJQUFJLEdBQUcsQ0FBQyxLQUFLLENBQUMsQ0FBQztJQUU1QixJQUFJLFVBQVUsR0FBRyxDQUFDLENBQUM7SUFDbkIsS0FBSyxJQUFJLENBQUMsR0FBRyxDQUFDLEVBQUUsQ0FBQyxHQUFHLEtBQUssQ0FBQyxNQUFNLEVBQUUsQ0FBQyxFQUFFLEVBQUU7UUFDckMsSUFBSSxDQUFDLElBQUksQ0FBQyxHQUFHLENBQUMsS0FBSyxDQUFDLENBQUMsQ0FBQyxDQUFDLEVBQUU7WUFDdkIsVUFBVSxFQUFFLENBQUM7U0FDZDtLQUNGO0lBRUQsTUFBTSxNQUFNLEdBQUcsSUFBSSxZQUFZLENBQUMsQ0FBQyxVQUFVLENBQUMsRUFBRSxFQUFFLENBQUMsS0FBSyxDQUFDLENBQUM7SUFDeEQsTUFBTSxPQUFPLEdBQUcsSUFBSSxZQUFZLENBQUMsQ0FBQyxVQUFVLENBQUMsRUFBRSxPQUFPLENBQUMsQ0FBQztJQUN4RCxLQUFLLElBQUksQ0FBQyxHQUFHLENBQUMsRUFBRSxDQUFDLEdBQUcsQ0FBQyxFQUFFLENBQUMsR0FBRyxLQUFLLENBQUMsTUFBTSxFQUFFLENBQUMsRUFBRSxFQUFFO1FBQzVDLElBQUksQ0FBQyxJQUFJLENBQUMsR0FBRyxDQUFDLEtBQUssQ0FBQyxDQUFDLENBQUMsQ0FBQyxFQUFFO1lBQ3ZCLE1BQU0sQ0FBQyxNQUFNLENBQUMsQ0FBQyxDQUFDLEdBQUcsS0FBSyxDQUFDLENBQUMsQ0FBQyxDQUFDO1lBQzVCLE9BQU8sQ0FBQyxNQUFNLENBQUMsQ0FBQyxDQUFDLEdBQUcsQ0FBQyxDQUFDO1lBQ3RCLENBQUMsRUFBRSxDQUFDO1NBQ0w7S0FDRjtJQUNELE9BQU8sQ0FBQyxNQUFNLENBQUMsUUFBUSxFQUFFLEVBQUUsT0FBTyxDQUFDLFFBQVEsRUFBRSxDQUFDLENBQUM7QUFDakQsQ0FBQztBQUNELE1BQU0sQ0FBQyxNQUFNLGNBQWMsR0FBRyxlQUFlLENBQUMiLCJzb3VyY2VzQ29udGVudCI6WyIvKipcbiAqIEBsaWNlbnNlXG4gKiBDb3B5cmlnaHQgMjAyMCBHb29nbGUgSW5jLiBBbGwgUmlnaHRzIFJlc2VydmVkLlxuICogTGljZW5zZWQgdW5kZXIgdGhlIEFwYWNoZSBMaWNlbnNlLCBWZXJzaW9uIDIuMCAodGhlIFwiTGljZW5zZVwiKTtcbiAqIHlvdSBtYXkgbm90IHVzZSB0aGlzIGZpbGUgZXhjZXB0IGluIGNvbXBsaWFuY2Ugd2l0aCB0aGUgTGljZW5zZS5cbiAqIFlvdSBtYXkgb2J0YWluIGEgY29weSBvZiB0aGUgTGljZW5zZSBhdFxuICpcbiAqIGh0dHA6Ly93d3cuYXBhY2hlLm9yZy9saWNlbnNlcy9MSUNFTlNFLTIuMFxuICpcbiAqIFVubGVzcyByZXF1aXJlZCBieSBhcHBsaWNhYmxlIGxhdyBvciBhZ3JlZWQgdG8gaW4gd3JpdGluZywgc29mdHdhcmVcbiAqIGRpc3RyaWJ1dGVkIHVuZGVyIHRoZSBMaWNlbnNlIGlzIGRpc3RyaWJ1dGVkIG9uIGFuIFwiQVMgSVNcIiBCQVNJUyxcbiAqIFdJVEhPVVQgV0FSUkFOVElFUyBPUiBDT05ESVRJT05TIE9GIEFOWSBLSU5ELCBlaXRoZXIgZXhwcmVzcyBvciBpbXBsaWVkLlxuICogU2VlIHRoZSBMaWNlbnNlIGZvciB0aGUgc3BlY2lmaWMgbGFuZ3VhZ2UgZ292ZXJuaW5nIHBlcm1pc3Npb25zIGFuZFxuICogbGltaXRhdGlvbnMgdW5kZXIgdGhlIExpY2Vuc2UuXG4gKiA9PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PVxuICovXG5pbXBvcnQge1RlbnNvciwgVGVuc29yQnVmZmVyfSBmcm9tICcuLi90ZW5zb3InO1xuaW1wb3J0IHtjb252ZXJ0VG9UZW5zb3J9IGZyb20gJy4uL3RlbnNvcl91dGlsX2Vudic7XG5pbXBvcnQge1RlbnNvckxpa2V9IGZyb20gJy4uL3R5cGVzJztcbmltcG9ydCAqIGFzIHV0aWwgZnJvbSAnLi4vdXRpbCc7XG5cbi8qKlxuICogQ29tcHV0ZXMgdGhlIGRpZmZlcmVuY2UgYmV0d2VlbiB0d28gbGlzdHMgb2YgbnVtYmVycy5cbiAqXG4gKiBHaXZlbiBhIFRlbnNvciBgeGAgYW5kIGEgVGVuc29yIGB5YCwgdGhpcyBvcGVyYXRpb24gcmV0dXJucyBhIFRlbnNvciBgb3V0YFxuICogdGhhdCByZXByZXNlbnRzIGFsbCB2YWx1ZXMgdGhhdCBhcmUgaW4gYHhgIGJ1dCBub3QgaW4gYHlgLiBUaGUgcmV0dXJuZWRcbiAqIFRlbnNvciBgb3V0YCBpcyBzb3J0ZWQgaW4gdGhlIHNhbWUgb3JkZXIgdGhhdCB0aGUgbnVtYmVycyBhcHBlYXIgaW4gYHhgXG4gKiAoZHVwbGljYXRlcyBhcmUgcHJlc2VydmVkKS4gVGhpcyBvcGVyYXRpb24gYWxzbyByZXR1cm5zIGEgVGVuc29yIGluZGljZXMgdGhhdFxuICogcmVwcmVzZW50cyB0aGUgcG9zaXRpb24gb2YgZWFjaCBvdXQgZWxlbWVudCBpbiBgeGAuIEluIG90aGVyIHdvcmRzOlxuICpcbiAqIGBvdXRbaV0gPSB4W2lkeFtpXV0gZm9yIGkgaW4gWzAsIDEsIC4uLiwgb3V0Lmxlbmd0aCAtIDFdYFxuICpcbiAqIGBgYGpzXG4gKiBjb25zdCB4ID0gWzEsIDIsIDMsIDQsIDUsIDZdO1xuICogY29uc3QgeSA9IFsxLCAzLCA1XTtcbiAqXG4gKiBjb25zdCBbb3V0LCBpbmRpY2VzXSA9IGF3YWl0IHRmLnNldGRpZmYxZEFzeW5jKHgsIHkpO1xuICogb3V0LnByaW50KCk7IC8vIFsyLCA0LCA2XVxuICogaW5kaWNlcy5wcmludCgpOyAvLyBbMSwgMywgNV1cbiAqIGBgYFxuICpcbiAqIEBwYXJhbSB4IDEtRCBUZW5zb3IuIFZhbHVlcyB0byBrZWVwLlxuICogQHBhcmFtIHkgMS1EIFRlbnNvci4gTXVzdCBoYXZlIHRoZSBzYW1lIHR5cGUgYXMgeC4gVmFsdWVzIHRvIGV4Y2x1ZGUgaW4gdGhlXG4gKiAgICAgb3V0cHV0LlxuICogQHJldHVybnMgUHJvbWlzZSBvZiBUZW5zb3IgdHVwbGUgW291dCwgaW5kaWNlc10uXG4gKiAgb3V0OiBUZW5zb3Igd2l0aCB0aGUgc2FtZSB0eXBlIGFzIHguXG4gKiAgaW5kaWNlczogQSBUZW5zb3Igb2YgdH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* @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sign } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns an element-wise indication of the sign of a number.\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3, NaN, 0]);\n *\n * x.sign().print(); // or tf.sign(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sign_(x) {\n const $x = convertToTensor(x, 'x', 'sign');\n const inputs = { x: $x };\n return ENGINE.runKernel(Sign, inputs);\n}\nexport const sign = op({ sign_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Softmax } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the softmax normalized vector given the logits.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n *\n * a.softmax().print(); // or tf.softmax(a)\n * ```\n *\n * ```js\n * const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]);\n *\n * a.softmax().print(); // or tf.softmax(a)\n * ```\n *\n * @param logits The logits array.\n * @param dim The dimension softmax would be performed on. Defaults to `-1`\n * which indicates the last dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction softmax_(logits, dim = -1) {\n const $logits = convertToTensor(logits, 'logits', 'softmax', 'float32');\n if (dim === -1) {\n dim = $logits.rank - 1;\n }\n if (dim !== $logits.rank - 1) {\n throw Error('Softmax along a non-last dimension is not yet supported. ' +\n `Logits was rank ${$logits.rank} and dim was ${dim}`);\n }\n const inputs = { logits: $logits };\n const attrs = { dim };\n return ENGINE.runKernel(Softmax, inputs, attrs);\n}\nexport const softmax = op({ softmax_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { StridedSlice } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Extracts a strided slice of a tensor.\n *\n * Roughly speaking, this op extracts a slice of size (end-begin)/stride from\n * the given input tensor (x). Starting at the location specified by begin the\n * slice continues by adding stride to the index until all dimensions are not\n * less than end. Note that a stride can be negative, which causes a reverse\n * slice.\n *\n * ```js\n * const t = tf.tensor3d([1, 1, 1 ,2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6],\n * [3, 2, 3]);\n * t.stridedSlice([1, 0, 0], [2, 1, 3], [1, 1, 1]).print() // [[[3, 3, 3]]]\n * t.stridedSlice([1, 0, 0], [2, 2, 3], [1, 1, 1]).print() // [[[3, 3, 3],\n * // [4, 4, 4]]]\n * t.stridedSlice([1, -1, 0], [2, -3, 3], [1, -1, 1]).print() // [[[4, 4, 4],\n * // [3, 3, 3]]]\n * ```\n *\n * @param x The tensor to stride slice.\n * @param begin The coordinates to start the slice from.\n * @param end: The coordinates to end the slice at.\n * @param strides: The size of the slice.\n * @param beginMask: If the ith bit of beginMask is set, begin[i] is ignored\n * and the fullest possible range in that dimension is used instead.\n * @param endMask: If the ith bit of endMask is set, end[i] is ignored\n * and the fullest possible range in that dimension is used instead.\n * @param shrinkAxisMask: a bitmask where bit i implies that\n * the ith specification should shrink the dimensionality. begin and end must\n * imply a slice of size 1 in the dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}\n */\nfunction stridedSlice_(x, begin, end, strides, beginMask = 0, endMask = 0, ellipsisMask = 0, newAxisMask = 0, shrinkAxisMask = 0) {\n const $x = convertToTensor(x, 'x', 'stridedSlice', 'string_or_numeric');\n const inputs = { x: $x };\n const attrs = {\n begin,\n end,\n strides,\n beginMask,\n endMask,\n ellipsisMask,\n newAxisMask,\n shrinkAxisMask\n };\n return ENGINE.runKernel(StridedSlice, inputs, attrs);\n}\nexport const stridedSlice = op({ stridedSlice_ });\n//# 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* @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Tan } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes tan of the input `tf.Tensor` element-wise, `tan(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);\n *\n * x.tan().print(); // or tf.tan(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction tan_(x) {\n const $x = convertToTensor(x, 'x', 'tan', 'float32');\n const inputs = { x: $x };\n return ENGINE.runKernel(Tan, inputs);\n}\nexport const tan = op({ tan_ });\n//# sourceMappingURL=data:application/json;base64,eyJ2ZXJzaW9uIjozLCJmaWxlIjoidGFuLmpzIiwic291cmNlUm9vdCI6IiIsInNvdXJjZXMiOlsiLi4vLi4vLi4vLi4vLi4vLi4vdGZqcy1jb3JlL3NyYy9vcHMvdGFuLnRzIl0sIm5hbWVzIjpbXSwibWFwcGluZ3MiOiJBQUFBOzs7Ozs7Ozs7Ozs7Ozs7R0FlRztBQUVILE9BQU8sRUFBQyxNQUFNLEVBQUMsTUFBTSxXQUFXLENBQUM7QUFDakMsT0FBTyxFQUFDLEdBQUcsRUFBWSxNQUFNLGlCQUFpQixDQUFDO0FBRy9DLE9BQU8sRUFBQyxlQUFlLEVBQUMsTUFBTSxvQkFBb0IsQ0FBQztBQUduRCxPQUFPLEVBQUMsRUFBRSxFQUFDLE1BQU0sYUFBYSxDQUFDO0FBRS9COzs7Ozs7Ozs7OztHQVdHO0FBQ0gsU0FBUyxJQUFJLENBQW1CLENBQWU7SUFDN0MsTUFBTSxFQUFFLEdBQUcsZUFBZSxDQUFDLENBQUMsRUFBRSxHQUFHLEVBQUUsS0FBSyxFQUFFLFNBQVMsQ0FBQyxDQUFDO0lBRXJELE1BQU0sTUFBTSxHQUFjLEVBQUMsQ0FBQyxFQUFFLEVBQUUsRUFBQyxDQUFDO0lBRWxDLE9BQU8sTUFBTSxDQUFDLFNBQVMsQ0FBQyxHQUFHLEVBQUUsTUFBOEIsQ0FBQyxDQUFDO0FBQy9ELENBQUM7QUFDRCxNQUFNLENBQUMsTUFBTSxHQUFHLEdBQUcsRUFBRSxDQUFDLEVBQUMsSUFBSSxFQUFDLENBQUMsQ0FBQyIsInNvdXJjZXNDb250ZW50IjpbIi8qKlxuICogQGxpY2Vuc2VcbiAqIENvcHlyaWdodCAyMDE4IEdvb2dsZSBMTEMuIEFsbCBSaWdodHMgUmVzZXJ2ZWQuXG4gKiBMaWNlbnNlZCB1bmRlciB0aGUgQXBhY2hlIExpY2Vuc2UsIFZlcnNpb24gMi4wICh0aGUgXCJMaWNlbnNlXCIpO1xuICogeW91IG1heSBub3QgdXNlIHRoaXMgZmlsZSBleGNlcHQgaW4gY29tcGxpYW5jZSB3aXRoIHRoZSBMaWNlbnNlLlxuICogWW91IG1heSBvYnRhaW4gYSBjb3B5IG9mIHRoZSBMaWNlbnNlIGF0XG4gKlxuICogaHR0cDovL3d3dy5hcGFjaGUub3JnL2xpY2Vuc2VzL0xJQ0VOU0UtMi4wXG4gKlxuICogVW5sZXNzIHJlcXVpcmVkIGJ5IGFwcGxpY2FibGUgbGF3IG9yIGFncmVlZCB0byBpbiB3cml0aW5nLCBzb2Z0d2FyZVxuICogZGlzdHJpYnV0ZWQgdW5kZXIgdGhlIExpY2Vuc2UgaXMgZGlzdHJpYnV0ZWQgb24gYW4gXCJBUyBJU1wiIEJBU0lTLFxuICogV0lUSE9VVCBXQVJSQU5USUVTIE9SIENPTkRJVElPTlMgT0YgQU5ZIEtJTkQsIGVpdGhlciBleHByZXNzIG9yIGltcGxpZWQuXG4gKiBTZWUgdGhlIExpY2Vuc2UgZm9yIHRoZSBzcGVjaWZpYyBsYW5ndWFnZSBnb3Zlcm5pbmcgcGVybWlzc2lvbnMgYW5kXG4gKiBsaW1pdGF0aW9ucyB1bmRlciB0aGUgTGljZW5zZS5cbiAqID09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09XG4gKi9cblxuaW1wb3J0IHtFTkdJTkV9IGZyb20gJy4uL2VuZ2luZSc7XG5pbXBvcnQge1RhbiwgVGFuSW5wdXRzfSBmcm9tICcuLi9rZXJuZWxfbmFtZXMnO1xuaW1wb3J0IHtUZW5zb3J9IGZyb20gJy4uL3RlbnNvcic7XG5pbXBvcnQge05hbWVkVGVuc29yTWFwfSBmcm9tICcuLi90ZW5zb3JfdHlwZXMnO1xuaW1wb3J0IHtjb252ZXJ0VG9UZW5zb3J9IGZyb20gJy4uL3RlbnNvcl91dGlsX2Vudic7XG5pbXBvcnQge1RlbnNvckxpa2V9IGZyb20gJy4uL3R5cGVzJztcblxuaW1wb3J0IHtvcH0gZnJvbSAnLi9vcGVyYXRpb24nO1xuXG4vKipcbiAqIENvbXB1dGVzIHRhbiBvZiB0aGUgaW5wdXQgYHRmLlRlbnNvcmAgZWxlbWVudC13aXNlLCBgdGFuKHgpYFxuICpcbiAqIGBgYGpzXG4gKiBjb25zdCB4ID0gdGYudGVuc29yMWQoWzAsIE1hdGguUEkgLyAyLCBNYXRoLlBJICogMyAvIDRdKTtcbiAqXG4gKiB4LnRhbigpLnByaW50KCk7ICAvLyBvciB0Zi50YW4oeClcbiAqIGBgYFxuICogQHBhcmFtIHggVGhlIGlucHV0IHRlbnNvci5cbiAqXG4gKiBAZG9jIHtoZWFkaW5nOiAnT3BlcmF0aW9ucycsIHN1YmhlYWRpbmc6ICdCYXNpYyBtYXRoJ31cbiAqL1xuZnVuY3Rpb24gdGFuXzxUIGV4dGVuZHMgVGVuc29yPih4OiBUfFRlbnNvckxpa2UpOiBUIHtcbiAgY29uc3QgJHggPSBjb252ZXJ0VG9UZW5zb3IoeCwgJ3gnLCAndGFuJywgJ2Zsb2F0MzInKTtcblxuICBjb25zdCBpbnB1dHM6IFRhbklucHV0cyA9IHt4OiAkeH07XG5cbiAgcmV0dXJuIEVOR0lORS5ydW5LZXJuZWwoVGFuLCBpbnB1dHMgYXMge30gYXMgTmFtZWRUZW5zb3JNYXApO1xufVxuZXhwb3J0IGNvbnN0IHRhbiA9IG9wKHt0YW5ffSk7XG4iXX0=","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { TopK } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Finds the values and indices of the `k` largest entries along the last\n * dimension.\n *\n * If the input is a vector (rank=1), finds the k largest entries in the vector\n * and outputs their values and indices as vectors. Thus values[j] is the j-th\n * largest entry in input, and its index is indices[j].\n * For higher rank inputs, computes the top k entries along the last dimension.\n *\n * If two elements are equal, the lower-index element appears first.\n *\n * ```js\n * const a = tf.tensor2d([[1, 5], [4, 3]]);\n * const {values, indices} = tf.topk(a);\n * values.print();\n * indices.print();\n * ```\n * @param x 1-D or higher `tf.Tensor` with last dimension being at least `k`.\n * @param k Number of top elements to look for along the last dimension.\n * @param sorted If true, the resulting `k` elements will be sorted by the\n * values in descending order.\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nfunction topk_(x, k = 1, sorted = true) {\n const $x = convertToTensor(x, 'x', 'topk');\n if ($x.rank === 0) {\n throw new Error('topk() expects the input to be of rank 1 or higher');\n }\n const lastDim = $x.shape[$x.shape.length - 1];\n if (k < 0) {\n throw new Error(`'k' passed to topk() must be >= 0 but got ${k}`);\n }\n if (k > lastDim) {\n throw new Error(`'k' passed to topk() must be <= the last dimension (${lastDim}) ` +\n `but got ${k}`);\n }\n const inputs = { x: $x };\n const attrs = { k, sorted };\n const [values, indices] = ENGINE.runKernel(TopK, inputs, attrs);\n return { values, indices };\n}\nexport const topk = op({ topk_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from './buffer';\nimport { op } from './operation';\nimport { MPRandGauss } from './rand_util';\n/**\n * Creates a `tf.Tensor` with values sampled from a truncated normal\n * distribution.\n *\n * ```js\n * tf.truncatedNormal([2, 2]).print();\n * ```\n *\n * The generated values follow a normal distribution with specified mean and\n * standard deviation, except that values whose magnitude is more than 2\n * standard deviations from the mean are dropped and re-picked.\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param mean The mean of the normal distribution.\n * @param stdDev The standard deviation of the normal distribution.\n * @param dtype The data type of the output tensor.\n * @param seed The seed for the random number generator.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction truncatedNormal_(shape, mean = 0, stdDev = 1, dtype, seed) {\n if (dtype != null && dtype === 'bool') {\n throw new Error(`Unsupported data type $ { dtype }`);\n }\n const randGauss = new MPRandGauss(mean, stdDev, dtype, true /* truncated */, seed);\n const res = buffer(shape, dtype);\n for (let i = 0; i < res.values.length; i++) {\n res.values[i] = randGauss.nextValue();\n }\n return res.toTensor();\n}\nexport const truncatedNormal = op({ truncatedNormal_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Unique } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assert } from '../util';\nimport { op } from './operation';\n/**\n * Finds unique elements along an axis of a tensor.\n *\n * It returns a tensor `values` containing all of the unique elements along the\n * `axis` of the given tensor `x` in the same order that they occur along the\n * `axis` in `x`; `x` does not need to be sorted. It also returns a tensor\n * `indices` the same size as the number of the elements in `x` along the `axis`\n * dimension. It contains the index in the unique output `values`.\n *\n * ```js\n * // A 1-D tensor\n * const a = tf.tensor1d([1, 1, 2, 4, 4, 4, 7, 8, 8]);\n * const {values, indices} = tf.unique(a);\n * values.print(); // [1, 2, 4, 7, 8,]\n * indices.print(); // [0, 0, 1, 2, 2, 2, 3, 4, 4]\n * ```\n *\n * ```js\n * // A 2-D tensor with axis=0\n * //\n * // 'a' is: [[1, 0, 0],\n * // [1, 0, 0],\n * // [2, 0, 0]]\n * const a = tf.tensor2d([[1, 0, 0], [1, 0, 0], [2, 0, 0]]);\n * const {values, indices} = tf.unique(a, 0)\n * values.print(); // [[1, 0, 0],\n * // [2, 0, 0]]\n * indices.print(); // [0, 0, 1]\n * ```\n *\n * ```js\n * // A 2-D tensor with axis=1\n * //\n * // 'a' is: [[1, 0, 0],\n * // [1, 0, 0],\n * // [2, 0, 0]]\n * const a = tf.tensor2d([[1, 0, 0], [1, 0, 0], [2, 0, 0]]);\n * const {values, indices} = tf.unique(a, 1)\n * values.print(); // [[1, 0],\n * // [1, 0],\n * // [2, 0]]\n * indices.print(); // [0, 1, 1]\n * ```\n * @param x A tensor (int32, string, bool).\n * @param axis The axis of the tensor to find the unique elements.\n * @returns [uniqueElements, indices] (see above for details)\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nfunction unique_(x, axis = 0) {\n const $x = convertToTensor(x, 'x', 'unique', 'string_or_numeric');\n assert($x.rank > 0, () => 'The input tensor must be at least 1D');\n const inputs = { x: $x };\n const attrs = { axis };\n const [values, indices] = ENGINE.runKernel(Unique, inputs, attrs);\n return { values, indices };\n}\nexport const unique = op({ unique_ });\n//# 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* @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ScatterNd } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport * as scatter_nd_util from './scatter_nd_util';\n/**\n * Creates a new tensor by applying sparse updates to individual\n * values or slices within a zero tensor of the given shape tensor according to\n * indices. This operator is the inverse of the `tf.gatherND` operator which\n * extracts values or slices from a given tensor.\n *\n * ```js\n * const indices = tf.tensor2d([4, 3, 1, 7], [4, 1], 'int32');\n * const updates = tf.tensor1d([9, 10, 11, 12]);\n * const shape = [8];\n * tf.scatterND(indices, updates, shape).print() //[0, 11, 0, 10, 9, 0, 0, 12]\n * ```\n *\n * @param indices The tensor contains the indices into the output tensor.\n * @param updates The tensor contains the value for the indices.\n * @param shape: The shape of the output tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}\n */\nfunction scatterND_(indices, updates, shape) {\n const $indices = convertToTensor(indices, 'indices', 'scatterND', 'int32');\n const $updates = convertToTensor(updates, 'updates', 'scatterND');\n scatter_nd_util.validateInput($updates, $indices, shape);\n const inputs = { indices: $indices, updates: $updates };\n const attrs = { shape };\n // tslint:disable-next-line: no-unnecessary-type-assertion\n return ENGINE.runKernel(ScatterNd, inputs, attrs);\n}\nexport const scatterND = op({ scatterND_ });\n//# 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* @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { GatherNd } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Gather slices from input tensor into a Tensor with shape specified by\n * `indices`.\n *\n * `indices` is an K-dimensional integer tensor, best thought of as a\n * (K-1)-dimensional tensor of indices into input, where each element defines a\n * slice of input:\n * output[\\\\(i_0, ..., i_{K-2}\\\\)] = input[indices[\\\\(i_0, ..., i_{K-2}\\\\)]]\n *\n * Whereas in `tf.gather`, `indices` defines slices into the first dimension of\n * input, in `tf.gatherND`, `indices` defines slices into the first N dimensions\n * of input, where N = indices.shape[-1].\n *\n * The last dimension of indices can be at most the rank of input:\n * indices.shape[-1] <= input.rank\n *\n * The last dimension of `indices` corresponds to elements\n * (if indices.shape[-1] == input.rank) or slices\n * (if indices.shape[-1] < input.rank) along dimension indices.shape[-1] of\n * input.\n * The output tensor has shape\n * indices.shape[:-1] + input.shape[indices.shape[-1]:]\n *\n * Note that on CPU, if an out of bound index is found, an error is returned. On\n * GPU, if an out of bound index is found, a 0 is stored in the corresponding\n * output value.\n *\n * ```js\n * const indices = tf.tensor2d([0, 1, 1, 0], [2,2], 'int32');\n * const input = tf.tensor2d([9, 10, 11, 12], [2, 2]);\n * tf.gatherND(input, indices).print() // [10, 11]\n * ```\n *\n * @param x The tensor from which to gather values.\n * @param indices Index tensor, must be of type int32.\n *\n * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}\n */\nfunction gatherND_(x, indices) {\n const $indices = convertToTensor(indices, 'indices', 'gatherND', 'int32');\n const $x = convertToTensor(x, 'x', 'gatherND', 'string_or_numeric');\n const inputs = { params: $x, indices: $indices };\n return ENGINE.runKernel(GatherNd, inputs);\n}\nexport const gatherND = op({ gatherND_ });\n//# 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JNYXApO1xufVxuXG5leHBvcnQgY29uc3QgZ2F0aGVyTkQgPSBvcCh7Z2F0aGVyTkRffSk7XG4iXX0=","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst delayCallback = (() => {\n if (typeof requestAnimationFrame !== 'undefined') {\n return requestAnimationFrame;\n }\n else if (typeof setImmediate !== 'undefined') {\n return setImmediate;\n }\n return (f) => f(); // no delays\n})();\n/**\n * Returns a promise that resolve when a requestAnimationFrame has completed.\n *\n * On Node.js this uses setImmediate instead of requestAnimationFrame.\n *\n * This is simply a sugar method so that users can do the following:\n * `await tf.nextFrame();`\n *\n * @doc {heading: 'Performance', subheading: 'Timing'}\n */\nfunction nextFrame() {\n return new Promise(resolve => delayCallback(() => resolve()));\n}\nexport { nextFrame };\n//# sourceMappingURL=data:application/json;base64,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","\"use strict\";\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * https://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nObject.defineProperty(exports, \"__esModule\", { value: true });\nexports.MESH_ANNOTATIONS = {\n silhouette: [\n 10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n 397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n 172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109\n ],\n lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n rightEyeIris: [473, 474, 475, 476, 477],\n leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n leftEyeIris: [468, 469, 470, 471, 472],\n midwayBetweenEyes: [168],\n noseTip: [1],\n noseBottom: [2],\n noseRightCorner: [98],\n noseLeftCorner: [327],\n rightCheek: [205],\n leftCheek: [425]\n};\n//# sourceMappingURL=keypoints.js.map","/**\n * Validate sparseToDense inputs.\n *\n * @param sparseIndices A 0-D, 1-D, or 2-D Tensor of type int32.\n * sparseIndices[i] contains the complete index where sparseValues[i] will be\n * placed.\n * @param sparseValues A 0-D or 1-D Tensor. Values\n * corresponding to each row of sparseIndices, or a scalar value to be used for\n * all sparse indices.\n * @param outputShape number[]. Shape of the dense output tensor.\n * @param validateIndices boolean. indice validation is not supported, error\n * will be thrown if it is set.\n */\nexport function validateInput(sparseIndices, sparseValues, outputShape, defaultValues) {\n if (sparseIndices.dtype !== 'int32') {\n throw new Error('tf.sparseToDense() expects the indices to be int32 type,' +\n ` but the dtype was ${sparseIndices.dtype}.`);\n }\n if (sparseIndices.rank > 2) {\n throw new Error('sparseIndices should be a scalar, vector, or matrix,' +\n ` but got shape ${sparseIndices.shape}.`);\n }\n const numElems = sparseIndices.rank > 0 ? sparseIndices.shape[0] : 1;\n const numDims = sparseIndices.rank > 1 ? sparseIndices.shape[1] : 1;\n if (outputShape.length !== numDims) {\n throw new Error('outputShape has incorrect number of elements:,' +\n ` ${outputShape.length}, should be: ${numDims}.`);\n }\n const numValues = sparseValues.size;\n if (!(sparseValues.rank === 0 ||\n sparseValues.rank === 1 && numValues === numElems)) {\n throw new Error('sparseValues has incorrect shape ' +\n `${sparseValues.shape}, should be [] or [${numElems}]`);\n }\n if (sparseValues.dtype !== defaultValues.dtype) {\n throw new Error('sparseValues.dtype must match defaultValues.dtype');\n }\n}\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { SparseToDense } from '../kernel_names';\nimport * as sparse_to_dense from '../ops/sparse_to_dense_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Converts a sparse representation into a dense tensor.\n *\n * Builds an array dense with shape outputShape such that:\n *\n * // If sparseIndices is scalar\n * dense[i] = (i == sparseIndices ? sparseValues : defaultValue)\n *\n * // If sparseIndices is a vector, then for each i\n * dense[sparseIndices[i]] = sparseValues[i]\n *\n * // If sparseIndices is an n by d matrix, then for each i in [0, n)\n * dense[sparseIndices[i][0], ..., sparseIndices[i][d-1]] = sparseValues[i]\n * All other values in dense are set to defaultValue. If sparseValues is a\n * scalar, all sparse indices are set to this single value.\n *\n * If indices are repeated the final value is summed over all values for those\n * indices.\n *\n * ```js\n * const indices = tf.tensor1d([4, 5, 6, 1, 2, 3], 'int32');\n * const values = tf.tensor1d([10, 11, 12, 13, 14, 15], 'float32');\n * const shape = [8];\n * tf.sparseToDense(indices, values, shape).print();\n * ```\n *\n * @param sparseIndices A 0-D, 1-D, or 2-D Tensor of type int32.\n * sparseIndices[i] contains the complete index where sparseValues[i] will be\n * placed.\n * @param sparseValues A 0-D or 1-D Tensor. Values\n * corresponding to each row of sparseIndices, or a scalar value to be used for\n * all sparse indices.\n * @param outputShape Shape of the dense output tensor. the type is inferred.\n * @param defaultValue Scalar. Value to set for indices not specified in\n * sparseIndices. Defaults to zero.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction sparseToDense_(sparseIndices, sparseValues, outputShape, defaultValue = 0) {\n const $sparseIndices = convertToTensor(sparseIndices, 'sparseIndices', 'sparseToDense', 'int32');\n const $sparseValues = convertToTensor(sparseValues, 'sparseValues', 'sparseToDense');\n const $defaultValue = convertToTensor(defaultValue, 'defaultValue', 'sparseToDense', $sparseValues.dtype);\n sparse_to_dense.validateInput($sparseIndices, $sparseValues, outputShape, $defaultValue);\n const inputs = {\n sparseIndices: $sparseIndices,\n sparseValues: $sparseValues,\n defaultValue: $defaultValue\n };\n const attrs = { outputShape };\n return ENGINE.runKernel(SparseToDense, inputs, attrs);\n}\nexport const sparseToDense = op({ sparseToDense_ });\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2022 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n {\n 'tfOpName': 'Add',\n 'category': 'arithmetic',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'a',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'b',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'AddV2',\n 'category': 'arithmetic',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'a',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'b',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'AddN',\n 'category': 'arithmetic',\n 'inputs': [\n {\n 'start': 0,\n 'end': 0,\n 'name': 'tensors',\n 'type': 'tensors'\n }\n ]\n },\n {\n 'tfOpName': 'BiasAdd',\n 'category': 'arithmetic',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'a',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'b',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n },\n {\n 'tfName': 'data_format',\n 'name': 'dataFormat',\n 'type': 'string',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Sub',\n 'category': 'arithmetic',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'a',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'b',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'RealDiv',\n 'category': 'arithmetic',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'a',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'b',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Div',\n 'category': 'arithmetic',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'a',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'b',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'DivNoNan',\n 'category': 'arithmetic',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'a',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'b',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'FloorDiv',\n 'category': 'arithmetic',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'a',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'b',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Mul',\n 'category': 'arithmetic',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'a',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'b',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Maximum',\n 'category': 'arithmetic',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'a',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'b',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Minimum',\n 'category': 'arithmetic',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'a',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'b',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Pow',\n 'category': 'arithmetic',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'a',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'b',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'SquaredDifference',\n 'category': 'arithmetic',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'a',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'b',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Mod',\n 'category': 'arithmetic',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'a',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'b',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'FloorMod',\n 'category': 'arithmetic',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'a',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'b',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n }\n];\n//# 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* @license\n * Copyright 2022 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {OpMapper} from '../types';\n\nexport const json: OpMapper[] = [\n  {\n    'tfOpName': 'Add',\n    'category': 'arithmetic',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'a',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'b',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'AddV2',\n    'category': 'arithmetic',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'a',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'b',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'AddN',\n    'category': 'arithmetic',\n    'inputs': [\n      {\n        'start': 0,\n        'end': 0,\n        'name': 'tensors',\n        'type': 'tensors'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'BiasAdd',\n    'category': 'arithmetic',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'a',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'b',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      },\n      {\n        'tfName': 'data_format',\n        'name': 'dataFormat',\n        'type': 'string',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Sub',\n    'category': 'arithmetic',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'a',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'b',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'RealDiv',\n    'category': 'arithmetic',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'a',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'b',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Div',\n    'category': 'arithmetic',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'a',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'b',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'DivNoNan',\n    'category': 'arithmetic',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'a',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'b',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'FloorDiv',\n    'category': 'arithmetic',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'a',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'b',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Mul',\n    'category': 'arithmetic',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'a',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'b',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Maximum',\n    'category': 'arithmetic',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'a',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'b',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Minimum',\n    'category': 'arithmetic',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'a',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'b',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Pow',\n    'category': 'arithmetic',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'a',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'b',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'SquaredDifference',\n    'category': 'arithmetic',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'a',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'b',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Mod',\n    'category': 'arithmetic',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'a',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'b',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'FloorMod',\n    'category': 'arithmetic',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'a',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'b',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  }\n];\n"]}","/**\n * @license\n * Copyright 2022 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n {\n 'tfOpName': 'Abs',\n 'category': 'basic_math',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Acos',\n 'category': 'basic_math',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Asin',\n 'category': 'basic_math',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Atan',\n 'category': 'basic_math',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Atan2',\n 'category': 'basic_math',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'y',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Ceil',\n 'category': 'basic_math',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'ClipByValue',\n 'category': 'basic_math',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'clipValueMin',\n 'type': 'number'\n },\n {\n 'start': 2,\n 'name': 'clipValueMax',\n 'type': 'number'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Complex',\n 'category': 'basic_math',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'real',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'imag',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'ComplexAbs',\n 'category': 'basic_math',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Cos',\n 'category': 'basic_math',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Cosh',\n 'category': 'basic_math',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Elu',\n 'category': 'basic_math',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Exp',\n 'category': 'basic_math',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Floor',\n 'category': 'basic_math',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Log',\n 'category': 'basic_math',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Imag',\n 'category': 'basic_math',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n },\n {\n 'tfName': 'Tout',\n 'name': 'outputType',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Neg',\n 'category': 'basic_math',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Real',\n 'category': 'basic_math',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n },\n {\n 'tfName': 'Tout',\n 'name': 'outputType',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Prelu',\n 'category': 'basic_math',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'alpha',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Relu',\n 'category': 'basic_math',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Relu6',\n 'category': 'basic_math',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Selu',\n 'category': 'basic_math',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Sigmoid',\n 'category': 'basic_math',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Sin',\n 'category': 'basic_math',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Sinh',\n 'category': 'basic_math',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Sqrt',\n 'category': 'basic_math',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Rsqrt',\n 'category': 'basic_math',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Square',\n 'category': 'basic_math',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Tan',\n 'category': 'basic_math',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Tanh',\n 'category': 'basic_math',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Sign',\n 'category': 'basic_math',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Round',\n 'category': 'basic_math',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Expm1',\n 'category': 'basic_math',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Log1p',\n 'category': 'basic_math',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Reciprocal',\n 'category': 'basic_math',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Softplus',\n 'category': 'basic_math',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Asinh',\n 'category': 'basic_math',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Acosh',\n 'category': 'basic_math',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Atanh',\n 'category': 'basic_math',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Erf',\n 'category': 'basic_math',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Prod',\n 'category': 'basic_math',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'axes',\n 'type': 'number[]'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'keep_dims',\n 'name': 'keepDims',\n 'type': 'bool',\n 'notSupported': true\n },\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'LeakyRelu',\n 'category': 'basic_math',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'alpha',\n 'name': 'alpha',\n 'type': 'number',\n 'defaultValue': 0.2\n },\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'IsNan',\n 'category': 'basic_math',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n }\n];\n//# 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* @license\n * Copyright 2022 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {OpMapper} from '../types';\n\nexport const json: OpMapper[] = [\n  {\n    'tfOpName': 'Abs',\n    'category': 'basic_math',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Acos',\n    'category': 'basic_math',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Asin',\n    'category': 'basic_math',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Atan',\n    'category': 'basic_math',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Atan2',\n    'category': 'basic_math',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'y',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Ceil',\n    'category': 'basic_math',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'ClipByValue',\n    'category': 'basic_math',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'clipValueMin',\n        'type': 'number'\n      },\n      {\n        'start': 2,\n        'name': 'clipValueMax',\n        'type': 'number'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Complex',\n    'category': 'basic_math',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'real',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'imag',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'ComplexAbs',\n    'category': 'basic_math',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n  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All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n {\n 'tfOpName': 'EmptyTensorList',\n 'category': 'control',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'elementShape',\n 'type': 'shape'\n },\n {\n 'start': 1,\n 'name': 'maxNumElements',\n 'type': 'number'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'element_dtype',\n 'name': 'elementDType',\n 'type': 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* @license\n * Copyright 2022 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {OpMapper} from '../types';\n\nexport const json: OpMapper[] = [\n  {\n    'tfOpName': 'EmptyTensorList',\n    'category': 'control',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'elementShape',\n        'type': 'shape'\n      },\n      {\n        'start': 1,\n        'name': 'maxNumElements',\n        'type': 'number'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'element_dtype',\n        'name': 'elementDType',\n        'type': 'dtype'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'LoopCond',\n    'category': 'control',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'pred',\n        'type': 'tensor'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Switch',\n    'category': 'control',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'data',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'pred',\n        'type': 'tensor'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Merge',\n    'category': 'control',\n    'inputs': [\n      {\n        'start': 0,\n        'end': 0,\n        'name': 'tensors',\n        'type': 'tensors'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Enter',\n    'category': 'control',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'tensor',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      },\n      {\n        'tfName': 'frame_name',\n        'name': 'frameName',\n        'type': 'string'\n      },\n      {\n        'tfName': 'is_constant',\n        'name': 'isConstant',\n        'type': 'bool'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Exit',\n    'category': 'control',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'tensor',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'NextIteration',\n    'category': 'control',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'tensor',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n       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'control',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'tensorArrayId',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'index',\n        'type': 'number'\n      },\n      {\n        'start': 2,\n        'name': 'tensor',\n        'type': 'tensor'\n      },\n      {\n        'start': 3,\n        'name': 'flowIn',\n        'type': 'number'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'TensorArrayReadV3',\n    'category': 'control',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'tensorArrayId',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'index',\n        'type': 'number'\n      },\n      {\n        'start': 2,\n        'name': 'flowIn',\n        'type': 'number'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'dtype',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'TensorArrayGatherV3',\n    'category': 'control',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'tensorArrayId',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'indices',\n        'type': 'number[]'\n      },\n      {\n        'start': 2,\n        'name': 'flowIn',\n        'type': 'number'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'dtype',\n        'name': 'dtype',\n        'type': 'dtype'\n      },\n      {\n        'tfName': 'element_shape',\n        'name': 'elementShape',\n        'type': 'shape'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'TensorArrayScatterV3',\n    'category': 'control',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'tensorArrayId',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'indices',\n        'type': 'number[]'\n      },\n      {\n        'start': 2,\n        'name': 'tensor',\n        'type': 'tensor'\n      },\n      {\n        'start': 3,\n        'name': 'flowIn',\n        'type': 'number'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'TensorArrayConcatV3',\n    'category': 'control',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'tensorArrayId',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'flowIn',\n        'type': 'number'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'dtype',\n        'name': 'dtype',\n        'type': 'dtype'\n      },\n      {\n        'tfName': 'element_shape_except0',\n        'name': 'elementShapeExcept0',\n        'type': 'shape',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'TensorArraySplitV3',\n    'category': 'control',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'tensorArrayId',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'tensor',\n        'type': 'tensor'\n      },\n      {\n        'start': 2,\n        'name': 'lengths',\n        'type': 'number[]'\n      },\n      {\n        'start': 3,\n        'name': 'flowIn',\n        'type': 'number'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'TensorArraySizeV3',\n    'category': 'control',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'tensorArrayId',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'flowIn',\n        'type': 'number'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'TensorArrayCloseV3',\n    'category': 'control',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'tensorArrayId',\n        'type': 'tensor'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'StatelessIf',\n    'category': 'control',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'cond',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'end': 0,\n        'name': 'args',\n        'type': 'tensors'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'then_branch',\n        'name': 'thenBranch',\n        'type': 'func'\n      },\n      {\n        'tfName': 'else_branch',\n        'name': 'elseBranch',\n        'type': 'func'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'If',\n    'category': 'control',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'cond',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'end': 0,\n        'name': 'args',\n        'type': 'tensors'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'then_branch',\n        'name': 'thenBranch',\n        'type': 'func'\n      },\n      {\n        'tfName': 'else_branch',\n        'name': 'elseBranch',\n        'type': 'func'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'StatelessWhile',\n    'category': 'control',\n    'inputs': [\n      {\n        'start': 0,\n        'end': 0,\n        'name': 'args',\n        'type': 'tensors'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'cond',\n        'name': 'cond',\n        'type': 'func'\n      },\n      {\n        'tfName': 'body',\n        'name': 'body',\n        'type': 'func'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'While',\n    'category': 'control',\n    'inputs': [\n      {\n        'start': 0,\n        'end': 0,\n        'name': 'args',\n        'type': 'tensors'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'cond',\n        'name': 'cond',\n        'type': 'func'\n      },\n      {\n        'tfName': 'body',\n        'name': 'body',\n        'type': 'func'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'TensorListScatter',\n    'category': 'control',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'tensor',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'indices',\n        'type': 'number[]'\n      },\n      {\n        'start': 2,\n        'name': 'elementShape',\n        'type': 'shape'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'element_dtype',\n        'name': 'elementDType',\n        'type': 'dtype'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'TensorListScatterV2',\n    'category': 'control',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'tensor',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'indices',\n        'type': 'number[]'\n      },\n      {\n        'start': 2,\n        'name': 'elementShape',\n        'type': 'shape'\n      },\n      {\n        'start': 3,\n        'name': 'numElements',\n        'type': 'number'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'element_dtype',\n        'name': 'elementDType',\n        'type': 'dtype'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'TensorListGather',\n    'category': 'control',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'tensorListId',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'indices',\n        'type': 'number[]'\n      },\n      {\n        'start': 2,\n        'name': 'elementShape',\n        'type': 'shape'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'element_dtype',\n        'name': 'elementDType',\n        'type': 'dtype'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'TensorListGetItem',\n    'category': 'control',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'tensorListId',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'index',\n        'type': 'number'\n      },\n      {\n        'start': 2,\n        'name': 'elementShape',\n        'type': 'shape'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'element_dtype',\n        'name': 'elementDType',\n        'type': 'dtype'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'TensorListSetItem',\n    'category': 'control',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'tensorListId',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'index',\n        'type': 'number'\n      },\n      {\n        'start': 2,\n        'name': 'tensor',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'element_dtype',\n        'name': 'elementDType',\n        'type': 'dtype'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'TensorListReserve',\n    'category': 'control',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'elementShape',\n        'type': 'shape'\n      },\n      {\n        'start': 1,\n        'name': 'numElements',\n        'type': 'number'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'element_dtype',\n        'name': 'elementDType',\n        'type': 'dtype'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'TensorListFromTensor',\n    'category': 'control',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'tensor',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'elementShape',\n        'type': 'shape'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'element_dtype',\n        'name': 'elementDType',\n        'type': 'dtype'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'TensorListStack',\n    'category': 'control',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'tensorListId',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'elementShape',\n        'type': 'shape'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'element_dtype',\n        'name': 'elementDType',\n        'type': 'dtype'\n      },\n      {\n        'tfName': 'num_elements',\n        'name': 'numElements',\n        'type': 'dtype'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'TensorListSplit',\n    'category': 'control',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'tensor',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'elementShape',\n        'type': 'shape'\n      },\n      {\n        'start': 2,\n        'name': 'lengths',\n        'type': 'number[]'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'element_dtype',\n        'name': 'elementDType',\n        'type': 'dtype'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'TensorListConcat',\n    'category': 'control',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'tensorListId',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'element_shape',\n        'name': 'elementShape',\n        'type': 'shape'\n      },\n      {\n        'tfName': 'element_dtype',\n        'name': 'elementDType',\n        'type': 'dtype'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'TensorListPopBack',\n    'category': 'control',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'tensorListId',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'elementShape',\n        'type': 'shape'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'element_dtype',\n        'name': 'elementDType',\n        'type': 'dtype'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'TensorListPushBack',\n    'category': 'control',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'tensorListId',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'tensor',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'element_dtype',\n        'name': 'elementDType',\n        'type': 'dtype'\n      }\n    ]\n  }\n];\n"]}","/**\n * @license\n * Copyright 2022 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n {\n 'tfOpName': 'AvgPool',\n 'category': 'convolution',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'strides',\n 'name': 'strides',\n 'type': 'number[]'\n },\n {\n 'tfName': 'padding',\n 'name': 'pad',\n 'type': 'string'\n },\n {\n 'tfName': 'data_format',\n 'name': 'dataFormat',\n 'type': 'string',\n 'notSupported': true\n },\n {\n 'tfName': 'ksize',\n 'name': 'kernelSize',\n 'type': 'number[]'\n },\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'MaxPool',\n 'category': 'convolution',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'strides',\n 'name': 'strides',\n 'type': 'number[]'\n },\n {\n 'tfName': 'padding',\n 'name': 'pad',\n 'type': 'string'\n },\n {\n 'tfName': 'data_format',\n 'name': 'dataFormat',\n 'type': 'string',\n 'notSupported': true\n },\n {\n 'tfName': 'ksize',\n 'name': 'kernelSize',\n 'type': 'number[]'\n },\n {\n 'tfName': 'explicit_paddings',\n 'name': 'explicitPaddings',\n 'type': 'number[]',\n 'defaultValue': [],\n 'notSupported': true\n },\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'MaxPoolWithArgmax',\n 'category': 'convolution',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'strides',\n 'name': 'strides',\n 'type': 'number[]'\n },\n {\n 'tfName': 'padding',\n 'name': 'pad',\n 'type': 'string'\n },\n {\n 'tfName': 'ksize',\n 'name': 'kernelSize',\n 'type': 'number[]'\n },\n {\n 'tfName': 'include_batch_in_index',\n 'name': 'includeBatchInIndex',\n 'type': 'bool'\n },\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'AvgPool3D',\n 'category': 'convolution',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'strides',\n 'name': 'strides',\n 'type': 'number[]'\n },\n {\n 'tfName': 'padding',\n 'name': 'pad',\n 'type': 'string'\n },\n {\n 'tfName': 'data_format',\n 'name': 'dataFormat',\n 'type': 'string',\n 'notSupported': true\n },\n {\n 'tfName': 'ksize',\n 'name': 'kernelSize',\n 'type': 'number[]'\n },\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'MaxPool3D',\n 'category': 'convolution',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'strides',\n 'name': 'strides',\n 'type': 'number[]'\n },\n {\n 'tfName': 'padding',\n 'name': 'pad',\n 'type': 'string'\n },\n {\n 'tfName': 'data_format',\n 'name': 'dataFormat',\n 'type': 'string',\n 'notSupported': true\n },\n {\n 'tfName': 'ksize',\n 'name': 'kernelSize',\n 'type': 'number[]'\n },\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Conv1D',\n 'category': 'convolution',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'filter',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'stride',\n 'name': 'stride',\n 'type': 'number'\n },\n {\n 'tfName': 'padding',\n 'name': 'pad',\n 'type': 'string'\n },\n {\n 'tfName': 'data_format',\n 'name': 'dataFormat',\n 'type': 'string',\n 'defaultValue': 'NWC'\n },\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n },\n {\n 'tfName': 'dilation',\n 'name': 'dilation',\n 'type': 'number',\n 'defaultValue': 1\n }\n ]\n },\n {\n 'tfOpName': 'Conv2D',\n 'category': 'convolution',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'filter',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n },\n {\n 'tfName': 'strides',\n 'name': 'strides',\n 'type': 'number[]'\n },\n {\n 'tfName': 'padding',\n 'name': 'pad',\n 'type': 'string'\n },\n {\n 'tfName': 'useCudnnOnGpu',\n 'name': 'useCudnnOnGpu',\n 'type': 'bool'\n },\n {\n 'tfName': 'data_format',\n 'name': 'dataFormat',\n 'type': 'string',\n 'defaultValue': 'NHWC'\n },\n {\n 'tfName': 'explicit_paddings',\n 'name': 'explicitPaddings',\n 'type': 'number[]',\n 'defaultValue': []\n },\n {\n 'tfName': 'dilations',\n 'name': 'dilations',\n 'type': 'number[]'\n }\n ]\n },\n {\n 'tfOpName': '_FusedConv2D',\n 'category': 'convolution',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'filter',\n 'type': 'tensor'\n },\n {\n 'start': 2,\n 'end': 0,\n 'name': 'args',\n 'type': 'tensors'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'num_args',\n 'name': 'numArgs',\n 'type': 'number'\n },\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n },\n {\n 'tfName': 'strides',\n 'name': 'strides',\n 'type': 'number[]'\n },\n {\n 'tfName': 'padding',\n 'name': 'pad',\n 'type': 'string'\n },\n {\n 'tfName': 'explicit_paddings',\n 'name': 'explicitPaddings',\n 'type': 'number[]',\n 'defaultValue': []\n },\n {\n 'tfName': 'use_cudnn_on_gpu',\n 'name': 'useCudnnOnGpu',\n 'type': 'bool',\n 'defaultValue': true\n },\n {\n 'tfName': 'data_format',\n 'name': 'dataFormat',\n 'type': 'string',\n 'defaultValue': 'NHWC'\n },\n {\n 'tfName': 'dilations',\n 'name': 'dilations',\n 'type': 'number[]',\n 'defaultValue': [\n 1,\n 1,\n 1,\n 1\n ]\n },\n {\n 'tfName': 'fused_ops',\n 'name': 'fusedOps',\n 'type': 'string[]',\n 'defaultValue': []\n },\n {\n 'tfName': 'epsilon',\n 'name': 'epsilon',\n 'type': 'number',\n 'defaultValue': 0.0001\n },\n {\n 'tfName': 'leakyrelu_alpha',\n 'name': 'leakyreluAlpha',\n 'type': 'number'\n }\n ]\n },\n {\n 'tfOpName': 'Conv2DBackpropInput',\n 'category': 'convolution',\n 'inputs': [\n {\n 'start': 2,\n 'name': 'x',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'filter',\n 'type': 'tensor'\n },\n {\n 'start': 0,\n 'name': 'outputShape',\n 'type': 'number[]'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'strides',\n 'name': 'strides',\n 'type': 'number[]'\n },\n {\n 'tfName': 'padding',\n 'name': 'pad',\n 'type': 'string'\n },\n {\n 'tfName': 'data_format',\n 'name': 'dataFormat',\n 'type': 'string',\n 'notSupported': true\n },\n {\n 'tfName': 'explicit_paddings',\n 'name': 'explicitPaddings',\n 'type': 'number[]',\n 'defaultValue': []\n },\n {\n 'tfName': 'dilations',\n 'name': 'dilations',\n 'type': 'number[]',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'DepthwiseConv2d',\n 'category': 'convolution',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'input',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'filter',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'strides',\n 'name': 'strides',\n 'type': 'number[]'\n },\n {\n 'tfName': 'padding',\n 'name': 'pad',\n 'type': 'string'\n },\n {\n 'tfName': 'data_format',\n 'name': 'dataFormat',\n 'type': 'string',\n 'defaultValue': 'NHWC'\n },\n {\n 'tfName': 'explicit_paddings',\n 'name': 'explicitPaddings',\n 'type': 'number[]',\n 'defaultValue': []\n },\n {\n 'tfName': 'dilations',\n 'name': 'dilations',\n 'type': 'number[]'\n }\n ]\n },\n {\n 'tfOpName': 'DepthwiseConv2dNative',\n 'category': 'convolution',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'input',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'filter',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'strides',\n 'name': 'strides',\n 'type': 'number[]'\n },\n {\n 'tfName': 'padding',\n 'name': 'pad',\n 'type': 'string'\n },\n {\n 'tfName': 'data_format',\n 'name': 'dataFormat',\n 'type': 'string',\n 'defaultValue': 'NHWC'\n },\n {\n 'tfName': 'explicit_paddings',\n 'name': 'explicitPaddings',\n 'type': 'number[]',\n 'defaultValue': []\n },\n {\n 'tfName': 'dilations',\n 'name': 'dilations',\n 'type': 'number[]'\n }\n ]\n },\n {\n 'tfOpName': 'FusedDepthwiseConv2dNative',\n 'category': 'convolution',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'filter',\n 'type': 'tensor'\n },\n {\n 'start': 2,\n 'end': 0,\n 'name': 'args',\n 'type': 'tensors'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'num_args',\n 'name': 'numArgs',\n 'type': 'number'\n },\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n },\n {\n 'tfName': 'strides',\n 'name': 'strides',\n 'type': 'number[]'\n },\n {\n 'tfName': 'padding',\n 'name': 'pad',\n 'type': 'string'\n },\n {\n 'tfName': 'data_format',\n 'name': 'dataFormat',\n 'type': 'string',\n 'defaultValue': 'NHWC'\n },\n {\n 'tfName': 'dilations',\n 'name': 'dilations',\n 'type': 'number[]',\n 'defaultValue': [\n 1,\n 1,\n 1,\n 1\n ]\n },\n {\n 'tfName': 'fused_ops',\n 'name': 'fusedOps',\n 'type': 'string[]',\n 'defaultValue': []\n },\n {\n 'tfName': 'explicit_paddings',\n 'name': 'explicitPaddings',\n 'type': 'number[]',\n 'defaultValue': []\n }\n ]\n },\n {\n 'tfOpName': 'Conv3D',\n 'category': 'convolution',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'filter',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'strides',\n 'name': 'strides',\n 'type': 'number[]'\n },\n {\n 'tfName': 'padding',\n 'name': 'pad',\n 'type': 'string'\n },\n {\n 'tfName': 'data_format',\n 'name': 'dataFormat',\n 'type': 'string',\n 'defaultValue': 'NHWC'\n },\n {\n 'tfName': 'dilations',\n 'name': 'dilations',\n 'type': 'number[]'\n }\n ]\n },\n {\n 'tfOpName': 'Dilation2D',\n 'category': 'convolution',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'filter',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'strides',\n 'name': 'strides',\n 'type': 'number[]'\n },\n {\n 'tfName': 'rates',\n 'name': 'dilations',\n 'type': 'number[]'\n },\n {\n 'tfName': 'padding',\n 'name': 'pad',\n 'type': 'string'\n }\n ]\n }\n];\n//# 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* @license\n * Copyright 2022 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {OpMapper} from '../types';\n\nexport const json: OpMapper[] = [\n  {\n    'tfOpName': 'AvgPool',\n    'category': 'convolution',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'strides',\n        'name': 'strides',\n        'type': 'number[]'\n      },\n      {\n        'tfName': 'padding',\n        'name': 'pad',\n        'type': 'string'\n      },\n      {\n        'tfName': 'data_format',\n        'name': 'dataFormat',\n        'type': 'string',\n        'notSupported': true\n      },\n      {\n        'tfName': 'ksize',\n        'name': 'kernelSize',\n        'type': 'number[]'\n      },\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'MaxPool',\n    'category': 'convolution',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'strides',\n        'name': 'strides',\n        'type': 'number[]'\n      },\n      {\n        'tfName': 'padding',\n        'name': 'pad',\n        'type': 'string'\n      },\n      {\n        'tfName': 'data_format',\n        'name': 'dataFormat',\n        'type': 'string',\n        'notSupported': true\n      },\n      {\n        'tfName': 'ksize',\n        'name': 'kernelSize',\n        'type': 'number[]'\n      },\n      {\n        'tfName': 'explicit_paddings',\n        'name': 'explicitPaddings',\n        'type': 'number[]',\n        'defaultValue': [],\n        'notSupported': true\n      },\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'MaxPoolWithArgmax',\n    'category': 'convolution',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'strides',\n        'name': 'strides',\n        'type': 'number[]'\n      },\n      {\n        'tfName': 'padding',\n        'name': 'pad',\n        'type': 'string'\n      },\n      {\n        'tfName': 'ksize',\n        'name': 'kernelSize',\n        'type': 'number[]'\n      },\n      {\n        'tfName': 'include_batch_in_index',\n        'name': 'includeBatchInIndex',\n        'type': 'bool'\n      },\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'AvgPool3D',\n    'category': 'convolution',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'strides',\n        'name': 'strides',\n        'type': 'number[]'\n      },\n      {\n        'tfName': 'padding',\n        'name': 'pad',\n        'type': 'string'\n      },\n      {\n        'tfName': 'data_format',\n        'name': 'dataFormat',\n        'type': 'string',\n        'notSupported': true\n      },\n      {\n        'tfName': 'ksize',\n        'name': 'kernelSize',\n        'type': 'number[]'\n      },\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'MaxPool3D',\n    'category': 'convolution',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'strides',\n        'name': 'strides',\n        'type': 'number[]'\n      },\n      {\n        'tfName': 'padding',\n        'name': 'pad',\n        'type': 'string'\n      },\n      {\n        'tfName': 'data_format',\n        'name': 'dataFormat',\n        'type': 'string',\n        'notSupported': true\n      },\n      {\n        'tfName': 'ksize',\n        'name': 'kernelSize',\n        'type': 'number[]'\n      },\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Conv1D',\n    'category': 'convolution',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'filter',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'stride',\n        'name': 'stride',\n        'type': 'number'\n      },\n      {\n        'tfName': 'padding',\n        'name': 'pad',\n        'type': 'string'\n      },\n      {\n        'tfName': 'data_format',\n        'name': 'dataFormat',\n        'type': 'string',\n        'defaultValue': 'NWC'\n      },\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      },\n      {\n        'tfName': 'dilation',\n        'name': 'dilation',\n        'type': 'number',\n        'defaultValue': 1\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Conv2D',\n    'category': 'convolution',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'filter',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      },\n      {\n        'tfName': 'strides',\n        'name': 'strides',\n        'type': 'number[]'\n      },\n      {\n        'tfName': 'padding',\n        'name': 'pad',\n        'type': 'string'\n      },\n      {\n        'tfName': 'useCudnnOnGpu',\n        'name': 'useCudnnOnGpu',\n        'type': 'bool'\n      },\n      {\n        'tfName': 'data_format',\n        'name': 'dataFormat',\n        'type': 'string',\n        'defaultValue': 'NHWC'\n      },\n      {\n        'tfName': 'explicit_paddings',\n        'name': 'explicitPaddings',\n        'type': 'number[]',\n        'defaultValue': []\n      },\n      {\n        'tfName': 'dilations',\n        'name': 'dilations',\n        'type': 'number[]'\n      }\n    ]\n  },\n  {\n    'tfOpName': '_FusedConv2D',\n    'category': 'convolution',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'filter',\n        'type': 'tensor'\n      },\n      {\n        'start': 2,\n        'end': 0,\n        'name': 'args',\n        'type': 'tensors'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'num_args',\n        'name': 'numArgs',\n        'type': 'number'\n      },\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      },\n      {\n        'tfName': 'strides',\n        'name': 'strides',\n        'type': 'number[]'\n      },\n      {\n        'tfName': 'padding',\n        'name': 'pad',\n        'type': 'string'\n      },\n      {\n        'tfName': 'explicit_paddings',\n        'name': 'explicitPaddings',\n        'type': 'number[]',\n        'defaultValue': []\n      },\n      {\n        'tfName': 'use_cudnn_on_gpu',\n        'name': 'useCudnnOnGpu',\n        'type': 'bool',\n        'defaultValue': true\n      },\n      {\n        'tfName': 'data_format',\n        'name': 'dataFormat',\n        'type': 'string',\n        'defaultValue': 'NHWC'\n      },\n      {\n        'tfName': 'dilations',\n        'name': 'dilations',\n        'type': 'number[]',\n        'defaultValue': [\n          1,\n          1,\n          1,\n          1\n        ]\n      },\n      {\n        'tfName': 'fused_ops',\n        'name': 'fusedOps',\n        'type': 'string[]',\n        'defaultValue': []\n      },\n      {\n        'tfName': 'epsilon',\n        'name': 'epsilon',\n        'type': 'number',\n        'defaultValue': 0.0001\n      },\n      {\n        'tfName': 'leakyrelu_alpha',\n        'name': 'leakyreluAlpha',\n        'type': 'number'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Conv2DBackpropInput',\n    'category': 'convolution',\n    'inputs': [\n      {\n        'start': 2,\n        'name': 'x',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'filter',\n        'type': 'tensor'\n      },\n      {\n        'start': 0,\n        'name': 'outputShape',\n        'type': 'number[]'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'strides',\n        'name': 'strides',\n        'type': 'number[]'\n      },\n      {\n        'tfName': 'padding',\n        'name': 'pad',\n        'type': 'string'\n      },\n      {\n        'tfName': 'data_format',\n        'name': 'dataFormat',\n        'type': 'string',\n        'notSupported': true\n      },\n      {\n        'tfName': 'explicit_paddings',\n        'name': 'explicitPaddings',\n        'type': 'number[]',\n        'defaultValue': []\n      },\n      {\n        'tfName': 'dilations',\n        'name': 'dilations',\n        'type': 'number[]',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'DepthwiseConv2d',\n    'category': 'convolution',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'input',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'filter',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'strides',\n        'name': 'strides',\n        'type': 'number[]'\n      },\n      {\n        'tfName': 'padding',\n        'name': 'pad',\n        'type': 'string'\n      },\n      {\n        'tfName': 'data_format',\n        'name': 'dataFormat',\n        'type': 'string',\n        'defaultValue': 'NHWC'\n      },\n      {\n        'tfName': 'explicit_paddings',\n        'name': 'explicitPaddings',\n        'type': 'number[]',\n        'defaultValue': []\n      },\n      {\n        'tfName': 'dilations',\n        'name': 'dilations',\n        'type': 'number[]'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'DepthwiseConv2dNative',\n    'category': 'convolution',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'input',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'filter',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'strides',\n        'name': 'strides',\n        'type': 'number[]'\n      },\n      {\n        'tfName': 'padding',\n        'name': 'pad',\n        'type': 'string'\n      },\n      {\n        'tfName': 'data_format',\n        'name': 'dataFormat',\n        'type': 'string',\n        'defaultValue': 'NHWC'\n      },\n      {\n        'tfName': 'explicit_paddings',\n        'name': 'explicitPaddings',\n        'type': 'number[]',\n        'defaultValue': []\n      },\n      {\n        'tfName': 'dilations',\n        'name': 'dilations',\n        'type': 'number[]'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'FusedDepthwiseConv2dNative',\n    'category': 'convolution',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'filter',\n        'type': 'tensor'\n      },\n      {\n        'start': 2,\n        'end': 0,\n        'name': 'args',\n        'type': 'tensors'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'num_args',\n        'name': 'numArgs',\n        'type': 'number'\n      },\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      },\n      {\n        'tfName': 'strides',\n        'name': 'strides',\n        'type': 'number[]'\n      },\n      {\n        'tfName': 'padding',\n        'name': 'pad',\n        'type': 'string'\n      },\n      {\n        'tfName': 'data_format',\n        'name': 'dataFormat',\n        'type': 'string',\n        'defaultValue': 'NHWC'\n      },\n      {\n        'tfName': 'dilations',\n        'name': 'dilations',\n        'type': 'number[]',\n        'defaultValue': [\n          1,\n          1,\n          1,\n          1\n        ]\n      },\n      {\n        'tfName': 'fused_ops',\n        'name': 'fusedOps',\n        'type': 'string[]',\n        'defaultValue': []\n      },\n      {\n        'tfName': 'explicit_paddings',\n        'name': 'explicitPaddings',\n        'type': 'number[]',\n        'defaultValue': []\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Conv3D',\n    'category': 'convolution',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'filter',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'strides',\n        'name': 'strides',\n        'type': 'number[]'\n      },\n      {\n        'tfName': 'padding',\n        'name': 'pad',\n        'type': 'string'\n      },\n      {\n        'tfName': 'data_format',\n        'name': 'dataFormat',\n        'type': 'string',\n        'defaultValue': 'NHWC'\n      },\n      {\n        'tfName': 'dilations',\n        'name': 'dilations',\n        'type': 'number[]'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Dilation2D',\n    'category': 'convolution',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'filter',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'strides',\n        'name': 'strides',\n        'type': 'number[]'\n      },\n      {\n        'tfName': 'rates',\n        'name': 'dilations',\n        'type': 'number[]'\n      },\n      {\n        'tfName': 'padding',\n        'name': 'pad',\n        'type': 'string'\n      }\n    ]\n  }\n];\n"]}","/**\n * @license\n * Copyright 2022 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n {\n 'tfOpName': 'Fill',\n 'category': 'creation',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'shape',\n 'type': 'number[]'\n },\n {\n 'start': 1,\n 'name': 'value',\n 'type': 'number'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype'\n }\n ]\n },\n {\n 'tfOpName': 'LinSpace',\n 'category': 'creation',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'start',\n 'type': 'number'\n },\n {\n 'start': 1,\n 'name': 'stop',\n 'type': 'number'\n },\n {\n 'start': 2,\n 'name': 'num',\n 'type': 'number'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'OneHot',\n 'category': 'creation',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'indices',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'depth',\n 'type': 'number'\n },\n {\n 'start': 2,\n 'name': 'onValue',\n 'type': 'number',\n 'defaultValue': 1\n },\n {\n 'start': 3,\n 'name': 'offValue',\n 'type': 'number',\n 'defaultValue': 0\n }\n ],\n 'attrs': [\n {\n 'tfName': 'axis',\n 'name': 'axis',\n 'type': 'number',\n 'notSupported': true\n },\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Ones',\n 'category': 'creation',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'shape',\n 'type': 'number[]'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype'\n }\n ]\n },\n {\n 'tfOpName': 'OnesLike',\n 'category': 'creation',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'dtype',\n 'name': 'dtype',\n 'type': 'dtype'\n }\n ]\n },\n {\n 'tfOpName': 'RandomUniform',\n 'category': 'creation',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'shape',\n 'type': 'number[]'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'minval',\n 'name': 'minval',\n 'type': 'number',\n 'defaultValue': 0\n },\n {\n 'tfName': 'maxval',\n 'name': 'maxval',\n 'type': 'number',\n 'defaultValue': 1\n },\n {\n 'tfName': 'dtype',\n 'name': 'dtype',\n 'type': 'dtype'\n },\n {\n 'tfName': 'seed',\n 'name': 'seed',\n 'type': 'number',\n 'defaultValue': 0\n },\n {\n 'tfName': 'seed2',\n 'name': 'seed2',\n 'type': 'number',\n 'defaultValue': 0,\n 'notSupported': true\n },\n {\n 'tfName': 'T',\n 'name': 'T',\n 'type': 'number',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Range',\n 'category': 'creation',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'start',\n 'type': 'number'\n },\n {\n 'start': 1,\n 'name': 'stop',\n 'type': 'number'\n },\n {\n 'start': 2,\n 'name': 'step',\n 'type': 'number',\n 'defaultValue': 0\n }\n ],\n 'attrs': [\n {\n 'tfName': 'Tidx',\n 'name': 'dtype',\n 'type': 'dtype'\n }\n ]\n },\n {\n 'tfOpName': 'TruncatedNormal',\n 'category': 'creation',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'shape',\n 'type': 'number[]'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'means',\n 'name': 'mean',\n 'type': 'number',\n 'defaultValue': 0\n },\n {\n 'tfName': 'stddev',\n 'name': 'stdDev',\n 'type': 'number',\n 'defaultValue': 1\n },\n {\n 'tfName': 'seed',\n 'name': 'seed',\n 'type': 'number'\n },\n {\n 'tfName': 'seed2',\n 'name': 'seed2',\n 'type': 'number',\n 'defaultValue': 0,\n 'notSupported': true\n },\n {\n 'tfName': 'dtype',\n 'name': 'dtype',\n 'type': 'dtype'\n },\n {\n 'tfName': 'T',\n 'name': 'T',\n 'type': 'number',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Zeros',\n 'category': 'creation',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'shape',\n 'type': 'number[]'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype'\n }\n ]\n },\n {\n 'tfOpName': 'ZerosLike',\n 'category': 'creation',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype'\n }\n ]\n },\n {\n 'tfOpName': 'Multinomial',\n 'category': 'creation',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'logits',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'numSamples',\n 'type': 'number'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'seed',\n 'name': 'seed',\n 'type': 'number'\n },\n {\n 'tfName': 'seed2',\n 'name': 'seed2',\n 'type': 'number'\n },\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype'\n },\n {\n 'tfName': 'output_dtype',\n 'name': 'output_dtype',\n 'type': 'dtype'\n }\n ]\n }\n];\n//# 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* @license\n * Copyright 2022 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {OpMapper} from '../types';\n\nexport const json: OpMapper[] = [\n  {\n    'tfOpName': 'Fill',\n    'category': 'creation',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'shape',\n        'type': 'number[]'\n      },\n      {\n        'start': 1,\n        'name': 'value',\n        'type': 'number'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'LinSpace',\n    'category': 'creation',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'start',\n        'type': 'number'\n      },\n      {\n        'start': 1,\n        'name': 'stop',\n        'type': 'number'\n      },\n      {\n        'start': 2,\n        'name': 'num',\n        'type': 'number'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'OneHot',\n    'category': 'creation',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'indices',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'depth',\n        'type': 'number'\n      },\n      {\n        'start': 2,\n        'name': 'onValue',\n        'type': 'number',\n        'defaultValue': 1\n      },\n      {\n        'start': 3,\n        'name': 'offValue',\n        'type': 'number',\n        'defaultValue': 0\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'axis',\n        'name': 'axis',\n        'type': 'number',\n        'notSupported': true\n      },\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Ones',\n    'category': 'creation',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'shape',\n        'type': 'number[]'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'OnesLike',\n    'category': 'creation',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'dtype',\n        'name': 'dtype',\n        'type': 'dtype'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'RandomUniform',\n    'category': 'creation',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'shape',\n        'type': 'number[]'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'minval',\n        'name': 'minval',\n        'type': 'number',\n        'defaultValue': 0\n      },\n      {\n        'tfName': 'maxval',\n        'name': 'maxval',\n        'type': 'number',\n        'defaultValue': 1\n      },\n      {\n        'tfName': 'dtype',\n        'name': 'dtype',\n        'type': 'dtype'\n      },\n      {\n        'tfName': 'seed',\n        'name': 'seed',\n        'type': 'number',\n        'defaultValue': 0\n      },\n      {\n        'tfName': 'seed2',\n        'name': 'seed2',\n        'type': 'number',\n        'defaultValue': 0,\n        'notSupported': true\n      },\n      {\n        'tfName': 'T',\n        'name': 'T',\n        'type': 'number',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Range',\n    'category': 'creation',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'start',\n        'type': 'number'\n      },\n      {\n        'start': 1,\n        'name': 'stop',\n        'type': 'number'\n      },\n      {\n        'start': 2,\n        'name': 'step',\n        'type': 'number',\n        'defaultValue': 0\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'Tidx',\n        'name': 'dtype',\n        'type': 'dtype'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'TruncatedNormal',\n    'category': 'creation',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'shape',\n        'type': 'number[]'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'means',\n        'name': 'mean',\n        'type': 'number',\n        'defaultValue': 0\n      },\n      {\n        'tfName': 'stddev',\n        'name': 'stdDev',\n        'type': 'number',\n        'defaultValue': 1\n      },\n      {\n        'tfName': 'seed',\n        'name': 'seed',\n        'type': 'number'\n      },\n      {\n        'tfName': 'seed2',\n        'name': 'seed2',\n        'type': 'number',\n        'defaultValue': 0,\n        'notSupported': true\n      },\n      {\n        'tfName': 'dtype',\n        'name': 'dtype',\n        'type': 'dtype'\n      },\n      {\n        'tfName': 'T',\n        'name': 'T',\n        'type': 'number',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Zeros',\n    'category': 'creation',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'shape',\n        'type': 'number[]'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'ZerosLike',\n    'category': 'creation',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Multinomial',\n    'category': 'creation',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'logits',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'numSamples',\n        'type': 'number'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'seed',\n        'name': 'seed',\n        'type': 'number'\n      },\n      {\n        'tfName': 'seed2',\n        'name': 'seed2',\n        'type': 'number'\n      },\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype'\n      },\n      {\n        'tfName': 'output_dtype',\n        'name': 'output_dtype',\n        'type': 'dtype'\n      }\n    ]\n  }\n];\n"]}","/**\n * @license\n * Copyright 2022 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n {\n 'tfOpName': 'NonMaxSuppressionV2',\n 'category': 'dynamic',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'boxes',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'scores',\n 'type': 'tensor'\n },\n {\n 'start': 2,\n 'name': 'maxOutputSize',\n 'type': 'number'\n },\n {\n 'start': 3,\n 'name': 'iouThreshold',\n 'type': 'number'\n }\n ]\n },\n {\n 'tfOpName': 'NonMaxSuppressionV3',\n 'category': 'dynamic',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'boxes',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'scores',\n 'type': 'tensor'\n },\n {\n 'start': 2,\n 'name': 'maxOutputSize',\n 'type': 'number'\n },\n {\n 'start': 3,\n 'name': 'iouThreshold',\n 'type': 'number'\n },\n {\n 'start': 4,\n 'name': 'scoreThreshold',\n 'type': 'number'\n }\n ]\n },\n {\n 'tfOpName': 'NonMaxSuppressionV4',\n 'category': 'dynamic',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'boxes',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'scores',\n 'type': 'tensor'\n },\n {\n 'start': 2,\n 'name': 'maxOutputSize',\n 'type': 'number'\n },\n {\n 'start': 3,\n 'name': 'iouThreshold',\n 'type': 'number'\n },\n {\n 'start': 4,\n 'name': 'scoreThreshold',\n 'type': 'number'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n },\n {\n 'tfName': 'T_threshold',\n 'name': 'threshold',\n 'type': 'dtype',\n 'notSupported': true\n },\n {\n 'tfName': 'pad_to_max_output_size',\n 'name': 'padToMaxOutputSize',\n 'type': 'bool'\n }\n ]\n },\n {\n 'tfOpName': 'NonMaxSuppressionV5',\n 'category': 'dynamic',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'boxes',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'scores',\n 'type': 'tensor'\n },\n {\n 'start': 2,\n 'name': 'maxOutputSize',\n 'type': 'number'\n },\n {\n 'start': 3,\n 'name': 'iouThreshold',\n 'type': 'number'\n },\n {\n 'start': 4,\n 'name': 'scoreThreshold',\n 'type': 'number'\n },\n {\n 'start': 5,\n 'name': 'softNmsSigma',\n 'type': 'number'\n }\n ]\n },\n {\n 'tfOpName': 'Where',\n 'category': 'dynamic',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'condition',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'ListDiff',\n 'category': 'dynamic',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'y',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n }\n];\n//# 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* @license\n * Copyright 2022 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {OpMapper} from '../types';\n\nexport const json: OpMapper[] = [\n  {\n    'tfOpName': 'NonMaxSuppressionV2',\n    'category': 'dynamic',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'boxes',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'scores',\n        'type': 'tensor'\n      },\n      {\n        'start': 2,\n        'name': 'maxOutputSize',\n        'type': 'number'\n      },\n      {\n        'start': 3,\n        'name': 'iouThreshold',\n        'type': 'number'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'NonMaxSuppressionV3',\n    'category': 'dynamic',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'boxes',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'scores',\n        'type': 'tensor'\n      },\n      {\n        'start': 2,\n        'name': 'maxOutputSize',\n        'type': 'number'\n      },\n      {\n        'start': 3,\n        'name': 'iouThreshold',\n        'type': 'number'\n      },\n      {\n        'start': 4,\n        'name': 'scoreThreshold',\n        'type': 'number'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'NonMaxSuppressionV4',\n    'category': 'dynamic',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'boxes',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'scores',\n        'type': 'tensor'\n      },\n      {\n        'start': 2,\n        'name': 'maxOutputSize',\n        'type': 'number'\n      },\n      {\n        'start': 3,\n        'name': 'iouThreshold',\n        'type': 'number'\n      },\n      {\n        'start': 4,\n        'name': 'scoreThreshold',\n        'type': 'number'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      },\n      {\n        'tfName': 'T_threshold',\n        'name': 'threshold',\n        'type': 'dtype',\n        'notSupported': true\n      },\n      {\n        'tfName': 'pad_to_max_output_size',\n        'name': 'padToMaxOutputSize',\n        'type': 'bool'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'NonMaxSuppressionV5',\n    'category': 'dynamic',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'boxes',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'scores',\n        'type': 'tensor'\n      },\n      {\n        'start': 2,\n        'name': 'maxOutputSize',\n        'type': 'number'\n      },\n      {\n        'start': 3,\n        'name': 'iouThreshold',\n        'type': 'number'\n      },\n      {\n        'start': 4,\n        'name': 'scoreThreshold',\n        'type': 'number'\n      },\n      {\n        'start': 5,\n        'name': 'softNmsSigma',\n        'type': 'number'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Where',\n    'category': 'dynamic',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'condition',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'ListDiff',\n    'category': 'dynamic',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'y',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  }\n];\n"]}","/**\n * @license\n * Copyright 2022 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n {\n 'tfOpName': 'TopKV2',\n 'category': 'evaluation',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'k',\n 'type': 'number'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'sorted',\n 'name': 'sorted',\n 'type': 'bool'\n }\n ]\n },\n {\n 'tfOpName': 'Unique',\n 'category': 'evaluation',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ]\n },\n {\n 'tfOpName': 'UniqueV2',\n 'category': 'evaluation',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'axis',\n 'type': 'number'\n }\n ]\n }\n];\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2022 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n {\n 'tfOpName': 'PlaceholderWithDefault',\n 'category': 'graph',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'default',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'shape',\n 'name': 'shape',\n 'type': 'shape'\n },\n {\n 'tfName': 'dtype',\n 'name': 'dtype',\n 'type': 'dtype'\n }\n ]\n },\n {\n 'tfOpName': 'Placeholder',\n 'category': 'graph',\n 'attrs': [\n {\n 'tfName': 'shape',\n 'name': 'shape',\n 'type': 'shape'\n },\n {\n 'tfName': 'dtype',\n 'name': 'dtype',\n 'type': 'dtype'\n }\n ]\n },\n {\n 'tfOpName': 'Const',\n 'category': 'graph'\n },\n {\n 'tfOpName': 'Identity',\n 'category': 'graph',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ]\n },\n {\n 'tfOpName': 'IdentityN',\n 'category': 'graph',\n 'inputs': [\n {\n 'start': 0,\n 'end': 0,\n 'name': 'x',\n 'type': 'tensors'\n }\n ]\n },\n {\n 'tfOpName': 'Snapshot',\n 'category': 'graph',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ]\n },\n {\n 'tfOpName': 'Rank',\n 'category': 'graph',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ]\n },\n {\n 'tfOpName': 'Size',\n 'category': 'graph',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ]\n },\n {\n 'tfOpName': 'Shape',\n 'category': 'graph',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ]\n },\n {\n 'tfOpName': 'ShapeN',\n 'category': 'graph',\n 'inputs': [\n {\n 'start': 0,\n 'end': 0,\n 'name': 'x',\n 'type': 'tensors'\n }\n ]\n },\n {\n 'tfOpName': 'Print',\n 'category': 'graph',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'data',\n 'type': 'tensors'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'message',\n 'name': 'message',\n 'type': 'string'\n },\n {\n 'tfName': 'first_n',\n 'name': 'firstN',\n 'type': 'number',\n 'notSupported': true\n },\n {\n 'tfName': 'summarize',\n 'name': 'summarize',\n 'type': 'number',\n 'defaultValue': 3\n }\n ]\n },\n {\n 'tfOpName': 'NoOp',\n 'category': 'graph',\n 'inputs': []\n },\n {\n 'tfOpName': 'StopGradient',\n 'category': 'graph',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ]\n },\n {\n 'tfOpName': 'FakeQuantWithMinMaxVars',\n 'category': 'graph',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'min',\n 'name': 'min',\n 'type': 'number'\n },\n {\n 'tfName': 'max',\n 'name': 'max',\n 'type': 'number'\n }\n ]\n }\n];\n//# 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* @license\n * Copyright 2022 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {OpMapper} from '../types';\n\nexport const json: OpMapper[] = [\n  {\n    'tfOpName': 'PlaceholderWithDefault',\n    'category': 'graph',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'default',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'shape',\n        'name': 'shape',\n        'type': 'shape'\n      },\n      {\n        'tfName': 'dtype',\n        'name': 'dtype',\n        'type': 'dtype'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Placeholder',\n    'category': 'graph',\n    'attrs': [\n      {\n        'tfName': 'shape',\n        'name': 'shape',\n        'type': 'shape'\n      },\n      {\n        'tfName': 'dtype',\n        'name': 'dtype',\n        'type': 'dtype'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Const',\n    'category': 'graph'\n  },\n  {\n    'tfOpName': 'Identity',\n    'category': 'graph',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'IdentityN',\n    'category': 'graph',\n    'inputs': [\n      {\n        'start': 0,\n        'end': 0,\n        'name': 'x',\n        'type': 'tensors'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Snapshot',\n    'category': 'graph',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Rank',\n    'category': 'graph',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Size',\n    'category': 'graph',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Shape',\n    'category': 'graph',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'ShapeN',\n    'category': 'graph',\n    'inputs': [\n      {\n        'start': 0,\n        'end': 0,\n        'name': 'x',\n        'type': 'tensors'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Print',\n    'category': 'graph',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'data',\n        'type': 'tensors'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'message',\n        'name': 'message',\n        'type': 'string'\n      },\n      {\n        'tfName': 'first_n',\n        'name': 'firstN',\n        'type': 'number',\n        'notSupported': true\n      },\n      {\n        'tfName': 'summarize',\n        'name': 'summarize',\n        'type': 'number',\n        'defaultValue': 3\n      }\n    ]\n  },\n  {\n    'tfOpName': 'NoOp',\n    'category': 'graph',\n    'inputs': []\n  },\n  {\n    'tfOpName': 'StopGradient',\n    'category': 'graph',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'FakeQuantWithMinMaxVars',\n    'category': 'graph',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'min',\n        'name': 'min',\n        'type': 'number'\n      },\n      {\n        'tfName': 'max',\n        'name': 'max',\n        'type': 'number'\n      }\n    ]\n  }\n];\n"]}","/**\n * @license\n * Copyright 2022 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n {\n 'tfOpName': 'HashTable',\n 'category': 'hash_table',\n 'inputs': [],\n 'attrs': [\n {\n 'tfName': 'shared_name',\n 'name': 'sharedName',\n 'type': 'string'\n },\n {\n 'tfName': 'use_node_name_sharing',\n 'name': 'useNodeNameSharing',\n 'type': 'bool'\n },\n {\n 'tfName': 'key_dtype',\n 'name': 'keyDType',\n 'type': 'dtype'\n },\n {\n 'tfName': 'value_dtype',\n 'name': 'valueDType',\n 'type': 'dtype'\n }\n ]\n },\n {\n 'tfOpName': 'HashTableV2',\n 'category': 'hash_table',\n 'inputs': [],\n 'attrs': [\n {\n 'tfName': 'shared_name',\n 'name': 'sharedName',\n 'type': 'string'\n },\n {\n 'tfName': 'use_node_name_sharing',\n 'name': 'useNodeNameSharing',\n 'type': 'bool'\n },\n {\n 'tfName': 'key_dtype',\n 'name': 'keyDType',\n 'type': 'dtype'\n },\n {\n 'tfName': 'value_dtype',\n 'name': 'valueDType',\n 'type': 'dtype'\n }\n ]\n },\n {\n 'tfOpName': 'LookupTableImport',\n 'category': 'hash_table',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'tableHandle',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'keys',\n 'type': 'tensor'\n },\n {\n 'start': 2,\n 'name': 'values',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'Tin',\n 'name': 'tIn',\n 'type': 'dtype',\n 'notSupported': true\n },\n {\n 'tfName': 'Tout',\n 'name': 'tOut',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'LookupTableImportV2',\n 'category': 'hash_table',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'tableHandle',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'keys',\n 'type': 'tensor'\n },\n {\n 'start': 2,\n 'name': 'values',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'Tin',\n 'name': 'tIn',\n 'type': 'dtype',\n 'notSupported': true\n },\n {\n 'tfName': 'Tout',\n 'name': 'tOut',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'LookupTableFind',\n 'category': 'hash_table',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'tableHandle',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'keys',\n 'type': 'tensor'\n },\n {\n 'start': 2,\n 'name': 'defaultValue',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'Tin',\n 'name': 'tIn',\n 'type': 'dtype',\n 'notSupported': true\n },\n {\n 'tfName': 'Tout',\n 'name': 'tOut',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 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* @license\n * Copyright 2022 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {OpMapper} from '../types';\n\nexport const json: OpMapper[] = [\n  {\n    'tfOpName': 'HashTable',\n    'category': 'hash_table',\n    'inputs': [],\n    'attrs': [\n      {\n        'tfName': 'shared_name',\n        'name': 'sharedName',\n        'type': 'string'\n      },\n      {\n        'tfName': 'use_node_name_sharing',\n        'name': 'useNodeNameSharing',\n        'type': 'bool'\n      },\n      {\n        'tfName': 'key_dtype',\n        'name': 'keyDType',\n        'type': 'dtype'\n      },\n      {\n        'tfName': 'value_dtype',\n        'name': 'valueDType',\n        'type': 'dtype'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'HashTableV2',\n    'category': 'hash_table',\n    'inputs': [],\n    'attrs': [\n      {\n        'tfName': 'shared_name',\n        'name': 'sharedName',\n        'type': 'string'\n      },\n      {\n        'tfName': 'use_node_name_sharing',\n        'name': 'useNodeNameSharing',\n        'type': 'bool'\n      },\n      {\n        'tfName': 'key_dtype',\n        'name': 'keyDType',\n        'type': 'dtype'\n      },\n      {\n        'tfName': 'value_dtype',\n        'name': 'valueDType',\n        'type': 'dtype'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'LookupTableImport',\n    'category': 'hash_table',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'tableHandle',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'keys',\n        'type': 'tensor'\n      },\n      {\n        'start': 2,\n        'name': 'values',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'Tin',\n        'name': 'tIn',\n        'type': 'dtype',\n        'notSupported': true\n      },\n      {\n        'tfName': 'Tout',\n        'name': 'tOut',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'LookupTableImportV2',\n    'category': 'hash_table',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'tableHandle',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'keys',\n        'type': 'tensor'\n      },\n      {\n        'start': 2,\n        'name': 'values',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'Tin',\n        'name': 'tIn',\n        'type': 'dtype',\n        'notSupported': true\n      },\n      {\n        'tfName': 'Tout',\n        'name': 'tOut',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'LookupTableFind',\n    'category': 'hash_table',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'tableHandle',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'keys',\n        'type': 'tensor'\n      },\n      {\n        'start': 2,\n        'name': 'defaultValue',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'Tin',\n        'name': 'tIn',\n        'type': 'dtype',\n        'notSupported': true\n      },\n      {\n        'tfName': 'Tout',\n        'name': 'tOut',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'LookupTableFindV2',\n    'category': 'hash_table',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'tableHandle',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'keys',\n        'type': 'tensor'\n      },\n      {\n        'start': 2,\n        'name': 'defaultValue',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'Tin',\n        'name': 'tIn',\n        'type': 'dtype',\n        'notSupported': true\n      },\n      {\n        'tfName': 'Tout',\n        'name': 'tOut',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'LookupTableSize',\n    'category': 'hash_table',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'tableHandle',\n        'type': 'tensor'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'LookupTableSizeV2',\n    'category': 'hash_table',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'tableHandle',\n        'type': 'tensor'\n      }\n    ]\n  }\n];\n"]}","/**\n * @license\n * Copyright 2022 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n {\n 'tfOpName': 'ResizeBilinear',\n 'category': 'image',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'images',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'size',\n 'type': 'number[]'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'align_corners',\n 'name': 'alignCorners',\n 'type': 'bool'\n },\n {\n 'tfName': 'half_pixel_centers',\n 'name': 'halfPixelCenters',\n 'type': 'bool'\n },\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'ResizeNearestNeighbor',\n 'category': 'image',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'images',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'size',\n 'type': 'number[]'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'align_corners',\n 'name': 'alignCorners',\n 'type': 'bool'\n },\n {\n 'tfName': 'half_pixel_centers',\n 'name': 'halfPixelCenters',\n 'type': 'bool'\n },\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'CropAndResize',\n 'category': 'image',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'image',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'boxes',\n 'type': 'tensor'\n },\n {\n 'start': 2,\n 'name': 'boxInd',\n 'type': 'tensor'\n },\n {\n 'start': 3,\n 'name': 'cropSize',\n 'type': 'number[]'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'method',\n 'name': 'method',\n 'type': 'string'\n },\n {\n 'tfName': 'extrapolation_value',\n 'name': 'extrapolationValue',\n 'type': 'number'\n }\n ]\n },\n {\n 'tfOpName': 'ImageProjectiveTransformV3',\n 'category': 'image',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'images',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'transforms',\n 'type': 'tensor'\n },\n {\n 'start': 2,\n 'name': 'outputShape',\n 'type': 'number[]'\n },\n {\n 'start': 3,\n 'name': 'fillValue',\n 'type': 'number'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'interpolation',\n 'name': 'interpolation',\n 'type': 'string'\n },\n {\n 'tfName': 'fill_mode',\n 'name': 'fillMode',\n 'type': 'string'\n }\n ]\n }\n];\n//# 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* @license\n * Copyright 2022 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n {\n 'tfOpName': 'Equal',\n 'category': 'logical',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'a',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'b',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'NotEqual',\n 'category': 'logical',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'a',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'b',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Greater',\n 'category': 'logical',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'a',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'b',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'GreaterEqual',\n 'category': 'logical',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'a',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'b',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Less',\n 'category': 'logical',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'a',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'b',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'LessEqual',\n 'category': 'logical',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'a',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'b',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'LogicalAnd',\n 'category': 'logical',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'a',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'b',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'LogicalNot',\n 'category': 'logical',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'a',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'LogicalOr',\n 'category': 'logical',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'a',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'b',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Select',\n 'category': 'logical',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'condition',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'a',\n 'type': 'tensor'\n },\n {\n 'start': 2,\n 'name': 'b',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'SelectV2',\n 'category': 'logical',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'condition',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'a',\n 'type': 'tensor'\n },\n {\n 'start': 2,\n 'name': 'b',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n }\n];\n//# 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* @license\n * Copyright 2022 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {OpMapper} from '../types';\n\nexport const json: OpMapper[] = [\n  {\n    'tfOpName': 'Equal',\n    'category': 'logical',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'a',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'b',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'NotEqual',\n    'category': 'logical',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'a',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'b',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Greater',\n    'category': 'logical',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'a',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'b',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'GreaterEqual',\n    'category': 'logical',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'a',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'b',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Less',\n    'category': 'logical',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'a',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'b',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'LessEqual',\n    'category': 'logical',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'a',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'b',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'LogicalAnd',\n    'category': 'logical',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'a',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'b',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'LogicalNot',\n    'category': 'logical',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'a',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'LogicalOr',\n    'category': 'logical',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'a',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'b',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Select',\n    'category': 'logical',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'condition',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'a',\n        'type': 'tensor'\n      },\n      {\n        'start': 2,\n        'name': 'b',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'SelectV2',\n    'category': 'logical',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'condition',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'a',\n        'type': 'tensor'\n      },\n      {\n        'start': 2,\n        'name': 'b',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  }\n];\n"]}","/**\n * @license\n * Copyright 2022 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n {\n 'tfOpName': '_FusedMatMul',\n 'category': 'matrices',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'a',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'b',\n 'type': 'tensor'\n },\n {\n 'start': 2,\n 'end': 0,\n 'name': 'args',\n 'type': 'tensors'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'num_args',\n 'name': 'numArgs',\n 'type': 'number'\n },\n {\n 'tfName': 'fused_ops',\n 'name': 'fusedOps',\n 'type': 'string[]',\n 'defaultValue': []\n },\n {\n 'tfName': 'epsilon',\n 'name': 'epsilon',\n 'type': 'number',\n 'defaultValue': 0.0001\n },\n {\n 'tfName': 'transpose_a',\n 'name': 'transposeA',\n 'type': 'bool',\n 'defaultValue': false\n },\n {\n 'tfName': 'transpose_b',\n 'name': 'transposeB',\n 'type': 'bool',\n 'defaultValue': false\n },\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'MatMul',\n 'category': 'matrices',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'a',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'b',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'transpose_a',\n 'name': 'transposeA',\n 'type': 'bool',\n 'defaultValue': false\n },\n {\n 'tfName': 'transpose_b',\n 'name': 'transposeB',\n 'type': 'bool',\n 'defaultValue': false\n },\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 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* @license\n * Copyright 2022 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {OpMapper} from '../types';\n\nexport const json: OpMapper[] = [\n  {\n    'tfOpName': '_FusedMatMul',\n    'category': 'matrices',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'a',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'b',\n        'type': 'tensor'\n      },\n      {\n        'start': 2,\n        'end': 0,\n        'name': 'args',\n        'type': 'tensors'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'num_args',\n        'name': 'numArgs',\n        'type': 'number'\n      },\n      {\n        'tfName': 'fused_ops',\n        'name': 'fusedOps',\n        'type': 'string[]',\n        'defaultValue': []\n      },\n      {\n        'tfName': 'epsilon',\n        'name': 'epsilon',\n        'type': 'number',\n        'defaultValue': 0.0001\n      },\n      {\n        'tfName': 'transpose_a',\n        'name': 'transposeA',\n        'type': 'bool',\n        'defaultValue': false\n      },\n      {\n        'tfName': 'transpose_b',\n        'name': 'transposeB',\n        'type': 'bool',\n        'defaultValue': false\n      },\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'MatMul',\n    'category': 'matrices',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'a',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'b',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'transpose_a',\n        'name': 'transposeA',\n        'type': 'bool',\n        'defaultValue': false\n      },\n      {\n        'tfName': 'transpose_b',\n        'name': 'transposeB',\n        'type': 'bool',\n        'defaultValue': false\n      },\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'BatchMatMul',\n    'category': 'matrices',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'a',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'b',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'adj_x',\n        'name': 'transposeA',\n        'type': 'bool',\n        'defaultValue': false\n      },\n      {\n        'tfName': 'adj_y',\n        'name': 'transposeB',\n        'type': 'bool',\n        'defaultValue': false\n      },\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'BatchMatMulV2',\n    'category': 'matrices',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'a',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'b',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'adj_x',\n        'name': 'transposeA',\n        'type': 'bool',\n        'defaultValue': false\n      },\n      {\n        'tfName': 'adj_y',\n        'name': 'transposeB',\n        'type': 'bool',\n        'defaultValue': false\n      },\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Transpose',\n    'category': 'matrices',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'perm',\n        'type': 'number[]'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Einsum',\n    'category': 'matrices',\n    'inputs': [\n      {\n        'start': 0,\n        'end': 0,\n        'name': 'tensors',\n        'type': 'tensors'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'equation',\n        'name': 'equation',\n        'type': 'string'\n      },\n      {\n        'tfName': 'N',\n        'name': 'n',\n        'type': 'number',\n        'defaultValue': 2\n      },\n      {\n        'tfName': 'T',\n        'name': 'dtype',\n        'type': 'dtype'\n      }\n    ]\n  }\n];\n"]}","/**\n * @license\n * Copyright 2022 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n {\n 'tfOpName': 'FusedBatchNorm',\n 'category': 'normalization',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'scale',\n 'type': 'tensor'\n },\n {\n 'start': 2,\n 'name': 'offset',\n 'type': 'tensor'\n },\n {\n 'start': 3,\n 'name': 'mean',\n 'type': 'tensor'\n },\n {\n 'start': 4,\n 'name': 'variance',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'epsilon',\n 'name': 'epsilon',\n 'type': 'number',\n 'defaultValue': 0.001\n },\n {\n 'tfName': 'data_format',\n 'name': 'dataFormat',\n 'type': 'string',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'FusedBatchNormV2',\n 'category': 'normalization',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'scale',\n 'type': 'tensor'\n },\n {\n 'start': 2,\n 'name': 'offset',\n 'type': 'tensor'\n },\n {\n 'start': 3,\n 'name': 'mean',\n 'type': 'tensor'\n },\n {\n 'start': 4,\n 'name': 'variance',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'epsilon',\n 'name': 'epsilon',\n 'type': 'number',\n 'defaultValue': 0.001\n },\n {\n 'tfName': 'data_format',\n 'name': 'dataFormat',\n 'type': 'string',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'FusedBatchNormV3',\n 'category': 'normalization',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'scale',\n 'type': 'tensor'\n },\n {\n 'start': 2,\n 'name': 'offset',\n 'type': 'tensor'\n },\n {\n 'start': 3,\n 'name': 'mean',\n 'type': 'tensor'\n },\n {\n 'start': 4,\n 'name': 'variance',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'epsilon',\n 'name': 'epsilon',\n 'type': 'number',\n 'defaultValue': 0.001\n },\n {\n 'tfName': 'data_format',\n 'name': 'dataFormat',\n 'type': 'string',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'LRN',\n 'category': 'normalization',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'depth_radius',\n 'name': 'radius',\n 'type': 'number',\n 'defaultValue': 5\n },\n {\n 'tfName': 'bias',\n 'name': 'bias',\n 'type': 'number',\n 'defaultValue': 1\n },\n {\n 'tfName': 'alpha',\n 'name': 'alpha',\n 'type': 'number',\n 'defaultValue': 1\n },\n {\n 'tfName': 'beta',\n 'name': 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* @license\n * Copyright 2022 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {OpMapper} from '../types';\n\nexport const json: OpMapper[] = [\n  {\n    'tfOpName': 'FusedBatchNorm',\n    'category': 'normalization',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'scale',\n        'type': 'tensor'\n      },\n      {\n        'start': 2,\n        'name': 'offset',\n        'type': 'tensor'\n      },\n      {\n        'start': 3,\n        'name': 'mean',\n        'type': 'tensor'\n      },\n      {\n        'start': 4,\n        'name': 'variance',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'epsilon',\n        'name': 'epsilon',\n        'type': 'number',\n        'defaultValue': 0.001\n      },\n      {\n        'tfName': 'data_format',\n        'name': 'dataFormat',\n        'type': 'string',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'FusedBatchNormV2',\n    'category': 'normalization',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'scale',\n        'type': 'tensor'\n      },\n      {\n        'start': 2,\n        'name': 'offset',\n        'type': 'tensor'\n      },\n      {\n        'start': 3,\n        'name': 'mean',\n        'type': 'tensor'\n      },\n      {\n        'start': 4,\n        'name': 'variance',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'epsilon',\n        'name': 'epsilon',\n        'type': 'number',\n        'defaultValue': 0.001\n      },\n      {\n        'tfName': 'data_format',\n        'name': 'dataFormat',\n        'type': 'string',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'FusedBatchNormV3',\n    'category': 'normalization',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'scale',\n        'type': 'tensor'\n      },\n      {\n        'start': 2,\n        'name': 'offset',\n        'type': 'tensor'\n      },\n      {\n        'start': 3,\n        'name': 'mean',\n        'type': 'tensor'\n      },\n      {\n        'start': 4,\n        'name': 'variance',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'epsilon',\n        'name': 'epsilon',\n        'type': 'number',\n        'defaultValue': 0.001\n      },\n      {\n        'tfName': 'data_format',\n        'name': 'dataFormat',\n        'type': 'string',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'LRN',\n    'category': 'normalization',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'depth_radius',\n        'name': 'radius',\n        'type': 'number',\n        'defaultValue': 5\n      },\n      {\n        'tfName': 'bias',\n        'name': 'bias',\n        'type': 'number',\n        'defaultValue': 1\n      },\n      {\n        'tfName': 'alpha',\n        'name': 'alpha',\n        'type': 'number',\n        'defaultValue': 1\n      },\n      {\n        'tfName': 'beta',\n        'name': 'beta',\n        'type': 'number',\n        'defaultValue': 0.5\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Softmax',\n    'category': 'normalization',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'LogSoftmax',\n    'category': 'normalization',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'SparseToDense',\n    'category': 'normalization',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'sparseIndices',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'outputShape',\n        'type': 'number[]'\n      },\n      {\n        'start': 2,\n        'name': 'sparseValues',\n        'type': 'tensor'\n      },\n      {\n        'start': 3,\n        'name': 'defaultValue',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'validate_indices',\n        'name': 'validateIndices',\n        'type': 'bool',\n        'defaultValue': true,\n        'notSupported': true\n      }\n    ]\n  }\n];\n"]}","/**\n * @license\n * Copyright 2022 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n {\n 'tfOpName': 'Bincount',\n 'category': 'reduction',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'size',\n 'type': 'number'\n },\n {\n 'start': 2,\n 'name': 'weights',\n 'type': 'tensor'\n }\n ]\n },\n {\n 'tfOpName': 'DenseBincount',\n 'category': 'reduction',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'size',\n 'type': 'number'\n },\n {\n 'start': 2,\n 'name': 'weights',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'binary_output',\n 'name': 'binaryOutput',\n 'type': 'bool'\n }\n ]\n },\n {\n 'tfOpName': 'Max',\n 'category': 'reduction',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'axis',\n 'type': 'number[]'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'keep_dims',\n 'name': 'keepDims',\n 'type': 'bool'\n }\n ]\n },\n {\n 'tfOpName': 'Mean',\n 'category': 'reduction',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'axis',\n 'type': 'number[]'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'keep_dims',\n 'name': 'keepDims',\n 'type': 'bool'\n }\n ]\n },\n {\n 'tfOpName': 'Min',\n 'category': 'reduction',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'axis',\n 'type': 'number[]'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'keep_dims',\n 'name': 'keepDims',\n 'type': 'bool'\n }\n ]\n },\n {\n 'tfOpName': 'Sum',\n 'category': 'reduction',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'axis',\n 'type': 'number[]'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'keep_dims',\n 'name': 'keepDims',\n 'type': 'bool'\n }\n ]\n },\n {\n 'tfOpName': 'All',\n 'category': 'reduction',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'axis',\n 'type': 'number[]'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'keep_dims',\n 'name': 'keepDims',\n 'type': 'bool'\n }\n ]\n },\n {\n 'tfOpName': 'Any',\n 'category': 'reduction',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'axis',\n 'type': 'number[]'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'keep_dims',\n 'name': 'keepDims',\n 'type': 'bool'\n }\n ]\n },\n {\n 'tfOpName': 'ArgMax',\n 'category': 'reduction',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'axis',\n 'type': 'number'\n }\n ]\n },\n {\n 'tfOpName': 'ArgMin',\n 'category': 'reduction',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'axis',\n 'type': 'number'\n }\n ]\n },\n {\n 'tfOpName': 'Prod',\n 'category': 'reduction',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'axis',\n 'type': 'number[]'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'keep_dims',\n 'name': 'keepDims',\n 'type': 'bool'\n }\n ]\n },\n {\n 'tfOpName': 'Cumprod',\n 'category': 'reduction',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'axis',\n 'type': 'number'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'exclusive',\n 'name': 'exclusive',\n 'type': 'bool'\n },\n {\n 'tfName': 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* @license\n * Copyright 2022 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {OpMapper} from '../types';\n\nexport const json: OpMapper[] = [\n  {\n    'tfOpName': 'Bincount',\n    'category': 'reduction',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'size',\n        'type': 'number'\n      },\n      {\n        'start': 2,\n        'name': 'weights',\n        'type': 'tensor'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'DenseBincount',\n    'category': 'reduction',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'size',\n        'type': 'number'\n      },\n      {\n        'start': 2,\n        'name': 'weights',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'binary_output',\n        'name': 'binaryOutput',\n        'type': 'bool'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Max',\n    'category': 'reduction',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'axis',\n        'type': 'number[]'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'keep_dims',\n        'name': 'keepDims',\n        'type': 'bool'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Mean',\n    'category': 'reduction',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'axis',\n        'type': 'number[]'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'keep_dims',\n        'name': 'keepDims',\n        'type': 'bool'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Min',\n    'category': 'reduction',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'axis',\n        'type': 'number[]'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'keep_dims',\n        'name': 'keepDims',\n        'type': 'bool'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Sum',\n    'category': 'reduction',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'axis',\n        'type': 'number[]'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'keep_dims',\n        'name': 'keepDims',\n        'type': 'bool'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'All',\n    'category': 'reduction',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'axis',\n        'type': 'number[]'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'keep_dims',\n        'name': 'keepDims',\n        'type': 'bool'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Any',\n    'category': 'reduction',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'axis',\n        'type': 'number[]'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'keep_dims',\n        'name': 'keepDims',\n        'type': 'bool'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'ArgMax',\n    'category': 'reduction',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'axis',\n        'type': 'number'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'ArgMin',\n    'category': 'reduction',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'axis',\n        'type': 'number'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Prod',\n    'category': 'reduction',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'axis',\n        'type': 'number[]'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'keep_dims',\n        'name': 'keepDims',\n        'type': 'bool'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Cumprod',\n    'category': 'reduction',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'axis',\n        'type': 'number'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'exclusive',\n        'name': 'exclusive',\n        'type': 'bool'\n      },\n      {\n        'tfName': 'reverse',\n        'name': 'reverse',\n        'type': 'bool'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Cumsum',\n    'category': 'reduction',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'axis',\n        'type': 'number'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'exclusive',\n        'name': 'exclusive',\n        'type': 'bool'\n      },\n      {\n        'tfName': 'reverse',\n        'name': 'reverse',\n        'type': 'bool'\n      }\n    ]\n  }\n]\n;\n"]}","/**\n * @license\n * Copyright 2022 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n {\n 'tfOpName': 'ConcatV2',\n 'category': 'slice_join',\n 'inputs': [\n {\n 'start': 0,\n 'end': -1,\n 'name': 'tensors',\n 'type': 'tensors'\n },\n {\n 'start': -1,\n 'name': 'axis',\n 'type': 'number'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'N',\n 'name': 'n',\n 'type': 'number',\n 'defaultValue': 2\n }\n ]\n },\n {\n 'tfOpName': 'Concat',\n 'category': 'slice_join',\n 'inputs': [\n {\n 'start': 1,\n 'end': 0,\n 'name': 'tensors',\n 'type': 'tensors'\n },\n {\n 'start': 0,\n 'name': 'axis',\n 'type': 'number'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'N',\n 'name': 'n',\n 'type': 'number',\n 'defaultValue': 2\n }\n ]\n },\n {\n 'tfOpName': 'GatherV2',\n 'category': 'slice_join',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'indices',\n 'type': 'tensor'\n },\n {\n 'start': 2,\n 'name': 'axis',\n 'type': 'number',\n 'defaultValue': 0\n }\n ],\n 'attrs': [\n {\n 'tfName': 'batch_dims',\n 'name': 'batchDims',\n 'type': 'number',\n 'defaultValue': 0\n }\n ]\n },\n {\n 'tfOpName': 'Gather',\n 'category': 'slice_join',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'indices',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'validate_indices',\n 'name': 'validateIndices',\n 'type': 'bool',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'Reverse',\n 'category': 'slice_join',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'dims',\n 'type': 'bool[]'\n }\n ]\n },\n {\n 'tfOpName': 'ReverseV2',\n 'category': 'slice_join',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'axis',\n 'type': 'number[]'\n }\n ]\n },\n {\n 'tfOpName': 'Slice',\n 'category': 'slice_join',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'begin',\n 'type': 'number[]'\n },\n {\n 'start': 2,\n 'name': 'size',\n 'type': 'number[]'\n }\n ]\n },\n {\n 'tfOpName': 'StridedSlice',\n 'category': 'slice_join',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'begin',\n 'type': 'number[]'\n },\n {\n 'start': 2,\n 'name': 'end',\n 'type': 'number[]'\n },\n {\n 'start': 3,\n 'name': 'strides',\n 'type': 'number[]'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'begin_mask',\n 'name': 'beginMask',\n 'type': 'number',\n 'defaultValue': 0\n },\n {\n 'tfName': 'end_mask',\n 'name': 'endMask',\n 'type': 'number',\n 'defaultValue': 0\n },\n {\n 'tfName': 'new_axis_mask',\n 'name': 'newAxisMask',\n 'type': 'number',\n 'defaultValue': 0\n },\n {\n 'tfName': 'ellipsis_mask',\n 'name': 'ellipsisMask',\n 'type': 'number',\n 'defaultValue': 0\n },\n {\n 'tfName': 'shrink_axis_mask',\n 'name': 'shrinkAxisMask',\n 'type': 'number',\n 'defaultValue': 0\n }\n ]\n },\n {\n 'tfOpName': 'Pack',\n 'category': 'slice_join',\n 'inputs': [\n {\n 'start': 0,\n 'end': 0,\n 'name': 'tensors',\n 'type': 'tensors'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'axis',\n 'name': 'axis',\n 'type': 'number',\n 'defaultValue': 0\n }\n ]\n },\n {\n 'tfOpName': 'Unpack',\n 'category': 'slice_join',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'tensor',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'axis',\n 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* @license\n * Copyright 2022 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {OpMapper} from '../types';\n\nexport const json: OpMapper[] = [\n  {\n    'tfOpName': 'ConcatV2',\n    'category': 'slice_join',\n    'inputs': [\n      {\n        'start': 0,\n        'end': -1,\n        'name': 'tensors',\n        'type': 'tensors'\n      },\n      {\n        'start': -1,\n        'name': 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'defaultValue': 0\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'batch_dims',\n        'name': 'batchDims',\n        'type': 'number',\n        'defaultValue': 0\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Gather',\n    'category': 'slice_join',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'indices',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'validate_indices',\n        'name': 'validateIndices',\n        'type': 'bool',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Reverse',\n    'category': 'slice_join',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'dims',\n        'type': 'bool[]'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'ReverseV2',\n    'category': 'slice_join',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'axis',\n        'type': 'number[]'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Slice',\n    'category': 'slice_join',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'begin',\n        'type': 'number[]'\n      },\n      {\n        'start': 2,\n        'name': 'size',\n        'type': 'number[]'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'StridedSlice',\n    'category': 'slice_join',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'begin',\n        'type': 'number[]'\n      },\n      {\n        'start': 2,\n        'name': 'end',\n        'type': 'number[]'\n      },\n      {\n        'start': 3,\n        'name': 'strides',\n        'type': 'number[]'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'begin_mask',\n        'name': 'beginMask',\n        'type': 'number',\n        'defaultValue': 0\n      },\n      {\n        'tfName': 'end_mask',\n        'name': 'endMask',\n        'type': 'number',\n        'defaultValue': 0\n      },\n      {\n        'tfName': 'new_axis_mask',\n        'name': 'newAxisMask',\n        'type': 'number',\n        'defaultValue': 0\n      },\n      {\n        'tfName': 'ellipsis_mask',\n        'name': 'ellipsisMask',\n        'type': 'number',\n        'defaultValue': 0\n      },\n      {\n        'tfName': 'shrink_axis_mask',\n        'name': 'shrinkAxisMask',\n        'type': 'number',\n        'defaultValue': 0\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Pack',\n    'category': 'slice_join',\n    'inputs': [\n      {\n        'start': 0,\n        'end': 0,\n        'name': 'tensors',\n        'type': 'tensors'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'axis',\n        'name': 'axis',\n        'type': 'number',\n        'defaultValue': 0\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Unpack',\n    'category': 'slice_join',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'tensor',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'axis',\n        'name': 'axis',\n        'type': 'number',\n        'defaultValue': 0\n      },\n      {\n        'tfName': 'num',\n        'name': 'num',\n        'type': 'number',\n        'defaultValue': 0,\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Tile',\n    'category': 'slice_join',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'reps',\n        'type': 'number[]'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'Split',\n    'category': 'slice_join',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'axis',\n        'type': 'number',\n        'defaultValue': 0\n      },\n      {\n        'start': 1,\n        'name': 'x',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'num_split',\n        'name': 'numOrSizeSplits',\n        'type': 'number',\n        'defaultValue': 1\n      }\n    ]\n  },\n  {\n    'tfOpName': 'SplitV',\n    'category': 'slice_join',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'numOrSizeSplits',\n        'type': 'number[]'\n      },\n      {\n        'start': 2,\n        'name': 'axis',\n        'type': 'number',\n        'defaultValue': 0\n      }\n    ]\n  },\n  {\n    'tfOpName': 'ScatterNd',\n    'category': 'slice_join',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'indices',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'values',\n        'type': 'tensor'\n      },\n      {\n        'start': 2,\n        'name': 'shape',\n        'type': 'number[]'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'GatherNd',\n    'category': 'slice_join',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'indices',\n        'type': 'tensor'\n      }\n    ]\n  },\n  {\n    'tfOpName': 'SparseToDense',\n    'category': 'slice_join',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'sparseIndices',\n        'type': 'tensor'\n      },\n      {\n        'start': 1,\n        'name': 'outputShape',\n        'type': 'number[]'\n      },\n      {\n        'start': 2,\n        'name': 'sparseValues',\n        'type': 'tensor'\n      },\n      {\n        'start': 3,\n        'name': 'defaultValue',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'validate_indices',\n        'name': 'validateIndices',\n        'type': 'bool',\n        'defaultValue': false,\n        'notSupported': true\n      }\n    ]\n  }\n];\n"]}","/**\n * @license\n * Copyright 2022 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n {\n 'tfOpName': 'SparseFillEmptyRows',\n 'category': 'sparse',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'indices',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'values',\n 'type': 'tensor'\n },\n {\n 'start': 2,\n 'name': 'denseShape',\n 'type': 'tensor'\n },\n {\n 'start': 3,\n 'name': 'defaultValue',\n 'type': 'tensor'\n }\n ]\n },\n {\n 'tfOpName': 'SparseReshape',\n 'category': 'sparse',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'inputIndices',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'inputShape',\n 'type': 'tensor'\n },\n {\n 'start': 2,\n 'name': 'newShape',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'T',\n 'name': 'dtype',\n 'type': 'dtype',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'SparseSegmentMean',\n 'category': 'sparse',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'data',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'indices',\n 'type': 'tensor'\n },\n {\n 'start': 2,\n 'name': 'segmentIds',\n 'type': 'tensor'\n }\n ]\n },\n {\n 'tfOpName': 'SparseSegmentSum',\n 'category': 'sparse',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'data',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'indices',\n 'type': 'tensor'\n },\n {\n 'start': 2,\n 'name': 'segmentIds',\n 'type': 'tensor'\n }\n ]\n }\n];\n//# 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* @license\n * Copyright 2022 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n {\n 'tfOpName': 'FFT',\n 'category': 'spectral',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ]\n },\n {\n 'tfOpName': 'IFFT',\n 'category': 'spectral',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ]\n },\n {\n 'tfOpName': 'RFFT',\n 'category': 'spectral',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'fft_length',\n 'type': 'number',\n 'notSupported': true\n }\n ]\n },\n {\n 'tfOpName': 'IRFFT',\n 'category': 'spectral',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'fft_length',\n 'type': 'number',\n 'notSupported': true\n }\n ]\n }\n];\n//# 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* @license\n * Copyright 2022 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n {\n 'tfOpName': 'StringNGrams',\n 'category': 'string',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'data',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'dataSplits',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'separator',\n 'name': 'separator',\n 'type': 'string'\n },\n {\n 'tfName': 'ngram_widths',\n 'name': 'nGramWidths',\n 'type': 'number[]'\n },\n {\n 'tfName': 'left_pad',\n 'name': 'leftPad',\n 'type': 'string'\n },\n {\n 'tfName': 'right_pad',\n 'name': 'rightPad',\n 'type': 'string'\n },\n {\n 'tfName': 'pad_width',\n 'name': 'padWidth',\n 'type': 'number'\n },\n {\n 'tfName': 'preserve_short_sequences',\n 'name': 'preserveShortSequences',\n 'type': 'bool'\n }\n ],\n 'outputs': [\n 'ngrams',\n 'ngrams_splits'\n ]\n },\n {\n 'tfOpName': 'StringSplit',\n 'category': 'string',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'input',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'delimiter',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'skip_empty',\n 'name': 'skipEmpty',\n 'type': 'bool'\n }\n ],\n 'outputs': [\n 'indices',\n 'values',\n 'shape'\n ]\n },\n {\n 'tfOpName': 'StringToHashBucketFast',\n 'category': 'string',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'input',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'num_buckets',\n 'name': 'numBuckets',\n 'type': 'number'\n }\n ]\n }\n];\n//# sourceMappingURL=data:application/json;base64,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","/**\n * @license\n * Copyright 2022 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n {\n 'tfOpName': 'Cast',\n 'category': 'transformation',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'SrcT',\n 'name': 'sdtype',\n 'type': 'dtype',\n 'notSupported': true\n },\n {\n 'tfName': 'DstT',\n 'name': 'dtype',\n 'type': 'dtype'\n }\n ]\n },\n {\n 'tfOpName': 'ExpandDims',\n 'category': 'transformation',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'axis',\n 'type': 'number'\n }\n ]\n },\n {\n 'tfOpName': 'MirrorPad',\n 'category': 'transformation',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'padding',\n 'type': 'number[]'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'mode',\n 'name': 'mode',\n 'type': 'string'\n }\n ]\n },\n {\n 'tfOpName': 'Pad',\n 'category': 'transformation',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'padding',\n 'type': 'number[]'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'constant_value',\n 'name': 'constantValue',\n 'type': 'number',\n 'defaultValue': 0\n }\n ]\n },\n {\n 'tfOpName': 'PadV2',\n 'category': 'transformation',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'padding',\n 'type': 'number[]'\n },\n {\n 'start': 2,\n 'name': 'constantValue',\n 'type': 'number',\n 'defaultValue': 0\n }\n ]\n },\n {\n 'tfOpName': 'Reshape',\n 'category': 'transformation',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'shape',\n 'type': 'number[]'\n }\n ]\n },\n {\n 'tfOpName': 'Squeeze',\n 'category': 'transformation',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'axis',\n 'tfDeprecatedName': 'squeeze_dims',\n 'name': 'axis',\n 'type': 'number[]'\n }\n ]\n },\n {\n 'tfOpName': 'SpaceToBatchND',\n 'category': 'transformation',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'blockShape',\n 'type': 'number[]'\n },\n {\n 'start': 2,\n 'name': 'paddings',\n 'type': 'number[]'\n }\n ]\n },\n {\n 'tfOpName': 'BatchToSpaceND',\n 'category': 'transformation',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'blockShape',\n 'type': 'number[]'\n },\n {\n 'start': 2,\n 'name': 'crops',\n 'type': 'number[]'\n }\n ]\n },\n {\n 'tfOpName': 'DepthToSpace',\n 'category': 'transformation',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n }\n ],\n 'attrs': [\n {\n 'tfName': 'block_size',\n 'name': 'blockSize',\n 'type': 'number'\n },\n {\n 'tfName': 'data_format',\n 'name': 'dataFormat',\n 'type': 'string'\n }\n ]\n },\n {\n 'tfOpName': 'BroadcastTo',\n 'category': 'transformation',\n 'inputs': [\n {\n 'start': 0,\n 'name': 'x',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 'shape',\n 'type': 'number[]'\n }\n ],\n 'attrs': []\n },\n {\n 'tfOpName': 'BroadcastArgs',\n 'category': 'transformation',\n 'inputs': [\n {\n 'start': 0,\n 'name': 's0',\n 'type': 'tensor'\n },\n {\n 'start': 1,\n 'name': 's1',\n 'type': 'tensor'\n }\n ],\n 'attrs': []\n }\n];\n//# 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All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {OpMapper} from '../types';\n\nexport const json: OpMapper[] = [\n  {\n    'tfOpName': 'Cast',\n    'category': 'transformation',\n    'inputs': [\n      {\n        'start': 0,\n        'name': 'x',\n        'type': 'tensor'\n      }\n    ],\n    'attrs': [\n      {\n        'tfName': 'SrcT',\n        'name': 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All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { LazyIterator, OneToManyIterator } from './lazy_iterator';\nimport { StringIterator } from './string_iterator';\nexport class ByteChunkIterator extends LazyIterator {\n /**\n * Decode a stream of UTF8-encoded byte arrays to a stream of strings.\n *\n * The byte arrays producetd from the ByteChunkIterator on which this is\n * called will be interpreted as concatenated. No assumptions are made about\n * the boundaries of the incoming chunks, so a multi-byte UTF8 encoding of a\n * character may span the boundary between chunks. This naturally happens,\n * for instance, when reading fixed-size byte arrays from a file.\n */\n decodeUTF8() {\n return new Utf8Iterator(this);\n }\n}\n// ============================================================================\n// The following private classes serve to implement the chainable methods\n// on ByteChunkIterator. 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Luckily with native decoder, TextDecoder in browser and\n * string_decoder in node, byte array boundaries are handled automatically.\n *\n * In the context of an input pipeline for machine learning, UTF8 decoding is\n * needed to parse text files containing training examples or prediction\n * requests (e.g., formatted as CSV or JSON). We cannot use the built-in\n * decoding provided by FileReader.readAsText() because here we are in a\n * streaming context, which FileReader does not support.\n *\n * @param upstream A `LazyIterator` of `Uint8Arrays` containing UTF8-encoded\n * text, which should be interpreted as concatenated. No assumptions are\n * made about the boundaries of the incoming chunks, so a multi-byte UTF8\n * encoding of a character may span the boundary between chunks. 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All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n\nimport {env} from '@tensorflow/tfjs-core';\nimport {LazyIterator, OneToManyIterator} from './lazy_iterator';\nimport {StringIterator} from './string_iterator';\n\nexport abstract class ByteChunkIterator extends LazyIterator<Uint8Array> {\n  /**\n   * Decode a stream of UTF8-encoded byte arrays to a stream of strings.\n   *\n   * The byte arrays producetd from the ByteChunkIterator on which this is\n   * called will be interpreted as concatenated.  No assumptions are made about\n   * the boundaries of the incoming chunks, so a multi-byte UTF8 encoding of a\n   * character may span the boundary between chunks.  This naturally happens,\n   * for instance, when reading fixed-size byte arrays from a file.\n   */\n  decodeUTF8(): StringIterator {\n    return new Utf8Iterator(this);\n  }\n}\n\n// ============================================================================\n// The following private classes serve to implement the chainable methods\n// on ByteChunkIterator.  Unfortunately they can't be placed in separate files,\n// due to resulting trouble with circular imports.\n// ============================================================================\n\n// We wanted multiple inheritance, e.g.\n//   class Utf8Iterator extends QueueIterator<string>, StringIterator\n// but the TypeScript mixin approach is a bit hacky, so we take this adapter\n// approach instead.\n\nclass Utf8Iterator extends StringIterator {\n  private impl: Utf8IteratorImpl;\n\n  constructor(protected upstream: LazyIterator<Uint8Array>) {\n    super();\n    this.impl = new Utf8IteratorImpl(upstream);\n  }\n\n  summary() {\n    return this.impl.summary();\n  }\n\n  async next() {\n    return this.impl.next();\n  }\n}\n\n/**\n * Decode a stream of UTF8-encoded byte arrays to a stream of strings.\n *\n * This is tricky because the incoming byte array boundaries may disrupt a\n * multi-byte UTF8 character. Thus any incomplete character data at the end of\n * a chunk must be carried over and prepended to the next chunk before\n * decoding. Luckily with native decoder, TextDecoder in browser and\n * string_decoder in node, byte array boundaries are handled automatically.\n *\n * In the context of an input pipeline for machine learning, UTF8 decoding is\n * needed to parse text files containing training examples or prediction\n * requests (e.g., formatted as CSV or JSON). We cannot use the built-in\n * decoding provided by FileReader.readAsText() because here we are in a\n * streaming context, which FileReader does not support.\n *\n * @param upstream A `LazyIterator` of `Uint8Arrays` containing UTF8-encoded\n *   text, which should be interpreted as concatenated.  No assumptions are\n *   made about the boundaries of the incoming chunks, so a multi-byte UTF8\n *   encoding of a character may span the boundary between chunks.  This\n *   naturally happens, for instance, when reading fixed-size byte arrays from a\n *   file.\n */\nclass Utf8IteratorImpl extends OneToManyIterator<string> {\n  // `decoder` as `any` here to dynamically assign value based on the\n  // environment.\n  // tslint:disable-next-line:no-any\n  decoder: any;\n\n  constructor(protected readonly upstream: LazyIterator<Uint8Array>) {\n    super();\n    if (env().get('IS_BROWSER')) {\n      this.decoder = new TextDecoder('utf-8');\n    } else {\n      // tslint:disable-next-line:no-require-imports\n      const {StringDecoder} = require('string_decoder');\n      this.decoder = new StringDecoder('utf8');\n    }\n  }\n  summary() {\n    return `${this.upstream.summary()} -> Utf8`;\n  }\n\n  async pump(): Promise<boolean> {\n    const chunkResult = await this.upstream.next();\n    let chunk;\n    if (chunkResult.done) {\n      return false;\n    } else {\n      chunk = chunkResult.value;\n    }\n\n    let text: string;\n    if (env().get('IS_BROWSER')) {\n      text = this.decoder.decode(chunk, {stream: true});\n    } else {\n      text = this.decoder.write(Buffer.from(chunk.buffer));\n    }\n    this.outputQueue.push(text);\n    return true;\n  }\n}\n"]}","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { LazyIterator, OneToManyIterator } from './lazy_iterator';\nexport class StringIterator extends LazyIterator {\n /**\n * Splits a string stream on a given separator.\n *\n * It is assumed that the incoming chunk boundaries have no semantic meaning,\n * so conceptually the incoming stream is treated simply as the concatenation\n * of its elements.\n *\n * The outgoing stream provides chunks corresponding to the results of the\n * standard string split() operation (even if such a chunk spanned incoming\n * chunks). The separators are not included.\n *\n * A typical usage is to split a text file (represented as a stream with\n * arbitrary chunk boundaries) into lines.\n *\n * @param upstream A readable stream of strings that can be treated as\n * concatenated.\n * @param separator A character to split on.\n */\n split(separator) {\n return new SplitIterator(this, separator);\n }\n}\n// ============================================================================\n// The following private classes serve to implement the chainable methods\n// on StringIterator. Unfortunately they can't be placed in separate files, due\n// to resulting trouble with circular imports.\n// ============================================================================\n// We wanted multiple inheritance, e.g.\n// class SplitIterator extends QueueIterator, StringIterator\n// but the TypeScript mixin approach is a bit hacky, so we take this adapter\n// approach instead.\nclass SplitIterator extends StringIterator {\n constructor(upstream, separator) {\n super();\n this.upstream = upstream;\n this.impl = new SplitIteratorImpl(upstream, separator);\n }\n summary() {\n return this.impl.summary();\n }\n async next() {\n return this.impl.next();\n }\n}\nclass SplitIteratorImpl extends OneToManyIterator {\n constructor(upstream, separator) {\n super();\n this.upstream = upstream;\n this.separator = separator;\n // A partial string at the end of an upstream chunk\n this.carryover = '';\n }\n summary() {\n return `${this.upstream.summary()} -> Split('${this.separator}')`;\n }\n async pump() {\n const chunkResult = await this.upstream.next();\n if (chunkResult.done) {\n if (this.carryover === '') {\n return false;\n }\n // Pretend that the pump succeeded in order to emit the small last batch.\n // The next pump() call will actually fail.\n this.outputQueue.push(this.carryover);\n this.carryover = '';\n return true;\n }\n const lines = chunkResult.value.split(this.separator);\n // Note the behavior: \" ab \".split(' ') === ['', 'ab', '']\n // Thus the carryover may be '' if the separator falls on a chunk\n // boundary; this produces the correct result.\n lines[0] = this.carryover + lines[0];\n for (const line of lines.slice(0, -1)) {\n this.outputQueue.push(line);\n }\n this.carryover = lines[lines.length - 1];\n return true;\n }\n}\n//# 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","// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random(); // 0 <= x < 1. Every bit is random.\n// var x = random.quick(); // 0 <= x < 1. 32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baagøe.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by François Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three. It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures. Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n","/**\n * @license\n * Copyright 2022 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n'use strict';\n\nObject.defineProperty(exports, '__esModule', { value: true });\n\nfunction _interopDefault (ex) { return (ex && (typeof ex === 'object') && 'default' in ex) ? ex['default'] : ex; }\n\nvar tfjsCore = require('@tensorflow/tfjs-core');\nvar LongExports = require('long');\nvar LongExports__default = _interopDefault(LongExports);\nvar tfjsLayers = require('@tensorflow/tfjs-layers');\nvar tfjsConverter = require('@tensorflow/tfjs-converter');\nvar tfjsData = require('@tensorflow/tfjs-data');\nvar tfjsBackendCpu = require('@tensorflow/tfjs-backend-cpu');\nvar tfjsBackendWebgl = require('@tensorflow/tfjs-backend-webgl');\n\nconst Abs = 'Abs';\nconst Acos = 'Acos';\nconst Acosh = 'Acosh';\nconst Add = 'Add';\nconst AddN = 'AddN';\nconst All = 'All';\nconst Any = 'Any';\nconst ArgMax = 'ArgMax';\nconst ArgMin = 'ArgMin';\nconst Asin = 'Asin';\nconst Asinh = 'Asinh';\nconst Atan = 'Atan';\nconst Atanh = 'Atanh';\nconst Atan2 = 'Atan2';\nconst AvgPool = 'AvgPool';\nconst AvgPoolGrad = 'AvgPoolGrad';\nconst AvgPool3D = 'AvgPool3D';\nconst AvgPool3DGrad = 'AvgPool3DGrad';\nconst BatchMatMul = 'BatchMatMul';\nconst BatchToSpaceND = 'BatchToSpaceND';\nconst Bincount = 'Bincount';\nconst BroadcastTo = 'BroadcastTo';\nconst BroadcastArgs = 'BroadcastArgs';\nconst Cast = 'Cast';\nconst Ceil = 'Ceil';\nconst ClipByValue = 'ClipByValue';\nconst Complex = 'Complex';\nconst ComplexAbs = 'ComplexAbs';\nconst Concat = 'Concat';\nconst Conv2D = 'Conv2D';\nconst Conv2DBackpropFilter = 'Conv2DBackpropFilter';\nconst Conv2DBackpropInput = 'Conv2DBackpropInput';\nconst Conv3D = 'Conv3D';\nconst Conv3DBackpropFilterV2 = 'Conv3DBackpropFilterV2';\nconst Conv3DBackpropInputV2 = 'Conv3DBackpropInputV2';\nconst Cos = 'Cos';\nconst Cosh = 'Cosh';\nconst Cumprod = 'Cumprod';\nconst Cumsum = 'Cumsum';\nconst CropAndResize = 'CropAndResize';\nconst DenseBincount = 'DenseBincount';\nconst DepthToSpace = 'DepthToSpace';\nconst DepthwiseConv2dNative = 'DepthwiseConv2dNative';\nconst DepthwiseConv2dNativeBackpropFilter = 'DepthwiseConv2dNativeBackpropFilter';\nconst DepthwiseConv2dNativeBackpropInput = 'DepthwiseConv2dNativeBackpropInput';\nconst Diag = 'Diag';\nconst Dilation2D = 'Dilation2D';\nconst Dilation2DBackpropInput = 'Dilation2DBackpropInput';\nconst Dilation2DBackpropFilter = 'Dilation2DBackpropFilter';\nconst RealDiv = 'RealDiv';\nconst Einsum = 'Einsum';\nconst Elu = 'Elu';\nconst EluGrad = 'EluGrad';\nconst Erf = 'Erf';\nconst Equal = 'Equal';\nconst Exp = 'Exp';\nconst ExpandDims = 'ExpandDims';\nconst Expm1 = 'Expm1';\nconst FFT = 'FFT';\nconst Fill = 'Fill';\nconst FlipLeftRight = 'FlipLeftRight';\nconst Floor = 'Floor';\nconst FloorDiv = 'FloorDiv';\nconst FusedBatchNorm = 'FusedBatchNorm';\nconst GatherV2 = 'GatherV2';\nconst GatherNd = 'GatherNd';\nconst Greater = 'Greater';\nconst GreaterEqual = 'GreaterEqual';\nconst Identity = 'Identity';\nconst IFFT = 'IFFT';\nconst Imag = 'Imag';\nconst IsFinite = 'IsFinite';\nconst IsInf = 'IsInf';\nconst IsNan = 'IsNan';\nconst LeakyRelu = 'LeakyRelu';\nconst Less = 'Less';\nconst LessEqual = 'LessEqual';\nconst LinSpace = 'LinSpace';\nconst Log = 'Log';\nconst Log1p = 'Log1p';\nconst LogicalAnd = 'LogicalAnd';\nconst LogicalNot = 'LogicalNot';\nconst LogicalOr = 'LogicalOr';\nconst LogSoftmax = 'LogSoftmax';\nconst LRN = 'LRN';\nconst LRNGrad = 'LRNGrad';\nconst Max = 'Max';\nconst Maximum = 'Maximum';\nconst MaxPool = 'MaxPool';\nconst MaxPoolGrad = 'MaxPoolGrad';\nconst MaxPool3D = 'MaxPool3D';\nconst MaxPool3DGrad = 'MaxPool3DGrad';\nconst MaxPoolWithArgmax = 'MaxPoolWithArgmax';\nconst Mean = 'Mean';\nconst Min = 'Min';\nconst Minimum = 'Minimum';\nconst MirrorPad = 'MirrorPad';\nconst Mod = 'Mod';\nconst Multinomial = 'Multinomial';\nconst Multiply = 'Multiply';\nconst Neg = 'Neg';\nconst NotEqual = 'NotEqual';\nconst NonMaxSuppressionV3 = 'NonMaxSuppressionV3';\nconst NonMaxSuppressionV4 = 'NonMaxSuppressionV4';\nconst NonMaxSuppressionV5 = 'NonMaxSuppressionV5';\nconst OnesLike = 'OnesLike';\nconst OneHot = 'OneHot';\nconst Pack = 'Pack';\nconst PadV2 = 'PadV2';\nconst Pool = 'Pool';\nconst Pow = 'Pow';\nconst Prelu = 'Prelu';\nconst Prod = 'Prod';\nconst Range = 'Range';\nconst Real = 'Real';\nconst Reciprocal = 'Reciprocal';\nconst Relu = 'Relu';\nconst Reshape = 'Reshape';\nconst ResizeNearestNeighbor = 'ResizeNearestNeighbor';\nconst ResizeNearestNeighborGrad = 'ResizeNearestNeighborGrad';\nconst ResizeBilinear = 'ResizeBilinear';\nconst ResizeBilinearGrad = 'ResizeBilinearGrad';\nconst Relu6 = 'Relu6';\nconst Reverse = 'Reverse';\nconst Round = 'Round';\nconst Rsqrt = 'Rsqrt';\nconst ScatterNd = 'ScatterNd';\nconst Select = 'Select';\nconst Selu = 'Selu';\nconst Slice = 'Slice';\nconst Sin = 'Sin';\nconst Sinh = 'Sinh';\nconst Sign = 'Sign';\nconst Sigmoid = 'Sigmoid';\nconst Softplus = 'Softplus';\nconst Sqrt = 'Sqrt';\nconst Sum = 'Sum';\nconst SpaceToBatchND = 'SpaceToBatchND';\nconst SplitV = 'SplitV';\nconst Softmax = 'Softmax';\nconst SparseFillEmptyRows = 'SparseFillEmptyRows';\nconst SparseReshape = 'SparseReshape';\nconst SparseSegmentMean = 'SparseSegmentMean';\nconst SparseSegmentSum = 'SparseSegmentSum';\nconst SparseToDense = 'SparseToDense';\nconst SquaredDifference = 'SquaredDifference';\nconst Square = 'Square';\nconst StridedSlice = 'StridedSlice';\nconst StringNGrams = 'StringNGrams';\nconst StringSplit = 'StringSplit';\nconst StringToHashBucketFast = 'StringToHashBucketFast';\nconst Sub = 'Sub';\nconst Tan = 'Tan';\nconst Tanh = 'Tanh';\nconst Tile = 'Tile';\nconst TopK = 'TopK';\nconst Transform = 'Transform';\nconst Transpose = 'Transpose';\nconst Unique = 'Unique';\nconst Unpack = 'Unpack';\nconst UnsortedSegmentSum = 'UnsortedSegmentSum';\nconst ZerosLike = 'ZerosLike';\n/**\n * TensorFlow.js-only kernels\n */\nconst Step = 'Step';\nconst FromPixels = 'FromPixels';\nconst RotateWithOffset = 'RotateWithOffset';\nconst _FusedMatMul = '_FusedMatMul';\nconst FusedConv2D = 'FusedConv2D';\nconst FusedDepthwiseConv2D = 'FusedDepthwiseConv2D';\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst EPSILON_FLOAT32 = 1e-7;\nconst EPSILON_FLOAT16 = 1e-4;\n/** Convenient class for storing tensor-related data. */\nclass DataStorage {\n constructor(backend, dataMover) {\n this.backend = backend;\n this.dataMover = dataMover;\n this.data = new WeakMap();\n this.dataIdsCount = 0;\n }\n get(dataId) {\n if (!this.data.has(dataId)) {\n this.dataMover.moveData(this.backend, dataId);\n }\n return this.data.get(dataId);\n }\n set(dataId, value) {\n this.dataIdsCount++;\n this.data.set(dataId, value);\n }\n has(dataId) {\n return this.data.has(dataId);\n }\n delete(dataId) {\n this.dataIdsCount--;\n return this.data.delete(dataId);\n }\n numDataIds() {\n return this.dataIdsCount;\n }\n}\n/**\n * The interface that defines the kernels that should be implemented when\n * adding a new backend. New backends don't need to implement every one of the\n * methods, this can be done gradually (throw an error for unimplemented\n * methods).\n */\nclass KernelBackend {\n refCount(dataId) {\n return notYetImplemented('refCount');\n }\n incRef(dataId) {\n return notYetImplemented('incRef');\n }\n timerAvailable() {\n return true;\n }\n time(f) {\n return notYetImplemented('time');\n }\n read(dataId) {\n return notYetImplemented('read');\n }\n readSync(dataId) {\n return notYetImplemented('readSync');\n }\n readToGPU(dataId, options) {\n return notYetImplemented('readToGPU');\n }\n numDataIds() {\n return notYetImplemented('numDataIds');\n }\n disposeData(dataId, force) {\n return notYetImplemented('disposeData');\n }\n write(values, shape, dtype) {\n return notYetImplemented('write');\n }\n move(dataId, values, shape, dtype, refCount) {\n return notYetImplemented('move');\n }\n memory() {\n return notYetImplemented('memory');\n }\n /** Returns the highest precision for floats in bits (e.g. 16 or 32) */\n floatPrecision() {\n return notYetImplemented('floatPrecision');\n }\n /** Returns the smallest representable number. */\n epsilon() {\n return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16;\n }\n dispose() {\n return notYetImplemented('dispose');\n }\n}\nfunction notYetImplemented(kernelName) {\n throw new Error(`'${kernelName}' not yet implemented or not found in the registry. ` +\n `This kernel may not be supported by the tfjs backend you have chosen`);\n}\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Shuffles the array in-place using Fisher-Yates algorithm.\n *\n * ```js\n * const a = [1, 2, 3, 4, 5];\n * tf.util.shuffle(a);\n * console.log(a);\n * ```\n *\n * @param array The array to shuffle in-place.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\n// tslint:disable-next-line:no-any\nfunction shuffle(array) {\n let counter = array.length;\n let index = 0;\n // While there are elements in the array\n while (counter > 0) {\n // Pick a random index\n index = (Math.random() * counter) | 0;\n // Decrease counter by 1\n counter--;\n // And swap the last element with it\n swap(array, counter, index);\n }\n}\n/**\n * Shuffles two arrays in-place the same way using Fisher-Yates algorithm.\n *\n * ```js\n * const a = [1,2,3,4,5];\n * const b = [11,22,33,44,55];\n * tf.util.shuffleCombo(a, b);\n * console.log(a, b);\n * ```\n *\n * @param array The first array to shuffle in-place.\n * @param array2 The second array to shuffle in-place with the same permutation\n * as the first array.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nfunction shuffleCombo(\n// tslint:disable-next-line:no-any\narray, \n// tslint:disable-next-line:no-any\narray2) {\n if (array.length !== array2.length) {\n throw new Error(`Array sizes must match to be shuffled together ` +\n `First array length was ${array.length}` +\n `Second array length was ${array2.length}`);\n }\n let counter = array.length;\n let index = 0;\n // While there are elements in the array\n while (counter > 0) {\n // Pick a random index\n index = (Math.random() * counter) | 0;\n // Decrease counter by 1\n counter--;\n // And swap the last element of each array with it\n swap(array, counter, index);\n swap(array2, counter, index);\n }\n}\n/** Clamps a value to a specified range. */\nfunction clamp(min, x, max) {\n return Math.max(min, Math.min(x, max));\n}\nfunction nearestLargerEven(val) {\n return val % 2 === 0 ? val : val + 1;\n}\nfunction swap(object, left, right) {\n const temp = object[left];\n object[left] = object[right];\n object[right] = temp;\n}\nfunction sum(arr) {\n let sum = 0;\n for (let i = 0; i < arr.length; i++) {\n sum += arr[i];\n }\n return sum;\n}\n/**\n * Returns a sample from a uniform [a, b) distribution.\n *\n * @param a The minimum support (inclusive).\n * @param b The maximum support (exclusive).\n * @return A pseudorandom number on the half-open interval [a,b).\n */\nfunction randUniform(a, b) {\n const r = Math.random();\n return (b * r) + (1 - r) * a;\n}\n/** Returns the squared Euclidean distance between two vectors. */\nfunction distSquared(a, b) {\n let result = 0;\n for (let i = 0; i < a.length; i++) {\n const diff = Number(a[i]) - Number(b[i]);\n result += diff * diff;\n }\n return result;\n}\n/**\n * Asserts that the expression is true. Otherwise throws an error with the\n * provided message.\n *\n * ```js\n * const x = 2;\n * tf.util.assert(x === 2, 'x is not 2');\n * ```\n *\n * @param expr The expression to assert (as a boolean).\n * @param msg A function that returns the message to report when throwing an\n * error. We use a function for performance reasons.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nfunction assert(expr, msg) {\n if (!expr) {\n throw new Error(typeof msg === 'string' ? msg : msg());\n }\n}\nfunction assertShapesMatch(shapeA, shapeB, errorMessagePrefix = '') {\n assert(arraysEqual(shapeA, shapeB), () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);\n}\nfunction assertNonNull(a) {\n assert(a != null, () => `The input to the tensor constructor must be a non-null value.`);\n}\n// NOTE: We explicitly type out what T extends instead of any so that\n// util.flatten on a nested array of number doesn't try to infer T as a\n// number[][], causing us to explicitly type util.flatten().\n/**\n * Flattens an arbitrarily nested array.\n *\n * ```js\n * const a = [[1, 2], [3, 4], [5, [6, [7]]]];\n * const flat = tf.util.flatten(a);\n * console.log(flat);\n * ```\n *\n * @param arr The nested array to flatten.\n * @param result The destination array which holds the elements.\n * @param skipTypedArray If true, avoids flattening the typed arrays. Defaults\n * to false.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nfunction flatten(arr, result = [], skipTypedArray = false) {\n if (result == null) {\n result = [];\n }\n if (Array.isArray(arr) || isTypedArray(arr) && !skipTypedArray) {\n for (let i = 0; i < arr.length; ++i) {\n flatten(arr[i], result, skipTypedArray);\n }\n }\n else {\n result.push(arr);\n }\n return result;\n}\n/**\n * Returns the size (number of elements) of the tensor given its shape.\n *\n * ```js\n * const shape = [3, 4, 2];\n * const size = tf.util.sizeFromShape(shape);\n * console.log(size);\n * ```\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nfunction sizeFromShape(shape) {\n if (shape.length === 0) {\n // Scalar.\n return 1;\n }\n let size = shape[0];\n for (let i = 1; i < shape.length; i++) {\n size *= shape[i];\n }\n return size;\n}\nfunction isScalarShape(shape) {\n return shape.length === 0;\n}\nfunction arraysEqual(n1, n2) {\n if (n1 === n2) {\n return true;\n }\n if (n1 == null || n2 == null) {\n return false;\n }\n if (n1.length !== n2.length) {\n return false;\n }\n for (let i = 0; i < n1.length; i++) {\n if (n1[i] !== n2[i]) {\n return false;\n }\n }\n return true;\n}\nfunction isInt(a) {\n return a % 1 === 0;\n}\nfunction tanh(x) {\n // tslint:disable-next-line:no-any\n if (Math.tanh != null) {\n // tslint:disable-next-line:no-any\n return Math.tanh(x);\n }\n if (x === Infinity) {\n return 1;\n }\n else if (x === -Infinity) {\n return -1;\n }\n else {\n const e2x = Math.exp(2 * x);\n return (e2x - 1) / (e2x + 1);\n }\n}\nfunction sizeToSquarishShape(size) {\n const width = Math.ceil(Math.sqrt(size));\n return [width, Math.ceil(size / width)];\n}\n/**\n * Creates a new array with randomized indicies to a given quantity.\n *\n * ```js\n * const randomTen = tf.util.createShuffledIndices(10);\n * console.log(randomTen);\n * ```\n *\n * @param number Quantity of how many shuffled indicies to create.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nfunction createShuffledIndices(n) {\n const shuffledIndices = new Uint32Array(n);\n for (let i = 0; i < n; ++i) {\n shuffledIndices[i] = i;\n }\n shuffle(shuffledIndices);\n return shuffledIndices;\n}\nfunction rightPad(a, size) {\n if (size <= a.length) {\n return a;\n }\n return a + ' '.repeat(size - a.length);\n}\nfunction repeatedTry(checkFn, delayFn = (counter) => 0, maxCounter) {\n return new Promise((resolve, reject) => {\n let tryCount = 0;\n const tryFn = () => {\n if (checkFn()) {\n resolve();\n return;\n }\n tryCount++;\n const nextBackoff = delayFn(tryCount);\n if (maxCounter != null && tryCount >= maxCounter) {\n reject();\n return;\n }\n setTimeout(tryFn, nextBackoff);\n };\n tryFn();\n });\n}\n/**\n * Given the full size of the array and a shape that may contain -1 as the\n * implicit dimension, returns the inferred shape where -1 is replaced.\n * E.g. For shape=[2, -1, 3] and size=24, it will return [2, 4, 3].\n *\n * @param shape The shape, which may contain -1 in some dimension.\n * @param size The full size (number of elements) of the array.\n * @return The inferred shape where -1 is replaced with the inferred size.\n */\nfunction inferFromImplicitShape(shape, size) {\n let shapeProd = 1;\n let implicitIdx = -1;\n for (let i = 0; i < shape.length; ++i) {\n if (shape[i] >= 0) {\n shapeProd *= shape[i];\n }\n else if (shape[i] === -1) {\n if (implicitIdx !== -1) {\n throw Error(`Shapes can only have 1 implicit size. ` +\n `Found -1 at dim ${implicitIdx} and dim ${i}`);\n }\n implicitIdx = i;\n }\n else if (shape[i] < 0) {\n throw Error(`Shapes can not be < 0. Found ${shape[i]} at dim ${i}`);\n }\n }\n if (implicitIdx === -1) {\n if (size > 0 && size !== shapeProd) {\n throw Error(`Size(${size}) must match the product of shape ${shape}`);\n }\n return shape;\n }\n if (shapeProd === 0) {\n throw Error(`Cannot infer the missing size in [${shape}] when ` +\n `there are 0 elements`);\n }\n if (size % shapeProd !== 0) {\n throw Error(`The implicit shape can't be a fractional number. ` +\n `Got ${size} / ${shapeProd}`);\n }\n const newShape = shape.slice();\n newShape[implicitIdx] = size / shapeProd;\n return newShape;\n}\nfunction parseAxisParam(axis, shape) {\n const rank = shape.length;\n // Normalize input\n axis = axis == null ? shape.map((s, i) => i) : [].concat(axis);\n // Check for valid range\n assert(axis.every(ax => ax >= -rank && ax < rank), () => `All values in axis param must be in range [-${rank}, ${rank}) but ` +\n `got axis ${axis}`);\n // Check for only integers\n assert(axis.every(ax => isInt(ax)), () => `All values in axis param must be integers but ` +\n `got axis ${axis}`);\n // Handle negative axis.\n return axis.map(a => a < 0 ? rank + a : a);\n}\n/** Reduces the shape by removing all dimensions of shape 1. */\nfunction squeezeShape(shape, axis) {\n const newShape = [];\n const keptDims = [];\n const isEmptyArray = axis != null && Array.isArray(axis) && axis.length === 0;\n const axes = (axis == null || isEmptyArray) ?\n null :\n parseAxisParam(axis, shape).sort();\n let j = 0;\n for (let i = 0; i < shape.length; ++i) {\n if (axes != null) {\n if (axes[j] === i && shape[i] !== 1) {\n throw new Error(`Can't squeeze axis ${i} since its dim '${shape[i]}' is not 1`);\n }\n if ((axes[j] == null || axes[j] > i) && shape[i] === 1) {\n newShape.push(shape[i]);\n keptDims.push(i);\n }\n if (axes[j] <= i) {\n j++;\n }\n }\n if (shape[i] !== 1) {\n newShape.push(shape[i]);\n keptDims.push(i);\n }\n }\n return { newShape, keptDims };\n}\nfunction getTypedArrayFromDType(dtype, size) {\n let values = null;\n if (dtype == null || dtype === 'float32') {\n values = new Float32Array(size);\n }\n else if (dtype === 'int32') {\n values = new Int32Array(size);\n }\n else if (dtype === 'bool') {\n values = new Uint8Array(size);\n }\n else {\n throw new Error(`Unknown data type ${dtype}`);\n }\n return values;\n}\nfunction getArrayFromDType(dtype, size) {\n let values = null;\n if (dtype == null || dtype === 'float32') {\n values = new Float32Array(size);\n }\n else if (dtype === 'int32') {\n values = new Int32Array(size);\n }\n else if (dtype === 'bool') {\n values = new Uint8Array(size);\n }\n else if (dtype === 'string') {\n values = new Array(size);\n }\n else {\n throw new Error(`Unknown data type ${dtype}`);\n }\n return values;\n}\nfunction checkConversionForErrors(vals, dtype) {\n for (let i = 0; i < vals.length; i++) {\n const num = vals[i];\n if (isNaN(num) || !isFinite(num)) {\n throw Error(`A tensor of type ${dtype} being uploaded contains ${num}.`);\n }\n }\n}\n/** Returns true if the dtype is valid. */\nfunction isValidDtype(dtype) {\n return dtype === 'bool' || dtype === 'complex64' || dtype === 'float32' ||\n dtype === 'int32' || dtype === 'string';\n}\n/**\n * Returns true if the new type can't encode the old type without loss of\n * precision.\n */\nfunction hasEncodingLoss(oldType, newType) {\n if (newType === 'complex64') {\n return false;\n }\n if (newType === 'float32' && oldType !== 'complex64') {\n return false;\n }\n if (newType === 'int32' && oldType !== 'float32' && oldType !== 'complex64') {\n return false;\n }\n if (newType === 'bool' && oldType === 'bool') {\n return false;\n }\n return true;\n}\nfunction isTypedArray(a) {\n return a instanceof Float32Array || a instanceof Int32Array ||\n a instanceof Uint8Array || a instanceof Uint8ClampedArray;\n}\nfunction bytesPerElement(dtype) {\n if (dtype === 'float32' || dtype === 'int32') {\n return 4;\n }\n else if (dtype === 'complex64') {\n return 8;\n }\n else if (dtype === 'bool') {\n return 1;\n }\n else {\n throw new Error(`Unknown dtype ${dtype}`);\n }\n}\n/**\n * Returns the approximate number of bytes allocated in the string array - 2\n * bytes per character. Computing the exact bytes for a native string in JS is\n * not possible since it depends on the encoding of the html page that serves\n * the website.\n */\nfunction bytesFromStringArray(arr) {\n if (arr == null) {\n return 0;\n }\n let bytes = 0;\n arr.forEach(x => bytes += x.length);\n return bytes;\n}\n/** Returns true if the value is a string. */\nfunction isString(value) {\n return typeof value === 'string' || value instanceof String;\n}\nfunction isBoolean(value) {\n return typeof value === 'boolean';\n}\nfunction isNumber(value) {\n return typeof value === 'number';\n}\nfunction inferDtype(values) {\n if (Array.isArray(values)) {\n return inferDtype(values[0]);\n }\n if (values instanceof Float32Array) {\n return 'float32';\n }\n else if (values instanceof Int32Array\n || values instanceof Uint8Array\n || values instanceof Uint8ClampedArray) {\n return 'int32';\n }\n else if (isNumber(values)) {\n return 'float32';\n }\n else if (isString(values)) {\n return 'string';\n }\n else if (isBoolean(values)) {\n return 'bool';\n }\n return 'float32';\n}\nfunction isFunction(f) {\n return !!(f && f.constructor && f.call && f.apply);\n}\nfunction nearestDivisor(size, start) {\n for (let i = start; i < size; ++i) {\n if (size % i === 0) {\n return i;\n }\n }\n return size;\n}\nfunction computeStrides(shape) {\n const rank = shape.length;\n if (rank < 2) {\n return [];\n }\n // Last dimension has implicit stride of 1, thus having D-1 (instead of D)\n // strides.\n const strides = new Array(rank - 1);\n strides[rank - 2] = shape[rank - 1];\n for (let i = rank - 3; i >= 0; --i) {\n strides[i] = strides[i + 1] * shape[i + 1];\n }\n return strides;\n}\nfunction createNestedArray(offset, shape, a, isComplex = false) {\n const ret = new Array();\n if (shape.length === 1) {\n const d = shape[0] * (isComplex ? 2 : 1);\n for (let i = 0; i < d; i++) {\n ret[i] = a[offset + i];\n }\n }\n else {\n const d = shape[0];\n const rest = shape.slice(1);\n const len = rest.reduce((acc, c) => acc * c) * (isComplex ? 2 : 1);\n for (let i = 0; i < d; i++) {\n ret[i] = createNestedArray(offset + i * len, rest, a, isComplex);\n }\n }\n return ret;\n}\n// Provide a nested array of TypedArray in given shape.\nfunction toNestedArray(shape, a, isComplex = false) {\n if (shape.length === 0) {\n // Scalar type should return a single number.\n return a[0];\n }\n const size = shape.reduce((acc, c) => acc * c) * (isComplex ? 2 : 1);\n if (size === 0) {\n // A tensor with shape zero should be turned into empty list.\n return [];\n }\n if (size !== a.length) {\n throw new Error(`[${shape}] does not match the input size ${a.length}${isComplex ? ' for a complex tensor' : ''}.`);\n }\n return createNestedArray(0, shape, a, isComplex);\n}\nfunction makeOnesTypedArray(size, dtype) {\n const array = makeZerosTypedArray(size, dtype);\n for (let i = 0; i < array.length; i++) {\n array[i] = 1;\n }\n return array;\n}\nfunction makeZerosTypedArray(size, dtype) {\n if (dtype == null || dtype === 'float32' || dtype === 'complex64') {\n return new Float32Array(size);\n }\n else if (dtype === 'int32') {\n return new Int32Array(size);\n }\n else if (dtype === 'bool') {\n return new Uint8Array(size);\n }\n else {\n throw new Error(`Unknown data type ${dtype}`);\n }\n}\n/**\n * Make nested `TypedArray` filled with zeros.\n * @param shape The shape information for the nested array.\n * @param dtype dtype of the array element.\n */\nfunction makeZerosNestedTypedArray(shape, dtype) {\n const size = shape.reduce((prev, curr) => prev * curr, 1);\n if (dtype == null || dtype === 'float32') {\n return toNestedArray(shape, new Float32Array(size));\n }\n else if (dtype === 'int32') {\n return toNestedArray(shape, new Int32Array(size));\n }\n else if (dtype === 'bool') {\n return toNestedArray(shape, new Uint8Array(size));\n }\n else {\n throw new Error(`Unknown data type ${dtype}`);\n }\n}\nfunction assertNonNegativeIntegerDimensions(shape) {\n shape.forEach(dimSize => {\n assert(Number.isInteger(dimSize) && dimSize >= 0, () => `Tensor must have a shape comprised of positive integers but got ` +\n `shape [${shape}].`);\n });\n}\n/**\n * Computes flat index for a given location (multidimentionsal index) in a\n * Tensor/multidimensional array.\n *\n * @param locs Location in the tensor.\n * @param rank Rank of the tensor.\n * @param strides Tensor strides.\n */\nfunction locToIndex(locs, rank, strides) {\n if (rank === 0) {\n return 0;\n }\n else if (rank === 1) {\n return locs[0];\n }\n let index = locs[locs.length - 1];\n for (let i = 0; i < locs.length - 1; ++i) {\n index += strides[i] * locs[i];\n }\n return index;\n}\n/**\n * Computes the location (multidimensional index) in a tensor/multidimentional\n * array for a given flat index.\n *\n * @param index Index in flat array.\n * @param rank Rank of tensor.\n * @param strides Strides of tensor.\n */\nfunction indexToLoc(index, rank, strides) {\n if (rank === 0) {\n return [];\n }\n else if (rank === 1) {\n return [index];\n }\n const locs = new Array(rank);\n for (let i = 0; i < locs.length - 1; ++i) {\n locs[i] = Math.floor(index / strides[i]);\n index -= locs[i] * strides[i];\n }\n locs[locs.length - 1] = index;\n return locs;\n}\n/**\n * This method asserts whether an object is a Promise instance.\n * @param object\n */\n// tslint:disable-next-line: no-any\nfunction isPromise(object) {\n // We chose to not use 'obj instanceOf Promise' for two reasons:\n // 1. It only reliably works for es6 Promise, not other Promise\n // implementations.\n // 2. It doesn't work with framework that uses zone.js. zone.js monkey patch\n // the async calls, so it is possible the obj (patched) is comparing to a\n // pre-patched Promise.\n return object && object.then && typeof object.then === 'function';\n}\n\n/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Expects flags from URL in the format ?tfjsflags=FLAG1:1,FLAG2:true.\nconst TENSORFLOWJS_FLAGS_PREFIX = 'tfjsflags';\n/**\n * The environment contains evaluated flags as well as the registered platform.\n * This is always used as a global singleton and can be retrieved with\n * `tf.env()`.\n *\n * @doc {heading: 'Environment'}\n */\nclass Environment {\n // tslint:disable-next-line: no-any\n constructor(global) {\n this.global = global;\n this.flags = {};\n this.flagRegistry = {};\n this.urlFlags = {};\n // Jasmine spies on this in 'environment_test.ts'\n this.getQueryParams = getQueryParams;\n this.populateURLFlags();\n }\n setPlatform(platformName, platform) {\n if (this.platform != null) {\n if (!(env().getBool('IS_TEST') || env().getBool('PROD'))) {\n console.warn(`Platform ${this.platformName} has already been set. ` +\n `Overwriting the platform with ${platformName}.`);\n }\n }\n this.platformName = platformName;\n this.platform = platform;\n }\n registerFlag(flagName, evaluationFn, setHook) {\n this.flagRegistry[flagName] = { evaluationFn, setHook };\n // Override the flag value from the URL. This has to happen here because\n // the environment is initialized before flags get registered.\n if (this.urlFlags[flagName] != null) {\n const flagValue = this.urlFlags[flagName];\n if (!(env().getBool('IS_TEST') || env().getBool('PROD'))) {\n console.warn(`Setting feature override from URL ${flagName}: ${flagValue}.`);\n }\n this.set(flagName, flagValue);\n }\n }\n async getAsync(flagName) {\n if (flagName in this.flags) {\n return this.flags[flagName];\n }\n this.flags[flagName] = await this.evaluateFlag(flagName);\n return this.flags[flagName];\n }\n get(flagName) {\n if (flagName in this.flags) {\n return this.flags[flagName];\n }\n const flagValue = this.evaluateFlag(flagName);\n if (isPromise(flagValue)) {\n throw new Error(`Flag ${flagName} cannot be synchronously evaluated. ` +\n `Please use getAsync() instead.`);\n }\n this.flags[flagName] = flagValue;\n return this.flags[flagName];\n }\n getNumber(flagName) {\n return this.get(flagName);\n }\n getBool(flagName) {\n return this.get(flagName);\n }\n getFlags() {\n return this.flags;\n }\n // For backwards compatibility.\n get features() {\n return this.flags;\n }\n set(flagName, value) {\n if (this.flagRegistry[flagName] == null) {\n throw new Error(`Cannot set flag ${flagName} as it has not been registered.`);\n }\n this.flags[flagName] = value;\n if (this.flagRegistry[flagName].setHook != null) {\n this.flagRegistry[flagName].setHook(value);\n }\n }\n evaluateFlag(flagName) {\n if (this.flagRegistry[flagName] == null) {\n throw new Error(`Cannot evaluate flag '${flagName}': no evaluation function found.`);\n }\n return this.flagRegistry[flagName].evaluationFn();\n }\n setFlags(flags) {\n this.flags = Object.assign({}, flags);\n }\n reset() {\n this.flags = {};\n this.urlFlags = {};\n this.populateURLFlags();\n }\n populateURLFlags() {\n if (typeof this.global === 'undefined' ||\n typeof this.global.location === 'undefined' ||\n typeof this.global.location.search === 'undefined') {\n return;\n }\n const urlParams = this.getQueryParams(this.global.location.search);\n if (TENSORFLOWJS_FLAGS_PREFIX in urlParams) {\n const keyValues = urlParams[TENSORFLOWJS_FLAGS_PREFIX].split(',');\n keyValues.forEach(keyValue => {\n const [key, value] = keyValue.split(':');\n this.urlFlags[key] = parseValue(key, value);\n });\n }\n }\n}\nfunction getQueryParams(queryString) {\n const params = {};\n queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (s, ...t) => {\n decodeParam(params, t[0], t[1]);\n return t.join('=');\n });\n return params;\n}\nfunction decodeParam(params, name, value) {\n params[decodeURIComponent(name)] = decodeURIComponent(value || '');\n}\nfunction parseValue(flagName, value) {\n value = value.toLowerCase();\n if (value === 'true' || value === 'false') {\n return value === 'true';\n }\n else if (`${+value}` === value) {\n return +value;\n }\n throw new Error(`Could not parse value flag value ${value} for flag ${flagName}.`);\n}\n/**\n * Returns the current environment (a global singleton).\n *\n * The environment object contains the evaluated feature values as well as the\n * active platform.\n *\n * @doc {heading: 'Environment'}\n */\nfunction env() {\n return ENV;\n}\nlet ENV = null;\nfunction setEnvironmentGlobal(environment) {\n ENV = environment;\n}\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Note that the identifier globalNameSpace is scoped to this module, but will\n// always resolve to the same global object regardless of how the module is\n// resolved.\n// tslint:disable-next-line:no-any\nlet globalNameSpace;\n// tslint:disable-next-line:no-any\nfunction getGlobalNamespace() {\n if (globalNameSpace == null) {\n // tslint:disable-next-line:no-any\n let ns;\n if (typeof (window) !== 'undefined') {\n ns = window;\n }\n else if (typeof (global) !== 'undefined') {\n ns = global;\n }\n else if (typeof (process) !== 'undefined') {\n ns = process;\n }\n else if (typeof (self) !== 'undefined') {\n ns = self;\n }\n else {\n throw new Error('Could not find a global object');\n }\n globalNameSpace = ns;\n }\n return globalNameSpace;\n}\n// tslint:disable-next-line:no-any\nfunction getGlobalMap() {\n const ns = getGlobalNamespace();\n if (ns._tfGlobals == null) {\n ns._tfGlobals = new Map();\n }\n return ns._tfGlobals;\n}\n/**\n * Returns a globally accessible 'singleton' object.\n *\n * @param key the name of the object\n * @param init a function to initialize to initialize this object\n * the first time it is fetched.\n */\nfunction getGlobal(key, init) {\n const globalMap = getGlobalMap();\n if (globalMap.has(key)) {\n return globalMap.get(key);\n }\n else {\n const singleton = init();\n globalMap.set(key, singleton);\n return globalMap.get(key);\n }\n}\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nfunction warn(...msg) {\n if (!(env().getBool('IS_TEST') || env().getBool('PROD'))) {\n console.warn(...msg);\n }\n}\nfunction log(...msg) {\n if (!(env().getBool('IS_TEST') || env().getBool('PROD'))) {\n console.log(...msg);\n }\n}\n\n/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst kernelRegistry = getGlobal('kernelRegistry', () => new Map());\nconst gradRegistry = getGlobal('gradRegistry', () => new Map());\n/**\n * Returns the kernel function (code) associated with the provided names.\n *\n * @param kernelName The official name of the kernel.\n * @param backendName The official name of the backend.\n */\nfunction getKernel(kernelName, backendName) {\n const key = makeKey(kernelName, backendName);\n return kernelRegistry.get(key);\n}\n/**\n * Returns the registered gradient info associated with the provided kernel.\n * @param kernelName The official TF kernel name.\n */\nfunction getGradient(kernelName) {\n return gradRegistry.get(kernelName);\n}\nfunction getKernelsForBackend(backendName) {\n const it = kernelRegistry.entries();\n const result = [];\n while (true) {\n const { done, value } = it.next();\n if (done) {\n break;\n }\n const [key, config] = value;\n const [backend,] = key.split('_');\n if (backend === backendName) {\n result.push(config);\n }\n }\n return result;\n}\n/**\n * Registers the function (forward pass) for the kernel in a global registry.\n *\n * @param config A config object with the following properties:\n * - `kernelName` The official name of the kernel.\n * - `backendName` The official name of the backend.\n * - `kernelFunc` The function to run during the forward pass of the kernel.\n * - `setupFunc` Optional. Gets called once, after the backend initializes.\n * - `disposeFunc` Optional. Gets called once, right before the backend is\n * disposed.\n */\nfunction registerKernel(config) {\n const { kernelName, backendName } = config;\n const key = makeKey(kernelName, backendName);\n if (kernelRegistry.has(key)) {\n warn(`The kernel '${kernelName}' for backend ` +\n `'${backendName}' is already registered`);\n }\n kernelRegistry.set(key, config);\n}\n/**\n * Registers a gradient function for a given kernel in the global registry,\n * to be used during the back-propagation of that kernel.\n *\n * @param config An object with the following properties:\n * - `kernelName` The name of the kernel that the gradient function is for.\n * - `gradFunc` The function to run during back-propagation.\n */\nfunction registerGradient(config) {\n const { kernelName } = config;\n if (gradRegistry.has(kernelName)) {\n // TODO (yassogba) after 3.0 assess whether we need to keep this gated\n // to debug mode.\n if (env().getBool('DEBUG')) {\n warn(`Overriding the gradient for '${kernelName}'`);\n }\n }\n gradRegistry.set(kernelName, config);\n}\n/**\n * Removes the kernel function from the registry.\n *\n * @param kernelName The official name of the kernel.\n * @param backendName The official name of the backend.\n *\n */\nfunction unregisterKernel(kernelName, backendName) {\n const key = makeKey(kernelName, backendName);\n if (!kernelRegistry.has(key)) {\n throw new Error(`The kernel '${kernelName}' for backend ` +\n `'${backendName}' is not registered`);\n }\n kernelRegistry.delete(key);\n}\n/** Removes the registered gradient from the global registry. */\nfunction unregisterGradient(kernelName) {\n if (!gradRegistry.has(kernelName)) {\n throw new Error(`The gradient '${kernelName}' for backend is not registered`);\n }\n gradRegistry.delete(kernelName);\n}\n/**\n * Finds kernels that have already been registered to a backend and re-registers\n * them for a new backend. Useful for registering custom backends.\n * @param registeredBackendName Already registered backend.\n * @param newBackendName New backend.\n */\nfunction copyRegisteredKernels(registeredBackendName, newBackendName) {\n const kernels = getKernelsForBackend(registeredBackendName);\n kernels.forEach(kernelConfig => {\n const newKernelConfig = Object.assign({}, kernelConfig, { backendName: newBackendName });\n registerKernel(newKernelConfig);\n });\n}\nfunction makeKey(kernelName, backendName) {\n return `${backendName}_${kernelName}`;\n}\n\n/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line\nconst Long = \n// tslint:disable-next-line\nLongExports__default || LongExports;\nfunction hexToLong(hex) {\n return Long.fromString(hex, true, 16);\n}\n// Some primes between 2^63 and 2^64 for various uses.\n// Hex 0xc3a5c85c97cb3127\nconst k0 = hexToLong('c3a5c85c97cb3127');\n// Hex 0xb492b66fbe98f273\nconst k1 = hexToLong('b492b66fbe98f273');\n// Hex 0x9ae16a3b2f90404f\nconst k2 = hexToLong('9ae16a3b2f90404f');\nfunction shiftMix(val) {\n return val.xor(val.shru(47));\n}\nfunction fetch(s, offset, numBytes) {\n const bytes = s.slice(offset, offset + numBytes);\n return Long.fromBytes(Array.from(bytes), true, true);\n}\nfunction fetch64(s, offset) {\n return fetch(s, offset, 8);\n}\nfunction fetch32(s, offset) {\n return fetch(s, offset, 4);\n}\nfunction rotate64(val, shift) {\n // Avoid shifting by 64: doing so yields an undefined result.\n return shift === 0 ? val : val.shru(shift).or(val.shl(64 - shift));\n}\nfunction hashLen16(u, v, mul = hexToLong('9ddfea08eb382d69')) {\n // Murmur-inspired hashing.\n let a = u.xor(v).mul(mul);\n a = a.xor(a.shru(47));\n let b = v.xor(a).mul(mul);\n b = b.xor(b.shru(47));\n b = b.mul(mul);\n return b;\n}\n// Return a 16-byte hash for 48 bytes. Quick and dirty.\n// Callers do best to use \"random-looking\" values for a and b.\nfunction weakHashLen32WithSeeds(w, x, y, z, a, b) {\n a = a.add(w);\n b = rotate64(b.add(a).add(z), 21);\n const c = a;\n a = a.add(x);\n a = a.add(y);\n b = b.add(rotate64(a, 44));\n return [a.add(z), b.add(c)];\n}\nfunction weakHashLen32WithSeedsStr(s, offset, a, b) {\n return weakHashLen32WithSeeds(fetch64(s, offset), fetch64(s, offset + 8), fetch64(s, offset + 16), fetch64(s, offset + 24), a, b);\n}\nfunction hashLen0to16(s, len = s.length) {\n if (len >= 8) {\n const mul = k2.add(len * 2);\n const a = fetch64(s, 0).add(k2);\n const b = fetch64(s, len - 8);\n const c = rotate64(b, 37).mul(mul).add(a);\n const d = rotate64(a, 25).add(b).mul(mul);\n return hashLen16(c, d, mul);\n }\n if (len >= 4) {\n const mul = k2.add(len * 2);\n const a = fetch32(s, 0);\n return hashLen16(a.shl(3).add(len), fetch32(s, len - 4), mul);\n }\n if (len > 0) {\n const a = s[0];\n const b = s[len >> 1];\n const c = s[len - 1];\n const y = a + (b << 8);\n const z = len + (c << 2);\n return shiftMix(k2.mul(y).xor(k0.mul(z))).mul(k2);\n }\n return k2;\n}\nfunction hashLen17to32(s, len = s.length) {\n const mul = k2.add(len * 2);\n const a = fetch64(s, 0).mul(k1);\n const b = fetch64(s, 8);\n const c = fetch64(s, len - 8).mul(mul);\n const d = fetch64(s, len - 16).mul(k2);\n return hashLen16(rotate64(a.add(b), 43).add(rotate64(c, 30)).add(d), a.add(rotate64(b.add(k2), 18)).add(c), mul);\n}\nfunction hashLen33to64(s, len = s.length) {\n const mul = k2.add(len * 2);\n const a = fetch64(s, 0).mul(k2);\n const b = fetch64(s, 8);\n const c = fetch64(s, len - 8).mul(mul);\n const d = fetch64(s, len - 16).mul(k2);\n const y = rotate64(a.add(b), 43).add(rotate64(c, 30)).add(d);\n const z = hashLen16(y, a.add(rotate64(b.add(k2), 18)).add(c), mul);\n const e = fetch64(s, 16).mul(mul);\n const f = fetch64(s, 24);\n const g = y.add(fetch64(s, len - 32)).mul(mul);\n const h = z.add(fetch64(s, len - 24)).mul(mul);\n return hashLen16(rotate64(e.add(f), 43).add(rotate64(g, 30)).add(h), e.add(rotate64(f.add(a), 18)).add(g), mul);\n}\nfunction fingerPrint64(s, len = s.length) {\n const seed = Long.fromNumber(81, true);\n if (len <= 32) {\n if (len <= 16) {\n return hashLen0to16(s, len);\n }\n else {\n return hashLen17to32(s, len);\n }\n }\n else if (len <= 64) {\n return hashLen33to64(s, len);\n }\n // For strings over 64 bytes we loop. Internal state consists of\n // 56 bytes: v, w, x, y, and z.\n let x = seed;\n let y = seed.mul(k1).add(113);\n let z = shiftMix(y.mul(k2).add(113)).mul(k2);\n let v = [Long.UZERO, Long.UZERO];\n let w = [Long.UZERO, Long.UZERO];\n x = x.mul(k2).add(fetch64(s, 0));\n let offset = 0;\n // Set end so that after the loop we have 1 to 64 bytes left to process.\n const end = ((len - 1) >> 6) * 64;\n const last64 = end + ((len - 1) & 63) - 63;\n do {\n x = rotate64(x.add(y).add(v[0]).add(fetch64(s, offset + 8)), 37).mul(k1);\n y = rotate64(y.add(v[1]).add(fetch64(s, offset + 48)), 42).mul(k1);\n x = x.xor(w[1]);\n y = y.add(v[0]).add(fetch64(s, offset + 40));\n z = rotate64(z.add(w[0]), 33).mul(k1);\n v = weakHashLen32WithSeedsStr(s, offset, v[1].mul(k1), x.add(w[0]));\n w = weakHashLen32WithSeedsStr(s, offset + 32, z.add(w[1]), y.add(fetch64(s, offset + 16)));\n [z, x] = [x, z];\n offset += 64;\n } while (offset !== end);\n const mul = k1.add(z.and(0xff).shl(1));\n // Point to the last 64 bytes of input.\n offset = last64;\n w[0] = w[0].add((len - 1) & 63);\n v[0] = v[0].add(w[0]);\n w[0] = w[0].add(v[0]);\n x = rotate64(x.add(y).add(v[0]).add(fetch64(s, offset + 8)), 37).mul(mul);\n y = rotate64(y.add(v[1]).add(fetch64(s, offset + 48)), 42).mul(mul);\n x = x.xor(w[1].mul(9));\n y = y.add(v[0].mul(9).add(fetch64(s, offset + 40)));\n z = rotate64(z.add(w[0]), 33).mul(mul);\n v = weakHashLen32WithSeedsStr(s, offset, v[1].mul(mul), x.add(w[0]));\n w = weakHashLen32WithSeedsStr(s, offset + 32, z.add(w[1]), y.add(fetch64(s, offset + 16)));\n [z, x] = [x, z];\n return hashLen16(hashLen16(v[0], w[0], mul).add(shiftMix(y).mul(k0)).add(z), hashLen16(v[1], w[1], mul).add(x), mul);\n}\n\n/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Create typed array for scalar value. Used for storing in `DataStorage`.\n */\nfunction createScalarValue(value, dtype) {\n if (dtype === 'string') {\n return encodeString(value);\n }\n return toTypedArray([value], dtype);\n}\nfunction noConversionNeeded(a, dtype) {\n return (a instanceof Float32Array && dtype === 'float32') ||\n (a instanceof Int32Array && dtype === 'int32') ||\n (a instanceof Uint8Array && dtype === 'bool');\n}\nfunction toTypedArray(a, dtype) {\n if (dtype === 'string') {\n throw new Error('Cannot convert a string[] to a TypedArray');\n }\n if (Array.isArray(a)) {\n a = flatten(a);\n }\n if (env().getBool('DEBUG')) {\n checkConversionForErrors(a, dtype);\n }\n if (noConversionNeeded(a, dtype)) {\n return a;\n }\n if (dtype == null || dtype === 'float32' || dtype === 'complex64') {\n return new Float32Array(a);\n }\n else if (dtype === 'int32') {\n return new Int32Array(a);\n }\n else if (dtype === 'bool') {\n const bool = new Uint8Array(a.length);\n for (let i = 0; i < bool.length; ++i) {\n if (Math.round(a[i]) !== 0) {\n bool[i] = 1;\n }\n }\n return bool;\n }\n else {\n throw new Error(`Unknown data type ${dtype}`);\n }\n}\n/**\n * Returns the current high-resolution time in milliseconds relative to an\n * arbitrary time in the past. It works across different platforms (node.js,\n * browsers).\n *\n * ```js\n * console.log(tf.util.now());\n * ```\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nfunction now() {\n return env().platform.now();\n}\n/**\n * Returns a platform-specific implementation of\n * [`fetch`](https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API).\n *\n * If `fetch` is defined on the global object (`window`, `process`, etc.),\n * `tf.util.fetch` returns that function.\n *\n * If not, `tf.util.fetch` returns a platform-specific solution.\n *\n * ```js\n * const resource = await tf.util.fetch('https://unpkg.com/@tensorflow/tfjs');\n * // handle response\n * ```\n *\n * @doc {heading: 'Util'}\n */\nfunction fetch$1(path, requestInits) {\n return env().platform.fetch(path, requestInits);\n}\n/**\n * Encodes the provided string into bytes using the provided encoding scheme.\n *\n * @param s The string to encode.\n * @param encoding The encoding scheme. Defaults to utf-8.\n *\n * @doc {heading: 'Util'}\n */\nfunction encodeString(s, encoding = 'utf-8') {\n encoding = encoding || 'utf-8';\n return env().platform.encode(s, encoding);\n}\n/**\n * Decodes the provided bytes into a string using the provided encoding scheme.\n * @param bytes The bytes to decode.\n *\n * @param encoding The encoding scheme. Defaults to utf-8.\n *\n * @doc {heading: 'Util'}\n */\nfunction decodeString(bytes, encoding = 'utf-8') {\n encoding = encoding || 'utf-8';\n return env().platform.decode(bytes, encoding);\n}\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nclass Profiler {\n constructor(backendTimer, logger) {\n this.backendTimer = backendTimer;\n this.logger = logger;\n if (logger == null) {\n this.logger = new Logger();\n }\n }\n profileKernel(kernelName, inputs, f) {\n let outputs;\n const holdResultWrapperFn = () => {\n outputs = f();\n };\n let timer;\n const start = now();\n if (this.backendTimer.timerAvailable()) {\n timer = this.backendTimer.time(holdResultWrapperFn);\n }\n else {\n holdResultWrapperFn();\n for (const output of outputs) {\n output.dataSync();\n }\n timer = Promise.resolve({ kernelMs: now() - start });\n }\n if (env().getBool('CHECK_COMPUTATION_FOR_ERRORS')) {\n for (let i = 0; i < outputs.length; i++) {\n const output = outputs[i];\n // Dangling promise here because we don't want to propagate up\n // asynchronicity.\n output.data().then(tensorVals => {\n checkComputationForErrors(tensorVals, output.dtype, kernelName);\n });\n }\n }\n const kernelProfile = {\n kernelName,\n outputs,\n inputs,\n timeMs: timer.then(timing => timing.kernelMs),\n extraInfo: timer.then(timing => timing.getExtraProfileInfo != null ?\n timing.getExtraProfileInfo() :\n '')\n };\n return kernelProfile;\n }\n logKernelProfile(kernelProfile) {\n const { kernelName, outputs, timeMs, inputs, extraInfo } = kernelProfile;\n outputs.forEach(result => {\n Promise.all([result.data(), timeMs, extraInfo]).then(valueContainer => {\n this.logger.logKernelProfile(kernelName, result, valueContainer[0], valueContainer[1], inputs, valueContainer[2]);\n });\n });\n }\n}\nfunction checkComputationForErrors(vals, dtype, kernelName) {\n if (dtype !== 'float32') {\n // Only floating point computations will generate NaN values\n return false;\n }\n for (let i = 0; i < vals.length; i++) {\n const num = vals[i];\n if (isNaN(num) || !isFinite(num)) {\n // Throwing custom exception so behavior is testable.\n console.warn(`Found ${num} in the result of '${kernelName}'`);\n return true;\n }\n }\n return false;\n}\nclass Logger {\n logKernelProfile(name, result, vals, timeMs, inputs, extraInfo) {\n const time = typeof timeMs === 'number' ? rightPad(`${timeMs}ms`, 9) :\n timeMs['error'];\n const paddedName = rightPad(name, 25);\n const rank = result.rank;\n const size = result.size;\n const shape = rightPad(result.shape.toString(), 14);\n let inputShapesDescription = '';\n for (const name in inputs) {\n const input = inputs[name];\n if (input != null) {\n // The input might be a non-tensor (e.g HTMLImageElement), in which case\n // we claim the output shape as input shape.\n const inputShape = input.shape || result.shape;\n const inputRank = inputShape.length;\n inputShapesDescription +=\n `${name}: ${inputRank}D ${inputRank > 0 ? inputShape : ''} `;\n }\n }\n console.log(`%c${paddedName}\\t%c${time}\\t%c${rank}D ${shape}\\t%c${size}\\t%c${inputShapesDescription}\\t%c${extraInfo}`, 'font-weight:bold', 'color:red', 'color:blue', 'color: orange', 'color: green', 'color: steelblue');\n }\n}\n\n/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes a list of TapeNodes that connect x to y, filtering everything else\n * out and preserving the order of the original tape elements.\n *\n * @param tape The tape elements to filter.\n * @param xs The input Tensors.\n * @param y The output Tensor.\n */\nfunction getFilteredNodesXToY(tape, xs, y) {\n // Forward pass to compute all the nodes and Tensors that are transitively a\n // function of x.\n const tensorsFromX = {};\n const nodesFromX = {};\n for (let i = 0; i < xs.length; i++) {\n tensorsFromX[xs[i].id] = true;\n }\n for (let i = 0; i < tape.length; i++) {\n const node = tape[i];\n const nodeInputs = node.inputs;\n for (const inputName in nodeInputs) {\n const input = nodeInputs[inputName];\n let anyInputFromX = false;\n for (let j = 0; j < xs.length; j++) {\n if (tensorsFromX[input.id]) {\n node.outputs.forEach(output => tensorsFromX[output.id] = true);\n anyInputFromX = true;\n nodesFromX[node.id] = true;\n break;\n }\n }\n if (anyInputFromX) {\n break;\n }\n }\n }\n // Backward pass to find all of the nodes and Tensors that lead to y.\n const tensorsLeadToY = {};\n tensorsLeadToY[y.id] = true;\n const nodesToY = {};\n for (let i = tape.length - 1; i >= 0; i--) {\n const node = tape[i];\n const nodeInputs = node.inputs;\n // If any of the outputs lead to y, mark all of the inputs as leading to y.\n for (let j = 0; j < node.outputs.length; j++) {\n if (tensorsLeadToY[node.outputs[j].id]) {\n for (const inputName in nodeInputs) {\n tensorsLeadToY[nodeInputs[inputName].id] = true;\n nodesToY[node.id] = true;\n }\n break;\n }\n }\n }\n // Return the paths that come from x and lead to y.\n const filteredTape = [];\n for (let i = 0; i < tape.length; i++) {\n const node = tape[i];\n if (nodesFromX[node.id] && nodesToY[node.id]) {\n // Prune the inputs from the node that aren't a function of x.\n const prunedInputs = {};\n for (const inputName in node.inputs) {\n const nodeInput = node.inputs[inputName];\n if (tensorsFromX[nodeInput.id]) {\n prunedInputs[inputName] = nodeInput;\n }\n }\n // Copy the node and overwrite inputsAndArgs to the pruned version.\n const prunedNode = Object.assign({}, node);\n prunedNode.inputs = prunedInputs;\n prunedNode.outputs = node.outputs;\n filteredTape.push(prunedNode);\n }\n }\n return filteredTape;\n}\n/**\n * Backpropagate gradients through the filtered TapeNodes.\n *\n * @param tensorAccumulatedGradientMap A map of Tensor to its gradient. This map\n * is mutated by this method.\n * @param filteredTape The filtered TapeNodes to backprop through.\n */\nfunction backpropagateGradients(tensorAccumulatedGradientMap, filteredTape, tidy, add) {\n // Walk the tape backward and keep a map of Tensor to its gradient.\n for (let i = filteredTape.length - 1; i >= 0; i--) {\n const node = filteredTape[i];\n const dys = [];\n node.outputs.forEach(o => {\n const gradTensor = tensorAccumulatedGradientMap[o.id];\n if (gradTensor != null) {\n dys.push(gradTensor);\n }\n else {\n // This particular output is not in the back-propagation subgraph, so it\n // does not affect the final output, thus we put null for its dy.\n dys.push(null);\n }\n });\n if (node.gradient == null) {\n throw new Error(`Cannot compute gradient: gradient function not found ` +\n `for ${node.kernelName}.`);\n }\n // Backprop dy through this node and accumulate gradients over the inputs.\n const inputGradients = node.gradient(dys);\n for (const inputName in node.inputs) {\n if (!(inputName in inputGradients)) {\n throw new Error(`Cannot backprop through input ${inputName}. ` +\n `Available gradients found: ${Object.keys(inputGradients)}.`);\n }\n // Call the gradient function.\n const dx = tidy(() => inputGradients[inputName]());\n if (dx.dtype !== 'float32') {\n throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input ` +\n `${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);\n }\n const x = node.inputs[inputName];\n if (!arraysEqual(dx.shape, x.shape)) {\n throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input ` +\n `'${inputName}' has shape '${dx.shape}', which does not match ` +\n `the shape of the input '${x.shape}'`);\n }\n if (tensorAccumulatedGradientMap[x.id] == null) {\n tensorAccumulatedGradientMap[x.id] = dx;\n }\n else {\n const curGradient = tensorAccumulatedGradientMap[x.id];\n tensorAccumulatedGradientMap[x.id] = add(curGradient, dx);\n curGradient.dispose();\n }\n }\n }\n}\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Maximum number of values before we decide to show ellipsis.\nconst FORMAT_LIMIT_NUM_VALS = 20;\n// Number of first and last values to show when displaying a, b,...,y, z.\nconst FORMAT_NUM_FIRST_LAST_VALS = 3;\n// Number of significant digits to show.\nconst FORMAT_NUM_SIG_DIGITS = 7;\nfunction tensorToString(vals, shape, dtype, verbose) {\n const strides = computeStrides(shape);\n const padPerCol = computeMaxSizePerColumn(vals, shape, dtype, strides);\n const rank = shape.length;\n const valsLines = subTensorToString(vals, shape, dtype, strides, padPerCol);\n const lines = ['Tensor'];\n if (verbose) {\n lines.push(` dtype: ${dtype}`);\n lines.push(` rank: ${rank}`);\n lines.push(` shape: [${shape}]`);\n lines.push(` values:`);\n }\n lines.push(valsLines.map(l => ' ' + l).join('\\n'));\n return lines.join('\\n');\n}\nfunction computeMaxSizePerColumn(vals, shape, dtype, strides) {\n const n = sizeFromShape(shape);\n const numCols = strides[strides.length - 1];\n const padPerCol = new Array(numCols).fill(0);\n const rank = shape.length;\n const valuesOrTuples = dtype === 'complex64' ? createComplexTuples(vals) : vals;\n if (rank > 1) {\n for (let row = 0; row < n / numCols; row++) {\n const offset = row * numCols;\n for (let j = 0; j < numCols; j++) {\n padPerCol[j] = Math.max(padPerCol[j], valToString(valuesOrTuples[offset + j], 0, dtype).length);\n }\n }\n }\n return padPerCol;\n}\nfunction valToString(val, pad, dtype) {\n let valStr;\n if (Array.isArray(val)) {\n valStr = `${parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS))} + ` +\n `${parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS))}j`;\n }\n else if (isString(val)) {\n valStr = `'${val}'`;\n }\n else if (dtype === 'bool') {\n valStr = boolNumToString(val);\n }\n else {\n valStr = parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS)).toString();\n }\n return rightPad(valStr, pad);\n}\nfunction boolNumToString(v) {\n return v === 0 ? 'false' : 'true';\n}\nfunction subTensorToString(vals, shape, dtype, strides, padPerCol, isLast = true) {\n const storagePerElement = dtype === 'complex64' ? 2 : 1;\n const size = shape[0];\n const rank = shape.length;\n if (rank === 0) {\n if (dtype === 'complex64') {\n const complexTuple = createComplexTuples(vals);\n return [valToString(complexTuple[0], 0, dtype)];\n }\n if (dtype === 'bool') {\n return [boolNumToString(vals[0])];\n }\n return [vals[0].toString()];\n }\n if (rank === 1) {\n if (size > FORMAT_LIMIT_NUM_VALS) {\n const firstValsSize = FORMAT_NUM_FIRST_LAST_VALS * storagePerElement;\n let firstVals = Array.from(vals.slice(0, firstValsSize));\n let lastVals = Array.from(vals.slice((size - FORMAT_NUM_FIRST_LAST_VALS) * storagePerElement, size * storagePerElement));\n if (dtype === 'complex64') {\n firstVals = createComplexTuples(firstVals);\n lastVals = createComplexTuples(lastVals);\n }\n return [\n '[' +\n firstVals.map((x, i) => valToString(x, padPerCol[i], dtype))\n .join(', ') +\n ', ..., ' +\n lastVals\n .map((x, i) => valToString(x, padPerCol[size - FORMAT_NUM_FIRST_LAST_VALS + i], dtype))\n .join(', ') +\n ']'\n ];\n }\n const displayVals = dtype === 'complex64' ? createComplexTuples(vals) :\n Array.from(vals);\n return [\n '[' +\n displayVals.map((x, i) => valToString(x, padPerCol[i], dtype))\n .join(', ') +\n ']'\n ];\n }\n // The array is rank 2 or more.\n const subshape = shape.slice(1);\n const substrides = strides.slice(1);\n const stride = strides[0] * storagePerElement;\n const lines = [];\n if (size > FORMAT_LIMIT_NUM_VALS) {\n for (let i = 0; i < FORMAT_NUM_FIRST_LAST_VALS; i++) {\n const start = i * stride;\n const end = start + stride;\n lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, false /* isLast */));\n }\n lines.push('...');\n for (let i = size - FORMAT_NUM_FIRST_LAST_VALS; i < size; i++) {\n const start = i * stride;\n const end = start + stride;\n lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1 /* isLast */));\n }\n }\n else {\n for (let i = 0; i < size; i++) {\n const start = i * stride;\n const end = start + stride;\n lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1 /* isLast */));\n }\n }\n const sep = rank === 2 ? ',' : '';\n lines[0] = '[' + lines[0] + sep;\n for (let i = 1; i < lines.length - 1; i++) {\n lines[i] = ' ' + lines[i] + sep;\n }\n let newLineSep = ',\\n';\n for (let i = 2; i < rank; i++) {\n newLineSep += '\\n';\n }\n lines[lines.length - 1] =\n ' ' + lines[lines.length - 1] + ']' + (isLast ? '' : newLineSep);\n return lines;\n}\nfunction createComplexTuples(vals) {\n const complexTuples = [];\n for (let i = 0; i < vals.length; i += 2) {\n complexTuples.push([vals[i], vals[i + 1]]);\n }\n return complexTuples;\n}\n\n/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * A mutable object, similar to `tf.Tensor`, that allows users to set values\n * at locations before converting to an immutable `tf.Tensor`.\n *\n * See `tf.buffer` for creating a tensor buffer.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nclass TensorBuffer {\n constructor(shape, dtype, values) {\n this.dtype = dtype;\n this.shape = shape.slice();\n this.size = sizeFromShape(shape);\n if (values != null) {\n const n = values.length;\n assert(n === this.size, () => `Length of values '${n}' does not match the size ` +\n `inferred by the shape '${this.size}'.`);\n }\n if (dtype === 'complex64') {\n throw new Error(`complex64 dtype TensorBuffers are not supported. Please create ` +\n `a TensorBuffer for the real and imaginary parts separately and ` +\n `call tf.complex(real, imag).`);\n }\n this.values = values || getArrayFromDType(dtype, this.size);\n this.strides = computeStrides(shape);\n }\n /**\n * Sets a value in the buffer at a given location.\n *\n * @param value The value to set.\n * @param locs The location indices.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\n set(value, ...locs) {\n if (locs.length === 0) {\n locs = [0];\n }\n assert(locs.length === this.rank, () => `The number of provided coordinates (${locs.length}) must ` +\n `match the rank (${this.rank})`);\n const index = this.locToIndex(locs);\n this.values[index] = value;\n }\n /**\n * Returns the value in the buffer at the provided location.\n *\n * @param locs The location indices.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\n get(...locs) {\n if (locs.length === 0) {\n locs = [0];\n }\n let i = 0;\n for (const loc of locs) {\n if (loc < 0 || loc >= this.shape[i]) {\n const msg = `Requested out of range element at ${locs}. ` +\n ` Buffer shape=${this.shape}`;\n throw new Error(msg);\n }\n i++;\n }\n let index = locs[locs.length - 1];\n for (let i = 0; i < locs.length - 1; ++i) {\n index += this.strides[i] * locs[i];\n }\n return this.values[index];\n }\n locToIndex(locs) {\n if (this.rank === 0) {\n return 0;\n }\n else if (this.rank === 1) {\n return locs[0];\n }\n let index = locs[locs.length - 1];\n for (let i = 0; i < locs.length - 1; ++i) {\n index += this.strides[i] * locs[i];\n }\n return index;\n }\n indexToLoc(index) {\n if (this.rank === 0) {\n return [];\n }\n else if (this.rank === 1) {\n return [index];\n }\n const locs = new Array(this.shape.length);\n for (let i = 0; i < locs.length - 1; ++i) {\n locs[i] = Math.floor(index / this.strides[i]);\n index -= locs[i] * this.strides[i];\n }\n locs[locs.length - 1] = index;\n return locs;\n }\n get rank() {\n return this.shape.length;\n }\n /**\n * Creates an immutable `tf.Tensor` object from the buffer.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\n toTensor() {\n return trackerFn().makeTensor(this.values, this.shape, this.dtype);\n }\n}\n// For tracking tensor creation and disposal.\nlet trackerFn = null;\n// Used by chaining methods to call into ops.\nlet opHandler = null;\n// Used to warn about deprecated methods.\nlet deprecationWarningFn = null;\n// This here so that we can use this method on dev branches and keep the\n// functionality at master.\n// tslint:disable-next-line:no-unused-expression\n[deprecationWarningFn];\n/**\n * An external consumer can register itself as the tensor tracker. This way\n * the Tensor class can notify the tracker for every tensor created and\n * disposed.\n */\nfunction setTensorTracker(fn) {\n trackerFn = fn;\n}\n/**\n * An external consumer can register itself as the op handler. This way the\n * Tensor class can have chaining methods that call into ops via the op\n * handler.\n */\nfunction setOpHandler(handler) {\n opHandler = handler;\n}\n/**\n * Sets the deprecation warning function to be used by this file. This way the\n * Tensor class can be a leaf but still use the environment.\n */\nfunction setDeprecationWarningFn(fn) {\n deprecationWarningFn = fn;\n}\n/**\n * A `tf.Tensor` object represents an immutable, multidimensional array of\n * numbers that has a shape and a data type.\n *\n * For performance reasons, functions that create tensors do not necessarily\n * perform a copy of the data passed to them (e.g. if the data is passed as a\n * `Float32Array`), and changes to the data will change the tensor. This is not\n * a feature and is not supported. To avoid this behavior, use the tensor before\n * changing the input data or create a copy with `copy = tf.add(yourTensor, 0)`.\n *\n * See `tf.tensor` for details on how to create a `tf.Tensor`.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nclass Tensor {\n constructor(shape, dtype, dataId, id) {\n /** Whether this tensor has been globally kept. */\n this.kept = false;\n this.isDisposedInternal = false;\n this.shape = shape.slice();\n this.dtype = dtype || 'float32';\n this.size = sizeFromShape(shape);\n this.strides = computeStrides(shape);\n this.dataId = dataId;\n this.id = id;\n this.rankType = (this.rank < 5 ? this.rank.toString() : 'higher');\n }\n get rank() {\n return this.shape.length;\n }\n /**\n * Returns a promise of `tf.TensorBuffer` that holds the underlying data.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n async buffer() {\n const vals = await this.data();\n return opHandler.buffer(this.shape, this.dtype, vals);\n }\n /**\n * Returns a `tf.TensorBuffer` that holds the underlying data.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n bufferSync() {\n return opHandler.buffer(this.shape, this.dtype, this.dataSync());\n }\n /**\n * Returns the tensor data as a nested array. The transfer of data is done\n * asynchronously.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n async array() {\n const vals = await this.data();\n return toNestedArray(this.shape, vals, this.dtype === 'complex64');\n }\n /**\n * Returns the tensor data as a nested array. The transfer of data is done\n * synchronously.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n arraySync() {\n return toNestedArray(this.shape, this.dataSync(), this.dtype === 'complex64');\n }\n /**\n * Asynchronously downloads the values from the `tf.Tensor`. Returns a\n * promise of `TypedArray` that resolves when the computation has finished.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n async data() {\n this.throwIfDisposed();\n const data = trackerFn().read(this.dataId);\n if (this.dtype === 'string') {\n const bytes = await data;\n try {\n return bytes.map(b => decodeString(b));\n }\n catch (_a) {\n throw new Error('Failed to decode the string bytes into utf-8. ' +\n 'To get the original bytes, call tensor.bytes().');\n }\n }\n return data;\n }\n /**\n * Copy the tensor's data to a new GPU resource. Comparing to the `dataSync()`\n * and `data()`, this method prevents data from being downloaded to CPU.\n *\n * For WebGL backend, the data will be stored on a densely packed texture.\n * This means that the texture will use the RGBA channels to store value.\n *\n * @param options:\n * For WebGL,\n * - customTexShape: Optional. If set, will use the user defined\n * texture shape to create the texture.\n *\n * @returns For WebGL backend, a GPUData contains the new texture and\n * its information.\n * {\n * tensorRef: The tensor that is associated with this texture,\n * texture: WebGLTexture,\n * texShape: [number, number] // [height, width]\n * }\n * Remember to dispose the GPUData after it is used by\n * `res.tensorRef.dispose()`.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n dataToGPU(options) {\n this.throwIfDisposed();\n return trackerFn().readToGPU(this.dataId, options);\n }\n /**\n * Synchronously downloads the values from the `tf.Tensor`. This blocks the\n * UI thread until the values are ready, which can cause performance issues.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n dataSync() {\n this.throwIfDisposed();\n const data = trackerFn().readSync(this.dataId);\n if (this.dtype === 'string') {\n try {\n return data.map(b => decodeString(b));\n }\n catch (_a) {\n throw new Error('Failed to decode the string bytes into utf-8. ' +\n 'To get the original bytes, call tensor.bytes().');\n }\n }\n return data;\n }\n /** Returns the underlying bytes of the tensor's data. */\n async bytes() {\n this.throwIfDisposed();\n const data = await trackerFn().read(this.dataId);\n if (this.dtype === 'string') {\n return data;\n }\n else {\n return new Uint8Array(data.buffer);\n }\n }\n /**\n * Disposes `tf.Tensor` from memory.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n dispose() {\n if (this.isDisposed) {\n return;\n }\n trackerFn().disposeTensor(this);\n this.isDisposedInternal = true;\n }\n get isDisposed() {\n return this.isDisposedInternal;\n }\n throwIfDisposed() {\n if (this.isDisposed) {\n throw new Error(`Tensor is disposed.`);\n }\n }\n /**\n * Prints the `tf.Tensor`. See `tf.print` for details.\n *\n * @param verbose Whether to print verbose information about the tensor,\n * including dtype and size.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n print(verbose = false) {\n return opHandler.print(this, verbose);\n }\n /**\n * Returns a copy of the tensor. See `tf.clone` for details.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n clone() {\n this.throwIfDisposed();\n return opHandler.clone(this);\n }\n /**\n * Returns a human-readable description of the tensor. Useful for logging.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n toString(verbose = false) {\n const vals = this.dataSync();\n return tensorToString(vals, this.shape, this.dtype, verbose);\n }\n cast(dtype) {\n this.throwIfDisposed();\n return opHandler.cast(this, dtype);\n }\n variable(trainable = true, name, dtype) {\n this.throwIfDisposed();\n return trackerFn().makeVariable(this, trainable, name, dtype);\n }\n}\nObject.defineProperty(Tensor, Symbol.hasInstance, {\n value: (instance) => {\n // Implementation note: we should use properties of the object that will be\n // defined before the constructor body has finished executing (methods).\n // This is because when this code is transpiled by babel, babel will call\n // classCallCheck before the constructor body is run.\n // See https://github.com/tensorflow/tfjs/issues/3384 for backstory.\n return !!instance && instance.data != null && instance.dataSync != null &&\n instance.throwIfDisposed != null;\n }\n});\nfunction getGlobalTensorClass() {\n // Use getGlobal so that we can augment the Tensor class across package\n // boundaries becase the node resolution alg may result in different modules\n // being returned for this file depending on the path they are loaded from.\n return getGlobal('Tensor', () => {\n return Tensor;\n });\n}\n// Global side effect. Cache global reference to Tensor class\ngetGlobalTensorClass();\n/**\n * A mutable `tf.Tensor`, useful for persisting state, e.g. for training.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nclass Variable extends Tensor {\n constructor(initialValue, trainable, name, tensorId) {\n super(initialValue.shape, initialValue.dtype, initialValue.dataId, tensorId);\n this.trainable = trainable;\n this.name = name;\n }\n /**\n * Assign a new `tf.Tensor` to this variable. The new `tf.Tensor` must have\n * the same shape and dtype as the old `tf.Tensor`.\n *\n * @param newValue New tensor to be assigned to this variable.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n assign(newValue) {\n if (newValue.dtype !== this.dtype) {\n throw new Error(`dtype of the new value (${newValue.dtype}) and ` +\n `previous value (${this.dtype}) must match`);\n }\n if (!arraysEqual(newValue.shape, this.shape)) {\n throw new Error(`shape of the new value (${newValue.shape}) and ` +\n `previous value (${this.shape}) must match`);\n }\n trackerFn().disposeTensor(this);\n this.dataId = newValue.dataId;\n trackerFn().incRef(this, null /* backend */);\n }\n dispose() {\n trackerFn().disposeVariable(this);\n this.isDisposedInternal = true;\n }\n}\nObject.defineProperty(Variable, Symbol.hasInstance, {\n value: (instance) => {\n return instance instanceof Tensor && instance.assign != null &&\n instance.assign instanceof Function;\n }\n});\n\n/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nvar Rank;\n(function (Rank) {\n Rank[\"R0\"] = \"R0\";\n Rank[\"R1\"] = \"R1\";\n Rank[\"R2\"] = \"R2\";\n Rank[\"R3\"] = \"R3\";\n Rank[\"R4\"] = \"R4\";\n Rank[\"R5\"] = \"R5\";\n Rank[\"R6\"] = \"R6\";\n})(Rank || (Rank = {}));\n// Looks for upcasting types. Used, for example, in operations with mixed dtype\n// inputs.\nvar UpcastInt32AndMap;\n(function (UpcastInt32AndMap) {\n UpcastInt32AndMap[\"float32\"] = \"float32\";\n UpcastInt32AndMap[\"int32\"] = \"int32\";\n UpcastInt32AndMap[\"bool\"] = \"int32\";\n UpcastInt32AndMap[\"complex64\"] = \"complex64\";\n})(UpcastInt32AndMap || (UpcastInt32AndMap = {}));\nvar UpcastBoolAndMap;\n(function (UpcastBoolAndMap) {\n UpcastBoolAndMap[\"float32\"] = \"float32\";\n UpcastBoolAndMap[\"int32\"] = \"int32\";\n UpcastBoolAndMap[\"bool\"] = \"bool\";\n UpcastBoolAndMap[\"complex64\"] = \"complex64\";\n})(UpcastBoolAndMap || (UpcastBoolAndMap = {}));\nvar UpcastFloat32AndMap;\n(function (UpcastFloat32AndMap) {\n UpcastFloat32AndMap[\"float32\"] = \"float32\";\n UpcastFloat32AndMap[\"int32\"] = \"float32\";\n UpcastFloat32AndMap[\"bool\"] = \"float32\";\n UpcastFloat32AndMap[\"complex64\"] = \"complex64\";\n})(UpcastFloat32AndMap || (UpcastFloat32AndMap = {}));\nvar UpcastComplex64AndMap;\n(function (UpcastComplex64AndMap) {\n UpcastComplex64AndMap[\"float32\"] = \"complex64\";\n UpcastComplex64AndMap[\"int32\"] = \"complex64\";\n UpcastComplex64AndMap[\"bool\"] = \"complex64\";\n UpcastComplex64AndMap[\"complex64\"] = \"complex64\";\n})(UpcastComplex64AndMap || (UpcastComplex64AndMap = {}));\nconst upcastTypeMap = {\n 'float32': UpcastFloat32AndMap,\n 'int32': UpcastInt32AndMap,\n 'bool': UpcastBoolAndMap,\n 'complex64': UpcastComplex64AndMap\n};\nfunction upcastType(typeA, typeB) {\n if (typeA === 'string' || typeB === 'string') {\n if (typeA === 'string' && typeB === 'string') {\n return 'string';\n }\n throw new Error(`Can not upcast ${typeA} with ${typeB}`);\n }\n return upcastTypeMap[typeA][typeB];\n}\n/** Returns the output type after summation. */\nfunction sumOutType(type) {\n return upcastType(type, 'int32');\n}\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nfunction makeTypesMatch(a, b) {\n if (a.dtype === b.dtype) {\n return [a, b];\n }\n const dtype = upcastType(a.dtype, b.dtype);\n return [a.cast(dtype), b.cast(dtype)];\n}\nfunction assertTypesMatch(a, b) {\n assert(a.dtype === b.dtype, () => `The dtypes of the first(${a.dtype}) and` +\n ` second(${b.dtype}) input must match`);\n}\nfunction isTensorInList(tensor, tensorList) {\n return tensorList.some(x => x.id === tensor.id);\n}\n/**\n * Extracts any `Tensor`s found within the provided object.\n *\n * @param container an object that may be a `Tensor` or may directly contain\n * `Tensor`s, such as a `Tensor[]` or `{key: Tensor, ...}`. In general it\n * is safe to pass any object here, except that `Promise`s are not\n * supported.\n * @returns An array of `Tensors` found within the passed object. If the\n * argument is simply a `Tensor', a list containing that `Tensor` is\n * returned. If the object is not a `Tensor` or does not\n * contain `Tensors`, an empty list is returned.\n */\nfunction getTensorsInContainer(result) {\n const list = [];\n const seen = new Set();\n walkTensorContainer(result, list, seen);\n return list;\n}\nfunction walkTensorContainer(container, list, seen) {\n if (container == null) {\n return;\n }\n if (container instanceof Tensor) {\n list.push(container);\n return;\n }\n if (!isIterable(container)) {\n return;\n }\n // Iteration over keys works also for arrays.\n const iterable = container;\n for (const k in iterable) {\n const val = iterable[k];\n if (!seen.has(val)) {\n seen.add(val);\n walkTensorContainer(val, list, seen);\n }\n }\n}\n// tslint:disable-next-line:no-any\nfunction isIterable(obj) {\n return Array.isArray(obj) || typeof obj === 'object';\n}\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nfunction isRegisteredKernelInvocation(kernelInvocation) {\n return kernelInvocation.kernelName != null;\n}\nclass EngineState {\n constructor() {\n // Public since optimizers will use it.\n this.registeredVariables = {};\n this.nextTapeNodeId = 0;\n this.numBytes = 0;\n this.numTensors = 0;\n this.numStringTensors = 0;\n this.numDataBuffers = 0;\n // Number of nested tf.grad() statements when computing higher-order\n // gradients. E.g. `1` for first-order gradients and `2` for second-order\n // gradients. Used to track if the tape should be removed after a backprop.\n this.gradientDepth = 0;\n // Number of nested kernel calls. When kernel depth is greater than 1, we turn\n // off the tape.\n this.kernelDepth = 0;\n this.scopeStack = [];\n /**\n * Keeps track of the number of data moves during a kernel execution. We\n * maintain a stack since kernels can call other kernels, recursively.\n */\n this.numDataMovesStack = [];\n this.nextScopeId = 0;\n this.tensorInfo = new WeakMap();\n this.profiling = false;\n this.activeProfile = {\n newBytes: 0,\n newTensors: 0,\n peakBytes: 0,\n kernels: [],\n result: null,\n get kernelNames() {\n return Array.from(new Set(this.kernels.map(k => k.name)));\n }\n };\n }\n dispose() {\n for (const variableName in this.registeredVariables) {\n this.registeredVariables[variableName].dispose();\n }\n }\n}\nclass Engine {\n constructor(ENV) {\n this.ENV = ENV;\n this.registry = {};\n this.registryFactory = {};\n this.pendingBackendInitId = 0;\n this.state = new EngineState();\n }\n async ready() {\n if (this.pendingBackendInit != null) {\n return this.pendingBackendInit.then(() => { });\n }\n if (this.backendInstance != null) {\n return;\n }\n const sortedBackends = this.getSortedBackends();\n for (let i = 0; i < sortedBackends.length; i++) {\n const backendName = sortedBackends[i];\n const success = await this.initializeBackend(backendName).success;\n if (success) {\n await this.setBackend(backendName);\n return;\n }\n }\n throw new Error(`Could not initialize any backends, all backend initializations ` +\n `failed.`);\n }\n get backend() {\n if (this.pendingBackendInit != null) {\n throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make ` +\n `sure to await tf.ready() or await tf.setBackend() before calling ` +\n `other methods`);\n }\n if (this.backendInstance == null) {\n const { name, asyncInit } = this.initializeBackendsAndReturnBest();\n if (asyncInit) {\n throw new Error(`The highest priority backend '${name}' has not yet been ` +\n `initialized. Make sure to await tf.ready() or ` +\n `await tf.setBackend() before calling other methods`);\n }\n this.setBackend(name);\n }\n return this.backendInstance;\n }\n backendNames() {\n return Object.keys(this.registryFactory);\n }\n findBackend(backendName) {\n if (!(backendName in this.registry)) {\n // If the backend hasn't been initialized but we have a registry entry for\n // it, initialize it and return it.\n if (backendName in this.registryFactory) {\n const { asyncInit } = this.initializeBackend(backendName);\n if (asyncInit) {\n // Backend is not ready yet.\n return null;\n }\n }\n else {\n return null;\n }\n }\n return this.registry[backendName];\n }\n findBackendFactory(backendName) {\n if (!(backendName in this.registryFactory)) {\n return null;\n }\n return this.registryFactory[backendName].factory;\n }\n registerBackend(backendName, factory, priority = 1) {\n if (backendName in this.registryFactory) {\n warn(`${backendName} backend was already registered. ` +\n `Reusing existing backend factory.`);\n return false;\n }\n this.registryFactory[backendName] = { factory, priority };\n return true;\n }\n async setBackend(backendName) {\n if (this.registryFactory[backendName] == null) {\n throw new Error(`Backend name '${backendName}' not found in registry`);\n }\n this.backendName = backendName;\n if (this.registry[backendName] == null) {\n this.backendInstance = null;\n const { success, asyncInit } = this.initializeBackend(backendName);\n const result = asyncInit ? await success : success;\n if (!result) {\n return false;\n }\n }\n this.backendInstance = this.registry[backendName];\n this.setupRegisteredKernels();\n // Reset the profiler.\n this.profiler = new Profiler(this.backendInstance);\n return true;\n }\n setupRegisteredKernels() {\n const kernels = getKernelsForBackend(this.backendName);\n kernels.forEach(kernel => {\n if (kernel.setupFunc != null) {\n kernel.setupFunc(this.backendInstance);\n }\n });\n }\n disposeRegisteredKernels(backendName) {\n const kernels = getKernelsForBackend(backendName);\n kernels.forEach(kernel => {\n if (kernel.disposeFunc != null) {\n kernel.disposeFunc(this.registry[backendName]);\n }\n });\n }\n /**\n * Initializes a backend by looking up the backend name in the factory\n * registry and calling the factory method. Returns a boolean representing\n * whether the initialization of the backend suceeded. Throws an error if\n * there is no backend in the factory registry.\n */\n initializeBackend(backendName) {\n const registryFactoryEntry = this.registryFactory[backendName];\n if (registryFactoryEntry == null) {\n throw new Error(`Cannot initialize backend ${backendName}, no registration found.`);\n }\n try {\n const backend = registryFactoryEntry.factory();\n /* Test if the factory returns a promise.\n Done in a more liberal way than\n previous 'Promise.resolve(backend)===backend'\n as we needed to account for custom Promise\n implementations (e.g. Angular) */\n if (backend && !(backend instanceof KernelBackend) &&\n typeof backend.then === 'function') {\n const promiseId = ++this.pendingBackendInitId;\n const success = backend\n .then(backendInstance => {\n // Outdated promise. Another backend was set in the meantime.\n if (promiseId < this.pendingBackendInitId) {\n return false;\n }\n this.registry[backendName] = backendInstance;\n this.pendingBackendInit = null;\n return true;\n })\n .catch(err => {\n // Outdated promise. Another backend was set in the meantime.\n if (promiseId < this.pendingBackendInitId) {\n return false;\n }\n this.pendingBackendInit = null;\n warn(`Initialization of backend ${backendName} failed`);\n warn(err.stack || err.message);\n return false;\n });\n this.pendingBackendInit = success;\n return { success, asyncInit: true };\n }\n else {\n this.registry[backendName] = backend;\n return { success: true, asyncInit: false };\n }\n }\n catch (err) {\n warn(`Initialization of backend ${backendName} failed`);\n warn(err.stack || err.message);\n return { success: false, asyncInit: false };\n }\n }\n removeBackend(backendName) {\n if (!(backendName in this.registryFactory)) {\n throw new Error(`${backendName} backend not found in registry`);\n }\n if (this.backendName === backendName && this.pendingBackendInit != null) {\n // There is a pending promise of the backend we want to remove. Make it\n // obsolete.\n this.pendingBackendInitId++;\n }\n if (backendName in this.registry) {\n this.disposeRegisteredKernels(backendName);\n this.registry[backendName].dispose();\n delete this.registry[backendName];\n }\n delete this.registryFactory[backendName];\n // Unset the backend if it is active.\n if (this.backendName === backendName) {\n this.pendingBackendInit = null;\n this.backendName = null;\n this.backendInstance = null;\n }\n }\n getSortedBackends() {\n if (Object.keys(this.registryFactory).length === 0) {\n throw new Error('No backend found in registry.');\n }\n return Object.keys(this.registryFactory).sort((a, b) => {\n // Highest priority comes first.\n return this.registryFactory[b].priority -\n this.registryFactory[a].priority;\n });\n }\n initializeBackendsAndReturnBest() {\n const sortedBackends = this.getSortedBackends();\n for (let i = 0; i < sortedBackends.length; i++) {\n const backendName = sortedBackends[i];\n const { success, asyncInit } = this.initializeBackend(backendName);\n if (asyncInit || success) {\n return { name: backendName, asyncInit };\n }\n }\n throw new Error(`Could not initialize any backends, all backend initializations ` +\n `failed.`);\n }\n moveData(backend, dataId) {\n const info = this.state.tensorInfo.get(dataId);\n const srcBackend = info.backend;\n const values = this.readSync(dataId);\n const refCount = srcBackend.refCount(dataId);\n // Delete the tensor from the old backend and move it to the new\n // backend.\n srcBackend.disposeData(dataId, true);\n info.backend = backend;\n backend.move(dataId, values, info.shape, info.dtype, refCount);\n if (this.shouldCheckForMemLeaks()) {\n // Track the number of moves during a kernel execution to correctly\n // detect memory leaks.\n this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]++;\n }\n }\n tidy(nameOrFn, fn) {\n let name = null;\n if (fn == null) {\n // Called with only 1 argument.\n if (typeof nameOrFn !== 'function') {\n throw new Error('Please provide a function to tidy()');\n }\n fn = nameOrFn;\n }\n else {\n // Called with 2 arguments.\n if (typeof nameOrFn !== 'string' && !(nameOrFn instanceof String)) {\n throw new Error('When calling with two arguments, the first argument ' +\n 'to tidy() must be a string');\n }\n if (typeof fn !== 'function') {\n throw new Error('When calling with two arguments, the 2nd argument ' +\n 'to tidy() must be a function');\n }\n name = nameOrFn;\n // TODO(nsthorat,smilkov): Do operation logging and performance\n // profiling.\n }\n let result;\n return this.scopedRun(() => this.startScope(name), () => this.endScope(result), () => {\n result = fn();\n if (result instanceof Promise) {\n console.error('Cannot return a Promise inside of tidy.');\n }\n return result;\n });\n }\n scopedRun(start, end, f) {\n start();\n try {\n const res = f();\n end();\n return res;\n }\n catch (ex) {\n end();\n throw ex;\n }\n }\n nextTensorId() {\n return Engine.nextTensorId++;\n }\n nextVariableId() {\n return Engine.nextVariableId++;\n }\n /**\n * This method is called instead of the public-facing tensor.clone() when\n * saving a tensor for backwards pass. It makes sure to add the clone\n * operation to the tape regardless of being called inside a kernel\n * execution.\n */\n clone(x) {\n const y = ENGINE.runKernel(Identity, { x });\n const inputs = { x };\n const grad = (dy) => ({\n x: () => {\n const dtype = 'float32';\n const gradInputs = { x: dy };\n const attrs = { dtype };\n return ENGINE.runKernel(Cast, gradInputs, \n // tslint:disable-next-line: no-unnecessary-type-assertion\n attrs);\n }\n });\n const saved = [];\n this.addTapeNode(this.state.activeScope.name, inputs, [y], grad, saved, {});\n return y;\n }\n /**\n * Execute a kernel with the given name and return the output tensor.\n *\n * @param kernelName The name of the kernel to execute.\n * @param inputs A map of input names to tensors.\n * @param attrs A map of attribute names to their values. An attribute is a\n * primitive (non-tensor) input to the kernel.\n * @param inputsToSave A list of tensors, inputs to save for the backprop\n * computation.\n * @param outputsToSave A list of booleans, specifying which output to save\n * for the backprop computation. These are booleans since the output\n * tensors are not visible to the user.\n */\n runKernel(kernelName, inputs, attrs) {\n if (this.backendName == null) {\n // backend has not been initialized yet (backend initialization is lazy\n // can be deferred until an op/ kernel is run).\n // The below getter has side effects that will try to initialize the\n // backend and set properties like this.backendName\n // tslint:disable-next-line: no-unused-expression\n this.backend;\n }\n const hasKernel = getKernel(kernelName, this.backendName) != null;\n if (!hasKernel) {\n throw new Error(`Kernel '${kernelName}' not registered for backend '${this.backendName}'`);\n }\n return this.runKernelFunc({ kernelName, inputs, attrs });\n }\n shouldCheckForMemLeaks() {\n return this.ENV.getBool('IS_TEST');\n }\n checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos) {\n const numDataIdsAfter = this.backend.numDataIds();\n // Count the number of data ids associated with the result of the kernel.\n let numOutputDataIds = 0;\n outInfos.forEach(info => {\n // Complex numbers allocate 3 data ids, one for 'real', one for\n // 'imaginary', and one for the container that holds the former two.\n numOutputDataIds += (info.dtype === 'complex64' ? 3 : 1);\n });\n // Account for the number of moves during kernel execution. A \"data move\"\n // can happen in the middle of a kernel execution, placing a new (key,value)\n // pair in the data storage. Since data moves have net zero effect (we\n // always remove the data from the old backend), we have to cancel them out\n // when detecting memory leaks.\n const numMoves = this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1];\n const dataIdsLeaked = numDataIdsAfter - numDataIdsBefore - numOutputDataIds - numMoves;\n if (dataIdsLeaked > 0) {\n throw new Error(`Backend '${this.backendName}' has an internal memory leak ` +\n `(${dataIdsLeaked} data ids) after running '${kernelName}'`);\n }\n }\n /**\n * Internal helper method to execute a kernel Func\n *\n * Use `runKernel` to execute kernels from outside of engine.\n */\n runKernelFunc(kernelParams) {\n let outputs;\n let saved = [];\n const isTapeOn = this.isTapeOn();\n const startingBytecount = this.state.numBytes;\n const startingNumTensors = this.state.numTensors;\n if (this.shouldCheckForMemLeaks()) {\n this.state.numDataMovesStack.push(0);\n }\n let kernelFunc;\n if (this.backendName == null) {\n // backend has not been initialized yet (backend initialization is lazy\n // can be deferred until an op/ kernel is run).\n // The below getter has side effects that will try to initialize the\n // backend and set properties like this.backendName\n // tslint:disable-next-line: no-unused-expression\n this.backend;\n }\n let out;\n const kernelOrScopeName = isRegisteredKernelInvocation(kernelParams) ?\n kernelParams.kernelName :\n this.state.activeScope != null ? this.state.activeScope.name : '';\n // Create the kernelFunc from either a registered kernel OR passed in\n // forward/backward functions (used by custom grad). In this context a\n // kernelFunc wraps a kernel implementation with some bookkeeping.\n if (isRegisteredKernelInvocation(kernelParams)) {\n const { kernelName, inputs, attrs } = kernelParams;\n if (this.backendName == null) {\n // backend has not been initialized yet (backend initialization is lazy\n // can be deferred until an op/ kernel is run).\n // The below getter has side effects that will try to initialize the\n // backend and set properties like this.backendName\n // tslint:disable-next-line: no-unused-expression\n this.backend;\n }\n const kernel = getKernel(kernelName, this.backendName);\n assert(kernel != null, () => `Cannot find registered kernel '${kernelName}' for backend '${this.backendName}'`);\n kernelFunc = () => {\n const numDataIdsBefore = this.backend.numDataIds();\n out = kernel.kernelFunc({ inputs, attrs, backend: this.backend });\n const outInfos = Array.isArray(out) ? out : [out];\n if (this.shouldCheckForMemLeaks()) {\n this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos);\n }\n const outTensors = outInfos.map((outInfo) => {\n // todo (yassogba) remove this option (Tensor) when node backend\n // methods have been modularized and they all return tensorInfo.\n // TensorInfos do not have a rank attribute.\n if (outInfo.rank != null) {\n return outInfo;\n }\n const { dataId, shape, dtype } = outInfo;\n return this.makeTensorFromDataId(dataId, shape, dtype);\n });\n // Save any required inputs and outputs.\n // Do not save unless we are recording to the tape. Otherwise it would\n // cause a mem leak since there would be no backprop for these tensors\n // (which would otherwise dispose them).\n if (isTapeOn) {\n const tensorsToSave = this.getTensorsForGradient(kernelName, inputs, outTensors);\n saved = this.saveTensorsForBackwardMode(tensorsToSave);\n }\n return outTensors;\n };\n }\n else {\n const { forwardFunc } = kernelParams;\n // Running a customGrad op.\n const saveFunc = (tensors) => {\n // Do not save unless we are recording to the tape. Otherwise it would\n // cause a mem leak since we would never run backprop, which disposes\n // the kept tensors.\n if (!isTapeOn) {\n return;\n }\n saved = tensors.map(tensor => this.keep(this.clone(tensor)));\n };\n kernelFunc = () => {\n const numDataIdsBefore = this.backend.numDataIds();\n out = this.tidy(() => forwardFunc(this.backend, saveFunc));\n const outs = (Array.isArray(out) ? out : [out]);\n if (this.shouldCheckForMemLeaks()) {\n // Scope name is used to print a more helpful error message if needed.\n this.checkKernelForMemLeak(kernelOrScopeName, numDataIdsBefore, outs);\n }\n return outs;\n };\n }\n //\n // Run the kernelFunc. Optionally profiling it.\n //\n const { inputs, attrs } = kernelParams;\n const backwardsFunc = isRegisteredKernelInvocation(kernelParams) ?\n null :\n kernelParams.backwardsFunc;\n let kernelProfile;\n this.scopedRun(\n // Stop recording to a tape when running a kernel.\n () => this.state.kernelDepth++, () => this.state.kernelDepth--, () => {\n if (!this.ENV.getBool('DEBUG') && !this.state.profiling) {\n outputs = kernelFunc();\n }\n else {\n kernelProfile = this.profiler.profileKernel(kernelOrScopeName, inputs, () => kernelFunc());\n if (this.ENV.getBool('DEBUG')) {\n this.profiler.logKernelProfile(kernelProfile);\n }\n outputs = kernelProfile.outputs;\n }\n });\n if (isTapeOn) {\n this.addTapeNode(kernelOrScopeName, inputs, outputs, backwardsFunc, saved, attrs);\n }\n if (this.state.profiling) {\n this.state.activeProfile.kernels.push({\n name: kernelOrScopeName,\n bytesAdded: this.state.numBytes - startingBytecount,\n totalBytesSnapshot: this.state.numBytes,\n tensorsAdded: this.state.numTensors - startingNumTensors,\n totalTensorsSnapshot: this.state.numTensors,\n inputShapes: Object.keys(inputs).map(key => inputs[key] != null ? inputs[key].shape : null),\n outputShapes: outputs.map(item => item.shape),\n kernelTimeMs: kernelProfile.timeMs,\n extraInfo: kernelProfile.extraInfo\n });\n }\n return (Array.isArray(out) ? outputs : outputs[0]);\n }\n /**\n * Saves tensors used in forward mode for use in backward mode.\n *\n * @param tensors the list of tensors to save.\n */\n saveTensorsForBackwardMode(tensors) {\n const saved = tensors.map(tensor => this.keep(this.clone(tensor)));\n return saved;\n }\n /**\n * Returns a list of tensors to save for a given gradient calculation.\n *\n * @param kernelName name of kernel to look up gradient for.\n * @param inputs a map of input tensors.\n * @param outputs an array of output tensors from forward mode of kernel.\n */\n getTensorsForGradient(kernelName, inputs, outputs) {\n const gradConfig = getGradient(kernelName);\n if (gradConfig != null) {\n const inputsToSave = gradConfig.inputsToSave || [];\n const outputsToSave = gradConfig.outputsToSave || [];\n // If saveAllInputs is true, all inputs will be saved. Otherwise, inputs\n // specified in inputsToSave will be saved.\n let inputTensorsToSave;\n if (gradConfig.saveAllInputs) {\n assert(Array.isArray(inputs), () => 'saveAllInputs is true, expected inputs to be an array.');\n inputTensorsToSave = Object.keys(inputs).map((key) => inputs[key]);\n }\n else {\n inputTensorsToSave = inputsToSave.map((inputName) => inputs[inputName]);\n }\n const outputTensorsToSave = outputs.filter((_, i) => outputsToSave[i]);\n return inputTensorsToSave.concat(outputTensorsToSave);\n }\n // We return an empty list rather than throw an error because the kernel we\n // are looking up may not actually be relevant to backproping through the\n // overall function\n //\n // See 'does not error if irrelevant (pruned) ops are missing grads' test\n // in gradients_test.ts for an example.\n return [];\n }\n /**\n * Internal method used by public APIs for tensor creation. Makes a new\n * tensor with the provided shape, dtype and values. It always\n * creates a new data id and writes the values to the underlying backend.\n */\n makeTensor(values, shape, dtype, backend) {\n if (values == null) {\n throw new Error('Values passed to engine.makeTensor() are null');\n }\n dtype = dtype || 'float32';\n backend = backend || this.backend;\n let backendVals = values;\n if (dtype === 'string' && isString(values[0])) {\n backendVals = values.map(d => encodeString(d));\n }\n const dataId = backend.write(backendVals, shape, dtype);\n const t = new Tensor(shape, dtype, dataId, this.nextTensorId());\n this.trackTensor(t, backend);\n // Count bytes for string tensors.\n if (dtype === 'string') {\n const info = this.state.tensorInfo.get(dataId);\n const newBytes = bytesFromStringArray(backendVals);\n this.state.numBytes += newBytes - info.bytes;\n info.bytes = newBytes;\n }\n return t;\n }\n /**\n * Internal method used by backends. Makes a new tensor\n * that is a wrapper around an existing data id. It doesn't create\n * a new data id, only increments the ref count used in memory tracking.\n */\n makeTensorFromDataId(dataId, shape, dtype, backend) {\n dtype = dtype || 'float32';\n const t = new Tensor(shape, dtype, dataId, this.nextTensorId());\n this.trackTensor(t, backend);\n return t;\n }\n makeVariable(initialValue, trainable = true, name, dtype) {\n name = name || this.nextVariableId().toString();\n if (dtype != null && dtype !== initialValue.dtype) {\n initialValue = initialValue.cast(dtype);\n }\n const v = new Variable(initialValue, trainable, name, this.nextTensorId());\n if (this.state.registeredVariables[v.name] != null) {\n throw new Error(`Variable with name ${v.name} was already registered`);\n }\n this.state.registeredVariables[v.name] = v;\n this.incRef(v, this.backend);\n return v;\n }\n trackTensor(a, backend) {\n this.state.numTensors++;\n if (a.dtype === 'string') {\n this.state.numStringTensors++;\n }\n // Bytes for complex numbers are counted by their components. Bytes for\n // string tensors are counted when writing values.\n let bytes = 0;\n if (a.dtype !== 'complex64' && a.dtype !== 'string') {\n bytes = a.size * bytesPerElement(a.dtype);\n }\n this.state.numBytes += bytes;\n if (!this.state.tensorInfo.has(a.dataId)) {\n this.state.numDataBuffers++;\n this.state.tensorInfo.set(a.dataId, {\n backend: backend || this.backend,\n dtype: a.dtype,\n shape: a.shape,\n bytes\n });\n }\n if (!(a instanceof Variable)) {\n this.track(a);\n }\n }\n // Track the tensor by dataId and increase the refCount for the dataId in the\n // backend.\n // TODO(pyu10055): This is currently used by makeVariable method, to increase\n // refCount on the backend for the dataId. It can potentially be replaced with\n // Identity op indead of calling backend directly.\n incRef(a, backend) {\n this.trackTensor(a, backend);\n this.backend.incRef(a.dataId);\n }\n removeDataId(dataId, backend) {\n if (this.state.tensorInfo.has(dataId) &&\n this.state.tensorInfo.get(dataId).backend === backend) {\n this.state.tensorInfo.delete(dataId);\n this.state.numDataBuffers--;\n }\n }\n disposeTensor(a) {\n if (!this.state.tensorInfo.has(a.dataId)) {\n return;\n }\n const info = this.state.tensorInfo.get(a.dataId);\n this.state.numTensors--;\n if (a.dtype === 'string') {\n this.state.numStringTensors--;\n this.state.numBytes -= info.bytes;\n }\n // Don't count bytes for complex numbers as they are counted by their\n // components.\n if (a.dtype !== 'complex64' && a.dtype !== 'string') {\n const bytes = a.size * bytesPerElement(a.dtype);\n this.state.numBytes -= bytes;\n }\n // Remove the reference to dataId if backend dispose the data successfully\n if (info.backend.disposeData(a.dataId)) {\n this.removeDataId(a.dataId, info.backend);\n }\n // TODO(nsthorat): Construct an error and save the stack trace for\n // debugging when in debug mode. Creating a stack trace is too expensive\n // to do unconditionally.\n }\n disposeVariables() {\n for (const varName in this.state.registeredVariables) {\n const v = this.state.registeredVariables[varName];\n this.disposeVariable(v);\n }\n }\n disposeVariable(v) {\n this.disposeTensor(v);\n if (this.state.registeredVariables[v.name] != null) {\n delete this.state.registeredVariables[v.name];\n }\n }\n memory() {\n const info = this.backend.memory();\n info.numTensors = this.state.numTensors;\n info.numDataBuffers = this.state.numDataBuffers;\n info.numBytes = this.state.numBytes;\n if (this.state.numStringTensors > 0) {\n info.unreliable = true;\n if (info.reasons == null) {\n info.reasons = [];\n }\n info.reasons.push('Memory usage by string tensors is approximate ' +\n '(2 bytes per character)');\n }\n return info;\n }\n async profile(query) {\n this.state.profiling = true;\n const startBytes = this.state.numBytes;\n const startNumTensors = this.state.numTensors;\n this.state.activeProfile.kernels = [];\n this.state.activeProfile.result = await query();\n this.state.profiling = false;\n this.state.activeProfile.peakBytes = Math.max(...this.state.activeProfile.kernels.map(d => d.totalBytesSnapshot));\n this.state.activeProfile.newBytes = this.state.numBytes - startBytes;\n this.state.activeProfile.newTensors =\n this.state.numTensors - startNumTensors;\n for (const kernel of this.state.activeProfile.kernels) {\n kernel.kernelTimeMs = await kernel.kernelTimeMs;\n kernel.extraInfo = await kernel.extraInfo;\n }\n return this.state.activeProfile;\n }\n isTapeOn() {\n return this.state.gradientDepth > 0 && this.state.kernelDepth === 0;\n }\n addTapeNode(kernelName, inputs, outputs, gradientsFunc, saved, attrs) {\n const tapeNode = { id: this.state.nextTapeNodeId++, kernelName, inputs, outputs, saved };\n const gradConfig = getGradient(kernelName);\n if (gradConfig != null) {\n gradientsFunc = gradConfig.gradFunc;\n }\n if (gradientsFunc != null) {\n tapeNode.gradient = (dys) => {\n // TODO(smilkov): To optimize back-prop, pass dys that are not used in\n // the backprop graph to the user as null instead of zeros\n dys = dys.map((dy, i) => {\n if (dy == null) {\n const output = outputs[i];\n const vals = makeZerosTypedArray(output.size, output.dtype);\n return this.makeTensor(vals, output.shape, output.dtype);\n }\n return dy;\n });\n // Grad functions of ops with single outputs expect a dy, while ops\n // with multiple outputs expect dys (array of dy).\n return gradientsFunc(dys.length > 1 ? dys : dys[0], saved, attrs);\n };\n }\n this.state.activeTape.push(tapeNode);\n }\n keep(result) {\n result.kept = true;\n return result;\n }\n startTape() {\n if (this.state.gradientDepth === 0) {\n this.state.activeTape = [];\n }\n this.state.gradientDepth++;\n }\n endTape() {\n this.state.gradientDepth--;\n }\n /**\n * Start a scope. Use this with endScope() to achieve the same functionality\n * as scope() without the need for a function closure.\n */\n startScope(name) {\n const scopeInfo = {\n track: [],\n name: 'unnamed scope',\n id: this.state.nextScopeId++\n };\n if (name) {\n scopeInfo.name = name;\n }\n this.state.scopeStack.push(scopeInfo);\n this.state.activeScope = scopeInfo;\n }\n /**\n * End a scope. Use this with startScope() to achieve the same functionality\n * as scope() without the need for a function closure.\n */\n endScope(result) {\n const tensorsToTrackInParent = getTensorsInContainer(result);\n const tensorsToTrackInParentSet = new Set(tensorsToTrackInParent.map(t => t.id));\n // Dispose the arrays tracked in this scope.\n for (let i = 0; i < this.state.activeScope.track.length; i++) {\n const tensor = this.state.activeScope.track[i];\n if (!tensor.kept && !tensorsToTrackInParentSet.has(tensor.id)) {\n tensor.dispose();\n }\n }\n const oldScope = this.state.scopeStack.pop();\n this.state.activeScope = this.state.scopeStack.length === 0 ?\n null :\n this.state.scopeStack[this.state.scopeStack.length - 1];\n // Track the current result in the parent scope.\n tensorsToTrackInParent.forEach(tensor => {\n // Only track the tensor if was allocated in the inner scope and is not\n // globally kept.\n if (!tensor.kept && tensor.scopeId === oldScope.id) {\n this.track(tensor);\n }\n });\n }\n /**\n * Returns gradients of `f` with respect to each of the `xs`. The gradients\n * returned are of the same length as `xs`, but some might be null if `f`\n * was not a function of that `x`. It also takes optional dy to multiply the\n * gradient, which defaults to `1`.\n */\n gradients(f, xs, dy, allowNoGradients = false) {\n assert(xs.length > 0, () => 'gradients() received an empty list of xs.');\n if (dy != null && dy.dtype !== 'float32') {\n throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`);\n }\n const y = this.scopedRun(() => this.startTape(), () => this.endTape(), () => this.tidy('forward', f));\n assert(y instanceof Tensor, () => 'The result y returned by f() must be a tensor.');\n // Filter out the nodes that don't connect x => y.\n const filteredTape = getFilteredNodesXToY(this.state.activeTape, xs, y);\n if (!allowNoGradients && filteredTape.length === 0 && xs.length > 0) {\n throw new Error('Cannot compute gradient of y=f(x) with respect to x. Make sure ' +\n 'that the f you passed encloses all operations that lead from x ' +\n 'to y.');\n }\n return this.tidy('backward', () => {\n const accumulatedGradientMap = {};\n accumulatedGradientMap[y.id] = (dy == null) ? ones(y.shape) : dy;\n // Backprop gradients through the filtered nodes.\n backpropagateGradients(accumulatedGradientMap, filteredTape, \n // Pass the tidy function to avoid circular dep with `tape.ts`.\n f => this.tidy(f), \n // Pass an add function to avoide a circular dep with `tape.ts`.\n add);\n const grads = xs.map(x => accumulatedGradientMap[x.id]);\n if (this.state.gradientDepth === 0) {\n // This means that we are not computing higher-order gradients\n // and can clean up the tape.\n this.state.activeTape.forEach(node => {\n for (const tensor of node.saved) {\n tensor.dispose();\n }\n });\n this.state.activeTape = null;\n }\n return { value: y, grads };\n });\n }\n customGrad(f) {\n assert(isFunction(f), () => 'The f passed in customGrad(f) must be a function.');\n return (...inputs) => {\n assert(inputs.every(t => t instanceof Tensor), () => 'The args passed in customGrad(f)(x1, x2,...) must all be ' +\n 'tensors');\n let res;\n const inputMap = {};\n inputs.forEach((input, i) => {\n inputMap[i] = input;\n });\n const forwardFunc = (_, save) => {\n res = f(...[...inputs, save]);\n assert(res.value instanceof Tensor, () => 'The function f passed in customGrad(f) must return an ' +\n 'object where `obj.value` is a tensor');\n assert(isFunction(res.gradFunc), () => 'The function f passed in customGrad(f) must return an ' +\n 'object where `obj.gradFunc` is a function.');\n return res.value;\n };\n const backwardsFunc = (dy, saved) => {\n const gradRes = res.gradFunc(dy, saved);\n const grads = Array.isArray(gradRes) ? gradRes : [gradRes];\n assert(grads.length === inputs.length, () => 'The function f passed in customGrad(f) must return an ' +\n 'object where `obj.gradFunc` is a function that returns ' +\n 'the same number of tensors as inputs passed to f(...).');\n assert(grads.every(t => t instanceof Tensor), () => 'The function f passed in customGrad(f) must return an ' +\n 'object where `obj.gradFunc` is a function that returns ' +\n 'a list of only tensors.');\n const gradMap = {};\n grads.forEach((grad, i) => {\n gradMap[i] = () => grad;\n });\n return gradMap;\n };\n return this.runKernelFunc({\n forwardFunc,\n backwardsFunc,\n inputs: inputMap,\n });\n };\n }\n readSync(dataId) {\n // Route the read to the correct backend.\n const info = this.state.tensorInfo.get(dataId);\n return info.backend.readSync(dataId);\n }\n read(dataId) {\n // Route the read to the correct backend.\n const info = this.state.tensorInfo.get(dataId);\n return info.backend.read(dataId);\n }\n readToGPU(dataId, options) {\n // Route the read to the correct backend.\n const info = this.state.tensorInfo.get(dataId);\n return info.backend.readToGPU(dataId, options);\n }\n async time(query) {\n const start = now();\n const timingInfo = await this.backend.time(query);\n timingInfo.wallMs = now() - start;\n return timingInfo;\n }\n /**\n * Tracks a Tensor in the current scope to be automatically cleaned up\n * when the current scope ends, and returns the value.\n *\n * @param result The Tensor to track in the current scope.\n */\n track(result) {\n if (this.state.activeScope != null) {\n result.scopeId = this.state.activeScope.id;\n this.state.activeScope.track.push(result);\n }\n return result;\n }\n get registeredVariables() {\n return this.state.registeredVariables;\n }\n /**\n * Resets the engine state. Removes all backends but does not remove\n * registered backend factories.\n */\n reset() {\n // Make any pending promise obsolete.\n this.pendingBackendInitId++;\n this.state.dispose();\n this.ENV.reset();\n this.state = new EngineState();\n for (const backendName in this.registry) {\n this.disposeRegisteredKernels(backendName);\n this.registry[backendName].dispose();\n delete this.registry[backendName];\n }\n this.backendName = null;\n this.backendInstance = null;\n this.pendingBackendInit = null;\n }\n}\nEngine.nextTensorId = 0;\nEngine.nextVariableId = 0;\nfunction ones(shape) {\n const values = makeOnesTypedArray(sizeFromShape(shape), 'float32');\n return ENGINE.makeTensor(values, shape, 'float32');\n}\nfunction getOrMakeEngine() {\n const ns = getGlobalNamespace();\n if (ns._tfengine == null) {\n const environment = new Environment(ns);\n ns._tfengine = new Engine(environment);\n }\n setEnvironmentGlobal(ns._tfengine.ENV);\n // Tell the current tensor interface that the global engine is responsible\n // for tracking.\n setTensorTracker(() => ns._tfengine);\n return ns._tfengine;\n}\nconst ENGINE = getOrMakeEngine();\n/**\n * A implementation of the add op for use within engine and tape.\n *\n * This allows us to avoid a circular dependency between add.ts and engine.\n * It is exported to be available in tape tests.\n */\nfunction add(a, b) {\n // We duplicate Add here to avoid a circular dependency with add.ts.\n const inputs = { a, b };\n return ENGINE.runKernel(Add, inputs);\n}\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nfunction inferShape(val, dtype) {\n let firstElem = val;\n if (isTypedArray(val)) {\n return dtype === 'string' ? [] : [val.length];\n }\n if (!Array.isArray(val)) {\n return []; // Scalar.\n }\n const shape = [];\n while (Array.isArray(firstElem) ||\n isTypedArray(firstElem) && dtype !== 'string') {\n shape.push(firstElem.length);\n firstElem = firstElem[0];\n }\n if (Array.isArray(val) &&\n env().getBool('TENSORLIKE_CHECK_SHAPE_CONSISTENCY')) {\n deepAssertShapeConsistency(val, shape, []);\n }\n return shape;\n}\nfunction deepAssertShapeConsistency(val, shape, indices) {\n indices = indices || [];\n if (!(Array.isArray(val)) && !isTypedArray(val)) {\n assert(shape.length === 0, () => `Element arr[${indices.join('][')}] is a primitive, ` +\n `but should be an array/TypedArray of ${shape[0]} elements`);\n return;\n }\n assert(shape.length > 0, () => `Element arr[${indices.join('][')}] should be a primitive, ` +\n `but is an array of ${val.length} elements`);\n assert(val.length === shape[0], () => `Element arr[${indices.join('][')}] should have ${shape[0]} ` +\n `elements, but has ${val.length} elements`);\n const subShape = shape.slice(1);\n for (let i = 0; i < val.length; ++i) {\n deepAssertShapeConsistency(val[i], subShape, indices.concat(i));\n }\n}\nfunction assertDtype(expectedDtype, actualDType, argName, functionName) {\n if (expectedDtype === 'string_or_numeric') {\n return;\n }\n if (expectedDtype == null) {\n throw new Error(`Expected dtype cannot be null.`);\n }\n if (expectedDtype !== 'numeric' && expectedDtype !== actualDType ||\n expectedDtype === 'numeric' && actualDType === 'string') {\n throw new Error(`Argument '${argName}' passed to '${functionName}' must ` +\n `be ${expectedDtype} tensor, but got ${actualDType} tensor`);\n }\n}\nfunction convertToTensor(x, argName, functionName, parseAsDtype = 'numeric') {\n if (x instanceof Tensor) {\n assertDtype(parseAsDtype, x.dtype, argName, functionName);\n return x;\n }\n let inferredDtype = inferDtype(x);\n // If the user expects a bool/int/float, use that info to update the\n // inferredDtype when it is not a string.\n if (inferredDtype !== 'string' &&\n ['bool', 'int32', 'float32'].indexOf(parseAsDtype) >= 0) {\n inferredDtype = parseAsDtype;\n }\n assertDtype(parseAsDtype, inferredDtype, argName, functionName);\n if ((x == null) ||\n (!isTypedArray(x) && !Array.isArray(x) && typeof x !== 'number' &&\n typeof x !== 'boolean' && typeof x !== 'string')) {\n const type = x == null ? 'null' : x.constructor.name;\n throw new Error(`Argument '${argName}' passed to '${functionName}' must be a ` +\n `Tensor or TensorLike, but got '${type}'`);\n }\n const inferredShape = inferShape(x, inferredDtype);\n if (!isTypedArray(x) && !Array.isArray(x)) {\n x = [x];\n }\n const skipTypedArray = true;\n const values = inferredDtype !== 'string' ?\n toTypedArray(x, inferredDtype) :\n flatten(x, [], skipTypedArray);\n return ENGINE.makeTensor(values, inferredShape, inferredDtype);\n}\nfunction convertToTensorArray(arg, argName, functionName, parseAsDtype = 'numeric') {\n if (!Array.isArray(arg)) {\n throw new Error(`Argument ${argName} passed to ${functionName} must be a ` +\n '`Tensor[]` or `TensorLike[]`');\n }\n const tensors = arg;\n return tensors.map((t, i) => convertToTensor(t, `${argName}[${i}]`, functionName, parseAsDtype));\n}\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst OP_SCOPE_SUFFIX = '__op';\n/**\n * Used for wrapping functions that perform math operations on\n * Tensors. The function will be wrapped in a named scope that cleans all\n * memory usage after the function is done.\n */\nfunction op(f) {\n const keys = Object.keys(f);\n if (keys.length !== 1) {\n throw new Error(`Please provide an object with a single key ` +\n `(operation name) mapping to a function. Got an object with ` +\n `${keys.length} keys.`);\n }\n let opName = keys[0];\n const fn = f[opName];\n // Strip the underscore from the end of the function name.\n if (opName.endsWith('_')) {\n opName = opName.substring(0, opName.length - 1);\n }\n // add an __op suffix to distinguish ops from kernels in tf.profile\n opName = opName + OP_SCOPE_SUFFIX;\n // tslint:disable-next-line:no-any\n const f2 = (...args) => {\n ENGINE.startScope(opName);\n try {\n const result = fn(...args);\n if (isPromise(result)) {\n console.error('Cannot return a Promise inside of tidy.');\n }\n ENGINE.endScope(result);\n return result;\n }\n catch (ex) {\n ENGINE.endScope(null);\n throw ex;\n }\n };\n Object.defineProperty(f2, 'name', { value: opName, configurable: true });\n // tslint:disable-next-line:no-any\n return f2;\n}\n\n/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Casts a `tf.Tensor` to a new dtype.\n *\n * ```js\n * const x = tf.tensor1d([1.5, 2.5, 3]);\n * tf.cast(x, 'int32').print();\n * ```\n * @param x The input tensor to be casted.\n * @param dtype The dtype to cast the input tensor to.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction cast_(x, dtype) {\n const $x = convertToTensor(x, 'x', 'cast');\n // Sanity checks.\n if (!isValidDtype(dtype)) {\n throw new Error(`Failed to cast to unknown dtype ${dtype}`);\n }\n if (dtype === 'string' && $x.dtype !== 'string' ||\n dtype !== 'string' && $x.dtype === 'string') {\n throw new Error('Only strings can be casted to strings');\n }\n const inputs = { x: $x };\n const attrs = { dtype };\n return ENGINE.runKernel(Cast, inputs, attrs);\n}\nconst cast = op({ cast_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Multiplies two `tf.Tensor`s element-wise, A * B. Supports broadcasting.\n *\n * We also expose `tf.mulStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3, 4]);\n * const b = tf.tensor1d([2, 3, 4, 5]);\n *\n * a.mul(b).print(); // or tf.mul(a, b)\n * ```\n *\n * ```js\n * // Broadcast mul a with b.\n * const a = tf.tensor1d([1, 2, 3, 4]);\n * const b = tf.scalar(5);\n *\n * a.mul(b).print(); // or tf.mul(a, b)\n * ```\n * @param a The first tensor to multiply.\n * @param b The second tensor to multiply. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction mul_(a, b) {\n let $a = convertToTensor(a, 'a', 'mul');\n let $b = convertToTensor(b, 'b', 'mul');\n [$a, $b] = makeTypesMatch($a, $b);\n const inputs = { a: $a, b: $b };\n return ENGINE.runKernel(Multiply, inputs);\n}\nconst mul = op({ mul_ });\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes step of the input `tf.Tensor` element-wise: `x > 0 ? 1 : alpha * x`\n *\n * ```js\n * const x = tf.tensor1d([0, 2, -1, -3]);\n *\n * x.step(.5).print(); // or tf.step(x, .5)\n * ```\n * @param x The input tensor.\n * @param alpha The gradient when input is negative.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction step_(x, alpha = 0.0) {\n const $x = convertToTensor(x, 'x', 'step');\n const inputs = { x: $x };\n const attrs = { alpha };\n return ENGINE.runKernel(Step, inputs, attrs);\n}\nconst step = op({ step_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst absGradConfig = {\n kernelName: Abs,\n inputsToSave: ['x'],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n return { x: () => mul(dy, step(cast(x, 'float32'), -1)) };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting.\n * The result is rounded with floor function.\n *\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 9, 16]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n *\n * a.floorDiv(b).print(); // or tf.div(a, b)\n * ```\n *\n * ```js\n * // Broadcast div a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(2);\n *\n * a.floorDiv(b).print(); // or tf.floorDiv(a, b)\n * ```\n *\n * @param a The first tensor as the numerator.\n * @param b The second tensor as the denominator. Must have the same dtype as\n * `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction floorDiv_(a, b) {\n let $a = convertToTensor(a, 'a', 'floorDiv');\n let $b = convertToTensor(b, 'b', 'floorDiv');\n [$a, $b] = makeTypesMatch($a, $b);\n const inputs = { a: $a, b: $b };\n return ENGINE.runKernel(FloorDiv, inputs);\n}\nconst floorDiv = op({ floorDiv_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 9, 16]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n *\n * a.div(b).print(); // or tf.div(a, b)\n * ```\n *\n * ```js\n * // Broadcast div a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(2);\n *\n * a.div(b).print(); // or tf.div(a, b)\n * ```\n *\n * @param a The first tensor as the numerator.\n * @param b The second tensor as the denominator. Must have the same dtype as\n * `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction div_(a, b) {\n let $a = convertToTensor(a, 'a', 'div');\n let $b = convertToTensor(b, 'b', 'div');\n [$a, $b] = makeTypesMatch($a, $b);\n if ($a.dtype === 'int32' && $b.dtype === 'int32') {\n return floorDiv($a, $b);\n }\n const inputs = { a: $a, b: $b };\n const attrs = {};\n // tslint:disable-next-line: no-unnecessary-type-assertion\n return ENGINE.runKernel(RealDiv, inputs, attrs);\n}\nconst div = op({ div_ });\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes `-1 * x` element-wise.\n *\n * ```js\n * const x = tf.tensor2d([1, 2, -2, 0], [2, 2]);\n *\n * x.neg().print(); // or tf.neg(x)\n * ```\n *\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction neg_(x) {\n const $x = convertToTensor(x, 'x', 'neg');\n const inputs = { x: $x };\n return ENGINE.runKernel(Neg, inputs);\n}\nconst neg = op({ neg_ });\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/** This is shared code across all tensor creation methods. */\nfunction makeTensor(values, shape, inferredShape, dtype) {\n if (dtype == null) {\n dtype = inferDtype(values);\n }\n if (dtype === 'complex64') {\n throw new Error(`Cannot construct a complex64 tensor directly. ` +\n `Please use tf.complex(real, imag).`);\n }\n if (!isTypedArray(values) && !Array.isArray(values) &&\n typeof values !== 'number' && typeof values !== 'boolean' &&\n typeof values !== 'string') {\n throw new Error('values passed to tensor(values) must be a number/boolean/string or ' +\n 'an array of numbers/booleans/strings, or a TypedArray');\n }\n if (shape != null) {\n assertNonNegativeIntegerDimensions(shape);\n const providedSize = sizeFromShape(shape);\n const inferredSize = sizeFromShape(inferredShape);\n assert(providedSize === inferredSize, () => `Based on the provided shape, [${shape}], the tensor should have ` +\n `${providedSize} values but has ${inferredSize}`);\n for (let i = 0; i < inferredShape.length; ++i) {\n const inferred = inferredShape[i];\n const flatDimsDontMatch = i === inferredShape.length - 1 ?\n inferred !== sizeFromShape(shape.slice(i)) :\n true;\n assert(inferredShape[i] === shape[i] || !flatDimsDontMatch, () => `Error creating a new Tensor. Inferred shape ` +\n `(${inferredShape}) does not match the provided ` +\n `shape (${shape}). `);\n }\n }\n if (!isTypedArray(values) && !Array.isArray(values)) {\n values = [values];\n }\n shape = shape || inferredShape;\n values = dtype !== 'string' ?\n toTypedArray(values, dtype) :\n flatten(values, [], true);\n return ENGINE.makeTensor(values, shape, dtype);\n}\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Creates rank-0 `tf.Tensor` (scalar) with the provided value and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.scalar` as it makes the code more readable.\n *\n * ```js\n * tf.scalar(3.14).print();\n * ```\n *\n * @param value The value of the scalar.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction scalar(value, dtype) {\n if (((isTypedArray(value) && dtype !== 'string') || Array.isArray(value)) &&\n dtype !== 'complex64') {\n throw new Error('Error creating a new Scalar: value must be a primitive ' +\n '(number|boolean|string)');\n }\n if (dtype === 'string' && isTypedArray(value) &&\n !(value instanceof Uint8Array)) {\n throw new Error('When making a scalar from encoded string, ' +\n 'the value must be `Uint8Array`.');\n }\n const shape = [];\n const inferredShape = [];\n return makeTensor(value, shape, inferredShape, dtype);\n}\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes square root of the input `tf.Tensor` element-wise: `y = sqrt(x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 4, -1]);\n *\n * x.sqrt().print(); // or tf.sqrt(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sqrt_(x) {\n const $x = convertToTensor(x, 'x', 'sqrt', 'float32');\n const inputs = { x: $x };\n return ENGINE.runKernel(Sqrt, inputs);\n}\nconst sqrt = op({ sqrt_ });\n\n/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes square of `x` element-wise: `x ^ 2`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, Math.sqrt(2), -1]);\n *\n * x.square().print(); // or tf.square(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction square_(x) {\n const $x = convertToTensor(x, 'x', 'square');\n const attrs = {};\n return ENGINE.runKernel('Square', { x: $x }, attrs);\n}\nconst square = op({ square_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Subtracts two `tf.Tensor`s element-wise, A - B. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([10, 20, 30, 40]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n *\n * a.sub(b).print(); // or tf.sub(a, b)\n * ```\n *\n * ```js\n * // Broadcast subtract a with b.\n * const a = tf.tensor1d([10, 20, 30, 40]);\n * const b = tf.scalar(5);\n *\n * a.sub(b).print(); // or tf.sub(a, b)\n * ```\n * @param a The first `tf.Tensor` to subtract from.\n * @param b The second `tf.Tensor` to be subtracted. Must have the same dtype as\n * `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction sub_(a, b) {\n let $a = convertToTensor(a, 'a', 'sub');\n let $b = convertToTensor(b, 'b', 'sub');\n [$a, $b] = makeTypesMatch($a, $b);\n const inputs = { a: $a, b: $b };\n return ENGINE.runKernel(Sub, inputs);\n}\nconst sub = op({ sub_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst acosGradConfig = {\n kernelName: Acos,\n inputsToSave: ['x'],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n return {\n x: () => {\n const a = square(cast(x, 'float32'));\n const b = sqrt(sub(scalar(1), a));\n return neg(div(dy, b));\n }\n };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst acoshGradConfig = {\n kernelName: Acosh,\n inputsToSave: ['x'],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n return {\n x: () => {\n const a = sqrt(sub(square(cast(x, 'float32')), 1));\n return div(dy, a);\n }\n };\n }\n};\n\n/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Returns the dimensions in the input shape that are broadcasted to\n * produce the provided output shape.\n *\n * The returned dimensions are 0-indexed and sorted. An example:\n * inShape = [4, 1, 3]\n * outShape = [5, 4, 3, 3]\n * result = [1]. Dimension 1 (2nd dimension of input) gets broadcasted 1 => 3.\n */\nfunction getBroadcastDims(inShape, outShape) {\n const inRank = inShape.length;\n const dims = [];\n for (let i = 0; i < inRank; i++) {\n const dim = inRank - 1 - i;\n const a = inShape[dim] || 1;\n const b = outShape[outShape.length - 1 - i] || 1;\n if (b > 1 && a === 1) {\n dims.unshift(dim);\n }\n }\n return dims;\n}\n/**\n * Returns the axes in the output space that should be reduced to produce\n * the input space.\n */\nfunction getReductionAxes(inShape, outShape) {\n const result = [];\n for (let i = 0; i < outShape.length; i++) {\n const inDim = inShape[inShape.length - i - 1];\n const outAxis = outShape.length - i - 1;\n const outDim = outShape[outAxis];\n if (inDim == null || (inDim === 1 && outDim > 1)) {\n result.unshift(outAxis);\n }\n }\n return result;\n}\nfunction assertAndGetBroadcastShape(shapeA, shapeB) {\n const result = [];\n const l = Math.max(shapeA.length, shapeB.length);\n for (let i = 0; i < l; i++) {\n let a = shapeA[shapeA.length - i - 1];\n if (a == null) {\n a = 1;\n }\n let b = shapeB[shapeB.length - i - 1];\n if (b == null) {\n b = 1;\n }\n if (a === 1) {\n result.unshift(b);\n }\n else if (b === 1) {\n result.unshift(a);\n }\n else if (a !== b) {\n const errMsg = `Operands could not be broadcast together with shapes ` +\n `${shapeA} and ${shapeB}.`;\n throw Error(errMsg);\n }\n else {\n result.unshift(a);\n }\n }\n return result;\n}\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Reshapes a `tf.Tensor` to a given shape.\n *\n * Given an input tensor, returns a new tensor with the same values as the\n * input tensor with shape `shape`.\n *\n * If one component of shape is the special value -1, the size of that\n * dimension is computed so that the total size remains constant. In\n * particular, a shape of [-1] flattens into 1-D. At most one component of\n * shape can be -1.\n *\n * If shape is 1-D or higher, then the operation returns a tensor with shape\n * shape filled with the values of tensor. In this case, the number of\n * elements implied by shape must be the same as the number of elements in\n * tensor.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * x.reshape([2, 2]).print();\n * ```\n *\n * @param x The input tensor to be reshaped.\n * @param shape An array of integers defining the output tensor shape.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction reshape_(x, shape) {\n const $x = convertToTensor(x, 'x', 'reshape', 'string_or_numeric');\n const inputs = { x: $x };\n const attrs = { shape };\n return ENGINE.runKernel(Reshape, inputs, attrs);\n}\nconst reshape = op({ reshape_ });\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes the sum of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If axes has no entries, all dimensions are reduced, and a\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.sum().print(); // or tf.sum(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.sum(axis).print(); // or tf.sum(x, axis)\n * ```\n *\n * @param x The input tensor to compute the sum over. If the dtype is `bool`\n * it will be converted to `int32` and the output dtype will be `int32`.\n * @param axis The dimension(s) to reduce. By default it reduces\n * all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction sum_(x, axis = null, keepDims = false) {\n let $x = convertToTensor(x, 'x', 'sum');\n if ($x.dtype === 'bool') {\n $x = cast($x, 'int32');\n }\n const inputs = { x: $x };\n const attrs = { axis, keepDims };\n return ENGINE.runKernel(Sum, inputs, attrs);\n}\nconst sum$1 = op({ sum_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst addGradConfig = {\n kernelName: Add,\n inputsToSave: ['a', 'b'],\n gradFunc: (dy, saved) => {\n const [a, b] = saved;\n const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n const derA = () => {\n let res = dy;\n const reduceAxes = getReductionAxes(a.shape, outShape);\n if (reduceAxes.length > 0) {\n res = sum$1(res, reduceAxes);\n }\n return reshape(res, a.shape);\n };\n const derB = () => {\n let res = dy;\n const reduceAxes = getReductionAxes(b.shape, outShape);\n if (reduceAxes.length > 0) {\n res = sum$1(res, reduceAxes);\n }\n return reshape(res, b.shape);\n };\n return { a: derA, b: derB };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst addNGradConfig = {\n kernelName: AddN,\n saveAllInputs: true,\n gradFunc: (dy, saved) => {\n const ders = {};\n saved.forEach((_, i) => {\n ders[i] = () => dy.clone();\n });\n return ders;\n }\n};\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Creates a `tf.Tensor` with all elements set to 0 with the same shape as the\n * given tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n * tf.zerosLike(x).print();\n * ```\n *\n * @param x The tensor of required shape.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction zerosLike_(x) {\n const $x = convertToTensor(x, 'x', 'zerosLike');\n const inputs = { x: $x };\n return ENGINE.runKernel(ZerosLike, inputs);\n}\nconst zerosLike = op({ zerosLike_ });\n\n/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst argMaxGradConfig = {\n kernelName: ArgMax,\n inputsToSave: ['x'],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n return { x: () => zerosLike(x) };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst argMinGradConfig = {\n kernelName: ArgMin,\n inputsToSave: ['x'],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n return { x: () => zerosLike(x) };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst asinGradConfig = {\n kernelName: Asin,\n inputsToSave: ['x'],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n return { x: () => div(dy, sqrt(sub(scalar(1), square(cast(x, 'float32'))))) };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Adds two `tf.Tensor`s element-wise, A + B. Supports broadcasting.\n *\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3, 4]);\n * const b = tf.tensor1d([10, 20, 30, 40]);\n *\n * a.add(b).print(); // or tf.add(a, b)\n * ```\n *\n * ```js\n * // Broadcast add a with b.\n * const a = tf.scalar(5);\n * const b = tf.tensor1d([10, 20, 30, 40]);\n *\n * a.add(b).print(); // or tf.add(a, b)\n * ```\n * @param a The first `tf.Tensor` to add.\n * @param b The second `tf.Tensor` to add. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction add_(a, b) {\n let $a = convertToTensor(a, 'a', 'add');\n let $b = convertToTensor(b, 'b', 'add');\n [$a, $b] = makeTypesMatch($a, $b);\n const inputs = { a: $a, b: $b };\n return ENGINE.runKernel(Add, inputs);\n}\nconst add$1 = op({ add_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst asinhGradConfig = {\n kernelName: Asinh,\n inputsToSave: ['x'],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n return {\n x: () => {\n const a = sqrt(add$1(scalar(1), square(cast(x, 'float32'))));\n return div(dy, a);\n }\n };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst atan2GradConfig = {\n kernelName: Atan2,\n inputsToSave: ['a', 'b'],\n gradFunc: (dy, saved) => {\n const [a, b] = saved;\n const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n const derA = () => {\n const d = add$1(square(a), square(b));\n let res = mul(dy, div(b, d));\n const reduceAxes = getReductionAxes(a.shape, outShape);\n if (reduceAxes.length > 0) {\n res = sum$1(res, reduceAxes);\n }\n return reshape(res, a.shape);\n };\n const derB = () => {\n const d = add$1(square(a), square(b));\n let res = neg(mul(dy, div(a, d)));\n const reduceAxes = getReductionAxes(b.shape, outShape);\n if (reduceAxes.length > 0) {\n res = sum$1(res, reduceAxes);\n }\n return reshape(res, b.shape);\n };\n return { a: derA, b: derB };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst atanGradConfig = {\n kernelName: Atan,\n inputsToSave: ['x'],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n return { x: () => div(dy, add$1(square(cast(x, 'float32')), 1)) };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst atanhGradConfig = {\n kernelName: Atanh,\n inputsToSave: ['x'],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n return { x: () => div(dy, sub(scalar(1), square(cast(x, 'float32')))) };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n *\n * @param inputShape Input tensor shape is of the following dimensions:\n * `[batch, height, width, inChannels]`.\n * @param filterShape The filter shape is of the following dimensions:\n * `[filterHeight, filterWidth, depth]`.\n * @param strides The strides of the sliding window for each dimension of the\n * input tensor: `[strideHeight, strideWidth]`.\n * If `strides` is a single number,\n * then `strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n * - `same` and stride 1: output will be of same size as input,\n * regardless of filter size.\n * - `valid`: output will be smaller than input if filter is larger\n * than 1*1x1.\n * - For more info, see this guide:\n * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](\n * https://www.tensorflow.org/api_docs/python/tf/nn/convolution)\n * @param dataFormat The data format of the input and output data.\n * Defaults to 'NHWC'.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`.\n * Defaults to `[1, 1]`. If `dilations` is a single number, then\n * `dilationHeight == dilationWidth`.\n */\nfunction computeDilation2DInfo(inputShape, filterShape, strides, pad, dataFormat = 'NHWC', dilations) {\n // `computerConv2DInfo` require filterShape to be in the dimension of:\n // `[filterHeight, filterWidth, depth, outDepth]`, dilation2d doesn't have\n // outDepth, it should have the same depth as the input.\n // Input shape: [batch, height, width, inChannels]\n const inputChannels = inputShape[3];\n const $filterShape = [...filterShape, inputChannels];\n const $dataFormat = convertConv2DDataFormat(dataFormat);\n return computeConv2DInfo(inputShape, $filterShape, strides, dilations, pad, null /* roundingMode */, null /* depthWise */, $dataFormat);\n}\nfunction computePool2DInfo(inShape, filterSize, strides, dilations, pad, roundingMode, dataFormat = 'channelsLast') {\n const [filterHeight, filterWidth] = parseTupleParam(filterSize);\n let filterShape;\n if (dataFormat === 'channelsLast') {\n filterShape = [filterHeight, filterWidth, inShape[3], inShape[3]];\n }\n else if (dataFormat === 'channelsFirst') {\n filterShape = [filterHeight, filterWidth, inShape[1], inShape[1]];\n }\n else {\n throw new Error(`Unknown dataFormat ${dataFormat}`);\n }\n return computeConv2DInfo(inShape, filterShape, strides, dilations, pad, roundingMode, false, dataFormat);\n}\n/**\n * Computes the information for a forward pass of a pooling3D operation.\n */\nfunction computePool3DInfo(inShape, filterSize, strides, dilations, pad, roundingMode, dataFormat = 'NDHWC') {\n const [filterDepth, filterHeight, filterWidth] = parse3TupleParam(filterSize);\n let filterShape;\n let $dataFormat;\n if (dataFormat === 'NDHWC') {\n $dataFormat = 'channelsLast';\n filterShape =\n [filterDepth, filterHeight, filterWidth, inShape[4], inShape[4]];\n }\n else if (dataFormat === 'NCDHW') {\n $dataFormat = 'channelsFirst';\n filterShape =\n [filterDepth, filterHeight, filterWidth, inShape[1], inShape[1]];\n }\n else {\n throw new Error(`Unknown dataFormat ${dataFormat}`);\n }\n return computeConv3DInfo(inShape, filterShape, strides, dilations, pad, false, $dataFormat, roundingMode);\n}\n/**\n * Computes the information for a forward pass of a convolution/pooling\n * operation.\n */\nfunction computeConv2DInfo(inShape, filterShape, strides, dilations, pad, roundingMode, depthwise = false, dataFormat = 'channelsLast') {\n let [batchSize, inHeight, inWidth, inChannels] = [-1, -1, -1, -1];\n if (dataFormat === 'channelsLast') {\n [batchSize, inHeight, inWidth, inChannels] = inShape;\n }\n else if (dataFormat === 'channelsFirst') {\n [batchSize, inChannels, inHeight, inWidth] = inShape;\n }\n else {\n throw new Error(`Unknown dataFormat ${dataFormat}`);\n }\n const [filterHeight, filterWidth, , filterChannels] = filterShape;\n const [strideHeight, strideWidth] = parseTupleParam(strides);\n const [dilationHeight, dilationWidth] = parseTupleParam(dilations);\n const effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);\n const effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);\n const { padInfo, outHeight, outWidth } = getPadAndOutInfo(pad, inHeight, inWidth, strideHeight, strideWidth, effectiveFilterHeight, effectiveFilterWidth, roundingMode, dataFormat);\n const outChannels = depthwise ? filterChannels * inChannels : filterChannels;\n let outShape;\n if (dataFormat === 'channelsFirst') {\n outShape = [batchSize, outChannels, outHeight, outWidth];\n }\n else if (dataFormat === 'channelsLast') {\n outShape = [batchSize, outHeight, outWidth, outChannels];\n }\n return {\n batchSize,\n dataFormat,\n inHeight,\n inWidth,\n inChannels,\n outHeight,\n outWidth,\n outChannels,\n padInfo,\n strideHeight,\n strideWidth,\n filterHeight,\n filterWidth,\n effectiveFilterHeight,\n effectiveFilterWidth,\n dilationHeight,\n dilationWidth,\n inShape,\n outShape,\n filterShape\n };\n}\n/**\n * Computes the information for a forward pass of a 3D convolution/pooling\n * operation.\n */\nfunction computeConv3DInfo(inShape, filterShape, strides, dilations, pad, depthwise = false, dataFormat = 'channelsLast', roundingMode) {\n let [batchSize, inDepth, inHeight, inWidth, inChannels] = [-1, -1, -1, -1, -1];\n if (dataFormat === 'channelsLast') {\n [batchSize, inDepth, inHeight, inWidth, inChannels] = inShape;\n }\n else if (dataFormat === 'channelsFirst') {\n [batchSize, inChannels, inDepth, inHeight, inWidth] = inShape;\n }\n else {\n throw new Error(`Unknown dataFormat ${dataFormat}`);\n }\n const [filterDepth, filterHeight, filterWidth, , filterChannels] = filterShape;\n const [strideDepth, strideHeight, strideWidth] = parse3TupleParam(strides);\n const [dilationDepth, dilationHeight, dilationWidth] = parse3TupleParam(dilations);\n const effectiveFilterDepth = getEffectiveFilterSize(filterDepth, dilationDepth);\n const effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);\n const effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);\n const { padInfo, outDepth, outHeight, outWidth } = get3DPadAndOutInfo(pad, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, effectiveFilterDepth, effectiveFilterHeight, effectiveFilterWidth, roundingMode);\n const outChannels = depthwise ? filterChannels * inChannels : filterChannels;\n let outShape;\n if (dataFormat === 'channelsFirst') {\n outShape = [batchSize, outChannels, outDepth, outHeight, outWidth];\n }\n else if (dataFormat === 'channelsLast') {\n outShape = [batchSize, outDepth, outHeight, outWidth, outChannels];\n }\n return {\n batchSize,\n dataFormat,\n inDepth,\n inHeight,\n inWidth,\n inChannels,\n outDepth,\n outHeight,\n outWidth,\n outChannels,\n padInfo,\n strideDepth,\n strideHeight,\n strideWidth,\n filterDepth,\n filterHeight,\n filterWidth,\n effectiveFilterDepth,\n effectiveFilterHeight,\n effectiveFilterWidth,\n dilationDepth,\n dilationHeight,\n dilationWidth,\n inShape,\n outShape,\n filterShape\n };\n}\nfunction computeOutputShape2D(inShape, fieldSize, stride, zeroPad, roundingMode) {\n if (zeroPad == null) {\n zeroPad = computeDefaultPad(inShape, fieldSize, stride);\n }\n const inputRows = inShape[0];\n const inputCols = inShape[1];\n const outputRows = round((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n const outputCols = round((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n return [outputRows, outputCols];\n}\nfunction computeOutputShape4D(inShape, fieldSize, outChannels, stride, zeroPad, roundingMode) {\n if (zeroPad == null) {\n zeroPad = computeDefaultPad(inShape, fieldSize, stride);\n }\n const inputDepth = inShape[0];\n const inputRows = inShape[1];\n const inputCols = inShape[2];\n const outputDepths = round((inputDepth - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n const outputRows = round((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n const outputCols = round((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n return [outputDepths, outputRows, outputCols, outChannels];\n}\nfunction computeDefaultPad(inputShape, fieldSize, stride, dilation = 1) {\n const effectiveFieldSize = getEffectiveFilterSize(fieldSize, dilation);\n return Math.floor((inputShape[0] * (stride - 1) - stride + effectiveFieldSize) / 2);\n}\nfunction parseTupleParam(param) {\n if (typeof param === 'number') {\n return [param, param, param];\n }\n if (param.length === 2) {\n return [param[0], param[1], 1];\n }\n return param;\n}\nfunction parse3TupleParam(param) {\n return typeof param === 'number' ? [param, param, param] : param;\n}\n/* See https://www.tensorflow.org/api_docs/python/tf/nn/atrous_conv2d\n * Atrous convolution is equivalent to standard convolution with upsampled\n * filters with effective_filter_height =\n * filter_height + (filter_height - 1) * (dilation - 1)\n * and effective_filter_width =\n * filter_width + (filter_width - 1) * (dilation - 1),\n * produced by inserting dilation - 1 zeros along consecutive elements across\n * the filters' spatial dimensions.\n * When there is a dilation, this converts a filter dimension to the\n * effective filter dimension, so it can be used in a standard convolution.\n */\nfunction getEffectiveFilterSize(filterSize, dilation) {\n if (dilation <= 1) {\n return filterSize;\n }\n return filterSize + (filterSize - 1) * (dilation - 1);\n}\nfunction getPadAndOutInfo(pad, inHeight, inWidth, strideHeight, strideWidth, filterHeight, filterWidth, roundingMode, dataFormat) {\n let padInfo;\n let outHeight;\n let outWidth;\n if (typeof pad === 'number') {\n const padType = (pad === 0) ? 'VALID' : 'NUMBER';\n padInfo = { top: pad, bottom: pad, left: pad, right: pad, type: padType };\n const outShape = computeOutputShape2D([inHeight, inWidth], filterHeight, strideHeight, pad, roundingMode);\n outHeight = outShape[0];\n outWidth = outShape[1];\n }\n else if (pad === 'same') {\n outHeight = Math.ceil(inHeight / strideHeight);\n outWidth = Math.ceil(inWidth / strideWidth);\n const padAlongHeight = Math.max(0, (outHeight - 1) * strideHeight + filterHeight - inHeight);\n const padAlongWidth = Math.max(0, (outWidth - 1) * strideWidth + filterWidth - inWidth);\n const top = Math.floor(padAlongHeight / 2);\n const bottom = padAlongHeight - top;\n const left = Math.floor(padAlongWidth / 2);\n const right = padAlongWidth - left;\n padInfo = { top, bottom, left, right, type: 'SAME' };\n }\n else if (pad === 'valid') {\n padInfo = { top: 0, bottom: 0, left: 0, right: 0, type: 'VALID' };\n outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);\n outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);\n }\n else if (typeof pad === 'object') {\n const top = dataFormat === 'channelsLast' ? pad[1][0] : pad[2][0];\n const bottom = dataFormat === 'channelsLast' ? pad[1][1] : pad[2][1];\n const left = dataFormat === 'channelsLast' ? pad[2][0] : pad[3][0];\n const right = dataFormat === 'channelsLast' ? pad[2][1] : pad[3][1];\n const padType = (top === 0 && bottom === 0 && left === 0 && right === 0) ?\n 'VALID' :\n 'EXPLICIT';\n padInfo = { top, bottom, left, right, type: padType };\n outHeight = round((inHeight - filterHeight + top + bottom) / strideHeight + 1, roundingMode);\n outWidth = round((inWidth - filterWidth + left + right) / strideWidth + 1, roundingMode);\n }\n else {\n throw Error(`Unknown padding parameter: ${pad}`);\n }\n return { padInfo, outHeight, outWidth };\n}\nfunction get3DPadAndOutInfo(pad, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, filterDepth, filterHeight, filterWidth, roundingMode) {\n let padInfo;\n let outDepth;\n let outHeight;\n let outWidth;\n if (typeof pad === 'number') {\n const padType = (pad === 0) ? 'VALID' : 'NUMBER';\n padInfo = {\n top: pad,\n bottom: pad,\n left: pad,\n right: pad,\n front: pad,\n back: pad,\n type: padType\n };\n const outShape = computeOutputShape4D([inDepth, inHeight, inWidth, 1], filterDepth, 1, strideDepth, pad, roundingMode);\n outDepth = outShape[0];\n outHeight = outShape[1];\n outWidth = outShape[2];\n }\n else if (pad === 'same') {\n outDepth = Math.ceil(inDepth / strideDepth);\n outHeight = Math.ceil(inHeight / strideHeight);\n outWidth = Math.ceil(inWidth / strideWidth);\n const padAlongDepth = (outDepth - 1) * strideDepth + filterDepth - inDepth;\n const padAlongHeight = (outHeight - 1) * strideHeight + filterHeight - inHeight;\n const padAlongWidth = (outWidth - 1) * strideWidth + filterWidth - inWidth;\n const front = Math.floor(padAlongDepth / 2);\n const back = padAlongDepth - front;\n const top = Math.floor(padAlongHeight / 2);\n const bottom = padAlongHeight - top;\n const left = Math.floor(padAlongWidth / 2);\n const right = padAlongWidth - left;\n padInfo = { top, bottom, left, right, front, back, type: 'SAME' };\n }\n else if (pad === 'valid') {\n padInfo = {\n top: 0,\n bottom: 0,\n left: 0,\n right: 0,\n front: 0,\n back: 0,\n type: 'VALID'\n };\n outDepth = Math.ceil((inDepth - filterDepth + 1) / strideDepth);\n outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);\n outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);\n }\n else {\n throw Error(`Unknown padding parameter: ${pad}`);\n }\n return { padInfo, outDepth, outHeight, outWidth };\n}\n/**\n * Rounds a value depending on the rounding mode\n * @param value\n * @param roundingMode A string from: 'ceil', 'round', 'floor'. If none is\n * provided, it will default to truncate.\n */\nfunction round(value, roundingMode) {\n if (!roundingMode) {\n return Math.trunc(value);\n }\n switch (roundingMode) {\n case 'round':\n // used for Caffe Conv\n return Math.round(value);\n case 'ceil':\n // used for Caffe Pool\n return Math.ceil(value);\n case 'floor':\n return Math.floor(value);\n default:\n throw new Error(`Unknown roundingMode ${roundingMode}`);\n }\n}\nfunction tupleValuesAreOne(param) {\n const [dimA, dimB, dimC] = parseTupleParam(param);\n return dimA === 1 && dimB === 1 && dimC === 1;\n}\nfunction eitherStridesOrDilationsAreOne(strides, dilations) {\n return tupleValuesAreOne(strides) || tupleValuesAreOne(dilations);\n}\n/**\n * Convert Conv2D dataFormat from 'NHWC'|'NCHW' to\n * 'channelsLast'|'channelsFirst'\n * @param dataFormat in 'NHWC'|'NCHW' mode\n * @return dataFormat in 'channelsLast'|'channelsFirst' mode\n * @throws unknown dataFormat\n */\nfunction convertConv2DDataFormat(dataFormat) {\n if (dataFormat === 'NHWC') {\n return 'channelsLast';\n }\n else if (dataFormat === 'NCHW') {\n return 'channelsFirst';\n }\n else {\n throw new Error(`Unknown dataFormat ${dataFormat}`);\n }\n}\n/**\n * Check validity of pad when using dimRoundingMode.\n * @param opDesc A string of op description\n * @param pad The type of padding algorithm.\n * - `same` and stride 1: output will be of same size as input,\n * regardless of filter size.\n * - `valid` output will be smaller than input if filter is larger\n * than 1x1.\n * - For more info, see this guide:\n * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](\n * https://www.tensorflow.org/api_docs/python/tf/nn/convolution)\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n * provided, it will default to truncate.\n * @throws unknown padding parameter\n */\nfunction checkPadOnDimRoundingMode(opDesc, pad, dimRoundingMode) {\n if (dimRoundingMode != null) {\n if (typeof pad === 'string') {\n throw Error(`Error in ${opDesc}: pad must be an integer when using ` +\n `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n }\n else if (typeof pad === 'number') {\n assert(isInt(pad), () => `Error in ${opDesc}: pad must be an integer when using ` +\n `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n }\n else if (typeof pad === 'object') {\n pad.forEach(p => {\n p.forEach(v => {\n assert(isInt(v), () => `Error in ${opDesc}: pad must be an integer when using ` +\n `dimRoundingMode ${dimRoundingMode} but got pad ${v}.`);\n });\n });\n }\n else {\n throw Error(`Error in ${opDesc}: Unknown padding parameter: ${pad}`);\n }\n }\n}\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes the backprop of a 3d avg pool.\n *\n * @param dy The dy error, of rank 5 of shape\n * [batchSize, depth, height, width, channels].\n * assumed.\n * @param input The original input image, of rank 5 or rank4 of shape\n * [batchSize, depth, height, width, channels].\n * @param filterSize The filter size:\n * `[filterDepth, filterHeight, filterWidth]`.\n * `filterSize` is a single number,\n * then `filterDepth == filterHeight == filterWidth`.\n * @param strides The strides of the pooling:\n * `[strideDepth, strideHeight, strideWidth]`. If\n * `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n * used in the forward prop of the op.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n * provided, it will default to truncate.\n */\nfunction avgPool3dGrad_(dy, input, filterSize, strides, pad, dimRoundingMode) {\n const $dy = convertToTensor(dy, 'dy', 'avgPool3dGrad');\n const $input = convertToTensor(input, 'input', 'avgPool3dGrad');\n let dy5D = $dy;\n let input5D = $input;\n let reshapedTo5D = false;\n if ($input.rank === 4) {\n reshapedTo5D = true;\n dy5D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]);\n input5D = reshape($input, [\n 1, $input.shape[0], $input.shape[1], $input.shape[2], $input.shape[3]\n ]);\n }\n assert(dy5D.rank === 5, () => `Error in avgPool3dGrad: dy must be rank 5 but got rank ` +\n `${dy5D.rank}.`);\n assert(input5D.rank === 5, () => `Error in avgPool3dGrad: input must be rank 5 but got rank ` +\n `${input5D.rank}.`);\n checkPadOnDimRoundingMode('avgPool3dGrad', pad, dimRoundingMode);\n const inputs = { dy: dy5D, input: input5D };\n const attrs = { filterSize, strides, pad, dimRoundingMode };\n // tslint:disable-next-line: no-unnecessary-type-assertion\n const res = ENGINE.runKernel(AvgPool3DGrad, inputs, attrs);\n if (reshapedTo5D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n }\n return res;\n}\nconst avgPool3dGrad = op({ avgPool3dGrad_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst avgPool3DGradConfig = {\n kernelName: AvgPool3D,\n inputsToSave: ['x'],\n gradFunc: (dy, saved, attrs) => {\n const [x] = saved;\n const { filterSize, strides, pad, dimRoundingMode } = attrs;\n return {\n x: () => avgPool3dGrad(dy, x, filterSize, strides, pad, dimRoundingMode)\n };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes the backprop of an 2D avg pool.\n *\n * @param dy The dy error, of rank 4 or rank 3 of shape\n * [batchSize, height, width, channels]. If rank 3, batch of 1 is\n * assumed.\n * @param input The input image, of rank 4 or rank 3 of shape\n * [batchSize, height, width, channels]. If rank 3, batch of 1 is\n * assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n * `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n * `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad The type of padding algorithm used in the forward prop of the op.\n * 'same', 'valid', for more info, see this guide:\n * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](\n * https://www.tensorflow.org/api_docs/python/tf/nn/convolution)\n */\nfunction avgPoolGrad_(dy, input, filterSize, strides, pad) {\n const $dy = convertToTensor(dy, 'dy', 'avgPoolGrad');\n const $input = convertToTensor(input, 'input', 'avgPoolGrad');\n assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);\n let input4D = $input;\n let dy4D = $dy;\n let reshapedTo4D = false;\n if ($input.rank === 3) {\n reshapedTo4D = true;\n input4D =\n reshape($input, [1, $input.shape[0], $input.shape[1], $input.shape[2]]);\n dy4D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2]]);\n }\n assert(dy4D.rank === 4, () => `Error in avgPoolGrad: dy must be rank 4 but got rank ` +\n `${dy4D.rank}.`);\n assert(input4D.rank === 4, () => `Error in avgPoolGrad: input must be rank 4 but got rank ` +\n `${input4D.rank}.`);\n const inputs = { dy: dy4D, input: input4D };\n const attrs = { filterSize, strides, pad };\n // tslint:disable-next-line: no-unnecessary-type-assertion\n const res = ENGINE.runKernel(AvgPoolGrad, inputs, attrs);\n if (reshapedTo4D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n }\n return res;\n}\nconst avgPoolGrad = op({ avgPoolGrad_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst avgPoolGradConfig = {\n kernelName: AvgPool,\n inputsToSave: ['x'],\n gradFunc: (dy, saved, attrs) => {\n const [x] = saved;\n const { filterSize, strides, pad } = attrs;\n return { x: () => avgPoolGrad(dy, x, filterSize, strides, pad) };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes the dot product of two matrices, A * B. These must be matrices.\n *\n * ```js\n * const a = tf.tensor2d([1, 2], [1, 2]);\n * const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * a.matMul(b).print(); // or tf.matMul(a, b)\n * ```\n * @param a First matrix in dot product operation.\n * @param b Second matrix in dot product operation.\n * @param transposeA If true, `a` is transposed before multiplication.\n * @param transposeB If true, `b` is transposed before multiplication.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction matMul_(a, b, transposeA = false, transposeB = false) {\n let $a = convertToTensor(a, 'a', 'matMul');\n let $b = convertToTensor(b, 'b', 'matMul');\n [$a, $b] = makeTypesMatch($a, $b);\n const inputs = { a: $a, b: $b };\n const attrs = { transposeA, transposeB };\n return ENGINE.runKernel(BatchMatMul, inputs, attrs);\n}\nconst matMul = op({ matMul_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst batchMatMulGradConfig = {\n kernelName: BatchMatMul,\n inputsToSave: ['a', 'b'],\n gradFunc: (dy, saved, attrs) => {\n const [a, b] = saved;\n const { transposeA, transposeB } = attrs;\n if (!transposeA && !transposeB) {\n return {\n a: () => matMul(dy, b, false, true),\n b: () => matMul(a, dy, true, false)\n };\n }\n else if (!transposeA && transposeB) {\n return {\n a: () => matMul(dy, b, false, false),\n b: () => matMul(dy, a, true, false)\n };\n }\n else if (transposeA && !transposeB) {\n return {\n a: () => matMul(b, dy, false, true),\n b: () => matMul(a, dy, false, false)\n };\n }\n else {\n return {\n a: () => matMul(b, dy, true, true),\n b: () => matMul(dy, a, true, true)\n };\n }\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * This operation divides \"spatial\" dimensions `[1, ..., M]` of the input into\n * a grid of blocks of shape `blockShape`, and interleaves these blocks with\n * the \"batch\" dimension (0) such that in the output, the spatial\n * dimensions `[1, ..., M]` correspond to the position within the grid,\n * and the batch dimension combines both the position within a spatial block\n * and the original batch position. Prior to division into blocks,\n * the spatial dimensions of the input are optionally zero padded\n * according to `paddings`. See below for a precise description.\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);\n * const blockShape = [2, 2];\n * const paddings = [[0, 0], [0, 0]];\n *\n * x.spaceToBatchND(blockShape, paddings).print();\n * ```\n *\n * @param x A `tf.Tensor`. N-D with `x.shape` = `[batch] + spatialShape +\n * remainingShape`, where spatialShape has `M` dimensions.\n * @param blockShape A 1-D array. Must have shape `[M]`, all values must\n * be >= 1.\n * @param paddings A 2-D array. Must have shape `[M, 2]`, all values must be >=\n * 0. `paddings[i] = [padStart, padEnd]` specifies the amount to zero-pad\n * from input dimension `i + 1`, which corresponds to spatial dimension `i`. It\n * is required that\n * `(inputShape[i + 1] + padStart + padEnd) % blockShape[i] === 0`\n *\n * This operation is equivalent to the following steps:\n *\n * 1. Zero-pad the start and end of dimensions `[1, ..., M]` of the input\n * according to `paddings` to produce `padded` of shape paddedShape.\n *\n * 2. Reshape `padded` to `reshapedPadded` of shape:\n * `[batch] + [paddedShape[1] / blockShape[0], blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1], blockShape[M-1]] + remainingShape`\n *\n * 3. Permute dimensions of `reshapedPadded` to produce `permutedReshapedPadded`\n * of shape: `blockShape + [batch] + [paddedShape[1] / blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1]] + remainingShape`\n *\n * 4. Reshape `permutedReshapedPadded` to flatten `blockShape` into the\n * batch dimension, producing an output tensor of shape:\n * `[batch * prod(blockShape)] + [paddedShape[1] / blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1]] + remainingShape`\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction spaceToBatchND_(x, blockShape, paddings) {\n const $x = convertToTensor(x, 'x', 'spaceToBatchND');\n assert($x.rank >= 1 + blockShape.length, () => `input rank ${$x.rank} should be > than [blockShape] ${blockShape.length}`);\n assert(paddings.length === blockShape.length, () => `paddings.shape[0] ${paddings.length} must be equal to [blockShape] ${blockShape.length}`);\n assert($x.shape.reduce((a, b, i) => {\n if (i > 0 && i <= blockShape.length) {\n return a &&\n ((b + paddings[i - 1][0] + paddings[i - 1][1]) %\n blockShape[i - 1] ===\n 0);\n }\n return a;\n }, true), () => `input spatial dimensions ${$x.shape.slice(1)} with paddings ${paddings.toString()} must be divisible by blockShapes ${blockShape.toString()}`);\n const inputs = { x: $x };\n const attrs = { blockShape, paddings };\n return ENGINE.runKernel(SpaceToBatchND, inputs, attrs);\n}\nconst spaceToBatchND = op({ spaceToBatchND_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst batchToSpaceNDGradConfig = {\n kernelName: BatchToSpaceND,\n gradFunc: (dy, saved, attrs) => {\n const { blockShape, crops } = attrs;\n return { x: () => spaceToBatchND(dy, blockShape, crops) };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst broadcastToGradConfig = {\n kernelName: BroadcastTo,\n gradFunc: (dy, saved, attrs) => {\n const broadCastToAttrs = attrs;\n const inputShape = broadCastToAttrs.inputShape;\n const outputShape = broadCastToAttrs.shape;\n const reps = Array.from(outputShape);\n for (let i = inputShape.length - 1; i >= 0; i--) {\n if (inputShape[i] === outputShape[i]) {\n reps[i] = 1;\n }\n else if (inputShape[i] !== 1) {\n throw new Error(`broadcastTo(): [${inputShape}] cannot be broadcast to [${outputShape}].`);\n }\n }\n const axes = [];\n for (let i = 0; i < reps.length; i++) {\n if (reps[i] > 1) {\n axes.push(i);\n }\n }\n return { x: () => sum$1(dy, axes, true /* keepDims */) };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst castGradConfig = {\n kernelName: Cast,\n gradFunc: (dy) => {\n return { x: () => dy.clone() };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst ceilGradConfig = {\n kernelName: Ceil,\n gradFunc: (dy) => {\n // TODO(manrajgrover): Return null for gradients when backprop supports it.\n return { x: () => zerosLike(dy) };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Returns the truth value of (a >= b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.greaterEqual(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction greaterEqual_(a, b) {\n let $a = convertToTensor(a, 'a', 'greaterEqual', 'string_or_numeric');\n let $b = convertToTensor(b, 'b', 'greaterEqual', 'string_or_numeric');\n [$a, $b] = makeTypesMatch($a, $b);\n assertAndGetBroadcastShape($a.shape, $b.shape);\n const inputs = { a: $a, b: $b };\n return ENGINE.runKernel(GreaterEqual, inputs);\n}\nconst greaterEqual = op({ greaterEqual_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Returns the truth value of (a <= b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.lessEqual(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction lessEqual_(a, b) {\n let $a = convertToTensor(a, 'a', 'lessEqual', 'string_or_numeric');\n let $b = convertToTensor(b, 'b', 'lessEqual', 'string_or_numeric');\n [$a, $b] = makeTypesMatch($a, $b);\n assertAndGetBroadcastShape($a.shape, $b.shape);\n const inputs = { a: $a, b: $b };\n return ENGINE.runKernel(LessEqual, inputs);\n}\nconst lessEqual = op({ lessEqual_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Returns the truth value of `a AND b` element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([false, false, true, true], 'bool');\n * const b = tf.tensor1d([false, true, false, true], 'bool');\n *\n * a.logicalAnd(b).print();\n * ```\n *\n * @param a The first input tensor. Must be of dtype bool.\n * @param b The second input tensor. Must be of dtype bool.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalAnd_(a, b) {\n const $a = convertToTensor(a, 'a', 'logicalAnd', 'bool');\n const $b = convertToTensor(b, 'b', 'logicalAnd', 'bool');\n assertAndGetBroadcastShape($a.shape, $b.shape);\n const inputs = { a: $a, b: $b };\n return ENGINE.runKernel(LogicalAnd, inputs);\n}\nconst logicalAnd = op({ logicalAnd_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Creates a new tensor with the same values and shape as the specified\n * tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n *\n * x.clone().print();\n * ```\n *\n * @param x The tensor to clone.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction clone_(x) {\n const $x = convertToTensor(x, 'x', 'clone', 'string_or_numeric');\n const inputs = { x: $x };\n // Note this op is called tf.identity in python. Hence the kernel name used\n // here.\n return ENGINE.runKernel(Identity, inputs);\n}\nconst clone = op({ clone_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Broadcast an array to a compatible shape NumPy-style.\n *\n * The tensor's shape is compared to the broadcast shape from end to beginning.\n * Ones are prepended to the tensor's shape until is has the same length as\n * the broadcast shape. If input.shape[i]==shape[i], the (i+1)-th axis is\n * already broadcast-compatible. If input.shape[i]==1 and shape[i]==N, then\n * the input tensor is tiled N times along that axis (using tf.tile).\n *\n * @param input The tensor that is to be broadcasted.\n * @param shape The input is to be broadcast to this shape.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction broadcastTo_(x, shape) {\n let input = convertToTensor(x, 'broadcastTo', 'x');\n const xShape = input.shape;\n if (shape.some(d => !(d > 0) || d % 1 !== 0)) {\n throw new Error(`broadcastTo(): Invalid broadcast shape [${shape}].`);\n }\n if (shape.length < input.rank) {\n throw new Error(`broadcastTo(): shape.length=${shape.length} < input.rank=${input.rank}.`);\n }\n if (shape.length > input.rank) {\n const newShape = input.shape.slice();\n while (newShape.length < shape.length) {\n newShape.unshift(1);\n }\n input = reshape(input, newShape);\n }\n const inputShape = input.shape;\n const reps = Array.from(shape);\n for (let i = shape.length - 1; i >= 0; i--) {\n if (inputShape[i] === shape[i]) {\n reps[i] = 1;\n }\n else if (input.shape[i] !== 1) {\n throw new Error(`broadcastTo(): [${xShape}] cannot be broadcast to [${shape}].`);\n }\n }\n const axes = reps.map((n, i) => n > 1 ? i : -1).filter(i => i >= 0);\n if (axes.length === 0) {\n return clone(input);\n }\n // TODO call broadcastTo kernel directly once backends implement broadcstTo\n const inputs = { x: input };\n const attrs = { reps };\n return ENGINE.runKernel(Tile, inputs, attrs);\n}\nconst broadcastTo = op({ broadcastTo_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Returns the elements, either `a` or `b` depending on the `condition`.\n *\n * If the condition is true, select from `a`, otherwise select from `b`.\n *\n * ```js\n * const cond = tf.tensor1d([false, false, true], 'bool');\n * const a = tf.tensor1d([1 , 2, 3]);\n * const b = tf.tensor1d([-1, -2, -3]);\n *\n * a.where(cond, b).print();\n * ```\n *\n * @param condition The input condition. Must be of dtype bool.\n * @param a If `condition` is rank 1, `a` may have a higher rank but\n * its first dimension must match the size of `condition`.\n * @param b A tensor with the same dtype as `a` and with shape that is\n * compatible with `a`.\n * @return A tensor with same dtype as `a` and `b`, and shape that is\n * broadcastable from `a` and `b`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction where_(condition, a, b) {\n const $a = convertToTensor(a, 'a', 'where');\n const $b = convertToTensor(b, 'b', 'where');\n const $condition = convertToTensor(condition, 'condition', 'where', 'bool');\n // TODO: move this logic to forward function when the broadcastTo op is\n // implemented in WASM.\n // Find the broadcastable shape for $condition, $a, and $b.\n const broadcastShape = assertAndGetBroadcastShape(assertAndGetBroadcastShape($condition.shape, $a.shape), $b.shape);\n const $broadcastedCondition = broadcastTo($condition, broadcastShape);\n const $broadcastedA = broadcastTo($a, broadcastShape);\n const $broadcastedB = broadcastTo($b, broadcastShape);\n const inputs = {\n condition: $broadcastedCondition,\n t: $broadcastedA,\n e: $broadcastedB\n };\n return ENGINE.runKernel(Select, inputs);\n}\nconst where = op({ where_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst clipByValueGradConfig = {\n kernelName: ClipByValue,\n inputsToSave: ['x'],\n gradFunc: (dy, saved, attrs) => {\n const [x] = saved;\n const { clipValueMin, clipValueMax } = attrs;\n return {\n x: () => where(logicalAnd(greaterEqual(x, clipValueMin), lessEqual(x, clipValueMax)), dy, zerosLike(dy)),\n };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst complexAbsGradConfig = {\n kernelName: ComplexAbs,\n inputsToSave: ['x'],\n gradFunc: absGradConfig.gradFunc,\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Splits a `tf.Tensor` into sub tensors.\n *\n * If `numOrSizeSplits` is a number, splits `x` along dimension `axis`\n * into `numOrSizeSplits` smaller tensors.\n * Requires that `numOrSizeSplits` evenly divides `x.shape[axis]`.\n *\n * If `numOrSizeSplits` is a number array, splits `x` into\n * `numOrSizeSplits.length` pieces. The shape of the `i`-th piece has the\n * same size as `x` except along dimension `axis` where the size is\n * `numOrSizeSplits[i]`.\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);\n * const [a, b] = tf.split(x, 2, 1);\n * a.print();\n * b.print();\n *\n * const [c, d, e] = tf.split(x, [1, 2, 1], 1);\n * c.print();\n * d.print();\n * e.print();\n * ```\n *\n * @param x The input tensor to split.\n * @param numOrSizeSplits Either an integer indicating the number of\n * splits along the axis or an array of integers containing the sizes of\n * each output tensor along the axis. If a number then it must evenly divide\n * `x.shape[axis]`; otherwise the sum of sizes must match `x.shape[axis]`.\n * Can contain one -1 indicating that dimension is to be inferred.\n * @param axis The dimension along which to split. Defaults to 0 (the first\n * dim).\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction split_(x, numOrSizeSplits, axis = 0) {\n const $x = convertToTensor(x, 'x', 'split');\n const inputs = { x: $x };\n const attr = { numOrSizeSplits, axis };\n return ENGINE.runKernel(SplitV, inputs, attr);\n}\nconst split = op({ split_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst concatGradConfig = {\n kernelName: Concat,\n saveAllInputs: true,\n gradFunc: (dy, saved, attrs) => {\n const shapes = saved.map(t => t.shape);\n const { axis } = attrs;\n const $axis = parseAxisParam(axis, saved[0].shape)[0];\n const sizeSplits = shapes.map(s => s[$axis]);\n const derTensors = split(dy, sizeSplits, $axis);\n return derTensors.map(t => () => t);\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes the derivative of the filter of a 2D convolution.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n * [batch, height, width, inChannels]. If rank 3, batch of 1 is assumed.\n * @param dy The dy image, of rank 4 or rank 3, of shape\n * [batch, height, width, outDepth]. If rank 3, batch of 1 is assumed.\n * @param filterShape The shape of the filter, length 4,\n * [filterHeight, filterWidth, inDepth, outDepth].\n * @param strides The strides of the convolution: [strideHeight,\n * strideWidth].\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n * used in the forward prop of the op.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n * \"NHWC\". Specify the data format of the input and output data. With the\n * default format \"NHWC\", the data is stored in the order of: [batch,\n * height, width, channels].\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n * provided, it will default to truncate.\n */\nfunction conv2DBackpropFilter_(x, dy, filterShape, strides, pad, dataFormat = 'NHWC', dimRoundingMode) {\n let x4D = x;\n if (x.rank === 3) {\n x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n }\n let dy4D = dy;\n if (dy4D.rank === 3) {\n dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n }\n assert(x4D.rank === 4, () => `Error in conv2dDerFilter: input must be rank 4, but got shape ` +\n `${x4D.shape}.`);\n assert(dy4D.rank === 4, () => `Error in conv2dDerFilter: dy must be rank 4, but got shape ` +\n `${dy4D.shape}.`);\n assert(filterShape.length === 4, () => `Error in conv2dDerFilter: filterShape must be length 4, but got ` +\n `${filterShape}.`);\n const inDepth = dataFormat === 'NHWC' ? x4D.shape[3] : x4D.shape[1];\n const outDepth = dataFormat === 'NHWC' ? dy4D.shape[3] : dy4D.shape[1];\n assert(inDepth === filterShape[2], () => `Error in conv2dDerFilter: depth of input ${inDepth}) must ` +\n `match input depth in filter (${filterShape[2]}.`);\n assert(outDepth === filterShape[3], () => `Error in conv2dDerFilter: depth of dy (${outDepth}) must ` +\n `match output depth for filter (${filterShape[3]}).`);\n checkPadOnDimRoundingMode('conv2dDerFilter', pad, dimRoundingMode);\n const inputs = { x: x4D, dy: dy4D };\n const attrs = { strides, pad, dataFormat, dimRoundingMode, filterShape };\n // tslint:disable-next-line: no-unnecessary-type-assertion\n return ENGINE.runKernel(Conv2DBackpropFilter, inputs, attrs);\n}\nconst conv2DBackpropFilter = op({ conv2DBackpropFilter_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes the derivative of the input of a 2D convolution.\n *\n * @param xShape The shape of the input: [batch, height, width, inDepth].\n * If length of 3, batch of 1 is assumed.\n * @param dy The derivative of the output, of rank 4 or rank 3 of shape\n * `[batch, outHeight, outWidth, outDepth]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter, rank 4, of shape\n * `[filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm used:\n * - `same` and stride 1: output will be of same size as input,\n * regardless of filter size.\n * - `valid`: output will be smaller than input if filter is larger\n * than 1x1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n * \"NHWC\". Specify the data format of the input and output data. With the\n * default format \"NHWC\", the data is stored in the order of: [batch,\n * height, width, channels].\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n * provided, it will default to truncate.\n */\nfunction conv2DBackpropInput_(xShape, dy, filter, strides, pad, dataFormat = 'NHWC', dimRoundingMode) {\n assert(xShape.length === dy.rank, () => `Length of inShape ` +\n `(${xShape.length}) and rank of dy (${dy.rank}) must match`);\n let xShape4D = xShape;\n let dy4D = dy;\n let reshapedTo4D = false;\n if (dy.rank === 3) {\n reshapedTo4D = true;\n dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n xShape4D = [1, xShape[0], xShape[1], xShape[2]];\n }\n assert(xShape4D.length === 4, () => `Error in conv2dDerInput: inShape must be length 4, but got length ` +\n `${xShape4D.length}.`);\n assert(dy4D.rank === 4, () => `Error in conv2dDerInput: dy must be rank 4, but got ` +\n `rank ${dy4D.rank}`);\n assert(filter.rank === 4, () => `Error in conv2dDerInput: filter must be rank 4, but got ` +\n `rank ${filter.rank}`);\n const inDepth = dataFormat === 'NHWC' ? xShape4D[3] : xShape4D[1];\n const outDepth = dataFormat === 'NHWC' ? dy4D.shape[3] : dy4D.shape[1];\n assert(inDepth === filter.shape[2], () => `Error in conv2dDerInput: depth of input (${inDepth}) must ` +\n `match input depth for filter ${filter.shape[2]}.`);\n assert(outDepth === filter.shape[3], () => `Error in conv2dDerInput: depth of output (${outDepth}) must ` +\n `match output depth for filter ${filter.shape[3]}.`);\n checkPadOnDimRoundingMode('conv2dDerInput', pad, dimRoundingMode);\n const inputs = { dy: dy4D, filter };\n const attrs = { strides, pad, dataFormat, dimRoundingMode, inputShape: xShape4D };\n // tslint:disable-next-line: no-unnecessary-type-assertion\n const res = ENGINE.runKernel(Conv2DBackpropInput, inputs, attrs);\n if (reshapedTo4D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n }\n return res;\n}\nconst conv2DBackpropInput = op({ conv2DBackpropInput_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst conv2DGradConfig = {\n kernelName: Conv2D,\n inputsToSave: ['x', 'filter'],\n gradFunc: (dy, saved, attrs) => {\n const [x4D, $filter] = saved;\n const { dilations, strides, pad, dataFormat } = attrs;\n assert(tupleValuesAreOne(dilations), () => 'Error in gradient of conv2D: dilation rates greater than 1 ' +\n `are not yet supported in gradients. Got dilations '${dilations}'`);\n return {\n x: () => conv2DBackpropInput(x4D.shape, dy, $filter, strides, pad, dataFormat),\n filter: () => conv2DBackpropFilter(x4D, dy, $filter.shape, strides, pad, dataFormat)\n };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes a 2D convolution over the input x.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter, rank 4, of shape\n * `[filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n * - `same` and stride 1: output will be of same size as input,\n * regardless of filter size.\n * - `valid`: output will be smaller than input if filter is larger\n * than 1x1.\n * - For more info, see this guide:\n * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](\n * https://www.tensorflow.org/api_docs/python/tf/nn/convolution)\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n * \"NHWC\". Specify the data format of the input and output data. With the\n * default format \"NHWC\", the data is stored in the order of: [batch,\n * height, width, channels].\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n * in which we sample input values across the height and width dimensions\n * in atrous convolution. Defaults to `[1, 1]`. If `dilations` is a single\n * number, then `dilationHeight == dilationWidth`. If it is greater than\n * 1, then all values of `strides` must be 1.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n * provided, it will default to truncate.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv2d_(x, filter, strides, pad, dataFormat = 'NHWC', dilations = [1, 1], dimRoundingMode) {\n const $x = convertToTensor(x, 'x', 'conv2d', 'float32');\n const $filter = convertToTensor(filter, 'filter', 'conv2d', 'float32');\n let x4D = $x;\n let reshapedTo4D = false;\n if ($x.rank === 3) {\n reshapedTo4D = true;\n x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n }\n assert(x4D.rank === 4, () => `Error in conv2d: input must be rank 4, but got rank ${x4D.rank}.`);\n assert($filter.rank === 4, () => `Error in conv2d: filter must be rank 4, but got rank ` +\n `${$filter.rank}.`);\n checkPadOnDimRoundingMode('conv2d', pad, dimRoundingMode);\n const inDepth = dataFormat === 'NHWC' ? x4D.shape[3] : x4D.shape[1];\n assert(inDepth === $filter.shape[2], () => `Error in conv2d: depth of input (${inDepth}) must match ` +\n `input depth for filter ${$filter.shape[2]}.`);\n assert(eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in conv2D: Either strides or dilations must be 1. ' +\n `Got strides ${strides} and dilations '${dilations}'`);\n const inputs = { x: x4D, filter: $filter };\n const attrs = { strides, pad, dataFormat, dilations, dimRoundingMode };\n // tslint:disable-next-line: no-unnecessary-type-assertion\n const res = ENGINE.runKernel(Conv2D, inputs, attrs);\n if (reshapedTo4D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n }\n return res;\n}\nconst conv2d = op({ conv2d_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst conv2DBackpropInputGradConfig = {\n kernelName: Conv2DBackpropInput,\n inputsToSave: ['dy', 'filter'],\n gradFunc: (ddx, saved, attrs) => {\n const [dy, filter] = saved;\n const { strides, pad, dataFormat, dimRoundingMode } = attrs;\n return {\n dy: () => conv2d(ddx, filter, strides, pad, dataFormat, 1 /* dilations */, dimRoundingMode),\n filter: () => conv2DBackpropFilter(ddx, dy, filter.shape, strides, pad, dataFormat, dimRoundingMode)\n };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes the derivative of the filter of a 3D convolution.\n *\n * @param x The input tensor, of rank 5 or rank 4 of shape\n * [batch, depth, height, width, inChannels]. If rank 4, batch of 1 is\n * assumed.\n * @param dy The dy image, of rank 5 or rank 4, of shape\n * [batch, depth, height, width, outDepth]. If rank 4, batch of 1 is\n * assumed.\n * @param filterShape The shape of the filter, length 5,\n * [filterDepth, filterHeight, filterWidth, inDepth, outDepth].\n * @param strides The strides of the convolution: [strideDepth, strideHeight,\n * strideWidth].\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n * used in the forward prop of the op.\n */\nfunction conv3DBackpropFilter_(x, dy, filterShape, strides, pad) {\n let x5D = x;\n if (x.rank === 4) {\n x5D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2], x.shape[3]]);\n }\n let dy5D = dy;\n if (dy5D.rank === 4) {\n dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);\n }\n assert(x5D.rank === 5, () => `Error in conv3dDerFilter: input must be rank 5, but got shape ` +\n `${x5D.shape}.`);\n assert(dy5D.rank === 5, () => `Error in conv3dDerFilter: dy must be rank 5, but got shape ` +\n `${dy5D.shape}.`);\n assert(filterShape.length === 5, () => `Error in conv3dDerFilter: filterShape must be length 5, but got ` +\n `${filterShape}.`);\n assert(x5D.shape[4] === filterShape[3], () => `Error in conv3dDerFilter: depth of input ${x5D.shape[4]}) must ` +\n `match input depth in filter (${filterShape[3]}.`);\n assert(dy5D.shape[4] === filterShape[4], () => `Error in conv3dDerFilter: depth of dy (${dy5D.shape[4]}) must ` +\n `match output depth for filter (${filterShape[4]}).`);\n const inputs = { x: x5D, dy: dy5D };\n const attrs = { strides, pad, filterShape };\n // tslint:disable-next-line: no-unnecessary-type-assertion\n return ENGINE.runKernel(Conv3DBackpropFilterV2, inputs, attrs);\n}\nconst conv3DBackpropFilter = op({ conv3DBackpropFilter_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes the derivative of the input of a 3D convolution.\n *\n * @param xShape The shape of the input: [batch, depth, height, width,\n * in_channels]. If length of 4, batch of 1 is assumed.\n * @param dy The derivative of the output, of rank 5 or rank 4 of shape\n * `[batch, outDepth, outHeight, outWidth, in_channels]`.\n * If rank 4, batch of 1 is assumed.\n * @param filter The filter, rank 5, of shape\n * `[filterDepth, filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideDepth, strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm used:\n * - `same` and stride 1: output will be of same size as input,\n * regardless of filter size.\n * - `valid`: output will be smaller than input if filter is larger\n * than 1x1.\n */\nfunction conv3DBackpropInput_(xShape, dy, filter, strides, pad) {\n assert(xShape.length === dy.rank, () => `Length of inShape ` +\n `(${xShape.length}) and rank of dy (${dy.rank}) must match`);\n let xShape5D = xShape;\n let dy5D = dy;\n let reshapedTo5D = false;\n if (dy.rank === 4) {\n reshapedTo5D = true;\n dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);\n xShape5D = [1, xShape[0], xShape[1], xShape[2], xShape[3]];\n }\n const inDepth = xShape5D[4];\n const outDepth = dy5D.shape[4];\n assert(xShape5D.length === 5, () => `Error in conv3dDerInput: inShape must be length 5, but got length ` +\n `${xShape5D.length}.`);\n assert(dy5D.rank === 5, () => `Error in conv3dDerInput: dy must be rank 5, but got ` +\n `rank ${dy5D.rank}`);\n assert(filter.rank === 5, () => `Error in conv3dDerInput: filter must be rank 5, but got ` +\n `rank ${filter.rank}`);\n assert(inDepth === filter.shape[3], () => `Error in conv3dDerInput: depth of input (${inDepth}) must ` +\n `match input depth for filter ${filter.shape[3]}.`);\n assert(outDepth === filter.shape[4], () => `Error in conv3dDerInput: depth of output (${outDepth}) must ` +\n `match output depth for filter ${filter.shape[4]}.`);\n const inputs = { dy: dy5D, filter };\n const attrs = { pad, strides, inputShape: xShape5D };\n // tslint:disable-next-line: no-unnecessary-type-assertion\n const res = ENGINE.runKernel(Conv3DBackpropInputV2, inputs, attrs);\n if (reshapedTo5D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n }\n return res;\n}\nconst conv3DBackpropInput = op({ conv3DBackpropInput_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst conv3DGradConfig = {\n kernelName: Conv3D,\n inputsToSave: ['x', 'filter'],\n gradFunc: (dy, saved, attrs) => {\n const { dilations, strides, pad } = attrs;\n assert(tupleValuesAreOne(dilations), () => 'Error in gradient of conv3D: dilation rates greater than 1 are ' +\n `not yet supported in gradients. Got dilations '${dilations}'`);\n const [x5D, $filter] = saved;\n return {\n x: () => conv3DBackpropInput(x5D.shape, dy, $filter, strides, pad),\n filter: () => conv3DBackpropFilter(x5D, dy, $filter.shape, strides, pad)\n };\n }\n};\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes sin of the input Tensor element-wise: `sin(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);\n *\n * x.sin().print(); // or tf.sin(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sin_(x) {\n const $x = convertToTensor(x, 'x', 'sin', 'float32');\n const inputs = { x: $x };\n return ENGINE.runKernel(Sin, inputs);\n}\nconst sin = op({ sin_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst cosGradConfig = {\n kernelName: Cos,\n inputsToSave: ['x'],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n return { x: () => mul(neg(sin(cast(x, 'float32'))), dy) };\n }\n};\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes hyperbolic sin of the input `tf.Tensor` element-wise: `sinh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.sinh().print(); // or tf.sinh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sinh_(x) {\n const $x = convertToTensor(x, 'x', 'sinh');\n const inputs = { x: $x };\n return ENGINE.runKernel(Sinh, inputs);\n}\nconst sinh = op({ sinh_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst coshGradConfig = {\n kernelName: Cosh,\n inputsToSave: ['x'],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n return { x: () => mul(sinh(cast(x, 'float32')), dy) };\n }\n};\n\n/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Returns true if the axis specifies the inner most dimensions of the\n * array.\n */\nfunction axesAreInnerMostDims(axes, rank) {\n for (let i = 0; i < axes.length; ++i) {\n if (axes[axes.length - i - 1] !== rank - 1 - i) {\n return false;\n }\n }\n return true;\n}\nfunction combineLocations(outputLoc, reduceLoc, axes) {\n const rank = outputLoc.length + reduceLoc.length;\n const loc = [];\n let outIdx = 0;\n let reduceIdx = 0;\n for (let dim = 0; dim < rank; dim++) {\n if (axes.indexOf(dim) === -1) {\n loc.push(outputLoc[outIdx++]);\n }\n else {\n loc.push(reduceLoc[reduceIdx++]);\n }\n }\n return loc;\n}\nfunction computeOutAndReduceShapes(aShape, axes) {\n const outShape = [];\n const rank = aShape.length;\n for (let dim = 0; dim < rank; dim++) {\n if (axes.indexOf(dim) === -1) {\n outShape.push(aShape[dim]);\n }\n }\n const reduceShape = axes.map(dim => aShape[dim]);\n return [outShape, reduceShape];\n}\nfunction expandShapeToKeepDim(shape, axes) {\n const reduceSubShape = axes.map(x => 1);\n return combineLocations(shape, reduceSubShape, axes);\n}\nfunction assertAxesAreInnerMostDims(msg, axes, rank) {\n assert(axesAreInnerMostDims(axes, rank), () => `${msg} supports only inner-most axes for now. ` +\n `Got axes ${axes} and rank-${rank} input.`);\n}\n/**\n * Returns the axes permutation to be used with `tf.transpose`, if such\n * permutation is necessary. Otherwise it returns null. This method is used by\n * operations that operate only on inner-most axes.\n */\nfunction getAxesPermutation(axes, rank) {\n if (axesAreInnerMostDims(axes, rank)) {\n return null;\n }\n const result = [];\n for (let i = 0; i < rank; ++i) {\n if (axes.indexOf(i) === -1) {\n result.push(i);\n }\n }\n axes.forEach(axis => result.push(axis));\n return result;\n}\n/** Returns the axes permutation that undoes the original permutation. */\nfunction getUndoAxesPermutation(axes) {\n return axes.map((axis, i) => [i, axis])\n .sort((a, b) => a[1] - b[1])\n .map(x => x[0]);\n}\nfunction getInnerMostAxes(numAxes, rank) {\n const res = [];\n for (let i = rank - numAxes; i < rank; ++i) {\n res.push(i);\n }\n return res;\n}\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes the cumulative sum of a `tf.Tensor` along `axis`.\n *\n * ```js\n * const x = tf.tensor([1, 2, 3, 4]);\n * x.cumsum().print();\n * ```\n * ```js\n * const x = tf.tensor([[1, 2], [3, 4]]);\n * x.cumsum().print();\n * ```\n *\n * @param x The input tensor to be summed.\n * @param axis The axis along which to sum. Optional. Defaults to 0.\n * @param exclusive Whether to perform exclusive cumulative sum. Optional.\n * Defaults to false. If set to true then the sum of each tensor entry\n * does not include its own value, but only the values previous to it\n * along the specified axis.\n * @param reverse Whether to sum in the opposite direction. Optional.\n * Defaults to false.\n *\n * @doc {heading: 'Operations', subheading: 'Scan'}\n */\nfunction cumsum_(x, axis = 0, exclusive = false, reverse = false) {\n const $x = convertToTensor(x, 'x', 'cumsum');\n const inputs = { x: $x };\n const attrs = { axis, exclusive, reverse };\n return ENGINE.runKernel(Cumsum, inputs, attrs);\n}\nconst cumsum = op({ cumsum_ });\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Transposes the `tf.Tensor`. Permutes the dimensions according to `perm`.\n *\n * The returned `tf.Tensor`'s dimension `i` will correspond to the input\n * dimension `perm[i]`. If `perm` is not given, it is set to `[n-1...0]`,\n * where `n` is the rank of the input `tf.Tensor`. Hence by default, this\n * operation performs a regular matrix transpose on 2-D input `tf.Tensor`s.\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);\n *\n * a.transpose().print(); // or tf.transpose(a)\n * ```\n *\n * @param x The tensor to transpose.\n * @param perm The permutation of the dimensions of a.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction transpose_(x, perm) {\n const $x = convertToTensor(x, 'x', 'transpose');\n if (perm == null) {\n perm = $x.shape.map((s, i) => i).reverse();\n }\n assert($x.rank === perm.length, () => `Error in transpose: rank of input ${$x.rank} ` +\n `must match length of perm ${perm}.`);\n perm.forEach(axis => {\n assert(axis >= 0 && axis < $x.rank, () => `All entries in 'perm' must be between 0 and ${$x.rank - 1}` +\n ` but got ${perm}`);\n });\n if ($x.rank <= 1) {\n return $x.clone();\n }\n const inputs = { x: $x };\n const attrs = { perm };\n return ENGINE.runKernel(Transpose, inputs, attrs);\n}\nconst transpose = op({ transpose_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst cumsumGradConfig = {\n kernelName: Cumsum,\n inputsToSave: ['x'],\n gradFunc: (dy, saved, attrs) => {\n const [x] = saved;\n const { axis, exclusive, reverse } = attrs;\n return {\n x: () => {\n const permutation = getAxesPermutation([axis], x.rank);\n let out = cumsum(dy, axis, exclusive, !reverse);\n if (permutation != null) {\n out = transpose(out, permutation);\n }\n return out;\n }\n };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nfunction depthwiseConv2dNativeBackpropFilter_(x, dy, filterShape, strides, pad, dilations = [1, 1], dimRoundingMode) {\n let x4D = x;\n if (x.rank === 3) {\n x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n }\n let dy4D = dy;\n if (dy4D.rank === 3) {\n dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n }\n const inputs = { x: x4D, dy: dy4D };\n const attrs = { strides, pad, dimRoundingMode, dilations, filterShape };\n // tslint:disable-next-line: no-unnecessary-type-assertion\n return ENGINE.runKernel(DepthwiseConv2dNativeBackpropFilter, inputs, attrs);\n}\nconst depthwiseConv2dNativeBackpropFilter = op({ depthwiseConv2dNativeBackpropFilter_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nfunction depthwiseConv2dNativeBackpropInput_(xShape, dy, filter, strides, pad, dilations = [1, 1], dimRoundingMode) {\n let dy4D = dy;\n let reshapedTo4D = false;\n if (dy.rank === 3) {\n reshapedTo4D = true;\n dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n }\n const inputs = { dy: dy4D, filter };\n const attrs = { strides, pad, dimRoundingMode, dilations, inputShape: xShape };\n const res = \n // tslint:disable-next-line: no-unnecessary-type-assertion\n ENGINE.runKernel(DepthwiseConv2dNativeBackpropInput, inputs, attrs);\n if (reshapedTo4D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n }\n return res;\n}\nconst depthwiseConv2dNativeBackpropInput = op({ depthwiseConv2dNativeBackpropInput_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst depthwiseConv2dNativeGradConfig = {\n kernelName: DepthwiseConv2dNative,\n inputsToSave: ['x', 'filter'],\n gradFunc: (dy, saved, attrs) => {\n const { dilations, strides, pad, dimRoundingMode } = attrs;\n const $dilations = dilations == null ? [1, 1] : dilations;\n assert(tupleValuesAreOne($dilations), () => 'Error in gradient of depthwiseConv2dNative: dilation rates ' +\n `greater than 1 are not yet supported. Got dilations ` +\n `'${$dilations}'`);\n const [x, filter] = saved;\n assert(x.rank === 4, () => `Error in gradient of depthwiseConv2dNative: input must be ` +\n `rank 4, but got rank ${x.rank}.`);\n assert(filter.rank === 4, () => `Error in gradient of depthwiseConv2dNative: filter must be ` +\n `rank 4, but got rank ${filter.rank}.`);\n assert(x.shape[3] === filter.shape[2], () => `Error in gradient of depthwiseConv2d: number of input ` +\n `channels (${x.shape[3]}) must match the inChannels dimension ` +\n `in filter ${filter.shape[2]}.`);\n assert(eitherStridesOrDilationsAreOne(strides, $dilations), () => 'Error in gradient of depthwiseConv2d: Either strides or ' +\n `dilations must be 1. Got strides ${strides} and dilations ` +\n `'${$dilations}'.`);\n checkPadOnDimRoundingMode('depthwiseConv2d', pad, dimRoundingMode);\n return {\n x: () => depthwiseConv2dNativeBackpropInput(x.shape, dy, filter, strides, pad, $dilations, dimRoundingMode),\n filter: () => depthwiseConv2dNativeBackpropFilter(x, dy, filter.shape, strides, pad, $dilations, dimRoundingMode),\n };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst dilation2dGradConfig = {\n kernelName: Dilation2D,\n inputsToSave: ['x', 'filter'],\n gradFunc: (dy, saved, attrs) => {\n const [x, filter] = saved;\n const inputInputs = { x, filter, dy };\n const filterInputs = { x, filter, dy };\n return {\n x: () => ENGINE.runKernel(Dilation2DBackpropInput, inputInputs, attrs),\n filter: () => ENGINE.runKernel(Dilation2DBackpropFilter, filterInputs, attrs)\n };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst eluGradConfig = {\n kernelName: Elu,\n outputsToSave: [true],\n gradFunc: (dy, saved) => {\n const [y] = saved;\n const inputs = { dy, y };\n return { x: () => ENGINE.runKernel(EluGrad, inputs) };\n }\n};\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes exponential of the input `tf.Tensor` element-wise. `e ^ x`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, -3]);\n *\n * x.exp().print(); // or tf.exp(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction exp_(x) {\n const $x = convertToTensor(x, 'x', 'exp');\n const inputs = { x: $x };\n return ENGINE.runKernel(Exp, inputs);\n}\nconst exp = op({ exp_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst erfGradConfig = {\n kernelName: Erf,\n inputsToSave: ['x'],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n const a = mul(exp(neg(square(x))), 2 / Math.sqrt(Math.PI));\n return { x: () => mul(dy, a) };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst expGradConfig = {\n kernelName: Exp,\n outputsToSave: [true],\n gradFunc: (dy, saved) => {\n const [y] = saved;\n return { x: () => mul(dy, y) };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst expandDimsGradConfig = {\n kernelName: ExpandDims,\n inputsToSave: ['input'],\n gradFunc: (dy, saved) => {\n const [input] = saved;\n return { input: () => reshape(dy, input.shape) };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst expm1GradConfig = {\n kernelName: Expm1,\n inputsToSave: ['x'],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n return { x: () => mul(dy, exp(x)) };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst floorGradConfig = {\n kernelName: Floor,\n gradFunc: (dy) => {\n return { x: () => zerosLike(dy) };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst floorDivGradConfig = {\n kernelName: FloorDiv,\n inputsToSave: ['a', 'b'],\n gradFunc: (dy, saved) => {\n const [a, b] = saved;\n const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n const derA = () => {\n const res = div(dy, cast(b, 'float32'));\n const reduceAxes = getReductionAxes(a.shape, outShape);\n if (reduceAxes.length > 0) {\n return reshape(sum$1(res, reduceAxes), a.shape);\n }\n return res;\n };\n const derB = () => {\n let res = mul(dy, cast(a, 'float32'));\n const reduceAxes = getReductionAxes(b.shape, outShape);\n if (reduceAxes.length > 0) {\n res = reshape(sum$1(res, reduceAxes), b.shape);\n }\n const tmp = square(b);\n return neg(div(res, cast(tmp, 'float32')));\n };\n return { a: derA, b: derB };\n }\n};\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes reciprocal of square root of the input `tf.Tensor` element-wise:\n * `y = 1 / sqrt(x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 4, -1]);\n *\n * x.rsqrt().print(); // or tf.rsqrt(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction rsqrt_(x) {\n const $x = convertToTensor(x, 'x', 'rsqrt', 'float32');\n const inputs = { x: $x };\n return ENGINE.runKernel(Rsqrt, inputs);\n}\nconst rsqrt = op({ rsqrt_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Construct a tensor by repeating it the number of times given by reps.\n *\n * This operation creates a new tensor by replicating `input` `reps`\n * times. The output tensor's i'th dimension has `input.shape[i] *\n * reps[i]` elements, and the values of `input` are replicated\n * `reps[i]` times along the i'th dimension. For example, tiling\n * `[a, b, c, d]` by `[2]` produces `[a, b, c, d, a, b, c, d]`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n *\n * a.tile([2]).print(); // or a.tile([2])\n * ```\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * a.tile([1, 2]).print(); // or a.tile([1, 2])\n * ```\n * @param x The tensor to tile.\n * @param reps Determines the number of replications per dimension.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction tile_(x, reps) {\n const $x = convertToTensor(x, 'x', 'tile', 'string_or_numeric');\n assert($x.rank === reps.length, () => `Error in transpose: rank of input ${$x.rank} ` +\n `must match length of reps ${reps}.`);\n const inputs = { x: $x };\n const attrs = { reps };\n return ENGINE.runKernel(Tile, inputs, attrs);\n}\nconst tile = op({ tile_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst fusedBatchNormGradConfig = {\n kernelName: FusedBatchNorm,\n inputsToSave: ['x', 'mean', 'variance', 'scale'],\n gradFunc: (dy, saved, attrs) => {\n const { varianceEpsilon } = attrs;\n const [x, mean, variance, scale] = saved;\n const scaleValue = scale == null ? scalar(1) : scale;\n const reductionAxes = getReductionAxes(mean.shape, x.shape);\n const tileShape = [];\n if (mean.rank === 1) {\n for (let i = 0; i < x.shape.length - 1; ++i) {\n tileShape.push(x.shape[i]);\n }\n tileShape.push(1);\n }\n const xMinusMean = sub(x, mean);\n const dyTimesScaleValue = mul(dy, scaleValue);\n const oneOverSqrtVariance = rsqrt(add$1(variance, scalar(varianceEpsilon)));\n const minusHalfRCube = mul(mul(mul(oneOverSqrtVariance, oneOverSqrtVariance), oneOverSqrtVariance), scalar(-0.5));\n const derX = () => {\n if (mean.rank === 1) {\n return reshape(mul(mul(dy, tile(reshape(oneOverSqrtVariance, [1, 1, 1, mean.shape[0]]), tileShape)), scaleValue), x.shape);\n }\n else {\n return reshape(mul(mul(dy, oneOverSqrtVariance), scaleValue), x.shape);\n }\n };\n const derMean = () => {\n let meanDer = mul(mul(oneOverSqrtVariance, scalar(-1)), dyTimesScaleValue);\n if (mean.rank === 1) {\n meanDer = sum$1(meanDer, reductionAxes);\n }\n return reshape(meanDer, mean.shape);\n };\n const derVariance = () => {\n let varianceDer = mul(mul(minusHalfRCube, xMinusMean), dyTimesScaleValue);\n if (mean.rank === 1) {\n varianceDer = sum$1(varianceDer, reductionAxes);\n }\n return reshape(varianceDer, mean.shape);\n };\n const derScale = () => {\n const xMinusMean2TimesRsqrt = mul(xMinusMean, oneOverSqrtVariance);\n let scaleDer = mul(dy, xMinusMean2TimesRsqrt);\n if (mean.rank === 1) {\n scaleDer = sum$1(scaleDer, reductionAxes);\n }\n return reshape(scaleDer, mean.shape);\n };\n const derOffset = () => {\n let offsetDer = dy;\n if (mean.rank === 1) {\n offsetDer = sum$1(offsetDer, reductionAxes);\n }\n return reshape(offsetDer, mean.shape);\n };\n return {\n x: derX,\n mean: derMean,\n variance: derVariance,\n scale: derScale,\n offset: derOffset\n };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes the sum along segments of a `tf.Tensor`.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * const segmentIds = tf.tensor1d([1, 2, 0, 1], 'int32');\n * const numSegments = 3;\n *\n * x.unsortedSegmentSum(segmentIds, numSegments).print()\n * //or tf.unsortedSegmentSum(x, segmentIds, numSegments)\n * ```\n * @param x The `tf.Tensor` that will be summed along its segments.\n * @param segmentIds A `tf.Tensor1D` whose rank is equal to the rank of `x`'s\n * dimension along the `axis`. Maps each element of `x` to a segment.\n * @param numSegments The number of distinct `segmentIds`.\n *\n * @doc {heading: 'Operations', subheading: 'Segment'}\n */\nfunction unsortedSegmentSum_(x, segmentIds, numSegments) {\n const $x = convertToTensor(x, 'x', 'unsortedSegmentSum');\n const $segmentIds = convertToTensor(segmentIds, 'segmentIds', 'unsortedSegmentSum', 'int32');\n assert(isInt(numSegments), () => 'numSegments must be of dtype int');\n const inputs = { x: $x, segmentIds: $segmentIds };\n const attrs = { numSegments };\n return ENGINE.runKernel(UnsortedSegmentSum, inputs, attrs);\n}\nconst unsortedSegmentSum = op({ unsortedSegmentSum_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst gatherGradConfig = {\n kernelName: GatherV2,\n inputsToSave: ['x', 'indices'],\n gradFunc: (dy, saved, attrs) => {\n const [x, indices] = saved;\n const { axis } = attrs;\n const parsedAxis = parseAxisParam(axis, x.shape)[0];\n const derX = () => {\n const paramsShape = x.shape;\n const indicesSize = indices.size;\n const outerShape = paramsShape.slice(0, parsedAxis);\n const outerDims = outerShape.length;\n const innerShape = paramsShape.slice(axis, paramsShape.length).slice(1);\n const innerDims = innerShape.length;\n const outerAxesIndices = arrayRange(0, outerDims);\n const innerAxesIndices = arrayRange(outerDims + 1, outerDims + 1 + innerDims);\n const valuesShape = arrayConcat([outerShape, [indicesSize], innerShape]);\n const values = reshape(dy, valuesShape);\n const reshapedIndices = reshape(indices, [indicesSize]);\n const transposeDims = arrayConcat([[outerDims], outerAxesIndices, innerAxesIndices]);\n const valuesTranspose = transpose(values, transposeDims);\n let paramsGrad = unsortedSegmentSum(valuesTranspose, reshapedIndices, x.shape[parsedAxis]);\n const invertTransposeDims = getUndoAxesPermutation(transposeDims);\n paramsGrad = transpose(paramsGrad, invertTransposeDims);\n return paramsGrad;\n };\n return { x: derX, indices: () => indices };\n }\n};\nfunction arrayRange(start, stop) {\n const result = [];\n for (let i = start; i < stop; ++i) {\n result.push(i);\n }\n return result;\n}\nfunction arrayConcat(arrays) {\n const result = [];\n for (let i = 0; i < arrays.length; ++i) {\n for (let j = 0; j < arrays[i].length; ++j) {\n result.push(arrays[i][j]);\n }\n }\n return result;\n}\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst greaterEqualGradConfig = {\n kernelName: GreaterEqual,\n inputsToSave: ['a', 'b'],\n gradFunc: (dy, saved) => {\n const [a, b] = saved;\n return { a: () => zerosLike(a), b: () => zerosLike(b) };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst identityGradConfig = {\n kernelName: Identity,\n gradFunc: (dy) => {\n return { x: () => cast(dy, 'float32') };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst isFiniteGradConfig = {\n kernelName: IsFinite,\n gradFunc: (dy) => {\n // TODO(nsthorat): Let gradients be null for cases where we want to stop\n // backpropgation.\n return { x: () => zerosLike(dy) };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst isInfGradConfig = {\n kernelName: IsInf,\n gradFunc: (dy) => {\n // TODO(nsthorat): Let gradients be null for cases where we want to stop\n // backpropgation.\n return { x: () => zerosLike(dy) };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst isNanGradConfig = {\n kernelName: IsNan,\n gradFunc: (dy) => {\n // TODO(nsthorat): Let gradients be null for cases where we want to stop\n // backpropgation.\n return { x: () => zerosLike(dy) };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Returns the truth value of (a > b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.greater(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction greater_(a, b) {\n let $a = convertToTensor(a, 'a', 'greater', 'string_or_numeric');\n let $b = convertToTensor(b, 'b', 'greater', 'string_or_numeric');\n [$a, $b] = makeTypesMatch($a, $b);\n assertAndGetBroadcastShape($a.shape, $b.shape);\n const inputs = { a: $a, b: $b };\n return ENGINE.runKernel(Greater, inputs);\n}\nconst greater = op({ greater_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst leakyReluGradConfig = {\n kernelName: LeakyRelu,\n inputsToSave: ['x'],\n gradFunc: (dy, saved, attrs) => {\n const [x] = saved;\n const { alpha } = attrs;\n const mask = greater(x, 0);\n // Returns `gradients * (features > 0) + alpha * gradients * (features <=\n // 0)`.\n return { x: () => where(mask, dy, mul(dy, alpha)) };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst log1pGradConfig = {\n kernelName: Log1p,\n inputsToSave: ['x'],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n return { x: () => div(dy, add$1(x, 1)) };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst logGradConfig = {\n kernelName: Log,\n inputsToSave: ['x'],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n return { x: () => div(dy, cast(x, 'float32')) };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst logSoftmaxGradConfig = {\n kernelName: LogSoftmax,\n inputsToSave: [],\n outputsToSave: [true],\n gradFunc: (dy, saved, attrs) => {\n const [value] = saved;\n const { axis } = attrs;\n return {\n logits: () => {\n const keepDims = true;\n const softmax = exp(value);\n return sub(dy, mul(sum$1(dy, axis, keepDims), softmax));\n }\n };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nfunction localResponseNormalizationBackprop_(x, y, dy, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5) {\n const inputs = { x, y, dy };\n const attrs = { depthRadius, bias, alpha, beta };\n return ENGINE.runKernel(LRNGrad, inputs, attrs);\n}\nconst localResponseNormalizationBackprop = op({ localResponseNormalizationBackprop_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst lrnGradConfig = {\n kernelName: LRN,\n inputsToSave: ['x'],\n outputsToSave: [true],\n gradFunc: (dy, saved, attrs) => {\n const [x, y] = saved;\n const { depthRadius, bias, alpha, beta } = attrs;\n return {\n x: () => localResponseNormalizationBackprop(x, y, dy, depthRadius, bias, alpha, beta)\n };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Returns the truth value of (a == b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.equal(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction equal_(a, b) {\n let $a = convertToTensor(a, 'a', 'equal', 'string_or_numeric');\n let $b = convertToTensor(b, 'b', 'equal', 'string_or_numeric');\n [$a, $b] = makeTypesMatch($a, $b);\n assertAndGetBroadcastShape($a.shape, $b.shape);\n const inputs = { a: $a, b: $b };\n return ENGINE.runKernel(Equal, inputs);\n}\nconst equal = op({ equal_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Gradient helper function for the min and max operations.\n */\nfunction gradForMinAndMax(dy, y, xOrig, origAxes) {\n if (y.rank < xOrig.rank) {\n y = reshape(y, expandShapeToKeepDim(y.shape, origAxes));\n }\n if (dy.rank < xOrig.rank) {\n dy = reshape(dy, expandShapeToKeepDim(dy.shape, origAxes));\n }\n return {\n x: () => {\n const dx = mul(dy, cast(equal(xOrig, y), dy.dtype));\n return dx;\n }\n };\n}\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst maxGradConfig = {\n kernelName: Max,\n inputsToSave: ['x'],\n outputsToSave: [true],\n gradFunc: (dy, saved, attrs) => {\n const maxAttrs = attrs;\n const { reductionIndices } = maxAttrs;\n const x = saved[0];\n const y = saved[1];\n const origAxes = parseAxisParam(reductionIndices, x.shape);\n const maxGrad = gradForMinAndMax(dy, y, x, origAxes);\n return {\n x: () => {\n return maxGrad['x']();\n }\n };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Returns the truth value of (a < b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.less(b).print();\n * ```\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction less_(a, b) {\n let $a = convertToTensor(a, 'a', 'less', 'string_or_numeric');\n let $b = convertToTensor(b, 'b', 'less', 'string_or_numeric');\n [$a, $b] = makeTypesMatch($a, $b);\n assertAndGetBroadcastShape($a.shape, $b.shape);\n const inputs = { a: $a, b: $b };\n return ENGINE.runKernel(Less, inputs);\n}\nconst less = op({ less_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst maximumGradConfig = {\n kernelName: Maximum,\n inputsToSave: ['a', 'b'],\n gradFunc: (dy, saved) => {\n const [a, b] = saved;\n const derA = () => mul(dy, cast(greaterEqual(a, b), 'float32'));\n const derB = () => mul(dy, cast(less(a, b), 'float32'));\n return { a: derA, b: derB };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes the backprop of a 3d max pool.\n *\n * @param dy The dy error, of rank 5 of shape\n * [batchSize, depth, height, width, channels].\n * assumed.\n * @param input The original input image, of rank 5 or rank 4 of shape\n * [batchSize, depth, height, width, channels].\n * @param output The original output image, of rank 5 of shape\n * [batchSize, outDepth, outHeight, outWidth, channels].\n * @param filterSize The filter size:\n * `[filterDepth, filterHeight, filterWidth]`.\n * `filterSize` is a single number,\n * then `filterDepth == filterHeight == filterWidth`.\n * @param strides The strides of the pooling:\n * `[strideDepth, strideHeight, strideWidth]`. If\n * `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n * used in the forward prop of the op.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n * provided, it will default to truncate.\n */\nfunction maxPool3dGrad_(dy, input, output, filterSize, strides, pad, dimRoundingMode) {\n const $dy = convertToTensor(dy, 'dy', 'maxPool3dGrad');\n const $input = convertToTensor(input, 'input', 'maxPool3dGrad');\n const $output = convertToTensor(output, 'output', 'maxPool3dGrad');\n let dy5D = $dy;\n let input5D = $input;\n let output5D = $output;\n let reshapedTo5D = false;\n if ($input.rank === 4) {\n reshapedTo5D = true;\n dy5D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]);\n input5D = reshape($input, [\n 1, $input.shape[0], $input.shape[1], $input.shape[2], $input.shape[3]\n ]);\n output5D = reshape($output, [\n 1, $output.shape[0], $output.shape[1], $output.shape[2], $output.shape[3]\n ]);\n }\n assert(dy5D.rank === 5, () => `Error in maxPool3dGrad: dy must be rank 5 but got rank ` +\n `${dy5D.rank}.`);\n assert(input5D.rank === 5, () => `Error in maxPool3dGrad: input must be rank 5 but got rank ` +\n `${input5D.rank}.`);\n assert(output5D.rank === 5, () => `Error in maxPool3dGrad: output must be rank 5 but got rank ` +\n `${output5D.rank}.`);\n checkPadOnDimRoundingMode('maxPool3dGrad', pad, dimRoundingMode);\n const inputs = { dy: dy5D, input: input5D, output: output5D };\n const attrs = { filterSize, strides, pad, dimRoundingMode };\n // tslint:disable-next-line: no-unnecessary-type-assertion\n const res = ENGINE.runKernel(MaxPool3DGrad, inputs, attrs);\n if (reshapedTo5D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n }\n return res;\n}\nconst maxPool3dGrad = op({ maxPool3dGrad_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst maxPool3DGradConfig = {\n kernelName: MaxPool3D,\n inputsToSave: ['x'],\n outputsToSave: [true],\n gradFunc: (dy, saved, attrs) => {\n const [x, y] = saved;\n const { filterSize, strides, pad, dimRoundingMode } = attrs;\n return {\n x: () => maxPool3dGrad(dy, x, y, filterSize, strides, pad, dimRoundingMode)\n };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes the backprop of a 2D max pool.\n *\n * @param dy The dy error, of rank 4 or rank 3 of shape\n * [batchSize, height, width, channels]. If rank 3, batch of 1 is\n * assumed.\n * @param input The original input image, of rank 4, of shape\n * [batchSize, height, width, channels].\n * @param output The original output image, of rank 4, of shape\n * [batchSize, outHeight, outWidth, channels].\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n * `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n * `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad The type of padding algorithm used in the forward prop of the op.\n * 'same', 'valid', for more info, see this guide:\n * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](\n * https://www.tensorflow.org/api_docs/python/tf/nn/convolution)\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n * provided, it will default to truncate.\n */\nfunction maxPoolGrad_(dy, input, output, filterSize, strides, pad, dimRoundingMode) {\n const $dy = convertToTensor(dy, 'dy', 'maxPoolGrad');\n const $input = convertToTensor(input, 'input', 'maxPoolGrad');\n const $output = convertToTensor(output, 'output', 'maxPoolGrad');\n assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy ` +\n `(${$dy.rank})`);\n assert($dy.rank === 4, () => `Error in maxPoolGrad: dy must be rank 4 but got rank ` +\n `${$dy.rank}.`);\n assert($input.rank === 4, () => `Error in maxPoolGrad: input must be rank 4 but got rank ` +\n `${$input.rank}.`);\n checkPadOnDimRoundingMode('maxPoolGrad', pad, dimRoundingMode);\n const inputs = { dy: $dy, input: $input, output: $output };\n const attrs = { filterSize, strides, pad, dimRoundingMode };\n // tslint:disable-next-line: no-unnecessary-type-assertion\n return ENGINE.runKernel(MaxPoolGrad, inputs, attrs);\n}\nconst maxPoolGrad = op({ maxPoolGrad_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst maxPoolGradConfig = {\n kernelName: MaxPool,\n inputsToSave: ['x'],\n outputsToSave: [true],\n gradFunc: (dy, saved, attrs) => {\n const [x, y] = saved;\n const { filterSize, strides, pad } = attrs;\n return {\n x: () => maxPoolGrad(dy, x, y, filterSize, strides, pad)\n };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Converts two real numbers to a complex number.\n *\n * Given a tensor `real` representing the real part of a complex number, and a\n * tensor `imag` representing the imaginary part of a complex number, this\n * operation returns complex numbers elementwise of the form [r0, i0, r1, i1],\n * where r represents the real part and i represents the imag part.\n *\n * The input tensors real and imag must have the same shape.\n *\n * ```js\n * const real = tf.tensor1d([2.25, 3.25]);\n * const imag = tf.tensor1d([4.75, 5.75]);\n * const complex = tf.complex(real, imag);\n *\n * complex.print();\n * ```\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction complex_(real, imag) {\n const $real = convertToTensor(real, 'real', 'complex');\n const $imag = convertToTensor(imag, 'imag', 'complex');\n assertShapesMatch($real.shape, $imag.shape, `real and imag shapes, ${$real.shape} and ${$imag.shape}, ` +\n `must match in call to tf.complex().`);\n const inputs = { real: $real, imag: $imag };\n return ENGINE.runKernel(Complex, inputs);\n}\nconst complex = op({ complex_ });\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Creates a `tf.Tensor` with all elements set to 0.\n *\n * ```js\n * tf.zeros([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The type of an element in the resulting tensor. Can\n * be 'float32', 'int32' or 'bool'. Defaults to 'float'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction zeros(shape, dtype = 'float32') {\n if (dtype === 'complex64') {\n const real = zeros(shape, 'float32');\n const imag = zeros(shape, 'float32');\n return complex(real, imag);\n }\n const values = makeZerosTypedArray(sizeFromShape(shape), dtype);\n return ENGINE.makeTensor(values, shape, dtype);\n}\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Creates a `tf.Tensor` with all elements set to 1.\n *\n * ```js\n * tf.ones([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The type of an element in the resulting tensor. Defaults to\n * 'float'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction ones$1(shape, dtype = 'float32') {\n if (dtype === 'complex64') {\n const real = ones$1(shape, 'float32');\n const imag = zeros(shape, 'float32');\n return complex(real, imag);\n }\n const values = makeOnesTypedArray(sizeFromShape(shape), dtype);\n return ENGINE.makeTensor(values, shape, dtype);\n}\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst meanGradConfig = {\n kernelName: Mean,\n inputsToSave: ['x'],\n gradFunc: (dy, saved, attrs) => {\n const [x] = saved;\n const { axis } = attrs;\n const axes = parseAxisParam(axis, x.shape);\n const shapes = computeOutAndReduceShapes(x.shape, axes);\n const reduceShape = shapes[1];\n const reduceSize = sizeFromShape(reduceShape);\n const derX = () => {\n const expandedDyShape = x.shape.slice();\n axes.forEach(axis => {\n expandedDyShape[axis] = 1;\n });\n const expandedDy = reshape(dy, expandedDyShape);\n const res = div(mul(expandedDy, ones$1(x.shape, 'float32')), reduceSize);\n return res;\n };\n return { x: derX };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst minGradConfig = {\n kernelName: Min,\n inputsToSave: ['x'],\n outputsToSave: [true],\n gradFunc: (dy, saved, attrs) => {\n const minAttrs = attrs;\n const { axis } = minAttrs;\n const [x, y] = saved;\n const origAxes = parseAxisParam(axis, x.shape);\n const minGrad = gradForMinAndMax(dy, y, x, origAxes);\n return {\n x: () => {\n return minGrad['x']();\n }\n };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst minimumGradConfig = {\n kernelName: Minimum,\n inputsToSave: ['a', 'b'],\n gradFunc: (dy, saved) => {\n const [a, b] = saved;\n const derA = () => mul(dy, cast(lessEqual(a, b), 'float32'));\n const derB = () => mul(dy, cast(greater(a, b), 'float32'));\n return { a: derA, b: derB };\n }\n};\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Extracts a slice from a `tf.Tensor` starting at coordinates `begin`\n * and is of size `size`.\n *\n * Also available are stricter rank-specific methods with the same signature\n * as this method that assert that `x` is of the given rank:\n * - `tf.slice1d`\n * - `tf.slice2d`\n * - `tf.slice3d`\n * - `tf.slice4d`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * x.slice([1], [2]).print();\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * x.slice([1, 0], [1, 2]).print();\n * ```\n * @param x The input `tf.Tensor` to slice from.\n * @param begin The coordinates to start the slice from. The length can be\n * less than the rank of x - the rest of the axes will have implicit 0 as\n * start. Can also be a single number, in which case it specifies the\n * first axis.\n * @param size The size of the slice. The length can be less than the rank of\n * x - the rest of the axes will have implicit -1. A value of -1 requests\n * the rest of the dimensions in the axis. Can also be a single number,\n * in which case it specifies the size of the first axis.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction slice_(x, begin, size) {\n const $x = convertToTensor(x, 'x', 'slice', 'string_or_numeric');\n if ($x.rank === 0) {\n throw new Error('Slicing scalar is not possible');\n }\n const inputs = { x: $x };\n const attrs = { begin, size };\n return ENGINE.runKernel(Slice, inputs, attrs);\n}\nconst slice = op({ slice_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst mirrorPadGradConfig = {\n kernelName: MirrorPad,\n inputsToSave: ['x'],\n gradFunc: (dy, saved, attrs) => {\n // Pad introduces values around the original tensor, so the gradient\n // slices the original shape out of the gradient.\n const x = saved[0];\n const { paddings } = attrs;\n const begin = paddings.map(p => p[0]);\n return { x: () => slice(dy, begin, x.shape) };\n }\n};\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes floor of input `tf.Tensor` element-wise: `floor(x)`.\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3]);\n *\n * x.floor().print(); // or tf.floor(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction floor_(x) {\n const $x = convertToTensor(x, 'x', 'floor', 'float32');\n const inputs = { x: $x };\n return ENGINE.runKernel(Floor, inputs);\n}\nconst floor = op({ floor_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst modGradConfig = {\n kernelName: Mod,\n inputsToSave: ['a', 'b'],\n gradFunc: (dy, saved) => {\n const [a, b] = saved;\n const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n const derA = () => {\n const reduceAxes = getReductionAxes(a.shape, outShape);\n if (reduceAxes.length > 0) {\n return reshape(sum$1(dy, reduceAxes), a.shape);\n }\n return dy;\n };\n const derB = () => {\n const res = mul(dy, neg(floor(div(a, b))));\n const reduceAxes = getReductionAxes(b.shape, outShape);\n if (reduceAxes.length > 0) {\n return reshape(sum$1(res, reduceAxes), b.shape);\n }\n return res;\n };\n return { a: derA, b: derB };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst multiplyGradConfig = {\n kernelName: Multiply,\n inputsToSave: ['a', 'b'],\n gradFunc: (dy, saved) => {\n const [a, b] = saved;\n const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n const derA = () => {\n const res = mul(dy, cast(b, 'float32'));\n const reduceAxes = getReductionAxes(a.shape, outShape);\n if (reduceAxes.length > 0) {\n return reshape(sum$1(res, reduceAxes), a.shape);\n }\n return res;\n };\n const derB = () => {\n const res = mul(dy, cast(a, 'float32'));\n const reduceAxes = getReductionAxes(b.shape, outShape);\n if (reduceAxes.length > 0) {\n return reshape(sum$1(res, reduceAxes), b.shape);\n }\n return res;\n };\n return { a: derA, b: derB };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst negGradConfig = {\n kernelName: Neg,\n gradFunc: (dy) => {\n return { x: () => neg(dy) };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst oneHotGradConfig = {\n kernelName: OneHot,\n inputsToSave: ['indices'],\n gradFunc: (dy, saved) => {\n const indices = saved[0];\n return { indices: () => zeros(indices.shape, 'float32') };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst onesLikeGradConfig = {\n kernelName: OnesLike,\n gradFunc: (dy) => {\n return { x: () => zerosLike(dy) };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Unstacks a `tf.Tensor` of rank-`R` into a list of rank-`(R-1)` `tf.Tensor`s.\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * tf.unstack(a).forEach(tensor => tensor.print());\n * ```\n *\n * @param x A tensor object.\n * @param axis The axis to unstack along. Defaults to 0 (the first dim).\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction unstack_(x, axis = 0) {\n const $x = convertToTensor(x, 'x', 'unstack', 'string_or_numeric');\n assert(axis >= -$x.shape.length && axis < $x.shape.length, () => `Axis = ${axis} is not in [-${$x.shape.length}, ${$x.shape.length})`);\n const inputs = { value: $x };\n const attrs = { axis };\n return ENGINE.runKernel(Unpack, inputs, attrs);\n}\nconst unstack = op({ unstack_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst packGradConfig = {\n kernelName: Pack,\n saveAllInputs: true,\n gradFunc: (dy, saved, attrs) => {\n const { axis } = attrs;\n const derTensors = unstack(dy, axis);\n return derTensors.map(t => () => t);\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst padV2GradConfig = {\n kernelName: PadV2,\n inputsToSave: ['x'],\n gradFunc: (dy, saved, attrs) => {\n // Pad introduces values around the original tensor, so the gradient\n // slices the original shape out of the gradient.\n const x = saved[0];\n const { paddings } = attrs;\n const begin = paddings.map(p => p[0]);\n return { x: () => slice(dy, begin, x.shape) };\n }\n};\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes natural logarithm of the input `tf.Tensor` element-wise: `ln(x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, Math.E]);\n *\n * x.log().print(); // or tf.log(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction log_(x) {\n const $x = convertToTensor(x, 'x', 'log', 'float32');\n const inputs = { x: $x };\n return ENGINE.runKernel(Log, inputs);\n}\nconst log$1 = op({ log_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes the power of one `tf.Tensor` to another. Supports broadcasting.\n *\n * Given a `tf.Tensor` x and a `tf.Tensor` y, this operation computes x^y for\n * corresponding elements in x and y. The result's dtype will be the upcasted\n * type of the `base` and `exp` dtypes.\n *\n * ```js\n * const a = tf.tensor([[2, 3], [4, 5]])\n * const b = tf.tensor([[1, 2], [3, 0]]).toInt();\n *\n * a.pow(b).print(); // or tf.pow(a, b)\n * ```\n *\n * ```js\n * const a = tf.tensor([[1, 2], [3, 4]])\n * const b = tf.tensor(2).toInt();\n *\n * a.pow(b).print(); // or tf.pow(a, b)\n * ```\n * We also expose `powStrict` which has the same signature as this op and\n * asserts that `base` and `exp` are the same shape (does not broadcast).\n *\n * @param base The base `tf.Tensor` to pow element-wise.\n * @param exp The exponent `tf.Tensor` to pow element-wise.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction pow_(base, exp) {\n let $base = convertToTensor(base, 'base', 'pow');\n let $exp = convertToTensor(exp, 'exp', 'pow');\n [$base, $exp] = makeTypesMatch($base, $exp);\n const inputs = { a: $base, b: $exp };\n return ENGINE.runKernel(Pow, inputs);\n}\nconst pow = op({ pow_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst powGradConfig = {\n kernelName: Pow,\n inputsToSave: ['a', 'b'],\n outputsToSave: [true],\n gradFunc: (dy, saved) => {\n const [a, b, y] = saved;\n const base = a;\n const exp = b;\n const outShape = assertAndGetBroadcastShape(base.shape, exp.shape);\n const derBase = () => {\n const expFloat = cast(exp, 'float32');\n let res = mul(dy, mul(expFloat, pow(base, sub(expFloat, scalar(1)))));\n const reduceAxes = getReductionAxes(base.shape, outShape);\n if (reduceAxes.length > 0) {\n res = sum$1(res, reduceAxes);\n }\n return reshape(res, base.shape);\n };\n const derExp = () => {\n const condition = greater(base, 0);\n const logBase = where(condition, log$1(base), zerosLike(base));\n let res = mul(dy, mul(y, logBase));\n const reduceAxes = getReductionAxes(exp.shape, outShape);\n if (reduceAxes.length > 0) {\n res = sum$1(res, reduceAxes);\n }\n return reshape(res, exp.shape);\n };\n return { a: derBase, b: derExp };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst preluGradConfig = {\n kernelName: Prelu,\n inputsToSave: ['x', 'alpha'],\n gradFunc: (dy, saved) => {\n const [x, alpha] = saved;\n const mask = greater(x, 0);\n return {\n x: () => where(mask, dy, mul(dy, alpha)),\n alpha: () => {\n let res = where(mask, zerosLike(dy), mul(dy, x));\n const reduceAxes = getReductionAxes(alpha.shape, dy.shape);\n if (reduceAxes.length > 0) {\n res = sum$1(res, reduceAxes);\n }\n return reshape(res, alpha.shape);\n }\n };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst divGradConfig = {\n kernelName: RealDiv,\n inputsToSave: ['a', 'b'],\n gradFunc: (dy, saved) => {\n const [a, b] = saved;\n const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n const derA = () => {\n const res = div(dy, cast(b, 'float32'));\n const reduceAxes = getReductionAxes(a.shape, outShape);\n if (reduceAxes.length > 0) {\n return reshape(sum$1(res, reduceAxes), a.shape);\n }\n return res;\n };\n const derB = () => {\n let res = mul(dy, cast(a, 'float32'));\n const reduceAxes = getReductionAxes(b.shape, outShape);\n if (reduceAxes.length > 0) {\n res = reshape(sum$1(res, reduceAxes), b.shape);\n }\n const tmp = square(b);\n return neg(div(res, cast(tmp, 'float32')));\n };\n return { a: derA, b: derB };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst reciprocalGradConfig = {\n kernelName: Reciprocal,\n inputsToSave: ['x'],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n return { x: () => div(dy, neg(square(x))) };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst relu6GradConfig = {\n kernelName: Relu6,\n inputsToSave: ['x'],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n const mask = mul(lessEqual(x, 6), step(x));\n return { x: () => mul(dy, cast(mask, 'float32')) };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst reluGradConfig = {\n kernelName: Relu,\n inputsToSave: ['x'],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n return { x: () => mul(dy, cast(step(x), 'float32')) };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst reshapeGradConfig = {\n kernelName: Reshape,\n inputsToSave: ['x'],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n return { x: () => reshape(dy, x.shape) };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst resizeBilinearGradConfig = {\n kernelName: ResizeBilinear,\n inputsToSave: ['images'],\n gradFunc: (dy, saved, attrs) => {\n const [images] = saved;\n const inputs = { dy, images };\n const imagesDer = () => \n // tslint:disable-next-line: no-unnecessary-type-assertion\n ENGINE.runKernel(ResizeBilinearGrad, inputs, attrs);\n return { images: imagesDer };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst resizeNearestNeighborGradConfig = {\n kernelName: ResizeNearestNeighbor,\n inputsToSave: ['images'],\n gradFunc: (dy, saved, attrs) => {\n const [images] = saved;\n const inputs = { dy, images };\n const imagesDer = () => \n // tslint:disable-next-line: no-unnecessary-type-assertion\n ENGINE.runKernel(ResizeNearestNeighborGrad, inputs, attrs);\n return { images: imagesDer };\n }\n};\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Reverses a `tf.Tensor` along a specified axis.\n *\n * Also available are stricter rank-specific methods that assert that `x` is\n * of the given rank:\n * - `tf.reverse1d`\n * - `tf.reverse2d`\n * - `tf.reverse3d`\n * - `tf.reverse4d`\n *\n * Except `tf.reverse1d` (which does not have axis param), all methods have\n * same signature as this method.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * x.reverse().print();\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.reverse(axis).print();\n * ```\n * @param x The input tensor to be reversed.\n * @param axis The set of dimensions to reverse. Must be in the\n * range [-rank(x), rank(x)). Defaults to all axes.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction reverse_(x, axis) {\n const $x = convertToTensor(x, 'x', 'reverse');\n const inputs = { x: $x };\n const attrs = { dims: axis };\n return ENGINE.runKernel(Reverse, inputs, attrs);\n}\nconst reverse = op({ reverse_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst reverseGradConfig = {\n kernelName: Reverse,\n gradFunc: (dy, saved, attrs) => {\n const { dims } = attrs;\n const axes = parseAxisParam(dims, dy.shape);\n return { x: () => reverse(dy, axes) };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst roundGradConfig = {\n kernelName: Round,\n gradFunc: (dy) => {\n // TODO(nsthorat): Let gradients be null for cases where we want to stop\n // backpropgation.\n return { x: () => zerosLike(dy) };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst rsqrtGradConfig = {\n kernelName: Rsqrt,\n inputsToSave: ['x'],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n return { x: () => neg(div(dy, mul(pow(x, 1.5), 2))) };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Returns the truth value of `NOT x` element-wise.\n *\n * ```js\n * const a = tf.tensor1d([false, true], 'bool');\n *\n * a.logicalNot().print();\n * ```\n *\n * @param x The input tensor. Must be of dtype 'bool'.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalNot_(x) {\n const $x = convertToTensor(x, 'x', 'logicalNot', 'bool');\n const inputs = { x: $x };\n return ENGINE.runKernel(LogicalNot, inputs);\n}\nconst logicalNot = op({ logicalNot_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst selectGradConfig = {\n kernelName: Select,\n inputsToSave: ['condition'],\n gradFunc: (dy, saved) => {\n const [condition] = saved;\n return {\n // TODO(julianoks): Return null for condition gradient\n // when backprop supports it.\n condition: () => cast(zerosLike(condition), 'float32'),\n t: () => mul(dy, cast(condition, dy.dtype)),\n e: () => mul(dy, cast(logicalNot(condition), dy.dtype))\n };\n }\n};\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst SELU_SCALEALPHA = 1.7580993408473768599402175208123;\nconst SELU_SCALE = 1.0507009873554804934193349852946;\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst seluGradConfig = {\n kernelName: Selu,\n inputsToSave: ['x'],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n return {\n x: () => {\n const mask = greater(x, scalar(0));\n const scaleAlpha = scalar(SELU_SCALEALPHA);\n const scale = scalar(SELU_SCALE);\n const greaterThanZeroDer = mul(dy, scale);\n const lessEqualZeroDer = mul(mul(dy, scaleAlpha), exp(cast(x, 'float32')));\n return where(mask, greaterThanZeroDer, lessEqualZeroDer);\n }\n };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst sigmoidGradConfig = {\n kernelName: Sigmoid,\n outputsToSave: [true],\n gradFunc: (dy, saved) => {\n const [y] = saved;\n return { x: () => mul(dy, mul(y, sub(scalar(1), y))) };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst signGradConfig = {\n kernelName: Sign,\n gradFunc: (dy) => {\n return { x: () => zerosLike(dy) };\n }\n};\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes cos of the input `tf.Tensor` element-wise: `cos(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);\n *\n * x.cos().print(); // or tf.cos(x)\n * ```\n * @param x The input tensor. Must be float32 type.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction cos_(x) {\n const $x = convertToTensor(x, 'x', 'cos', 'float32');\n const inputs = { x: $x };\n return ENGINE.runKernel(Cos, inputs);\n}\nconst cos = op({ cos_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst sinGradConfig = {\n kernelName: Sin,\n inputsToSave: ['x'],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n return { x: () => mul(cos(cast(x, 'float32')), dy) };\n }\n};\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes hyperbolic cos of the input `tf.Tensor` element-wise: `cosh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.cosh().print(); // or tf.cosh(x)\n * ```\n * @param x The input tensor. Must be float32 type.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction cosh_(x) {\n const $x = convertToTensor(x, 'x', 'cosh', 'float32');\n const inputs = { x: $x };\n return ENGINE.runKernel(Cosh, inputs);\n}\nconst cosh = op({ cosh_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst sinhGradConfig = {\n kernelName: Sinh,\n inputsToSave: ['x'],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n return { x: () => mul(cosh(cast(x, 'float32')), dy) };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Pads a `tf.Tensor` with a given value and paddings.\n *\n * This operation implements `CONSTANT` mode. For `REFLECT` and `SYMMETRIC`,\n * refer to `tf.mirrorPad`\n *\n * Also available are stricter rank-specific methods with the same signature\n * as this method that assert that `paddings` is of given length.\n * - `tf.pad1d`\n * - `tf.pad2d`\n * - `tf.pad3d`\n * - `tf.pad4d`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * x.pad([[1, 2]]).print();\n * ```\n * @param x The tensor to pad.\n * @param paddings An array of length `R` (the rank of the tensor), where\n * each element is a length-2 tuple of ints `[padBefore, padAfter]`,\n * specifying how much to pad along each dimension of the tensor.\n * @param constantValue The pad value to use. Defaults to 0.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction pad_(x, paddings, constantValue = 0) {\n const $x = convertToTensor(x, 'x', 'pad');\n if ($x.rank === 0) {\n throw new Error('pad(scalar) is not defined. Pass non-scalar to pad');\n }\n const attrs = { paddings, constantValue };\n const inputs = { x: $x };\n return ENGINE.runKernel(PadV2, inputs, attrs);\n}\nconst pad = op({ pad_ });\n\n/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst NEW_AXIS = -2;\nconst SHRINK_AXIS = -1;\nfunction assertParamsValid(input, begin, size) {\n const inputRank = input.shape.length;\n assert(inputRank === begin.length, () => `Error in slice${inputRank}D: Length of begin ${begin} must ` +\n `match the rank of the array (${inputRank}).`);\n assert(inputRank === size.length, () => `Error in slice${inputRank}D: Length of size ${size} must ` +\n `match the rank of the array (${inputRank}).`);\n for (let i = 0; i < inputRank; ++i) {\n assert(begin[i] + size[i] <= input.shape[i], () => `Error in slice${inputRank}D: begin[${i}] + size[${i}] ` +\n `(${begin[i] + size[i]}) would overflow input.shape[${i}] (${input.shape[i]})`);\n }\n}\n/** Converts a binary mask to an array of axes. Used in stridedSlice(). */\nfunction maskToAxes(mask) {\n const axes = [];\n let axis = 0;\n while (mask > 0) {\n if (mask & 1) {\n axes.push(axis);\n }\n mask /= 2;\n axis++;\n }\n return axes;\n}\n/** Computes the output shape given the strided slice params. */\nfunction computeOutShape(begin, end, strides) {\n const size = [];\n for (let axis = 0; axis < begin.length; axis++) {\n size[axis] = Math.ceil((end[axis] - begin[axis]) / strides[axis]);\n }\n return size;\n}\n// Creates full selection at the elided dimensions. If the dimension matches\n// the ellipsis mask, override the current stride value. Otherwise, insert.\nfunction stridesWithElidedDims(strides, ellipsisInsertionIndex, numElidedAxes, inputShape) {\n const newStrides = [...strides];\n for (let i = newStrides.length; i < inputShape.length; i++) {\n newStrides.push(1);\n }\n for (let i = 0; i < numElidedAxes; i++) {\n if (i === 0) {\n newStrides[ellipsisInsertionIndex] = 1;\n }\n else {\n newStrides.splice(ellipsisInsertionIndex, 0 /* num elements to delete */, 1 /* element to add */);\n newStrides.pop();\n }\n }\n return newStrides;\n}\nfunction unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, normalizedAxis) {\n if (normalizedAxis <= ellipsisInsertionIndex) {\n return normalizedAxis;\n }\n return normalizedAxis - (numElidedAxes - 1);\n}\nfunction getElidedAxes(numElidedAxes, ellipsisInsertionIndex) {\n const elidedAxes = [];\n for (let i = 0; i < numElidedAxes; i++) {\n elidedAxes.push(ellipsisInsertionIndex + i);\n }\n return elidedAxes;\n}\n// Normalize the start, end and strides.\nfunction getNormalizedAxes(inputShape, ellipsisAxes, numInterpolatedAxes, begin, end, strides, beginMask, endMask, ellipsisMask) {\n const inputRank = inputShape.length;\n let normalizedBegin = new Array(inputRank), normalizedEnd = new Array(inputRank), normalizedStrides = new Array(inputRank);\n if (ellipsisAxes.length && numInterpolatedAxes > 0) {\n const fullIndex = ellipsisAxes[0];\n // The ellipsis applies to the masked index as well as any dimensions\n // that are interpolated.\n const numElidedAxes = numInterpolatedAxes + 1;\n normalizedBegin = startIndicesWithElidedDims(beginMask, fullIndex, numElidedAxes, begin, inputShape);\n normalizedEnd = stopIndicesWithElidedDims(endMask, fullIndex, numElidedAxes, end, inputShape);\n normalizedStrides =\n stridesWithElidedDims(strides, fullIndex, numElidedAxes, inputShape);\n }\n else {\n for (let axis = 0; axis < inputRank; axis++) {\n normalizedBegin[axis] = startForAxis(beginMask, begin, strides, inputShape, axis, ellipsisMask);\n normalizedEnd[axis] =\n stopForAxis(endMask, end, strides, inputShape, axis, ellipsisMask);\n normalizedStrides[axis] = stridesForAxis(strides, axis, ellipsisMask);\n }\n }\n return {\n begin: normalizedBegin,\n end: normalizedEnd,\n strides: normalizedStrides\n };\n}\n// Creates full selection at the elided dimensions. If the dimension matches\n// the ellipsis mask, override the current start value. Otherwise, insert.\nfunction startIndicesWithElidedDims(beginMask, ellipsisInsertionIndex, numElidedAxes, originalBegin, inputShape) {\n const newIndices = [...inputShape];\n const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);\n for (let axis = 0; axis < newIndices.length; axis++) {\n if (elidedAxes.indexOf(axis) > -1) {\n newIndices[axis] = 0;\n }\n else {\n const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);\n let originalValue = originalBegin[originalAxis];\n if (beginMask & 1 << originalAxis) {\n originalValue = 0;\n }\n newIndices[axis] = originalValue;\n }\n }\n return newIndices;\n}\n// Creates full selection at the elided dimensions. If the dimension matches\n// the ellipsis mask, override the current stop value. Otherwise, insert.\nfunction stopIndicesWithElidedDims(endMask, ellipsisInsertionIndex, numElidedAxes, originalEnd, inputShape) {\n const newIndices = [...inputShape];\n const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);\n for (let axis = 0; axis < newIndices.length; axis++) {\n if (elidedAxes.indexOf(axis) > -1) {\n newIndices[axis] = Number.MAX_SAFE_INTEGER;\n }\n else {\n const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);\n let originalValue = originalEnd[originalAxis];\n if (endMask & 1 << originalAxis) {\n originalValue = Number.MAX_SAFE_INTEGER;\n }\n newIndices[axis] = originalValue;\n }\n }\n for (let i = 0; i < newIndices.length; i++) {\n // Handle negative indices\n const axisSize = inputShape[i];\n if (newIndices[i] < 0) {\n newIndices[i] += axisSize;\n }\n newIndices[i] = clamp(0, newIndices[i], inputShape[i]);\n }\n return newIndices;\n}\nfunction stridesForAxis(strides, axis, ellipsisMask) {\n let stride = strides[axis];\n if (ellipsisMask & (1 << axis) || stride == null) {\n stride = 1;\n }\n return stride;\n}\nfunction startForAxis(beginMask, startIndices, strides, inputShape, axis, ellipsisMask) {\n // Begin with the specified index\n let start = startIndices[axis];\n const stride = strides[axis] || 1;\n // Check the axis bit from right of masked axes, or the begin index is not set\n // for the axis.\n if (beginMask & 1 << axis || ellipsisMask & 1 << axis || start == null) {\n if (stride > 0) {\n // Forward iteration - use the first element. These values will get\n // clamped below (Note: We could have set them to 0 and axis_size-1, but\n // use lowest() and max() to maintain symmetry with StopForAxis())\n start = Number.MIN_SAFE_INTEGER;\n }\n else {\n // Backward iteration - use the last element.\n start = Number.MAX_SAFE_INTEGER;\n }\n }\n // Handle negative indices\n const axisSize = inputShape[axis];\n if (start < 0) {\n start += axisSize;\n }\n // Clamping\n start = clamp(0, start, axisSize - 1);\n return start;\n}\nfunction stopForAxis(endMask, stopIndices, strides, inputShape, axis, ellipsisMask) {\n // Begin with the specified index\n let stop = stopIndices[axis];\n const stride = strides[axis] || 1;\n // Check the axis bit from right of masked axes, or if the stop index is not\n // set for this axis.\n if (endMask & (1 << axis) || ellipsisMask & (1 << axis) || stop == null) {\n if (stride > 0) {\n // Forward iteration - use the last element. These values will get\n // clamped below\n stop = Number.MAX_SAFE_INTEGER;\n }\n else {\n // Backward iteration - use the first element.\n stop = Number.MIN_SAFE_INTEGER;\n }\n }\n // Handle negative indices\n const axisSize = inputShape[axis];\n if (stop < 0) {\n stop += axisSize;\n }\n // Clamping\n // Because the end index points one past the last element, we need slightly\n // different clamping ranges depending on the direction.\n if (stride > 0) {\n // Forward iteration\n stop = clamp(0, stop, axisSize);\n }\n else {\n // Backward iteration\n stop = clamp(-1, stop, axisSize - 1);\n }\n return stop;\n}\n/**\n * Returns true if the slice occupies a continous set of elements in the\n * 'flat' space.\n */\nfunction isSliceContinous(shape, begin, size) {\n // Index of the first axis that has size > 1.\n let firstNonOneAxis = size.length;\n for (let i = 0; i < size.length; i++) {\n if (size[i] > 1) {\n firstNonOneAxis = i;\n break;\n }\n }\n for (let i = firstNonOneAxis + 1; i < size.length; i++) {\n if (begin[i] > 0 || size[i] !== shape[i]) {\n return false;\n }\n }\n return true;\n}\nfunction computeFlatOffset(begin, strides) {\n let flatOffset = begin.length > 0 ? begin[begin.length - 1] : 1;\n for (let i = 0; i < begin.length - 1; i++) {\n flatOffset += begin[i] * strides[i];\n }\n return flatOffset;\n}\nfunction parseSliceParams(x, begin, size) {\n // The following logic allows for more ergonomic calls.\n let begin_;\n const xRank = x.shape.length;\n if (typeof begin === 'number') {\n begin_ = [begin, ...new Array(xRank - 1).fill(0)];\n }\n else if (begin.length < xRank) {\n begin_ = begin.concat(new Array(xRank - begin.length).fill(0));\n }\n else {\n begin_ = begin.slice();\n }\n begin_.forEach(d => {\n assert(d !== -1, () => 'slice() does not support negative begin indexing.');\n });\n let size_;\n if (size == null) {\n size_ = new Array(xRank).fill(-1);\n }\n else if (typeof size === 'number') {\n size_ = [size, ...new Array(xRank - 1).fill(-1)];\n }\n else if (size.length < xRank) {\n size_ = size.concat(new Array(xRank - size.length).fill(-1));\n }\n else {\n size_ = size;\n }\n size_ = size_.map((d, i) => {\n if (d >= 0) {\n return d;\n }\n else {\n assert(d === -1, () => `Negative size values should be exactly -1 but got ` +\n `${d} for the slice() size at index ${i}.`);\n return x.shape[i] - begin_[i];\n }\n });\n return [begin_, size_];\n}\n// Convert the slicing specification from a sparse representation to a dense\n// representation. This means that all ellipses and newaxis are expanded out.\nfunction sliceInfo(xShape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask) {\n let stridesNonNull;\n if (strides == null) {\n stridesNonNull = new Array(begin.length);\n stridesNonNull.fill(1);\n }\n else {\n stridesNonNull = strides;\n }\n // Only one non-zero bit is allowed in ellipsisMask, which means ellipsisMask\n // is a power of 2. Use bit compares to ensure ellipsisMask is 0 or a power\n // of 2. When i is a power of 2, i & (i - 1) is always 0.\n // Also ref:\n // https://stackoverflow.com/questions/600293/how-to-check-if-a-number-is-a-power-of-2\n if (ellipsisMask != null && (ellipsisMask & (ellipsisMask - 1)) !== 0) {\n throw new Error('Multiple ellipses in slice is not allowed.');\n }\n // Step 1: Account for ellipsis and new axis.\n // Check for ellipsis and count how many non-newaxis there are after.\n let ellipsisSeen = false;\n const sparseSpec = {\n dims: stridesNonNull.length,\n numAddAxisAfterEllipsis: 0,\n begin: begin.slice(),\n end: end.slice(),\n strides: stridesNonNull.slice(),\n beginMask,\n endMask,\n ellipsisMask,\n newAxisMask,\n shrinkAxisMask\n };\n for (let i = 0; i < sparseSpec.dims; i++) {\n if (ellipsisSeen && ((1 << i) & newAxisMask) !== 0) {\n sparseSpec.numAddAxisAfterEllipsis++;\n }\n if ((1 << i) & ellipsisMask) {\n ellipsisSeen = true;\n }\n }\n // If no ellipsis insert one at the end.\n if (!ellipsisSeen) {\n sparseSpec.ellipsisMask |= (1 << sparseSpec.dims);\n sparseSpec.dims++; // this effects loop iteration below\n }\n // Step 2: Make a sparse spec into a full index spec.\n //\n // The sparse spec deos not correspond to the number of dimensions.\n // Make a dense spec that cooresponds to the number of dimensions.\n //\n // For example suppose foo[...,3:] on foo.shape = [2, 2, 3] then we need to\n // produce the missing beginMask for the first two dimensions i.e. from\n // beginMaskSpec = 0, endMaskSpec = 2, we achieve beginMask = 6 (110),\n // endMask = 7 (111).\n const denseSpec = {\n dims: xShape.length,\n beginMask: 0,\n endMask: 0,\n beginValid: false,\n endValid: false\n };\n buildDenseSpec(sparseSpec, denseSpec);\n // Step 3: Make implicit ranges (non-zero beginMasks and endMasks) explicit\n // and bounds check.\n let isIdentity = true;\n let sliceDim0 = true;\n let isSimpleSlice = true;\n const processingShape = [];\n const finalShape = [];\n for (let i = 0; i < xShape.length; ++i) {\n if (denseSpec.strides[i] === 0) {\n throw Error(`strides[${i}] must be non-zero`);\n }\n const shrinkI = !!(denseSpec.shrinkAxisMask & (1 << i));\n const dimI = xShape[i];\n if (dimI === -1) {\n processingShape.push(shrinkI ? 1 : -1);\n continue;\n }\n const masks = [denseSpec.beginMask & (1 << i), denseSpec.endMask & (1 << i)];\n const validRange = [\n denseSpec.strides[i] > 0 ? 0 : -1,\n denseSpec.strides[i] > 0 ? dimI : dimI - 1\n ];\n if (shrinkI && denseSpec.strides[i] <= 0) {\n throw Error('only stride 1 allowed on non-range indexing.');\n }\n isSimpleSlice = isSimpleSlice && (denseSpec.strides[i] === 1);\n const beginAndEndMasked = !!((denseSpec.beginMask & (1 << i)) && (denseSpec.endMask & (1 << i)));\n if (denseSpec.beginValid && denseSpec.endValid) {\n if (shrinkI) {\n // If we are shrinking, the end index is now possibly incorrect. In\n // particular foo[-1] produces sparseBegin = -1, sparseEnd = 0.\n // and canonical puts these to n-1 and 0, which implies a degenerate\n // interval. Fortunately, it is now safe to re-create end as begin + 1.\n const xFwd = denseSpec.begin[i] < 0 ? dimI + denseSpec.begin[i] :\n denseSpec.begin[i];\n denseSpec.begin[i] = xFwd;\n denseSpec.end[i] = denseSpec.begin[i] + 1;\n if (xFwd < 0 || xFwd >= dimI) {\n throw Error(`slice index ${denseSpec.begin[i]} of dimension ${i} out of bounds.`);\n }\n }\n else {\n denseSpec.begin[i] = canonical(denseSpec.begin[i], 0, denseSpec.strides[i], dimI, masks, validRange);\n denseSpec.end[i] = canonical(denseSpec.end[i], 1, denseSpec.strides[i], dimI, masks, validRange);\n }\n // Update optimization values\n const takeAllInDimension = denseSpec.strides[i] === 1 &&\n denseSpec.begin[i] === 0 && denseSpec.end[i] === dimI;\n isIdentity = isIdentity && takeAllInDimension;\n sliceDim0 = sliceDim0 &&\n ((i === 0 && denseSpec.strides[i] === 1) || takeAllInDimension);\n }\n else {\n isIdentity =\n isIdentity && ((denseSpec.strides[i] === 1) && beginAndEndMasked);\n sliceDim0 = sliceDim0 &&\n ((i === 0 && denseSpec.strides[i] === 1) || beginAndEndMasked);\n }\n // Compute the processing shape (the intermediate Eigen will produce)\n let intervalLength;\n let knownInterval = false;\n if (denseSpec.beginValid && denseSpec.endValid) {\n intervalLength = denseSpec.end[i] - denseSpec.begin[i];\n knownInterval = true;\n }\n else if (shrinkI) {\n // The dimension is still known as 1 for the processingShape, but will be\n // discarded for the final shape.\n intervalLength = 1;\n knownInterval = true;\n }\n else if (beginAndEndMasked) {\n // Even if we don't have values for begin or end, we do know that this\n // dimension covers the whole interval. If we have shape information for\n // this dimension, that tells us the interval length.\n if (dimI >= 0) {\n if (denseSpec.strides[i] < 0) {\n intervalLength = -dimI;\n }\n else {\n intervalLength = dimI;\n }\n knownInterval = true;\n }\n }\n if (knownInterval) {\n let sizeI;\n // Hold zero if the interval is degenerate, otherwise account for\n // remainder\n if (intervalLength === 0 ||\n ((intervalLength < 0) !== (denseSpec.strides[i] < 0))) {\n sizeI = 0;\n }\n else {\n sizeI = Math.trunc(intervalLength / denseSpec.strides[i]) +\n (intervalLength % denseSpec.strides[i] !== 0 ? 1 : 0);\n }\n processingShape.push(sizeI);\n }\n else {\n processingShape.push(-1);\n }\n }\n // Step 4: Compute the final shape\n //\n // newAxis will increase dimension by 1 (with a one-size dimension)\n // slices like foo[3, ...] will reduce dimension by 1.\n // This cannot be done earlier, because it depends on Step 3.\n for (let denseDim = 0; denseDim < denseSpec.finalShapeGatherIndices.length; ++denseDim) {\n const gatherIndex = denseSpec.finalShapeGatherIndices[denseDim];\n if (gatherIndex >= 0) {\n finalShape.push(processingShape[gatherIndex]);\n }\n else if (gatherIndex === NEW_AXIS) {\n finalShape.push(1);\n }\n }\n const finalShapeSparse = finalShape.filter((dim, i) => denseSpec.finalShapeGatherIndices[i] !== NEW_AXIS);\n return {\n finalShapeSparse,\n finalShape,\n isIdentity,\n sliceDim0,\n isSimpleSlice,\n begin: denseSpec.begin,\n end: denseSpec.end,\n strides: denseSpec.strides\n };\n}\nfunction buildDenseSpec(sparse, dense) {\n dense.beginMask = 0;\n dense.endMask = 0;\n dense.shrinkAxisMask = 0;\n let fullIndex = 0;\n dense.beginValid = sparse.begin != null;\n dense.endValid = sparse.end != null;\n dense.begin = new Array(dense.dims);\n dense.end = new Array(dense.dims);\n dense.strides = new Array(dense.dims);\n dense.finalShapeGatherIndices = [];\n dense.finalShapeGatherIndicesSparse = [];\n dense.inputShapeGatherIndicesSparse = new Array(dense.dims);\n for (let i = 0; i < sparse.dims; i++) {\n if ((1 << i) & sparse.ellipsisMask) {\n // Only the bit that has ellipsis will fall in this condition.\n // Expand the ellipsis into the appropriate indices\n // Note: this only works because we guaranteed one ellipsis.\n const nextIndex = Math.min(dense.dims - (sparse.dims - i) + 1 + sparse.numAddAxisAfterEllipsis, dense.dims);\n for (; fullIndex < nextIndex; fullIndex++) {\n // newAxis aren't real axis so you have to skip.\n dense.begin[fullIndex] = 0;\n dense.end[fullIndex] = 0;\n dense.strides[fullIndex] = 1;\n dense.beginMask |= (1 << fullIndex);\n dense.endMask |= (1 << fullIndex);\n dense.finalShapeGatherIndices.push(fullIndex);\n dense.finalShapeGatherIndicesSparse.push(-1);\n dense.inputShapeGatherIndicesSparse[fullIndex] = i;\n }\n }\n else if ((1 << i) & sparse.newAxisMask) {\n // Only the bit that has newAxis will fall in this condition.\n dense.finalShapeGatherIndices.push(NEW_AXIS);\n dense.finalShapeGatherIndicesSparse.push(-1);\n }\n else {\n if (fullIndex === dense.begin.length) {\n throw Error(`Index out of range using input dim ${fullIndex}; input ` +\n `has only ${dense.dims} dims, ${dense.begin.length}.`);\n }\n // Gather slicing spec into appropriate index.\n if (sparse.begin != null) {\n dense.begin[fullIndex] = sparse.begin[i];\n }\n if (sparse.end != null) {\n dense.end[fullIndex] = sparse.end[i];\n }\n dense.strides[fullIndex] = sparse.strides[i];\n if (sparse.beginMask & (1 << i)) {\n dense.beginMask |= (1 << fullIndex);\n }\n if (sparse.endMask & (1 << i)) {\n dense.endMask |= (1 << fullIndex);\n }\n // If shrink, record where to get the dimensionality from (i.e. newAxis)\n // creates a fake 1 size dimension. Also remember shrink axis (now in\n // dense form) so we can ignore dense.end below.\n if (sparse.shrinkAxisMask & (1 << i)) {\n dense.finalShapeGatherIndices.push(SHRINK_AXIS);\n dense.finalShapeGatherIndicesSparse.push(-1);\n dense.shrinkAxisMask |= (1 << fullIndex);\n }\n else {\n dense.finalShapeGatherIndices.push(fullIndex);\n // Remember that where in the sparse shape the dense dim comes from.\n dense.finalShapeGatherIndicesSparse.push(i);\n }\n dense.inputShapeGatherIndicesSparse[fullIndex] = i;\n fullIndex++;\n }\n }\n}\nfunction canonical(x, c, strideI, dimI, masks, validRange) {\n if (masks[c]) {\n return strideI > 0 ? validRange[c] : validRange[(c + 1) & 1];\n }\n else {\n const xFwd = x < 0 ? dimI + x : x; // make negative indices positive\n return xFwd < validRange[0] ? validRange[0] :\n xFwd > validRange[1] ? validRange[1] : xFwd;\n }\n}\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst sliceGradConfig = {\n kernelName: Slice,\n inputsToSave: ['x'],\n gradFunc: (dy, saved, attrs) => {\n const [x] = saved;\n const { begin, size } = attrs;\n const inputShape = x.shape;\n const [begin_, size_] = parseSliceParams(x, begin, size);\n // Create an Nx2 padding where the first column represents how many\n // zeros are prepended (at start) for each dimension, and the second\n // column indicates how many zeros are appended (at end).\n // The number of zeros to append is the shape of the input\n // elementwise-subtracted by both the begin vector and sizes vector.\n const paddings = [];\n for (let i = 0; i < dy.rank; i++) {\n paddings.push([begin_[i], inputShape[i] - begin_[i] - size_[i]]);\n }\n return { x: () => pad(dy, paddings) };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst softmaxGradConfig = {\n kernelName: Softmax,\n outputsToSave: [true],\n gradFunc: (dy, saved, attrs) => {\n const [y] = saved;\n const { dim } = attrs;\n const keepDims = true;\n const dyTimesY = mul(dy, y);\n return {\n logits: () => sub(dyTimesY, mul(sum$1(dyTimesY, [dim], keepDims), y))\n };\n }\n};\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes sigmoid element-wise, `1 / (1 + exp(-x))`\n *\n * ```js\n * const x = tf.tensor1d([0, -1, 2, -3]);\n *\n * x.sigmoid().print(); // or tf.sigmoid(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sigmoid_(x) {\n const $x = convertToTensor(x, 'x', 'sigmoid', 'float32');\n const inputs = { x: $x };\n return ENGINE.runKernel(Sigmoid, inputs);\n}\nconst sigmoid = op({ sigmoid_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst softplusGradConfig = {\n kernelName: Softplus,\n inputsToSave: ['x'],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n return { x: () => mul(dy, sigmoid(x)) };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * This operation reshapes the \"batch\" dimension 0 into `M + 1` dimensions of\n * shape `blockShape + [batch]`, interleaves these blocks back into the grid\n * defined by the spatial dimensions `[1, ..., M]`, to obtain a result with\n * the same rank as the input. The spatial dimensions of this intermediate\n * result are then optionally cropped according to `crops` to produce the\n * output. This is the reverse of `tf.spaceToBatchND`. See below for a precise\n * description.\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [4, 1, 1, 1]);\n * const blockShape = [2, 2];\n * const crops = [[0, 0], [0, 0]];\n *\n * x.batchToSpaceND(blockShape, crops).print();\n * ```\n *\n * @param x A `tf.Tensor`. N-D with `x.shape` = `[batch] + spatialShape +\n * remainingShape`, where spatialShape has `M` dimensions.\n * @param blockShape A 1-D array. Must have shape `[M]`, all values must\n * be >= 1.\n * @param crops A 2-D array. Must have shape `[M, 2]`, all values must be >= 0.\n * `crops[i] = [cropStart, cropEnd]` specifies the amount to crop from input\n * dimension `i + 1`, which corresponds to spatial dimension `i`. It is required\n * that `cropStart[i] + cropEnd[i] <= blockShape[i] * inputShape[i + 1]`\n *\n * This operation is equivalent to the following steps:\n *\n * 1. Reshape `x` to `reshaped` of shape: `[blockShape[0], ...,\n * blockShape[M-1], batch / prod(blockShape), x.shape[1], ...,\n * x.shape[N-1]]`\n *\n * 2. Permute dimensions of `reshaped`to produce `permuted` of shape `[batch /\n * prod(blockShape),x.shape[1], blockShape[0], ..., x.shape[M],\n * blockShape[M-1],x.shape[M+1], ..., x.shape[N-1]]`\n *\n * 3. Reshape `permuted` to produce `reshapedPermuted` of shape `[batch /\n * prod(blockShape),x.shape[1] * blockShape[0], ..., x.shape[M] *\n * blockShape[M-1],x.shape[M+1], ..., x.shape[N-1]]`\n *\n * 4. Crop the start and end of dimensions `[1, ..., M]` of `reshapedPermuted`\n * according to `crops` to produce the output of shape: `[batch /\n * prod(blockShape),x.shape[1] * blockShape[0] - crops[0,0] - crops[0,1],\n * ..., x.shape[M] * blockShape[M-1] - crops[M-1,0] -\n * crops[M-1,1],x.shape[M+1], ..., x.shape[N-1]]`\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction batchToSpaceND_(x, blockShape, crops) {\n const $x = convertToTensor(x, 'x', 'batchToSpaceND');\n const prod = blockShape.reduce((a, b) => a * b);\n assert($x.rank >= 1 + blockShape.length, () => `input rank is ${$x.rank} but should be > than blockShape.length ${blockShape.length}`);\n assert(crops.length === blockShape.length, () => `crops.length is ${crops.length} but should be equal to blockShape.length ${blockShape.length}`);\n assert($x.shape[0] % prod === 0, () => `input tensor batch is ${$x.shape[0]} but is not divisible by the product of ` +\n `the elements of blockShape ${blockShape.join(' * ')} === ${prod}`);\n const inputs = { x: $x };\n const attrs = { blockShape, crops };\n return ENGINE.runKernel(BatchToSpaceND, inputs, attrs);\n}\nconst batchToSpaceND = op({ batchToSpaceND_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst spaceToBatchNDGradConfig = {\n kernelName: SpaceToBatchND,\n gradFunc: (dy, saved, attrs) => {\n const { blockShape, paddings } = attrs;\n return { x: () => batchToSpaceND(dy, blockShape, paddings) };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Concatenates a list of `tf.Tensor`s along a given axis.\n *\n * The tensors ranks and types must match, and their sizes must match in all\n * dimensions except `axis`.\n *\n * Also available are stricter rank-specific methods that assert that\n * `tensors` are of the given rank:\n * - `tf.concat1d`\n * - `tf.concat2d`\n * - `tf.concat3d`\n * - `tf.concat4d`\n *\n * Except `tf.concat1d` (which does not have axis param), all methods have\n * same signature as this method.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * a.concat(b).print(); // or a.concat(b)\n * ```\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * const c = tf.tensor1d([5, 6]);\n * tf.concat([a, b, c]).print();\n * ```\n *\n * ```js\n * const a = tf.tensor2d([[1, 2], [10, 20]]);\n * const b = tf.tensor2d([[3, 4], [30, 40]]);\n * const axis = 1;\n * tf.concat([a, b], axis).print();\n * ```\n * @param tensors A list of tensors to concatenate.\n * @param axis The axis to concate along. Defaults to 0 (the first dim).\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction concat_(tensors, axis = 0) {\n assert(tensors.length >= 1, () => 'Pass at least one tensor to concat');\n const $tensors = convertToTensorArray(tensors, 'tensors', 'concat', 'string_or_numeric');\n if ($tensors[0].dtype === 'complex64') {\n $tensors.forEach(tensor => {\n if (tensor.dtype !== 'complex64') {\n throw new Error(`Cannot concatenate complex64 tensors with a tensor\n with dtype ${tensor.dtype}. `);\n }\n });\n }\n if ($tensors.length === 1) {\n return clone($tensors[0]);\n }\n const inputs = $tensors;\n const attr = { axis };\n return ENGINE.runKernel(Concat, inputs, attr);\n}\nconst concat = op({ concat_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst splitVGradConfig = {\n kernelName: SplitV,\n gradFunc: (dy, saved, attrs) => {\n const { axis } = attrs;\n return { x: () => concat(dy, axis) };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst sqrtGradConfig = {\n kernelName: Sqrt,\n inputsToSave: ['x'],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n return { x: () => div(dy, mul(sqrt(cast(x, 'float32')), 2)) };\n }\n};\n\n/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst squareGradConfig = {\n kernelName: Square,\n inputsToSave: ['x'],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n return { x: () => mul(dy, mul(cast(x, 'float32'), 2)) };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst squaredDifferenceGradConfig = {\n kernelName: SquaredDifference,\n inputsToSave: ['a', 'b'],\n gradFunc: (dy, saved) => {\n const [a, b] = saved;\n const two = scalar(2);\n const derA = () => mul(dy, mul(two, sub(a, b)));\n const derB = () => mul(dy, mul(two, sub(b, a)));\n return { a: derA, b: derB };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst stepGradConfig = {\n kernelName: Step,\n gradFunc: (dy) => {\n // TODO(manrajgrover): Return null for gradients when backprop supports\n // it.\n return { x: () => zerosLike(dy) };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst subGradConfig = {\n kernelName: Sub,\n inputsToSave: ['a', 'b'],\n gradFunc: (dy, saved) => {\n const [a, b] = saved;\n const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n const derA = () => {\n let res = dy;\n const reduceAxes = getReductionAxes(a.shape, outShape);\n if (reduceAxes.length > 0) {\n res = sum$1(res, reduceAxes);\n }\n return reshape(res, a.shape);\n };\n const derB = () => {\n let res = dy;\n const reduceAxes = getReductionAxes(b.shape, outShape);\n if (reduceAxes.length > 0) {\n res = sum$1(res, reduceAxes);\n }\n return reshape(neg(res), b.shape);\n };\n return { a: derA, b: derB };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst sumGradConfig = {\n kernelName: Sum,\n inputsToSave: ['x'],\n gradFunc: (dy, saved, attrs) => {\n const [x] = saved;\n const expandedDyShape = x.shape.slice();\n const { axis } = attrs;\n const axes = parseAxisParam(axis, x.shape);\n axes.forEach(axis => {\n expandedDyShape[axis] = 1;\n });\n const expandedDy = reshape(dy, expandedDyShape);\n const derX = mul(expandedDy, ones$1(x.shape, 'float32'));\n return { x: () => derX };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst tanGradConfig = {\n kernelName: Tan,\n inputsToSave: ['x'],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n return { x: () => div(dy, square(cos(x))) };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst tanhGradConfig = {\n kernelName: Tanh,\n outputsToSave: [true],\n gradFunc: (dy, saved) => {\n const [y] = saved;\n return { x: () => mul(sub(scalar(1), square(y)), dy) };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst tileGradConfig = {\n kernelName: Tile,\n inputsToSave: ['x'],\n gradFunc: (dy, saved, attrs) => {\n const [x] = saved;\n const { reps } = attrs;\n const derX = () => {\n let xGrad = zerosLike(x);\n // TODO(cais): Maybe reduce memory footprint by avoiding repeated\n // slicing.\n if (x.rank === 1) {\n for (let i = 0; i < reps[0]; ++i) {\n xGrad = add$1(xGrad, slice(dy, [i * x.shape[0]], [x.shape[0]]));\n }\n }\n else if (x.rank === 2) {\n for (let i = 0; i < reps[0]; ++i) {\n for (let j = 0; j < reps[1]; ++j) {\n xGrad = add$1(xGrad, slice(dy, [i * x.shape[0], j * x.shape[1]], [\n x.shape[0], x.shape[1]\n ]));\n }\n }\n }\n else if (x.rank === 3) {\n for (let i = 0; i < reps[0]; ++i) {\n for (let j = 0; j < reps[1]; ++j) {\n for (let k = 0; k < reps[2]; ++k) {\n xGrad =\n add$1(xGrad, slice(dy, [i * x.shape[0], j * x.shape[1], k * x.shape[2]], [x.shape[0], x.shape[1], x.shape[2]]));\n }\n }\n }\n }\n else if (x.rank === 4) {\n for (let i = 0; i < reps[0]; ++i) {\n for (let j = 0; j < reps[1]; ++j) {\n for (let k = 0; k < reps[2]; ++k) {\n for (let l = 0; l < reps[3]; ++l) {\n xGrad =\n add$1(xGrad, slice(dy, [\n i * x.shape[0], j * x.shape[1], k * x.shape[2],\n l * x.shape[3]\n ], [x.shape[0], x.shape[1], x.shape[2], x.shape[3]]));\n }\n }\n }\n }\n }\n else {\n throw new Error(`Gradient for tile operation is not implemented for rank-` +\n `${x.rank} tensors yet.`);\n }\n return xGrad;\n };\n return { x: derX };\n },\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst transposeGradConfig = {\n kernelName: Transpose,\n gradFunc: (dy, saved, attrs) => {\n const transposeAttrs = attrs;\n const { perm } = transposeAttrs;\n const undoPerm = getUndoAxesPermutation(perm);\n return { x: () => transpose(dy, undoPerm) };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Stacks a list of rank-`R` `tf.Tensor`s into one rank-`(R+1)` `tf.Tensor`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * const c = tf.tensor1d([5, 6]);\n * tf.stack([a, b, c]).print();\n * ```\n *\n * @param tensors A list of tensor objects with the same shape and dtype.\n * @param axis The axis to stack along. Defaults to 0 (the first dim).\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction stack_(tensors, axis = 0) {\n const $tensors = convertToTensorArray(tensors, 'tensors', 'stack', 'string_or_numeric');\n assert($tensors.length >= 1, () => 'Pass at least one tensor to tf.stack');\n if ($tensors.length > 0) {\n assert(axis <= $tensors[0].rank, () => 'Axis must be <= rank of the tensor');\n }\n const inputs = $tensors;\n const attrs = { axis };\n return ENGINE.runKernel(Pack, inputs, attrs);\n}\nconst stack = op({ stack_ });\n\n/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst unpackGradConfig = {\n kernelName: Unpack,\n gradFunc: (dy, saved, attrs) => {\n const unpackAttrs = attrs;\n const { axis } = unpackAttrs;\n return { value: () => stack(dy, axis) };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Returns a `tf.Tensor` that has expanded rank, by inserting a dimension\n * into the tensor's shape.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * const axis = 1;\n * x.expandDims(axis).print();\n * ```\n *\n * @param x The input tensor whose dimensions to be expanded.\n * @param axis The dimension index at which to insert shape of `1`. Defaults\n * to 0 (the first dimension).\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction expandDims_(x, axis = 0) {\n const $x = convertToTensor(x, 'x', 'expandDims', 'string_or_numeric');\n assert(axis <= $x.rank, () => 'Axis must be <= rank of the tensor');\n const inputs = { input: $x };\n const attrs = { dim: axis };\n return ENGINE.runKernel(ExpandDims, inputs, attrs);\n}\nconst expandDims = op({ expandDims_ });\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Gather slices from tensor `x`'s axis `axis` according to `indices`.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * const indices = tf.tensor1d([1, 3, 3], 'int32');\n *\n * x.gather(indices).print();\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const indices = tf.tensor1d([1, 1, 0], 'int32');\n *\n * x.gather(indices).print();\n * ```\n * @param x The input tensor whose slices to be gathered.\n * @param indices The indices of the values to extract.\n * @param axis The axis over which to select values. Defaults to 0.\n * @param batchDims Optional. The number of batch dimensions. It must be less\n * than or equal to rank(indices). Defaults to 0.\n * The output tensor will have shape of\n * `x.shape[:axis] + indices.shape[batchDims:] + x.shape[axis + 1:]`\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction gather_(x, indices, axis = 0, batchDims = 0) {\n const $x = convertToTensor(x, 'x', 'gather');\n const $indices = convertToTensor(indices, 'indices', 'gather', 'int32');\n const inputs = { x: $x, indices: $indices };\n const attrs = { axis, batchDims };\n return ENGINE.runKernel(GatherV2, inputs, attrs);\n}\nconst gather = op({ gather_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Returns the max of a and b (`a > b ? a : b`) element-wise.\n * Supports broadcasting.\n *\n * We also expose `tf.maximumStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.maximum(b).print(); // or tf.maximum(a, b)\n * ```\n *\n * ```js\n * // Broadcast maximum a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.maximum(b).print(); // or tf.maximum(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction maximum_(a, b) {\n let $a = convertToTensor(a, 'a', 'maximum');\n let $b = convertToTensor(b, 'b', 'maximum');\n [$a, $b] = makeTypesMatch($a, $b);\n if ($a.dtype === 'bool') {\n $a = cast($a, 'int32');\n $b = cast($b, 'int32');\n }\n assertAndGetBroadcastShape($a.shape, $b.shape);\n const inputs = { a: $a, b: $b };\n return ENGINE.runKernel(Maximum, inputs);\n}\nconst maximum = op({ maximum_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst unsortedSegmentSumGradConfig = {\n kernelName: UnsortedSegmentSum,\n inputsToSave: ['segmentIds'],\n gradFunc: (dy, saved) => {\n const [segmentIds] = saved;\n const derX = () => {\n return gatherDropNegatives(dy, segmentIds);\n };\n return { x: derX };\n }\n};\nfunction gatherDropNegatives(x, indices) {\n // Helper function for unsorted segment ops. Gathers params for\n // positive segment ids and gathers 0 for inputs with negative segment id.\n // Mirrors _GatherDropNegatives from tensorflow/python/ops/math_grad.py\n const zeroClippedIndices = maximum(indices, zerosLike(indices));\n const gathered = gather(x, zeroClippedIndices);\n let isPositive = greaterEqual(indices, scalar(0, 'int32'));\n const numIters = gathered.rank - isPositive.rank;\n for (let i = 0; i < numIters; ++i) {\n isPositive = expandDims(isPositive, i + 1);\n }\n isPositive = logicalAnd(isPositive, ones$1(gathered.shape, 'bool'));\n const zeroSlice = zerosLike(gathered);\n return where(isPositive, gathered, zeroSlice);\n}\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst zerosLikeGradConfig = {\n kernelName: ZerosLike,\n gradFunc: (dy) => {\n return { x: () => zerosLike(dy) };\n }\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Export all kernel configs here so that the package can auto register them\nconst gradConfigs = [\n absGradConfig,\n acosGradConfig,\n acoshGradConfig,\n addGradConfig,\n addNGradConfig,\n argMaxGradConfig,\n argMinGradConfig,\n asinGradConfig,\n asinhGradConfig,\n atan2GradConfig,\n atanGradConfig,\n atanhGradConfig,\n avgPool3DGradConfig,\n avgPoolGradConfig,\n batchMatMulGradConfig,\n batchToSpaceNDGradConfig,\n broadcastToGradConfig,\n castGradConfig,\n ceilGradConfig,\n clipByValueGradConfig,\n complexAbsGradConfig,\n concatGradConfig,\n conv2DBackpropInputGradConfig,\n conv2DGradConfig,\n conv3DGradConfig,\n cosGradConfig,\n coshGradConfig,\n cumsumGradConfig,\n depthwiseConv2dNativeGradConfig,\n dilation2dGradConfig,\n divGradConfig,\n eluGradConfig,\n erfGradConfig,\n expGradConfig,\n expandDimsGradConfig,\n expm1GradConfig,\n floorDivGradConfig,\n floorGradConfig,\n fusedBatchNormGradConfig,\n gatherGradConfig,\n greaterEqualGradConfig,\n identityGradConfig,\n isFiniteGradConfig,\n isInfGradConfig,\n isNanGradConfig,\n leakyReluGradConfig,\n log1pGradConfig,\n logGradConfig,\n logSoftmaxGradConfig,\n lrnGradConfig,\n maxGradConfig,\n maxGradConfig,\n maximumGradConfig,\n maxPool3DGradConfig,\n maxPoolGradConfig,\n meanGradConfig,\n minGradConfig,\n minimumGradConfig,\n mirrorPadGradConfig,\n modGradConfig,\n multiplyGradConfig,\n negGradConfig,\n oneHotGradConfig,\n onesLikeGradConfig,\n packGradConfig,\n padV2GradConfig,\n padV2GradConfig,\n powGradConfig,\n preluGradConfig,\n reciprocalGradConfig,\n relu6GradConfig,\n reluGradConfig,\n reshapeGradConfig,\n resizeBilinearGradConfig,\n resizeNearestNeighborGradConfig,\n reverseGradConfig,\n roundGradConfig,\n rsqrtGradConfig,\n selectGradConfig,\n seluGradConfig,\n sigmoidGradConfig,\n signGradConfig,\n sinGradConfig,\n sinhGradConfig,\n sliceGradConfig,\n softmaxGradConfig,\n softplusGradConfig,\n spaceToBatchNDGradConfig,\n spaceToBatchNDGradConfig,\n splitVGradConfig,\n splitVGradConfig,\n sqrtGradConfig,\n squaredDifferenceGradConfig,\n squareGradConfig,\n stepGradConfig,\n subGradConfig,\n sumGradConfig,\n tanGradConfig,\n tanhGradConfig,\n tileGradConfig,\n transposeGradConfig,\n unpackGradConfig,\n unsortedSegmentSumGradConfig,\n zerosLikeGradConfig\n];\nfor (const gradientConfig of gradConfigs) {\n registerGradient(gradientConfig);\n}\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes absolute value element-wise: `abs(x)`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.abs().print(); // or tf.abs(x)\n * ```\n * @param x The input `tf.Tensor`.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction abs_(x) {\n const $x = convertToTensor(x, 'x', 'abs');\n if ($x.dtype === 'complex64') {\n const inputs = { x: $x };\n return ENGINE.runKernel(ComplexAbs, inputs);\n }\n else {\n const inputs = { x: $x };\n return ENGINE.runKernel(Abs, inputs);\n }\n}\nconst abs = op({ abs_ });\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes acos of the input `tf.Tensor` element-wise: `acos(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.acos().print(); // or tf.acos(x)\n * ```\n * @param x The input tensor.\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction acos_(x) {\n const $x = convertToTensor(x, 'x', 'acos');\n const inputs = { x: $x };\n return ENGINE.runKernel(Acos, inputs);\n}\nconst acos = op({ acos_ });\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes the inverse hyperbolic cos of the input `tf.Tensor` element-wise:\n * `acosh(x)`\n *\n * ```js\n * const x = tf.tensor1d([10, 1, 3, 5.7]);\n *\n * x.acosh().print(); // or tf.acosh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction acosh_(x) {\n const $x = convertToTensor(x, 'x', 'acosh');\n const inputs = { x: $x };\n return ENGINE.runKernel(Acosh, inputs);\n}\nconst acosh = op({ acosh_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Adds a list of `tf.Tensor`s element-wise, each with the same shape and dtype.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * const c = tf.tensor1d([5, 6]);\n *\n * tf.addN([a, b, c]).print();\n * ```\n * @param tensors A list of tensors with the same shape and dtype.\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction addN_(tensors) {\n assert(Array.isArray(tensors), () => 'The argument passed to tf.addN() must be a list of tensors');\n assert(tensors.length >= 1, () => `Must pass at least one tensor to tf.addN(), but got ` +\n `${tensors.length}`);\n const $tensors = tensors.map((t, i) => convertToTensor(t, `tensors${i}`, 'addN'));\n const firstTensor = $tensors[0];\n $tensors.forEach(t => {\n if (t.dtype !== firstTensor.dtype) {\n throw new Error('All tensors passed to tf.addN() must have the same dtype');\n }\n });\n $tensors.forEach(t => {\n if (!arraysEqual(t.shape, firstTensor.shape)) {\n throw new Error('All tensors passed to tf.addN() must have the same shape');\n }\n });\n const inputs = $tensors;\n return ENGINE.runKernel(AddN, inputs);\n}\nconst addN = op({ addN_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes the logical and of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and an\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 1, 1], 'bool');\n *\n * x.all().print(); // or tf.all(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');\n *\n * const axis = 1;\n * x.all(axis).print(); // or tf.all(x, axis)\n * ```\n *\n * @param x The input tensor. Must be of dtype bool.\n * @param axis The dimension(s) to reduce. By default it reduces\n * all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction all_(x, axis = null, keepDims = false) {\n const $x = convertToTensor(x, 'x', 'all', 'bool');\n const inputs = { x: $x };\n const attrs = { axis, keepDims };\n return ENGINE.runKernel(All, inputs, attrs);\n}\nconst all = op({ all_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes the logical or of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and an\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 1, 1], 'bool');\n *\n * x.any().print(); // or tf.any(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');\n *\n * const axis = 1;\n * x.any(axis).print(); // or tf.any(x, axis)\n * ```\n *\n * @param x The input tensor. Must be of dtype bool.\n * @param axis The dimension(s) to reduce. By default it reduces\n * all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction any_(x, axis = null, keepDims = false) {\n const $x = convertToTensor(x, 'x', 'any', 'bool');\n const inputs = { x: $x };\n const attrs = { axis, keepDims };\n return ENGINE.runKernel(Any, inputs, attrs);\n}\n// tslint:disable-next-line:variable-name\nconst any = op({ any_ });\n\n/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Returns the indices of the maximum values along an `axis`.\n *\n * The result has the same shape as `input` with the dimension along `axis`\n * removed.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.argMax().print(); // or tf.argMax(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 4, 3], [2, 2]);\n *\n * const axis = 1;\n * x.argMax(axis).print(); // or tf.argMax(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension to reduce. Defaults to 0 (outer-most dimension).\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction argMax_(x, axis = 0) {\n const $x = convertToTensor(x, 'x', 'argMax');\n const inputs = { x: $x };\n const attrs = { axis };\n return ENGINE.runKernel(ArgMax, inputs, attrs);\n}\nconst argMax = op({ argMax_ });\n\n/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Returns the indices of the minimum values along an `axis`.\n *\n * The result has the same shape as `input` with the dimension along `axis`\n * removed.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.argMin().print(); // or tf.argMin(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 4, 3], [2, 2]);\n *\n * const axis = 1;\n * x.argMin(axis).print(); // or tf.argMin(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension to reduce. Defaults to 0 (outer-most dimension).\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction argMin_(x, axis = 0) {\n const $x = convertToTensor(x, 'x', 'argMin');\n const inputs = { x: $x };\n const attrs = { axis };\n return ENGINE.runKernel(ArgMin, inputs, attrs);\n}\nconst argMin = op({ argMin_ });\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes asin of the input `tf.Tensor` element-wise: `asin(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.asin().print(); // or tf.asin(x)\n * ```\n * @param x The input tensor.\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction asin_(x) {\n const $x = convertToTensor(x, 'x', 'asin');\n const inputs = { x: $x };\n return ENGINE.runKernel(Asin, inputs);\n}\nconst asin = op({ asin_ });\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes inverse hyperbolic sin of the input `tf.Tensor` element-wise:\n * `asinh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.asinh().print(); // or tf.asinh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction asinh_(x) {\n const $x = convertToTensor(x, 'x', 'asinh');\n const inputs = { x: $x };\n return ENGINE.runKernel(Asinh, inputs);\n}\nconst asinh = op({ asinh_ });\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes atan of the input `tf.Tensor` element-wise: `atan(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.atan().print(); // or tf.atan(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction atan_(x) {\n const $x = convertToTensor(x, 'x', 'atan');\n const inputs = { x: $x };\n return ENGINE.runKernel(Atan, inputs);\n}\nconst atan = op({ atan_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes arctangent of `tf.Tensor`s a / b element-wise: `atan2(a, b)`.\n * Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1.0, 1.0, -1.0, .7]);\n * const b = tf.tensor1d([2.0, 13.0, 3.5, .21]);\n *\n * tf.atan2(a, b).print()\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction atan2_(a, b) {\n let $a = convertToTensor(a, 'a', 'atan2');\n let $b = convertToTensor(b, 'b', 'atan2');\n [$a, $b] = makeTypesMatch($a, $b);\n const inputs = { a: $a, b: $b };\n return ENGINE.runKernel(Atan2, inputs);\n}\nconst atan2 = op({ atan2_ });\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes inverse hyperbolic tan of the input `tf.Tensor` element-wise:\n * `atanh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, .1, -.1, .7]);\n *\n * x.atanh().print(); // or tf.atanh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction atanh_(x) {\n const $x = convertToTensor(x, 'x', 'atanh');\n const inputs = { x: $x };\n return ENGINE.runKernel(Atanh, inputs);\n}\nconst atanh = op({ atanh_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes the 2D average pooling of an image.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n * `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n * `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad The type of padding algorithm:\n * - `same` and stride 1: output will be of same size as input,\n * regardless of filter size.\n * - `valid`: output will be smaller than input if filter is larger\n * than 1x1.\n * - For more info, see this guide:\n * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](\n * https://www.tensorflow.org/api_docs/python/tf/nn/convolution)\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n * provided, it will default to truncate.\n */\nfunction avgPool_(x, filterSize, strides, pad, dimRoundingMode) {\n const $x = convertToTensor(x, 'x', 'avgPool', 'float32');\n const dilations = 1;\n assert(eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in avgPool: Either strides or dilations must be 1. ' +\n `Got strides ${strides} and dilations '${dilations}'`);\n let x4D = $x;\n let reshapedTo4D = false;\n if ($x.rank === 3) {\n reshapedTo4D = true;\n x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n }\n assert(x4D.rank === 4, () => `Error in avgPool: x must be rank 4 but got rank ${x4D.rank}.`);\n checkPadOnDimRoundingMode('avgPool', pad, dimRoundingMode);\n const inputs = { x: x4D };\n const attrs = { filterSize, strides, pad, dimRoundingMode };\n // tslint:disable-next-line: no-unnecessary-type-assertion\n let res = ENGINE.runKernel(AvgPool, inputs, attrs);\n res = cast(res, $x.dtype);\n if (reshapedTo4D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n }\n return res;\n}\nconst avgPool = op({ avgPool_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes the 3D average pooling.\n *\n * ```js\n * const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);\n * const result = tf.avgPool3d(x, 2, 1, 'valid');\n * result.print();\n * ```\n *\n * @param x The input tensor, of rank 5 or rank 4 of shape\n * `[batch, depth, height, width, inChannels]`.\n * @param filterSize The filter size:\n * `[filterDepth, filterHeight, filterWidth]`.\n * If `filterSize` is a single number,\n * then `filterDepth == filterHeight == filterWidth`.\n * @param strides The strides of the pooling:\n * `[strideDepth, strideHeight, strideWidth]`.\n * If `strides` is a single number,\n * then `strideDepth == strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n * - `same` and stride 1: output will be of same size as input,\n * regardless of filter size.\n * - `valid`: output will be smaller than input if filter is larger\n * than 1*1x1.\n * - For more info, see this guide:\n * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](\n * https://www.tensorflow.org/api_docs/python/tf/nn/convolution)\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n * provided, it will default to truncate.\n * @param dataFormat An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n * \"NDHWC\". Specify the data format of the input and output data. With the\n * default format \"NDHWC\", the data is stored in the order of: [batch,\n * depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction avgPool3d_(x, filterSize, strides, pad, dimRoundingMode, dataFormat = 'NDHWC') {\n const $x = convertToTensor(x, 'x', 'avgPool3d', 'float32');\n let x5D = $x;\n let reshapedTo5D = false;\n if ($x.rank === 4) {\n reshapedTo5D = true;\n x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n }\n assert(x5D.rank === 5, () => `Error in avgPool3d: x must be rank 5 but got rank ${x5D.rank}.`);\n assert(dataFormat === 'NDHWC', () => `Error in avgPool3d: Only NDHWC is currently supported, ` +\n `but got dataFormat of ${dataFormat}`);\n checkPadOnDimRoundingMode('avgPool3d', pad, dimRoundingMode);\n const inputs = { x: x5D };\n const attrs = { filterSize, strides, pad, dimRoundingMode, dataFormat };\n // tslint:disable-next-line: no-unnecessary-type-assertion\n let res = ENGINE.runKernel(AvgPool3D, inputs, attrs);\n res = cast(res, x5D.dtype);\n if (reshapedTo5D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n }\n return res;\n}\nconst avgPool3d = op({ avgPool3d_ });\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes hyperbolic tangent of the input `tf.Tensor` element-wise: `tanh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, 70]);\n *\n * x.tanh().print(); // or tf.tanh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction tanh_(x) {\n const $x = convertToTensor(x, 'x', 'tanh', 'float32');\n const inputs = { x: $x };\n return ENGINE.runKernel(Tanh, inputs);\n}\nconst tanh$1 = op({ tanh_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes the next state and output of a BasicLSTMCell.\n *\n * Returns `[newC, newH]`.\n *\n * Derived from tf.contrib.rnn.BasicLSTMCell.\n *\n * @param forgetBias Forget bias for the cell.\n * @param lstmKernel The weights for the cell.\n * @param lstmBias The bias for the cell.\n * @param data The input to the cell.\n * @param c Previous cell state.\n * @param h Previous cell output.\n *\n * @doc {heading: 'Operations', subheading: 'RNN'}\n */\nfunction basicLSTMCell_(forgetBias, lstmKernel, lstmBias, data, c, h) {\n const $forgetBias = convertToTensor(forgetBias, 'forgetBias', 'basicLSTMCell');\n const $lstmKernel = convertToTensor(lstmKernel, 'lstmKernel', 'basicLSTMCell');\n const $lstmBias = convertToTensor(lstmBias, 'lstmBias', 'basicLSTMCell');\n const $data = convertToTensor(data, 'data', 'basicLSTMCell');\n const $c = convertToTensor(c, 'c', 'basicLSTMCell');\n const $h = convertToTensor(h, 'h', 'basicLSTMCell');\n const combined = concat([$data, $h], 1);\n const weighted = matMul(combined, $lstmKernel);\n const res = add$1(weighted, $lstmBias);\n // i = input_gate, j = new_input, f = forget_gate, o = output_gate\n const batchSize = res.shape[0];\n const sliceCols = res.shape[1] / 4;\n const sliceSize = [batchSize, sliceCols];\n const i = slice(res, [0, 0], sliceSize);\n const j = slice(res, [0, sliceCols], sliceSize);\n const f = slice(res, [0, sliceCols * 2], sliceSize);\n const o = slice(res, [0, sliceCols * 3], sliceSize);\n const newC = add$1(mul(sigmoid(i), tanh$1(j)), mul($c, sigmoid(add$1($forgetBias, f))));\n const newH = mul(tanh$1(newC), sigmoid(o));\n return [newC, newH];\n}\nconst basicLSTMCell = op({ basicLSTMCell_ });\n\nfunction xAs4D(x) {\n let x4D;\n if (x.rank === 0 || x.rank === 1) {\n x4D = reshape(x, [1, 1, 1, x.size]);\n }\n else if (x.rank === 2) {\n x4D = reshape(x, [1, 1, x.shape[0], x.shape[1]]);\n }\n else if (x.rank === 3) {\n x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n }\n else {\n x4D = x;\n }\n return x4D;\n}\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Batch normalization.\n *\n * As described in\n * [http://arxiv.org/abs/1502.03167](http://arxiv.org/abs/1502.03167).\n *\n * Mean, variance, scale, and offset can be of two shapes:\n * - The same shape as the input.\n * - In the common case, the depth dimension is the last dimension of x, so\n * the values would be an `tf.Tensor1D` of shape [depth].\n *\n * Also available are stricter rank-specific methods with the same signature\n * as this method that assert that parameters passed are of given rank\n * - `tf.batchNorm2d`\n * - `tf.batchNorm3d`\n * - `tf.batchNorm4d`\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction batchNorm_(x, mean, variance, offset, scale, varianceEpsilon) {\n if (varianceEpsilon == null) {\n varianceEpsilon = 0.001;\n }\n const $x = convertToTensor(x, 'x', 'batchNorm');\n const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n let $scale;\n if (scale != null) {\n $scale = convertToTensor(scale, 'scale', 'batchNorm');\n }\n let $offset;\n if (offset != null) {\n $offset = convertToTensor(offset, 'offset', 'batchNorm');\n }\n assert($mean.rank === $variance.rank, () => 'Batch normalization gradient requires mean and variance to have ' +\n 'equal ranks.');\n assert($offset == null || $mean.rank === $offset.rank, () => 'Batch normalization gradient requires mean and offset to have ' +\n 'equal ranks.');\n assert($scale == null || $mean.rank === $scale.rank, () => 'Batch normalization gradient requires mean and scale to have ' +\n 'equal ranks.');\n const x4D = xAs4D($x);\n const inputs = {\n x: x4D,\n scale: $scale,\n offset: $offset,\n mean: $mean,\n variance: $variance\n };\n const attrs = { varianceEpsilon };\n // tslint:disable-next-line: no-unnecessary-type-assertion\n const res = ENGINE.runKernel(FusedBatchNorm, inputs, attrs);\n return reshape(res, $x.shape);\n}\nconst batchNorm = op({ batchNorm_ });\n\n/**\n * Batch normalization, strictly for 2D. For the more relaxed version, see\n * `tf.batchNorm`.\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n */\nfunction batchNorm2d_(x, mean, variance, offset, scale, varianceEpsilon) {\n const $x = convertToTensor(x, 'x', 'batchNorm');\n const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n let $scale;\n if (scale != null) {\n $scale = convertToTensor(scale, 'scale', 'batchNorm');\n }\n let $offset;\n if (offset != null) {\n $offset = convertToTensor(offset, 'offset', 'batchNorm');\n }\n assert($x.rank === 2, () => `Error in batchNorm2D: x must be rank 2 but got rank ` +\n `${$x.rank}.`);\n assert($mean.rank === 2 || $mean.rank === 1, () => `Error in batchNorm2D: mean must be rank 2 or rank 1 but ` +\n `got rank ${$mean.rank}.`);\n assert($variance.rank === 2 || $variance.rank === 1, () => `Error in batchNorm2D: variance must be rank 2 or rank 1 ` +\n `but got rank ${$variance.rank}.`);\n if ($scale != null) {\n assert($scale.rank === 2 || $scale.rank === 1, () => `Error in batchNorm2D: scale must be rank 2 or rank 1 ` +\n `but got rank ${$scale.rank}.`);\n }\n if ($offset != null) {\n assert($offset.rank === 2 || $offset.rank === 1, () => `Error in batchNorm2D: offset must be rank 2 or rank 1 ` +\n `but got rank ${$offset.rank}.`);\n }\n return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\nconst batchNorm2d = op({ batchNorm2d_ });\n\n/**\n * Batch normalization, strictly for 3D. For the more relaxed version, see\n * `tf.batchNorm`.\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n */\nfunction batchNorm3d_(x, mean, variance, offset, scale, varianceEpsilon) {\n const $x = convertToTensor(x, 'x', 'batchNorm');\n const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n let $scale;\n if (scale != null) {\n $scale = convertToTensor(scale, 'scale', 'batchNorm');\n }\n let $offset;\n if (offset != null) {\n $offset = convertToTensor(offset, 'offset', 'batchNorm');\n }\n assert($x.rank === 3, () => `Error in batchNorm3D: x must be rank 3 but got rank ` +\n `${$x.rank}.`);\n assert($mean.rank === 3 || $mean.rank === 1, () => `Error in batchNorm3D: mean must be rank 3 or rank 1 but ` +\n `got rank ${$mean.rank}.`);\n assert($variance.rank === 3 || $variance.rank === 1, () => `Error in batchNorm3D: variance must be rank 3 or rank 1 ` +\n `but got rank ${$variance.rank}.`);\n if ($scale != null) {\n assert($scale.rank === 3 || $scale.rank === 1, () => `Error in batchNorm3D: scale must be rank 3 or rank 1 ` +\n `but got rank ${$scale.rank}.`);\n }\n if ($offset != null) {\n assert($offset.rank === 3 || $offset.rank === 1, () => `Error in batchNorm3D: offset must be rank 3 or rank 1 ` +\n `but got rank ${$offset.rank}.`);\n }\n return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\nconst batchNorm3d = op({ batchNorm3d_ });\n\n/**\n * Batch normalization, strictly for 4D. For the more relaxed version, see\n * `tf.batchNorm`.\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n */\nfunction batchNorm4d_(x, mean, variance, offset, scale, varianceEpsilon) {\n const $x = convertToTensor(x, 'x', 'batchNorm');\n const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n let $scale;\n if (scale != null) {\n $scale = convertToTensor(scale, 'scale', 'batchNorm');\n }\n let $offset;\n if (offset != null) {\n $offset = convertToTensor(offset, 'offset', 'batchNorm');\n }\n assert($x.rank === 4, () => `Error in batchNorm4D: x must be rank 4 but got rank ` +\n `${$x.rank}.`);\n assert($mean.rank === 4 || $mean.rank === 1, () => `Error in batchNorm4D: mean must be rank 4 or rank 1 but ` +\n `got rank ${$mean.rank}.`);\n assert($variance.rank === 4 || $variance.rank === 1, () => `Error in batchNorm4D: variance must be rank 4 or rank 1 ` +\n `but got rank ${$variance.rank}.`);\n if ($scale != null) {\n assert($scale.rank === 4 || $scale.rank === 1, () => `Error in batchNorm4D: scale must be rank 4 or rank 1 ` +\n `but got rank ${$scale.rank}.`);\n }\n if ($offset != null) {\n assert($offset.rank === 4 || $offset.rank === 1, () => `Error in batchNorm4D: offset must be rank 4 or rank 1 ` +\n `but got rank ${$offset.rank}.`);\n }\n return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\nconst batchNorm4d = op({ batchNorm4d_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Outputs a vector with length `size` and the same dtype as `weights`.\n *\n * If `weights` are empty, then index `i` stores the number of times the value\n * `i` is counted in `x`. If `weights` are non-empty, then index `i` stores the\n * sum of the value in `weights` at each index where the corresponding value in\n * `x` is `i`.\n *\n * Values in `x` outside of the range [0, size) are ignored.\n *\n * @param x The input int tensor, rank 1.\n * @param weights The weights tensor, must have the same shape as x, or a\n * length-0 Tensor, in which case it acts as all weights equal to 1.\n * @param size Non-negative integer.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction bincount_(x, weights, size) {\n const $x = convertToTensor(x, 'x', 'bincount');\n const $weights = convertToTensor(weights, 'weights', 'bincount');\n assert($x.dtype === 'int32', () => `Error in bincount: input ` +\n `dtype must be int32, but got ${$x.dtype}`);\n assert(size >= 0, () => `size must be non-negative, but got ${size}.`);\n assert($weights.size === $x.size || $weights.size === 0, () => `Error in bincount: weights must have the same size as input or` +\n `0-length, but got input shape: ${$x.shape}, weights shape: ` +\n `${$weights.shape}.`);\n const inputs = { x: $x, weights: $weights };\n const attrs = { size };\n return ENGINE.runKernel(Bincount, inputs, attrs);\n}\nconst bincount = op({ bincount_ });\n\n/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Return the shape of s0 op s1 with broadcast.\n *\n * compute r0, the broadcasted shape as a tensor.\n * s0, s1 and r0 are all integer vectors.\n *\n * This function returns the shape of the result of an operation between\n * two tensors of size s0 and s1 performed with broadcast.\n *\n * @param s0 A tensor representing a shape\n * @param s1 A tensor representing a shape\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction broadcastArgs_(s0, s1) {\n const shape1Input = convertToTensor(s0, 's0', 'broadcastArgs', 'int32');\n const shape2Input = convertToTensor(s1, 's1', 'broadcastArgs', 'int32');\n if (shape1Input.rank !== 1) {\n throw new Error('broadcastArgs(): first input must be a vector (rank=1). ' +\n `Has rank ${shape1Input.rank}`);\n }\n if (shape2Input.rank !== 1) {\n throw new Error('broadcastArgs(): second input must be a vector (rank=1). ' +\n `Has rank ${shape2Input.rank}`);\n }\n const inputs = { s0: shape1Input, s1: shape2Input };\n return ENGINE.runKernel(BroadcastArgs, inputs);\n}\nconst broadcastArgs = op({ broadcastArgs_ });\n\n/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Creates an empty `tf.TensorBuffer` with the specified `shape` and `dtype`.\n *\n * The values are stored in CPU as `TypedArray`. Fill the buffer using\n * `buffer.set()`, or by modifying directly `buffer.values`.\n *\n * When done, call `buffer.toTensor()` to get an immutable `tf.Tensor` with\n * those values.\n *\n * ```js\n * // Create a buffer and set values at particular indices.\n * const buffer = tf.buffer([2, 2]);\n * buffer.set(3, 0, 0);\n * buffer.set(5, 1, 0);\n *\n * // Convert the buffer back to a tensor.\n * buffer.toTensor().print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The dtype of the buffer. Defaults to 'float32'.\n * @param values The values of the buffer as `TypedArray`. Defaults to\n * zeros.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction buffer(shape, dtype = 'float32', values) {\n dtype = dtype || 'float32';\n assertNonNegativeIntegerDimensions(shape);\n return new TensorBuffer(shape, dtype, values);\n}\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes ceiling of input `tf.Tensor` element-wise: `ceil(x)`\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3]);\n *\n * x.ceil().print(); // or tf.ceil(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction ceil_(x) {\n const $x = convertToTensor(x, 'x', 'ceil', 'float32');\n const inputs = { x: $x };\n return ENGINE.runKernel(Ceil, inputs);\n}\nconst ceil = op({ ceil_ });\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Clips values element-wise. `max(min(x, clipValueMax), clipValueMin)`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.clipByValue(-2, 3).print(); // or tf.clipByValue(x, -2, 3)\n * ```\n * @param x The input tensor.\n * @param clipValueMin Lower-bound of range to be clipped to.\n * @param clipValueMax Upper-bound of range to be clipped to.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction clipByValue_(x, clipValueMin, clipValueMax) {\n const $x = convertToTensor(x, 'x', 'clipByValue');\n assert((clipValueMin <= clipValueMax), () => `Error in clip: min (${clipValueMin}) must be ` +\n `less than or equal to max (${clipValueMax}).`);\n const inputs = { x: $x };\n const attrs = { clipValueMin, clipValueMax };\n return ENGINE.runKernel(ClipByValue, inputs, attrs);\n}\nconst clipByValue = op({ clipByValue_ });\n\n/**\n * Concatenates a list of`tf.Tensor1D`s along an axis. See `concat` for details.\n *\n * For example, if:\n * A: shape(3) = |r1, g1, b1|\n * B: shape(2) = |r2, g2|\n * C = tf.concat1d([A, B]) == |r1, g1, b1, r2, g2|\n *\n * @param tensors A list of`tf.Tensor`s to concatenate.\n * @return The concatenated array.\n */\nfunction concat1d_(tensors) {\n return concat(tensors, 0 /* axis */);\n}\nconst concat1d = op({ concat1d_ });\n\n/**\n * Concatenates a list of`tf.Tensor2D`s along an axis. See `concat` for details.\n *\n * For example, if:\n * A: shape(2, 3) = | r1, g1, b1 |\n * | r2, g2, b2 |\n *\n * B: shape(2, 3) = | r3, g3, b3 |\n * | r4, g4, b4 |\n *\n * C = tf.concat2d([A, B], axis)\n *\n * if axis = 0:\n * C: shape(4, 3) = | r1, g1, b1 |\n * | r2, g2, b2 |\n * | r3, g3, b3 |\n * | r4, g4, b4 |\n *\n * if axis = 1:\n * C = shape(2, 6) = | r1, g1, b1, r3, g3, b3 |\n * | r2, g2, b2, r4, g4, b4 |\n *\n *\n * @param tensors A list of `tf.Tensor`s to concatenate.\n * @param axis The axis to concatenate along.\n * @return The concatenated array.\n */\nfunction concat2d_(tensors, axis) {\n return concat(tensors, axis);\n}\nconst concat2d = op({ concat2d_ });\n\n/**\n * Concatenates a list of `tf.Tensor3D`s along an axis.\n * See `concat` for details.\n *\n * For example, if:\n * A: shape(2, 1, 3) = | r1, g1, b1 |\n * | r2, g2, b2 |\n *\n * B: shape(2, 1, 3) = | r3, g3, b3 |\n * | r4, g4, b4 |\n *\n * C = tf.concat3d([A, B], axis)\n *\n * if axis = 0:\n * C: shape(4, 1, 3) = | r1, g1, b1 |\n * | r2, g2, b2 |\n * | r3, g3, b3 |\n * | r4, g4, b4 |\n *\n * if axis = 1:\n * C: shape(2, 2, 3) = | r1, g1, b1, r3, g3, b3 |\n * | r2, g2, b2, r4, g4, b4 |\n *\n * if axis = 2:\n * C = shape(2, 1, 6) = | r1, g1, b1, r3, g3, b3 |\n * | r2, g2, b2, r4, g4, b4 |\n *\n * @param tensors A list of`tf.Tensor`s to concatenate.\n * @param axis The axis to concate along.\n * @return The concatenated array.\n */\nfunction concat3d_(tensors, axis) {\n return concat(tensors, axis);\n}\nconst concat3d = op({ concat3d_ });\n\n/**\n * Concatenates a list of `tf.Tensor4D`s along an axis.\n * See `concat` for details.\n *\n * @param tensors A list of `tf.Tensor`s to concatenate.\n * @param axis The axis to concate along.\n * @return The concatenated array.\n */\nfunction concat4d_(tensors, axis) {\n return concat(tensors, axis);\n}\nconst concat4d = op({ concat4d_ });\n\n/**\n * Computes a 1D convolution over the input x.\n *\n * @param x The input tensor, of rank 3 or rank 2, of shape\n * `[batch, width, inChannels]`. If rank 2, batch of 1 is assumed.\n * @param filter The filter, rank 3, of shape\n * `[filterWidth, inDepth, outDepth]`.\n * @param stride The number of entries by which the filter is moved right at\n * each step.\n * @param pad The type of padding algorithm.\n * - `same` and stride 1: output will be of same size as input,\n * regardless of filter size.\n * - `valid`: output will be smaller than input if filter is larger\n * than 1x1.\n * - For more info, see this guide:\n * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](\n * https://www.tensorflow.org/api_docs/python/tf/nn/convolution)\n * @param dataFormat An optional string from \"NWC\", \"NCW\". Defaults to \"NWC\",\n * the data is stored in the order of [batch, in_width, in_channels]. Only\n * \"NWC\" is currently supported.\n * @param dilation The dilation rate in which we sample input values in\n * atrous convolution. Defaults to `1`. If it is greater than 1, then\n * stride must be `1`.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n * provided, it will default to truncate.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv1d_(x, filter, stride, pad, dataFormat = 'NWC', dilation = 1, dimRoundingMode) {\n const $x = convertToTensor(x, 'x', 'conv1d');\n const $filter = convertToTensor(filter, 'filter', 'conv1d');\n let x3D = $x;\n let reshapedTo3D = false;\n if ($x.rank === 2) {\n reshapedTo3D = true;\n x3D = reshape($x, [1, $x.shape[0], $x.shape[1]]);\n }\n assert(x3D.rank === 3, () => `Error in conv1d: input must be rank 3, but got rank ${x3D.rank}.`);\n assert($filter.rank === 3, () => `Error in conv1d: filter must be rank 3, but got rank ` +\n `${$filter.rank}.`);\n checkPadOnDimRoundingMode('conv1d', pad, dimRoundingMode);\n assert(x3D.shape[2] === $filter.shape[1], () => `Error in conv1d: depth of input (${x3D.shape[2]}) must match ` +\n `input depth for filter ${$filter.shape[1]}.`);\n assert(eitherStridesOrDilationsAreOne(stride, dilation), () => 'Error in conv1D: Either stride or dilation must be 1. ' +\n `Got stride ${stride} and dilation '${dilation}'`);\n assert(dataFormat === 'NWC', () => `Error in conv1d: got dataFormat of ${dataFormat} but only NWC is currently supported.`);\n const filter4D = reshape($filter, [1, $filter.shape[0], $filter.shape[1], $filter.shape[2]]);\n const input4D = reshape(x3D, [x3D.shape[0], 1, x3D.shape[1], x3D.shape[2]]);\n const strides = [1, stride];\n const dilations = [1, dilation];\n const conv2dDataFormat = 'NHWC';\n const res = conv2d(input4D, filter4D, strides, pad, conv2dDataFormat, dilations, dimRoundingMode);\n if (reshapedTo3D) {\n return reshape(res, [res.shape[2], res.shape[3]]);\n }\n return reshape(res, [res.shape[0], res.shape[2], res.shape[3]]);\n}\nconst conv1d = op({ conv1d_ });\n\n/**\n * Computes the transposed 2D convolution of an image, also known as a\n * deconvolution.\n *\n * @param x The input image, of rank 4 or rank 3, of shape\n * `[batch, height, width, inDepth]`. If rank 3, batch of 1 is assumed.\n * @param filter The filter, rank 4, of shape\n * `[filterHeight, filterWidth, outDepth, inDepth]`.\n * `inDepth` must match `inDepth` in `x`.\n * @param outputShape Output shape, of rank 4 or rank 3:\n * `[batch, height, width, outDepth]`. If rank 3, batch of 1 is assumed.\n * @param strides The strides of the original convolution:\n * `[strideHeight, strideWidth]`.\n * @param pad The type of padding algorithm used in the non-transpose version\n * of the op.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n * provided, it will default to truncate.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv2dTranspose_(x, filter, outputShape, strides, pad, dimRoundingMode) {\n const $x = convertToTensor(x, 'x', 'conv2dTranspose');\n const $filter = convertToTensor(filter, 'filter', 'conv2dTranspose');\n return conv2DBackpropInput(outputShape, $x, $filter, strides, pad, 'NHWC', dimRoundingMode);\n}\nconst conv2dTranspose = op({ conv2dTranspose_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes a 3D convolution over the input x.\n *\n * @param x The input tensor, of rank 5 or rank 4, of shape\n * `[batch, depth, height, width, channels]`. If rank 4,\n * batch of 1 is assumed.\n * @param filter The filter, rank 5, of shape\n * `[filterDepth, filterHeight, filterWidth, inChannels, outChannels]`.\n * inChannels must match between input and filter.\n * @param strides The strides of the convolution: `[strideDepth, strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n * - `same` and stride 1: output will be of same size as input,\n * regardless of filter size.\n * - `valid`: output will be smaller than input if filter is larger\n * than 1x1.\n * - For more info, see this guide:\n * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](\n * https://www.tensorflow.org/api_docs/python/tf/nn/convolution)\n * @param dataFormat: An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n * \"NDHWC\". Specify the data format of the input and output data. With the\n * default format \"NDHWC\", the data is stored in the order of: [batch,\n * depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @param dilations The dilation rates: `[dilationDepth, dilationHeight,\n * dilationWidth]` in which we sample input values across the height\n * and width dimensions in atrous convolution. Defaults to `[1, 1, 1]`.\n * If `dilations` is a single number, then\n * `dilationDepth == dilationHeight == dilationWidth`. If it is greater\n * than 1, then all values of `strides` must be 1.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv3d_(x, filter, strides, pad, dataFormat = 'NDHWC', dilations = [1, 1, 1]) {\n const $x = convertToTensor(x, 'x', 'conv3d');\n const $filter = convertToTensor(filter, 'filter', 'conv3d');\n let x5D = $x;\n let reshapedTo5D = false;\n if ($x.rank === 4) {\n reshapedTo5D = true;\n x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n }\n assert(x5D.rank === 5, () => `Error in conv3d: input must be rank 5, but got rank ${x5D.rank}.`);\n assert($filter.rank === 5, () => `Error in conv3d: filter must be rank 5, but got rank ` +\n `${$filter.rank}.`);\n assert(x5D.shape[4] === $filter.shape[3], () => `Error in conv3d: depth of input (${x5D.shape[4]}) must match ` +\n `input depth for filter ${$filter.shape[3]}.`);\n assert(eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in conv3D: Either strides or dilations must be 1. ' +\n `Got strides ${strides} and dilations '${dilations}'`);\n assert(dataFormat === 'NDHWC', () => `Error in conv3d: got dataFormat of ${dataFormat} but only NDHWC is currently supported.`);\n const inputs = { x: x5D, filter: $filter };\n const attrs = { strides, pad, dataFormat, dilations };\n // tslint:disable-next-line: no-unnecessary-type-assertion\n const res = ENGINE.runKernel(Conv3D, inputs, attrs);\n if (reshapedTo5D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n }\n return res;\n}\nconst conv3d = op({ conv3d_ });\n\n/**\n * Computes the transposed 3D convolution of a volume, also known as a\n * deconvolution.\n *\n * @param x The input image, of rank 5 or rank 4, of shape\n * `[batch, depth, height, width, inDepth]`. If rank 4, batch of 1 is assumed.\n * @param filter The filter, rank 4, of shape\n * `[depth, filterHeight, filterWidth, outDepth, inDepth]`.\n * `inDepth` must match `inDepth` in `x`.\n * @param outputShape Output shape, of rank 5 or rank 4:\n * `[batch, depth, height, width, outDepth]`. If rank 3, batch of 1 is\n * assumed.\n * @param strides The strides of the original convolution:\n * `[strideDepth, strideHeight, strideWidth]`.\n * @param pad The type of padding algorithm used in the non-transpose version\n * of the op.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv3dTranspose_(x, filter, outputShape, strides, pad) {\n const $x = convertToTensor(x, 'x', 'conv3dTranspose');\n const $filter = convertToTensor(filter, 'filter', 'conv3dTranspose');\n return conv3DBackpropInput(outputShape, $x, $filter, strides, pad);\n}\nconst conv3dTranspose = op({ conv3dTranspose_ });\n\n/**\n * @license\n * Copyright 2022 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the 'License');\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an 'AS IS' BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes the cumulative product of a `tf.Tensor` along `axis`.\n *\n * ```js\n * const x = tf.tensor([1, 2, 3, 4]);\n * x.cumprod().print();\n * ```\n * ```js\n * const x = tf.tensor([[1, 2], [3, 4]]);\n * x.cumprod().print();\n * ```\n *\n * @param x The input tensor to cumulatively multiply.\n * @param axis The axis along which to multiply. Optional. Defaults to 0.\n * @param exclusive Whether to perform exclusive cumulative product. Optional.\n * Defaults to false. If set to true then the product of each tensor entry\n * does not include its own value, but only the values previous to it\n * along the specified axis.\n * @param reverse Whether to multiply in the opposite direction. Optional.\n * Defaults to false.\n *\n * @doc {heading: 'Operations', subheading: 'Scan'}\n */\nfunction cumprod_(x, axis = 0, exclusive = false, reverse = false) {\n const $x = convertToTensor(x, 'x', 'cumprod');\n const inputs = { x: $x };\n const attrs = { axis, exclusive, reverse };\n return ENGINE.runKernel(Cumprod, inputs, attrs);\n}\nconst cumprod = op({ cumprod_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Outputs a vector with length `size` and the same dtype as `weights`.\n *\n * If `weights` are empty, then index `i` stores the number of times the value\n * `i` is counted in `x`. If `weights` are non-empty, then index `i` stores the\n * sum of the value in `weights` at each index where the corresponding value in\n * `x` is `i`.\n *\n * Values in `x` outside of the range [0, size) are ignored.\n *\n * @param x The input int tensor, rank 1 or rank 2.\n * @param weights The weights tensor, must have the same shape as x, or a\n * length-0 Tensor, in which case it acts as all weights equal to 1.\n * @param size Non-negative integer.\n * @param binaryOutput Optional. Whether the kernel should count the appearance\n * or number of occurrences. Defaults to False.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction denseBincount_(x, weights, size, binaryOutput = false) {\n const $x = convertToTensor(x, 'x', 'denseBincount');\n const $weights = convertToTensor(weights, 'weights', 'denseBincount');\n assert($x.dtype === 'int32', () => `Error in denseBincount: input ` +\n `dtype must be int32, but got ${$x.dtype}`);\n assert($x.rank <= 2, () => `Error in denseBincount: input must be at most rank 2, but got ` +\n `rank ${$x.rank}.`);\n assert(size >= 0, () => `size must be non-negative, but got ${size}.`);\n assert($weights.size === $x.size || $weights.size === 0, () => `Error in denseBincount: weights must have the same shape as x or ` +\n `0-length, but got x shape: ${$x.shape}, weights shape: ` +\n `${$weights.shape}.`);\n const inputs = { x: $x, weights: $weights };\n const attrs = { size, binaryOutput };\n return ENGINE.runKernel(DenseBincount, inputs, attrs);\n}\nconst denseBincount = op({ denseBincount_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Rearranges data from depth into blocks of spatial data. More specifically,\n * this op outputs a copy of the input tensor where values from the `depth`\n * dimension are moved in spatial blocks to the `height` and `width` dimensions.\n * The attr `blockSize` indicates the input block size and how the data is\n * moved.\n *\n * - Chunks of data of size `blockSize * blockSize` from depth are rearranged\n * into non-overlapping blocks of size `blockSize x blockSize`\n *\n * - The width the output tensor is `inputWidth * blockSize`, whereas the\n * height is `inputHeight * blockSize`\n *\n * - The Y, X coordinates within each block of the output image are determined\n * by the high order component of the input channel index\n *\n * - The depth of the input tensor must be divisible by `blockSize *\n * blockSize`\n *\n * The `dataFormat` attr specifies the layout of the input and output tensors\n * with the following options: \"NHWC\": [ `batch, height, width, channels` ]\n * \"NCHW\": [ `batch, channels, height, width` ]\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [1, 1, 1, 4]);\n * const blockSize = 2;\n * const dataFormat = \"NHWC\";\n *\n * tf.depthToSpace(x, blockSize, dataFormat).print();\n * ```\n *\n * @param x The input tensor of rank 4\n * @param blockSIze An `int` that is `>= 2`. The size of the spatial block\n * @param dataFormat An optional string from: \"NHWC\", \"NCHW\". Defaults to \"NHWC\"\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction depthToSpace_(x, blockSize, dataFormat = 'NHWC') {\n const $x = convertToTensor(x, 'x', 'depthToSpace', 'float32');\n const inputHeight = (dataFormat === 'NHWC') ? $x.shape[1] : $x.shape[2];\n const inputWidth = (dataFormat === 'NHWC') ? $x.shape[2] : $x.shape[3];\n const inputDepth = (dataFormat === 'NHWC') ? $x.shape[3] : $x.shape[1];\n assert(blockSize > 1, () => `blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);\n assert(inputHeight * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying\n ${inputHeight} and ${blockSize} for depthToSpace with input shape\n ${$x.shape}`);\n assert(inputWidth * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying\n ${inputWidth} and ${blockSize} for depthToSpace with input shape\n ${$x.shape}`);\n assert((inputDepth % (blockSize * blockSize) === 0), () => `Dimension size must be evenly divisible by ${blockSize * blockSize} but is ${inputDepth} for depthToSpace with input shape ${$x.shape}`);\n const inputs = { x: $x };\n const attrs = { blockSize, dataFormat };\n return ENGINE.runKernel(DepthToSpace, inputs, attrs);\n}\nconst depthToSpace = op({ depthToSpace_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Depthwise 2D convolution.\n *\n * Given a 4D `input` array and a `filter` array of shape\n * `[filterHeight, filterWidth, inChannels, channelMultiplier]` containing\n * `inChannels` convolutional filters of depth 1, this op applies a\n * different filter to each input channel (expanding from 1 channel to\n * `channelMultiplier` channels for each), then concatenates the results\n * together. The output has `inChannels * channelMultiplier` channels.\n *\n * See\n * [https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d](\n * https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d)\n * for more details.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter tensor, rank 4, of shape\n * `[filterHeight, filterWidth, inChannels, channelMultiplier]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`. If strides is a single number, then `strideHeight ==\n * strideWidth`.\n * @param pad The type of padding algorithm.\n * - `same` and stride 1: output will be of same size as input,\n * regardless of filter size.\n * - `valid`: output will be smaller than input if filter is larger\n * than 1x1.\n * - For more info, see this guide:\n * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](\n * https://www.tensorflow.org/api_docs/python/tf/nn/convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n * in which we sample input values across the height and width dimensions\n * in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single\n * number, then `dilationHeight == dilationWidth`. If it is greater than\n * 1, then all values of `strides` must be 1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n * \"NHWC\". Specify the data format of the input and output data. With the\n * default format \"NHWC\", the data is stored in the order of: [batch,\n * height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n * provided, it will default to truncate.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction depthwiseConv2d_(x, filter, strides, pad, dataFormat = 'NHWC', dilations = [1, 1], dimRoundingMode) {\n const $x = convertToTensor(x, 'x', 'depthwiseConv2d', 'float32');\n const $filter = convertToTensor(filter, 'filter', 'depthwiseConv2d', 'float32');\n let x4D = $x;\n let reshapedTo4D = false;\n if ($x.rank === 3) {\n reshapedTo4D = true;\n x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n }\n assert(x4D.rank === 4, () => `Error in depthwiseConv2d: input must be rank 4, but got ` +\n `rank ${x4D.rank}.`);\n assert($filter.rank === 4, () => `Error in depthwiseConv2d: filter must be rank 4, but got rank ` +\n `${$filter.rank}.`);\n assert(x4D.shape[3] === $filter.shape[2], () => `Error in depthwiseConv2d: number of input channels ` +\n `(${x4D.shape[3]}) must match the inChannels dimension in ` +\n `filter ${$filter.shape[2]}.`);\n checkPadOnDimRoundingMode('depthwiseConv2d', pad, dimRoundingMode);\n const inputs = { x: x4D, filter: $filter };\n const attrs = { strides, pad, dataFormat, dilations, dimRoundingMode };\n // tslint:disable-next-line: no-unnecessary-type-assertion\n const res = ENGINE.runKernel(DepthwiseConv2dNative, inputs, attrs);\n if (reshapedTo4D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n }\n return res;\n}\nconst depthwiseConv2d = op({ depthwiseConv2d_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Returns a diagonal tensor with a given diagonal values.\n *\n * Given a diagonal, this operation returns a tensor with the diagonal and\n * everything else padded with zeros.\n *\n * Assume the input has dimensions `[D1,..., Dk]`, then the output is a tensor\n * of rank 2k with dimensions `[D1,..., Dk, D1,..., Dk]`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * tf.diag(x).print()\n * ```\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4, 5, 6, 6, 8], [4, 2])\n *\n * tf.diag(x).print()\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction diag_(x) {\n const $x = convertToTensor(x, 'x', 'diag');\n const inputs = { x: $x };\n return ENGINE.runKernel(Diag, inputs);\n}\nconst diag = op({ diag_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes the grayscale dilation over the input `x`.\n *\n * @param x The input tensor, rank 3 or rank 4 of shape\n * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filter The filter tensor, rank 3, of shape\n * `[filterHeight, filterWidth, depth]`.\n * @param strides The strides of the sliding window for each dimension of the\n * input tensor: `[strideHeight, strideWidth]`.\n * If `strides` is a single number,\n * then `strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n * - `same` and stride 1: output will be of same size as input,\n * regardless of filter size.\n * - `valid`: output will be smaller than input if filter is larger\n * than 1*1x1.\n * - For more info, see this guide:\n * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](\n * https://www.tensorflow.org/api_docs/python/tf/nn/convolution)\n * @param dataFormat Specify the data format of the input and output data.\n * Defaults to 'NHWC'. Only 'NHWC' is currently supported. With the\n * default format \"NHWC\", the data is stored in the order of: [batch,\n * height, width, channels].\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n * in which we sample input values across the height and width dimensions\n * for atrous morphological dilation. Defaults to `[1, 1]`. If `dilations`\n * is a single number, then `dilationHeight == dilationWidth`. If it is\n * greater than 1, then all values of `strides` must be 1.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction dilation2d_(x, filter, strides, pad, dilations = [1, 1], dataFormat = 'NHWC') {\n const $x = convertToTensor(x, 'x', 'dilation2d');\n const $filter = convertToTensor(filter, 'filter', 'dilation2d');\n assert($x.rank === 3 || $x.rank === 4, () => `Error in dilation2d: input must be rank 3 or 4, but got rank ` +\n `${$x.rank}.`);\n assert($filter.rank === 3, () => `Error in dilation2d: filter must be rank 3, but got rank ` +\n `${$filter.rank}.`);\n assert(dataFormat === 'NHWC', () => `Error in dilation2d: Only NHWC is currently supported, ` +\n `but got dataFormat of ${dataFormat}`);\n let x4D = $x;\n let reshapedTo4D = false;\n if ($x.rank === 3) {\n x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n reshapedTo4D = true;\n }\n const inputs = { x: x4D, filter: $filter };\n const attrs = { strides, pad, dilations };\n // tslint:disable-next-line: no-unnecessary-type-assertion\n const res = ENGINE.runKernel(Dilation2D, inputs, attrs);\n if (reshapedTo4D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n }\n return res;\n}\nconst dilation2d = op({ dilation2d_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting. Return 0\n * if denominator is 0.\n *\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 9, 16]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n * const c = tf.tensor1d([0, 0, 0, 0]);\n *\n * a.divNoNan(b).print(); // or tf.divNoNan(a, b)\n * a.divNoNan(c).print(); // or tf.divNoNan(a, c)\n * ```\n *\n * ```js\n * // Broadcast div a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(2);\n * const c = tf.scalar(0);\n *\n * a.divNoNan(b).print(); // or tf.divNoNan(a, b)\n * a.divNoNan(c).print(); // or tf.divNoNan(a, c)\n * ```\n *\n * @param a The first tensor as the numerator.\n * @param b The second tensor as the denominator. Must have the same dtype as\n * `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction divNoNan_(a, b) {\n // TODO: Make this into its own kernel.\n let $a = convertToTensor(a, 'a', 'div');\n let $b = convertToTensor(b, 'b', 'div');\n [$a, $b] = makeTypesMatch($a, $b);\n const divResult = div($a, $b);\n const zeros = zerosLike(divResult);\n const bEqualsZero = equal($b, zeros);\n return where(bEqualsZero, zeros, divResult);\n}\nconst divNoNan = op({ divNoNan_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes the dot product of two matrices and/or vectors, `t1` and `t2`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor2d([[1, 2], [3, 4]]);\n * const c = tf.tensor2d([[1, 2, 3], [4, 5, 6]]);\n *\n * a.dot(b).print(); // or tf.dot(a, b)\n * b.dot(a).print();\n * b.dot(c).print();\n * ```\n * @param t1 The first tensor in the dot operation.\n * @param t2 The second tensor in the dot operation.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction dot_(t1, t2) {\n const $t1 = convertToTensor(t1, 't1', 'dot');\n const $t2 = convertToTensor(t2, 't2', 'dot');\n assert(($t1.rank === 1 || $t1.rank === 2) && ($t2.rank === 1 || $t2.rank === 2), () => `Error in dot: inputs must all be rank 1 or 2, but got ranks ` +\n `${$t1.rank} and ${$t2.rank}.`);\n const t1Inner = ($t1.rank === 1 ? $t1.size : $t1.shape[1]);\n const t2Inner = ($t2.rank === 1 ? $t2.size : $t2.shape[0]);\n assert(t1Inner === t2Inner, () => `Error in dot: inner dimensions of inputs must match, but got ` +\n `${t1Inner} and ${t2Inner}.`);\n if ($t1.rank === 1 && $t2.rank === 1) {\n const t12D = reshape($t1, [1, -1]);\n const t22D = reshape($t2, [-1, 1]);\n const t1t2 = matMul(t12D, t22D);\n return reshape(t1t2, []);\n }\n else if ($t1.rank === 1 && $t2.rank === 2) {\n const t12D = reshape($t1, [1, -1]);\n const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);\n const t1t2 = matMul(t12D, t22D);\n return reshape(t1t2, [t1t2.size]);\n }\n else if ($t1.rank === 2 && $t2.rank === 1) {\n const t22D = reshape($t2, [-1, 1]);\n const t1t2 = matMul($t1, t22D);\n return reshape(t1t2, [t1t2.size]);\n }\n else {\n const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);\n const t1t2 = matMul($t1, t22D);\n return t1t2;\n }\n}\nconst dot = op({ dot_ });\n\n/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Tensor contraction over specified indices and outer product.\n *\n * `einsum` allows defining Tensors by defining their element-wise computation.\n * This computation is based on\n * [Einstein summation](https://en.wikipedia.org/wiki/Einstein_notation).\n *\n * Some special cases include:\n *\n * Matrix multiplication:\n * ```js\n * const x = tf.tensor2d([[1, 2, 3], [4, 5, 6]]);\n * const y = tf.tensor2d([[0, 1], [2, 3], [4, 5]]);\n * x.print();\n * y.print();\n * tf.einsum('ij,jk->ik', x, y).print();\n * ```\n *\n * Dot product:\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n * const y = tf.tensor1d([0, 1, 2]);\n * x.print();\n * y.print();\n * tf.einsum('i,i->', x, y).print();\n * ```\n *\n * Batch dot product:\n * ```js\n * const x = tf.tensor2d([[1, 2, 3], [4, 5, 6]]);\n * const y = tf.tensor2d([[0, 1, 2], [3, 4, 5]]);\n * x.print();\n * y.print();\n * tf.einsum('bi,bi->b', x, y).print();\n * ```\n *\n * Outer prouduct:\n * ```js\n * const x = tf.tensor1d([1, 3, 5]);\n * const y = tf.tensor1d([2, 4, 6]);\n * x.print();\n * y.print();\n * tf.einsum('i,j->ij', x, y).print();\n * ```\n *\n * Matrix transpose:\n * ```js\n * const x = tf.tensor2d([[1, 2], [3, 4]]);\n * x.print();\n * tf.einsum('ij->ji', x).print();\n * ```\n *\n * Batch matrix transpose:\n * ```js\n * const x = tf.tensor3d([[[1, 2], [3, 4]], [[-1, -2], [-3, -4]]]);\n * x.print();\n * tf.einsum('bij->bji', x).print();\n * ```\n *\n * Limitations:\n *\n * This implementation of einsum has the following limitations:\n *\n * - Does not support >2 input tensors.\n * - Does not support duplicate axes for any given input tensor. E.g., equation\n * 'ii->' is not suppoted.\n * - The `...` notation is not supported.\n *\n * @param equation a string describing the contraction, in the same format as\n * [numpy.einsum](https://numpy.org/doc/stable/reference/generated/numpy.einsum.html).\n * @param tensors the input(s) to contract (each one a Tensor), whose shapes\n * should be consistent with equation.\n * @returns The output tensor.\n *\n * @doc {heading: 'Tensors', subheading: 'Matrices'}\n */\nfunction einsum_(equation, ...tensors) {\n const $tensors = tensors.map((t, i) => convertToTensor(t, `tensors${i}`, 'einsum'));\n const attrs = { equation };\n return ENGINE.runKernel(Einsum, $tensors, attrs);\n}\nconst einsum = op({ einsum_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes exponential linear element-wise: `x > 0 ? x : (e ^ x) - 1`.\n *\n * ```js\n * const x = tf.tensor1d([-1, 1, -3, 2]);\n *\n * x.elu().print(); // or tf.elu(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction elu_(x) {\n const $x = convertToTensor(x, 'x', 'elu', 'float32');\n const inputs = { x: $x };\n return ENGINE.runKernel(Elu, inputs);\n}\nconst elu = op({ elu_ });\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes gause error function of the input `tf.Tensor` element-wise:\n * `erf(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, .1, -.1, .7]);\n *\n * x.erf().print(); // or tf.erf(x);\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction erf_(x) {\n let $x = convertToTensor(x, 'x', 'erf');\n assert($x.dtype === 'int32' || $x.dtype === 'float32', () => 'Input dtype must be `int32` or `float32`.');\n if ($x.dtype === 'int32') {\n $x = cast($x, 'float32');\n }\n const inputs = { x: $x };\n return ENGINE.runKernel(Erf, inputs);\n}\nconst erf = op({ erf_ });\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes exponential of the input `tf.Tensor` minus one element-wise.\n * `e ^ x - 1`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, -3]);\n *\n * x.expm1().print(); // or tf.expm1(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction expm1_(x) {\n const $x = convertToTensor(x, 'x', 'expm1');\n const inputs = { x: $x };\n return ENGINE.runKernel(Expm1, inputs);\n}\nconst expm1 = op({ expm1_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Create an identity matrix.\n *\n * @param numRows Number of rows.\n * @param numColumns Number of columns. Defaults to `numRows`.\n * @param batchShape If provided, will add the batch shape to the beginning\n * of the shape of the returned `tf.Tensor` by repeating the identity\n * matrix.\n * @param dtype Data type.\n * @returns Identity matrix of the specified size and data type, possibly\n * with batch repetition if `batchShape` is specified.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction eye_(numRows, numColumns, batchShape, dtype = 'float32') {\n if (numColumns == null) {\n numColumns = numRows;\n }\n const buff = buffer([numRows, numColumns], dtype);\n const n = numRows <= numColumns ? numRows : numColumns;\n for (let i = 0; i < n; ++i) {\n buff.set(1, i, i);\n }\n const out = reshape(buff.toTensor(), [numRows, numColumns]);\n if (batchShape == null) {\n return out;\n }\n else {\n if (batchShape.length === 1) {\n return tile(expandDims(out, 0), [batchShape[0], 1, 1]);\n }\n else if (batchShape.length === 2) {\n // tslint:disable-next-line:no-unnecessary-type-assertion\n return tile(expandDims(expandDims(out, 0), 0), [batchShape[0], batchShape[1], 1, 1]);\n }\n else if (batchShape.length === 3) {\n // tslint:disable-next-line:no-unnecessary-type-assertion\n return tile(expandDims(expandDims(expandDims(out, 0), 0), 0), [\n batchShape[0], batchShape[1], batchShape[2], 1, 1\n ]);\n }\n else {\n throw new Error(`eye() currently supports only 1D and 2D ` +\n // tslint:disable-next-line:no-any\n `batchShapes, but received ${batchShape.length}D.`);\n }\n }\n}\nconst eye = op({ eye_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Creates a `tf.Tensor` filled with a scalar value.\n *\n * ```js\n * tf.fill([2, 2], 4).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param value The scalar value to fill the tensor with.\n * @param dtype The type of an element in the resulting tensor. Defaults to\n * 'float'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction fill(shape, value, dtype) {\n const attrs = { shape, value, dtype };\n return ENGINE.runKernel(Fill, {}, attrs);\n}\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Returns the imaginary part of a complex (or real) tensor.\n *\n * Given a tensor input, this operation returns a tensor of type float that is\n * the imaginary part of each element in input considered as a complex number.\n * If input is real, a tensor of all zeros is returned.\n *\n * ```js\n * const x = tf.complex([-2.25, 3.25], [4.75, 5.75]);\n * tf.imag(x).print();\n * ```\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction imag_(input) {\n const $input = convertToTensor(input, 'input', 'imag');\n const inputs = { input: $input };\n return ENGINE.runKernel(Imag, inputs);\n}\nconst imag = op({ imag_ });\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Returns which elements of x are finite.\n *\n * ```js\n * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);\n *\n * x.isFinite().print(); // or tf.isNaN(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction isFinite_(x) {\n const $x = convertToTensor(x, 'x', 'isFinite');\n const inputs = { x: $x };\n return ENGINE.runKernel(IsFinite, inputs);\n}\nconst isFinite$1 = op({ isFinite_ });\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Returns which elements of x are Infinity or -Infinity.\n *\n * ```js\n * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);\n *\n * x.isInf().print(); // or tf.isNaN(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction isInf_(x) {\n const $x = convertToTensor(x, 'x', 'isInf');\n const inputs = { x: $x };\n return ENGINE.runKernel(IsInf, inputs);\n}\nconst isInf = op({ isInf_ });\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * RReturns which elements of x are NaN.\n *\n * ```js\n * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);\n *\n * x.isNaN().print(); // or tf.isNaN(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction isNaN_(x) {\n const $x = convertToTensor(x, 'x', 'isNaN');\n const inputs = { x: $x };\n return ENGINE.runKernel(IsNan, inputs);\n}\nconst isNaN$1 = op({ isNaN_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes leaky rectified linear element-wise.\n *\n * See\n * [http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf](\n * http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf)\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.leakyRelu(0.1).print(); // or tf.leakyRelu(x, 0.1)\n * ```\n * @param x The input tensor.\n * @param alpha The scaling factor for negative values, defaults to 0.2.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction leakyRelu_(x, alpha = 0.2) {\n const $x = convertToTensor(x, 'x', 'leakyRelu');\n const inputs = { x: $x };\n const attrs = { alpha };\n return ENGINE.runKernel(LeakyRelu, inputs, attrs);\n}\nconst leakyRelu = op({ leakyRelu_ });\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Return an evenly spaced sequence of numbers over the given interval.\n *\n * ```js\n * tf.linspace(0, 9, 10).print();\n * ```\n * @param start The start value of the sequence.\n * @param stop The end value of the sequence.\n * @param num The number of values to generate.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction linspace(start, stop, num) {\n if (num <= 0) {\n throw new Error('The number of values should be positive.');\n }\n const attrs = { start, stop, num };\n return ENGINE.runKernel(LinSpace, {}, attrs);\n}\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Normalizes the activation of a local neighborhood across or within\n * channels.\n *\n * @param x The input tensor. The 4-D input tensor is treated as a 3-D array\n * of 1D vectors (along the last dimension), and each vector is\n * normalized independently.\n * @param depthRadius The number of adjacent channels in the 1D normalization\n * window.\n * @param bias A constant bias term for the basis.\n * @param alpha A scale factor, usually positive.\n * @param beta An exponent.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction localResponseNormalization_(x, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5) {\n const $x = convertToTensor(x, 'x', 'localResponseNormalization');\n assert($x.rank === 4 || $x.rank === 3, () => `Error in localResponseNormalization: x must be rank 3 or 4 but got\n rank ${$x.rank}.`);\n assert(isInt(depthRadius), () => `Error in localResponseNormalization: depthRadius must be an ` +\n `integer but got depthRadius ${depthRadius}.`);\n let x4D = $x;\n let reshapedTo4D = false;\n if ($x.rank === 3) {\n reshapedTo4D = true;\n x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n }\n const inputs = { x: x4D };\n const attrs = { depthRadius, bias, alpha, beta };\n // tslint:disable-next-line: no-unnecessary-type-assertion\n const res = ENGINE.runKernel(LRN, inputs, attrs);\n if (reshapedTo4D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n }\n else {\n return res;\n }\n}\nconst localResponseNormalization = op({ localResponseNormalization_ });\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes natural logarithm of the input `tf.Tensor` plus one\n * element-wise: `ln(1 + x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, Math.E - 1]);\n *\n * x.log1p().print(); // or tf.log1p(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction log1p_(x) {\n const $x = convertToTensor(x, 'x', 'log1p');\n const inputs = { x: $x };\n return ENGINE.runKernel(Log1p, inputs);\n}\nconst log1p = op({ log1p_ });\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Provided `f(x)`, returns another function `g(x, dy?)`, which gives the\n * gradient of `f(x)` with respect to `x`.\n *\n * If `dy` is provided, the gradient of `f(x).mul(dy).sum()` with respect to\n * `x` is computed instead. `f(x)` must take a single tensor `x` and return a\n * single tensor `y`. If `f()` takes multiple inputs, use `tf.grads` instead.\n *\n * ```js\n * // f(x) = x ^ 2\n * const f = x => x.square();\n * // f'(x) = 2x\n * const g = tf.grad(f);\n *\n * const x = tf.tensor1d([2, 3]);\n * g(x).print();\n * ```\n *\n * ```js\n * // f(x) = x ^ 3\n * const f = x => x.pow(tf.scalar(3, 'int32'));\n * // f'(x) = 3x ^ 2\n * const g = tf.grad(f);\n * // f''(x) = 6x\n * const gg = tf.grad(g);\n *\n * const x = tf.tensor1d([2, 3]);\n * gg(x).print();\n * ```\n *\n * @param f The function f(x), to compute gradient for.\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction grad(f) {\n assert(isFunction(f), () => 'The f passed in grad(f) must be a function');\n return (x, dy) => {\n // x can be of any dtype, thus null as the last argument.\n const $x = convertToTensor(x, 'x', 'tf.grad', 'string_or_numeric');\n const $dy = (dy != null) ? convertToTensor(dy, 'dy', 'tf.grad') : null;\n return ENGINE.tidy(() => {\n const { value, grads } = ENGINE.gradients(() => f($x), [$x], $dy);\n if ($dy != null) {\n assertShapesMatch(value.shape, $dy.shape, 'The shape of dy passed in grad(f)(x, dy) must match the shape ' +\n 'returned by f(x)');\n }\n checkGrads(grads);\n return grads[0];\n });\n };\n}\n/**\n * Provided `f(x1, x2,...)`, returns another function `g([x1, x2,...], dy?)`,\n * which gives an array of gradients of `f()` with respect to each input\n * [`x1`,`x2`,...].\n *\n * If `dy` is passed when calling `g()`, the gradient of\n * `f(x1,...).mul(dy).sum()` with respect to each input is computed instead.\n * The provided `f` must take one or more tensors and return a single tensor\n * `y`. If `f()` takes a single input, we recommend using `tf.grad` instead.\n *\n * ```js\n * // f(a, b) = a * b\n * const f = (a, b) => a.mul(b);\n * // df / da = b, df / db = a\n * const g = tf.grads(f);\n *\n * const a = tf.tensor1d([2, 3]);\n * const b = tf.tensor1d([-2, -3]);\n * const [da, db] = g([a, b]);\n * console.log('da');\n * da.print();\n * console.log('db');\n * db.print();\n * ```\n *\n * @param f The function `f(x1, x2,...)` to compute gradients for.\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction grads(f) {\n assert(isFunction(f), () => 'The f passed in grads(f) must be a function');\n return (args, dy) => {\n assert(Array.isArray(args), () => 'The args passed in grads(f)(args) must be an array ' +\n 'of `Tensor`s or `TensorLike`s');\n // args can be of any dtype, thus null as the last argument.\n const $args = convertToTensorArray(args, 'args', 'tf.grads', 'string_or_numeric');\n const $dy = (dy != null) ? convertToTensor(dy, 'dy', 'tf.grads') : null;\n return ENGINE.tidy(() => {\n const { value, grads } = ENGINE.gradients(() => f(...$args), $args, $dy);\n if ($dy != null) {\n assertShapesMatch(value.shape, $dy.shape, 'The shape of dy passed in grads(f)([x1,...], dy) must ' +\n 'match the shape returned by f([x1,...])');\n }\n checkGrads(grads);\n return grads;\n });\n };\n}\n/**\n * Like `tf.grad`, but also returns the value of `f()`. Useful when `f()`\n * returns a metric you want to show.\n *\n * The result is a rich object with the following properties:\n * - grad: The gradient of `f(x)` w.r.t `x` (result of `tf.grad`).\n * - value: The value returned by `f(x)`.\n *\n * ```js\n * // f(x) = x ^ 2\n * const f = x => x.square();\n * // f'(x) = 2x\n * const g = tf.valueAndGrad(f);\n *\n * const x = tf.tensor1d([2, 3]);\n * const {value, grad} = g(x);\n *\n * console.log('value');\n * value.print();\n * console.log('grad');\n * grad.print();\n * ```\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction valueAndGrad(f) {\n assert(isFunction(f), () => 'The f passed in valueAndGrad(f) must be a function');\n return (x, dy) => {\n assert(x instanceof Tensor, () => 'The x passed in valueAndGrad(f)(x) must be a tensor');\n assert(dy == null || dy instanceof Tensor, () => 'The dy passed in valueAndGrad(f)(x, dy) must be a tensor');\n const { grads, value } = ENGINE.gradients(() => f(x), [x], dy);\n checkGrads(grads);\n return { grad: grads[0], value };\n };\n}\n/**\n * Like `tf.grads`, but returns also the value of `f()`. Useful when `f()`\n * returns a metric you want to show.\n *\n * The result is a rich object with the following properties:\n * - grads: The gradients of `f()` w.r.t each input (result of `tf.grads`).\n * - value: The value returned by `f(x)`.\n *\n * ```js\n * // f(a, b) = a * b\n * const f = (a, b) => a.mul(b);\n * // df/da = b, df/db = a\n * const g = tf.valueAndGrads(f);\n *\n * const a = tf.tensor1d([2, 3]);\n * const b = tf.tensor1d([-2, -3]);\n * const {value, grads} = g([a, b]);\n *\n * const [da, db] = grads;\n *\n * console.log('value');\n * value.print();\n *\n * console.log('da');\n * da.print();\n * console.log('db');\n * db.print();\n * ```\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction valueAndGrads(f) {\n assert(isFunction(f), () => 'The f passed in valueAndGrads(f) must be a function');\n return (args, dy) => {\n assert(Array.isArray(args) && args.every(arg => arg instanceof Tensor), () => 'The args passed in valueAndGrads(f)(args) must be array of ' +\n 'tensors');\n assert(dy == null || dy instanceof Tensor, () => 'The dy passed in valueAndGrads(f)(args, dy) must be a tensor');\n const res = ENGINE.gradients(() => f(...args), args, dy);\n if (dy != null) {\n assertShapesMatch(res.value.shape, dy.shape, 'The shape of dy passed in valueAndGrads(f)([x1,...], dy) must ' +\n 'match the shape returned by f([x1,...])');\n }\n checkGrads(res.grads);\n return res;\n };\n}\n/**\n * Computes and returns the gradient of f(x) with respect to the list of\n * trainable variables provided by `varList`. If no list is provided, it\n * defaults to all trainable variables.\n *\n * ```js\n * const a = tf.variable(tf.tensor1d([3, 4]));\n * const b = tf.variable(tf.tensor1d([5, 6]));\n * const x = tf.tensor1d([1, 2]);\n *\n * // f(a, b) = a * x ^ 2 + b * x\n * const f = () => a.mul(x.square()).add(b.mul(x)).sum();\n * // df/da = x ^ 2, df/db = x\n * const {value, grads} = tf.variableGrads(f);\n *\n * Object.keys(grads).forEach(varName => grads[varName].print());\n * ```\n *\n * @param f The function to execute. f() should return a scalar.\n * @param varList The list of variables to compute the gradients with respect\n * to. Defaults to all trainable variables.\n * @returns An object with the following keys and values:\n * - `value`: The value of the function `f`.\n * - `grads`: A map from the names of the variables to the gradients.\n * If the `varList` argument is provided explicitly and contains a subset of\n * non-trainable variables, this map in the return value will contain keys\n * that map the names of the non-trainable variables to `null`.\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction variableGrads(f, varList) {\n assert(isFunction(f), () => 'The f passed in variableGrads(f) must be a function');\n assert(varList == null ||\n Array.isArray(varList) && varList.every(v => v instanceof Variable), () => 'The varList passed in variableGrads(f, varList) must be an array ' +\n 'of variables');\n const specifiedVarList = varList != null;\n if (!specifiedVarList) {\n // Get all of the trainable variables.\n varList = [];\n for (const varName in ENGINE.registeredVariables) {\n varList.push(ENGINE.registeredVariables[varName]);\n }\n }\n const specifiedNonTrainable = specifiedVarList ? varList.filter(variable => !variable.trainable) : null;\n // Prune non-trainable variables.\n const originalVarCount = varList.length;\n varList = varList.filter(variable => variable.trainable);\n assert(varList.length > 0, () => `variableGrads() expects at least one of the input variables to ` +\n `be trainable, but none of the ${originalVarCount} variables is ` +\n `trainable.`);\n const allowNoGradients = true;\n const { value, grads } = ENGINE.gradients(f, varList, null, allowNoGradients);\n assert(grads.some(g => g != null), () => 'Cannot find a connection between any variable and the result of ' +\n 'the loss function y=f(x). Please make sure the operations that ' +\n 'use variables are inside the function f passed to minimize().');\n assert(value.rank === 0, () => `The f passed in variableGrads(f) must return a scalar, but it ` +\n `returned a rank-${value.rank} tensor`);\n const namedGrads = {};\n varList.forEach((v, i) => {\n if (grads[i] != null) {\n namedGrads[v.name] = grads[i];\n }\n });\n if (specifiedNonTrainable != null) {\n // If varList is explicitly provided and contains non-trainable values,\n // add them to the returned gradients with `null` values.\n specifiedNonTrainable.forEach(v => namedGrads[v.name] = null);\n }\n return { value, grads: namedGrads };\n}\n/**\n * Overrides the gradient computation of a function `f`.\n *\n * Takes a function\n * `f(...inputs, save) => {value: Tensor, gradFunc: (dy, saved) => Tensor[]}`\n * and returns another function `g(...inputs)` which takes the same inputs as\n * `f`. When called, `g` returns `f().value`. In backward mode, custom gradients\n * with respect to each input of `f` are computed using `f().gradFunc`.\n *\n * The `save` function passsed to `f` should be used for saving tensors needed\n * in the gradient. And the `saved` passed to the `gradFunc` is a\n * `NamedTensorMap`, which contains those saved tensor.\n *\n * ```js\n * const customOp = tf.customGrad((x, save) => {\n * // Save x to make sure it's available later for the gradient.\n * save([x]);\n * // Override gradient of our custom x ^ 2 op to be dy * abs(x);\n * return {\n * value: x.square(),\n * // Note `saved.x` which points to the `x` we saved earlier.\n * gradFunc: (dy, saved) => [dy.mul(saved[0].abs())]\n * };\n * });\n *\n * const x = tf.tensor1d([-1, -2, 3]);\n * const dx = tf.grad(x => customOp(x));\n *\n * console.log(`f(x):`);\n * customOp(x).print();\n * console.log(`f'(x):`);\n * dx(x).print();\n * ```\n *\n * @param f The function to evaluate in forward mode, which should return\n * `{value: Tensor, gradFunc: (dy, saved) => Tensor[]}`, where `gradFunc`\n * returns the custom gradients of `f` with respect to its inputs.\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction customGrad(f) {\n return ENGINE.customGrad(f);\n}\nfunction checkGrads(grads) {\n const numNullGradients = grads.filter(g => g == null).length;\n if (numNullGradients > 0) {\n throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that\n the f you passed encloses all operations that lead from x to y.`);\n }\n}\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes softplus of the input `tf.Tensor` element-wise: `log(exp(x) + 1)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.softplus().print(); // or tf.softplus(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction softplus_(x) {\n const $x = convertToTensor(x, 'x', 'softplus');\n const inputs = { x: $x };\n return ENGINE.runKernel(Softplus, inputs);\n}\nconst softplus = op({ softplus_ });\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes log sigmoid of the input `tf.Tensor` element-wise:\n * `logSigmoid(x)`. For numerical stability, we use `-tf.softplus(-x)`.\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.logSigmoid().print(); // or tf.logSigmoid(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction logSigmoid_(x) {\n const $x = convertToTensor(x, 'x', 'logSigmoid');\n // Use a custom gradient to maintain previous implementation.\n // There is no LogSigmoid kernel in TF so we can't use engine.runKernel\n // directly\n const customOp = customGrad((x) => {\n // TODO(yassogba) we can remove the chained softplus call here only\n // after backends have modualrized softplus at which point we can call\n // engine runKernel(..., Sotfplus, ...) directly.\n const value = neg(softplus(neg(x)));\n const gradFunc = (dy) => {\n const derX = mul(dy, sigmoid(neg(x)));\n return derX;\n };\n return { value, gradFunc };\n });\n return customOp($x);\n}\nconst logSigmoid = op({ logSigmoid_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes the maximum of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and an\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.max().print(); // or tf.max(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.max(axis).print(); // or tf.max(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension(s) to reduce. By default it reduces\n * all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction max_(x, axis = null, keepDims = false) {\n const $x = convertToTensor(x, 'x', 'max');\n const inputs = { x: $x };\n const attrs = { reductionIndices: axis, keepDims };\n return ENGINE.runKernel(Max, inputs, attrs);\n}\nconst max = op({ max_ });\n\n/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes the log softmax.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n *\n * a.logSoftmax().print(); // or tf.logSoftmax(a)\n * ```\n *\n * ```js\n * const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]);\n *\n * a.logSoftmax().print(); // or tf.logSoftmax(a)\n * ```\n *\n * @param logits The logits array.\n * @param axis The dimension softmax would be performed on. Defaults to `-1`\n * which indicates the last dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction logSoftmax_(logits, axis = -1) {\n const $logits = convertToTensor(logits, 'logits', 'logSoftmax');\n if (axis === -1) {\n axis = $logits.rank - 1;\n }\n if (axis !== $logits.rank - 1) {\n throw Error('Log Softmax along a non-last dimension is not yet supported. ' +\n `Logits was rank ${$logits.rank} and axis was ${axis}`);\n }\n // const forward: ForwardFunc = (backend, save) => {\n // const keepDims = true;\n // const xMax = max(logits, axis, true);\n // const shifted = sub(logits, xMax);\n // const value =\n // sub(cast(shifted, 'float32'), log(sum(exp(shifted), axis,\n // keepDims)));\n // save([value]);\n // return value;\n // };\n // Use a custom gradient for numerical stability.\n const customOp = customGrad((logits, save) => {\n const keepDims = true;\n const xMax = max(logits, axis, true);\n const shifted = sub(logits, xMax);\n const value = sub(cast(shifted, 'float32'), log$1(sum$1(exp(shifted), axis, keepDims)));\n save([value]);\n const gradFunc = (dy, saved) => {\n const [value] = saved;\n const keepDims = true;\n const softmax = exp(value);\n return sub(dy, mul(sum$1(dy, axis, keepDims), softmax));\n };\n return { value, gradFunc };\n });\n return customOp($logits);\n // TODO Use Engine.runKernel when CPU/WebGL/WASM backends implement this.\n // const inputs: LogSoftmaxInputs = {logits: $logits};\n // const attrs: LogSoftmaxAttrs = {axis};\n // return ENGINE.runKernel(\n // LogSoftmax, inputs as {} as NamedTensorMap,\n // attrs as {} as NamedAttrMap);\n}\nconst logSoftmax = op({ logSoftmax_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes the log(sum(exp(elements across the reduction dimensions)).\n *\n * Reduces the input along the dimensions given in `axis`. Unless `keepDims`\n * is true, the rank of the array is reduced by 1 for each entry in `axis`.\n * If `keepDims` is true, the reduced dimensions are retained with length 1.\n * If `axis` has no entries, all dimensions are reduced, and an array with a\n * single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.logSumExp().print(); // or tf.logSumExp(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.logSumExp(axis).print(); // or tf.logSumExp(a, axis)\n * ```\n * @param x The input tensor.\n * @param axis The dimension(s) to reduce. If null (the default),\n * reduces all dimensions.\n * @param keepDims If true, retains reduced dimensions with length\n * of 1. Defaults to false.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction logSumExp_(x, axis = null, keepDims = false) {\n const $x = convertToTensor(x, 'x', 'logSumExp');\n const axes = parseAxisParam(axis, $x.shape);\n const xMax = max($x, axes, true /* keepDims */);\n const a = sub($x, xMax);\n const b = exp(a);\n const c = sum$1(b, axes);\n const d = log$1(c);\n const res = add$1(reshape(xMax, d.shape), d);\n if (keepDims) {\n const newShape = expandShapeToKeepDim(res.shape, axes);\n return reshape(res, newShape);\n }\n return res;\n}\nconst logSumExp = op({ logSumExp_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Returns the truth value of `a OR b` element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([false, false, true, true], 'bool');\n * const b = tf.tensor1d([false, true, false, true], 'bool');\n *\n * a.logicalOr(b).print();\n * ```\n * @param a The first input tensor. Must be of dtype bool.\n * @param b The second input tensor. Must be of dtype bool.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalOr_(a, b) {\n const $a = convertToTensor(a, 'a', 'logicalOr', 'bool');\n const $b = convertToTensor(b, 'b', 'logicalOr', 'bool');\n assertAndGetBroadcastShape($a.shape, $b.shape);\n const inputs = { a: $a, b: $b };\n return ENGINE.runKernel(LogicalOr, inputs);\n}\nconst logicalOr = op({ logicalOr_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Returns the truth value of `a XOR b` element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([false, false, true, true], 'bool');\n * const b = tf.tensor1d([false, true, false, true], 'bool');\n *\n * a.logicalXor(b).print();\n * ```\n *\n * @param a The first input tensor. Must be of dtype bool.\n * @param b The second input tensor. Must be of dtype bool.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalXor_(a, b) {\n const $a = convertToTensor(a, 'a', 'logicalXor', 'bool');\n const $b = convertToTensor(b, 'b', 'logicalXor', 'bool');\n assertAndGetBroadcastShape($a.shape, $b.shape);\n // x ^ y = (x | y) & ~(x & y)\n return logicalAnd(logicalOr(a, b), logicalNot(logicalAnd(a, b)));\n}\nconst logicalXor = op({ logicalXor_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes the 2D max pooling of an image.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n * `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n * `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n * in which we sample input values across the height and width dimensions\n * in dilated pooling. Defaults to `[1, 1]`. If `dilations` is a single\n * number, then `dilationHeight == dilationWidth`. If it is greater than\n * 1, then all values of `strides` must be 1.\n * @param pad The type of padding algorithm.\n * - `same` and stride 1: output will be of same size as input,\n * regardless of filter size.\n * - `valid`: output will be smaller than input if filter is larger\n * than 1x1.\n * - For more info, see this guide:\n * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](\n * https://www.tensorflow.org/api_docs/python/tf/nn/convolution)\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n * provided, it will default to truncate.\n */\nfunction maxPool_(x, filterSize, strides, pad, dimRoundingMode) {\n const $x = convertToTensor(x, 'x', 'maxPool');\n const dilations = 1;\n let x4D = $x;\n let reshapedTo4D = false;\n if ($x.rank === 3) {\n reshapedTo4D = true;\n x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n }\n assert(x4D.rank === 4, () => `Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`);\n assert(eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool: Either strides or dilations must be 1. ' +\n `Got strides ${strides} and dilations '${dilations}'`);\n checkPadOnDimRoundingMode('maxPool', pad, dimRoundingMode);\n const inputs = { x: x4D };\n const attrs = { filterSize, strides, pad, dimRoundingMode };\n // tslint:disable-next-line: no-unnecessary-type-assertion\n const res = ENGINE.runKernel(MaxPool, inputs, attrs);\n if (reshapedTo4D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n }\n return res;\n}\nconst maxPool = op({ maxPool_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes the 3D max pooling.\n *\n * ```js\n * const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);\n * const result = tf.maxPool3d(x, 2, 1, 'valid');\n * result.print();\n * ```\n *\n * @param x The input tensor, of rank 5 or rank 4 of shape\n * `[batch, depth, height, width, inChannels]`.\n * @param filterSize The filter size:\n * `[filterDepth, filterHeight, filterWidth]`.\n * If `filterSize` is a single number,\n * then `filterDepth == filterHeight == filterWidth`.\n * @param strides The strides of the pooling:\n * `[strideDepth, strideHeight, strideWidth]`.\n * If `strides` is a single number,\n * then `strideDepth == strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n * - `same` and stride 1: output will be of same size as input,\n * regardless of filter size.\n * - `valid`: output will be smaller than input if filter is larger\n * than 1*1x1.\n * - For more info, see this guide:\n * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](\n * https://www.tensorflow.org/api_docs/python/tf/nn/convolution)\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n * provided, it will default to truncate.\n * @param dataFormat An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n * \"NDHWC\". Specify the data format of the input and output data. With the\n * default format \"NDHWC\", the data is stored in the order of: [batch,\n * depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction maxPool3d_(x, filterSize = [1, 1, 1], strides, pad, dimRoundingMode, dataFormat = 'NDHWC') {\n const $x = convertToTensor(x, 'x', 'maxPool3d');\n let x5D = $x;\n let reshapedTo5D = false;\n if ($x.rank === 4) {\n reshapedTo5D = true;\n x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n }\n assert(x5D.rank === 5, () => `Error in maxPool3d: x must be rank 5 but got rank ${x5D.rank}.`);\n assert(dataFormat === 'NDHWC', () => `Error in maxPool3d: Only NDHWC is currently supported, ` +\n `but got dataFormat of ${dataFormat}`);\n checkPadOnDimRoundingMode('maxPool3d', pad, dimRoundingMode);\n const inputs = { x: x5D };\n const attrs = { filterSize, strides, pad, dimRoundingMode, dataFormat };\n // tslint:disable-next-line: no-unnecessary-type-assertion\n const res = ENGINE.runKernel(MaxPool3D, inputs, attrs);\n if (reshapedTo5D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n }\n return res;\n}\nconst maxPool3d = op({ maxPool3d_ });\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes the 2D max pooling of an image with Argmax index.\n * The indices in argmax are flattened, so that a maximum value at position `[b,\n * y, x, c]` becomes flattened index: `(y * width + x) * channels + c` if\n * include_batch_in_index is False; `((b * height + y) * width + x) * channels\n * +c` if include_batch_in_index is True.\n *\n * The indices returned are always in `[0, height) x [0, width)` before\n * flattening.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n * `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n * `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dataFormat An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n * \"NDHWC\". Specify the data format of the input and output data. With the\n * default format \"NDHWC\", the data is stored in the order of: [batch,\n * depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @param pad The type of padding algorithm.\n * - `same` and stride 1: output will be of same size as input,\n * regardless of filter size.\n * - `valid`: output will be smaller than input if filter is larger\n * than 1x1.\n * - For more info, see this guide:\n * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](\n * https://www.tensorflow.org/api_docs/python/tf/nn/convolution)\n * @param includeBatchIndex Defaults to False. Whether to include batch\n * dimension in flattened index of argmax.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction maxPoolWithArgmax_(x, filterSize, strides, pad, includeBatchInIndex = false) {\n const $x = convertToTensor(x, 'x', 'maxPoolWithArgmax');\n const inputs = { x: $x };\n const attrs = { filterSize, strides, pad, includeBatchInIndex };\n // tslint:disable-next-line: no-unnecessary-type-assertion\n const result = ENGINE.runKernel(MaxPoolWithArgmax, inputs, attrs);\n return { result: result[0], indexes: result[1] };\n}\nconst maxPoolWithArgmax = op({ maxPoolWithArgmax_ });\n\n/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes the mean of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces `x` along the dimensions given in `axis`. Unless `keepDims` is\n * true, the rank of the `tf.Tensor` is reduced by 1 for each entry in `axis`.\n * If `keepDims` is true, the reduced dimensions are retained with length 1.\n * If `axis` has no entries, all dimensions are reduced, and a `tf.Tensor` with\n * a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.mean().print(); // or tf.mean(a)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.mean(axis).print(); // or tf.mean(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension(s) to reduce. By default it reduces\n * all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction mean_(x, axis = null, keepDims = false) {\n const $x = convertToTensor(x, 'x', 'mean');\n const inputs = { x: $x };\n const attrs = { axis, keepDims };\n return ENGINE.runKernel(Mean, inputs, attrs);\n}\nconst mean = op({ mean_ });\n\n/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Broadcasts parameters for evaluation on an N-D grid.\n *\n * Given N one-dimensional coordinate arrays `*args`, returns a list `outputs`\n * of N-D coordinate arrays for evaluating expressions on an N-D grid.\n *\n * Notes:\n * `meshgrid` supports cartesian ('xy') and matrix ('ij') indexing conventions.\n * When the `indexing` argument is set to 'xy' (the default), the broadcasting\n * instructions for the first two dimensions are swapped.\n * Examples:\n * Calling `const [X, Y] = meshgrid(x, y)` with the tensors\n *\n * ```javascript\n * const x = [1, 2, 3];\n * const y = [4, 5, 6];\n * const [X, Y] = tf.meshgrid(x, y);\n * // X = [[1, 2, 3],\n * // [1, 2, 3],\n * // [1, 2, 3]]\n * // Y = [[4, 4, 4],\n * // [5, 5, 5],\n * // [6, 6, 6]]\n * ```\n *\n * @param x Tensor with rank geq 1.\n * @param y Tensor with rank geq 1.\n * @param indexing\n *\n * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}\n */\nfunction meshgrid(x, y, { indexing = 'xy' } = {}) {\n if (indexing !== 'xy' && indexing !== 'ij') {\n throw new TypeError(`${indexing} is not a valid third argument to meshgrid`);\n }\n if (x === undefined) {\n return [];\n }\n let $x = convertToTensor(x, 'x', 'meshgrid', x instanceof Tensor ? x.dtype : 'float32');\n if (y === undefined) {\n return [$x];\n }\n let $y = convertToTensor(y, 'y', 'meshgrid', y instanceof Tensor ? y.dtype : 'float32');\n const w = sizeFromShape($x.shape);\n const h = sizeFromShape($y.shape);\n if (indexing === 'xy') {\n $x = reshape($x, [1, -1]);\n $y = reshape($y, [-1, 1]);\n return [\n matMul(ones$1([h, 1], $x.dtype), $x),\n matMul($y, ones$1([1, w], $y.dtype)),\n ];\n }\n $x = reshape($x, [-1, 1]);\n $y = reshape($y, [1, -1]);\n return [\n matMul($x, ones$1([1, h], $x.dtype)),\n matMul(ones$1([w, 1], $y.dtype), $y),\n ];\n}\n\n/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes the minimum value from the input.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the array is reduced by 1 for each entry in `axes`.\n * If `keepDims` is true, the reduced dimensions are retained with length 1.\n * If `axes` has no entries, all dimensions are reduced, and an array with a\n * single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.min().print(); // or tf.min(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.min(axis).print(); // or tf.min(x, axis)\n * ```\n *\n * @param x The input Tensor.\n * @param axis The dimension(s) to reduce. By default it reduces\n * all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction min_(x, axis = null, keepDims = false) {\n const $x = convertToTensor(x, 'x', 'min');\n const inputs = { x: $x };\n const attrs = { axis, keepDims };\n // tslint:disable-next-line: no-unnecessary-type-assertion\n return ENGINE.runKernel(Min, inputs, attrs);\n}\nconst min = op({ min_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Returns the min of a and b (`a < b ? a : b`) element-wise.\n * Supports broadcasting.\n *\n * We also expose `minimumStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.minimum(b).print(); // or tf.minimum(a, b)\n * ```\n *\n * ```js\n * // Broadcast minimum a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.minimum(b).print(); // or tf.minimum(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction minimum_(a, b) {\n let $a = convertToTensor(a, 'a', 'minimum');\n let $b = convertToTensor(b, 'b', 'minimum');\n [$a, $b] = makeTypesMatch($a, $b);\n if ($a.dtype === 'bool') {\n $a = cast($a, 'int32');\n $b = cast($b, 'int32');\n }\n assertAndGetBroadcastShape($a.shape, $b.shape);\n const inputs = { a: $a, b: $b };\n return ENGINE.runKernel(Minimum, inputs);\n}\nconst minimum = op({ minimum_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Pads a `tf.Tensor` using mirror padding.\n *\n * This operation implements the `REFLECT` and `SYMMETRIC` modes of pad.\n *\n * ```js\n * const x = tf.range(0, 9).reshape([1, 1, 3, 3]);\n * x.mirrorPad([[0, 0], [0, 0], [2, 2], [2, 2]], 'reflect').print();\n * ```\n * @param x The tensor to pad.\n * @param paddings An array of length `R` (the rank of the tensor), where\n * each element is a length-2 tuple of ints `[padBefore, padAfter]`,\n * specifying how much to pad along each dimension of the tensor.\n * In \"reflect\" mode, the padded regions do not include the borders,\n * while in \"symmetric\" mode the padded regions do include the borders.\n * For example, if the input is `[1, 2, 3]` and paddings is `[0, 2]`,\n * then the output is `[1, 2, 3, 2, 1]` in \"reflect\" mode, and\n * `[1, 2, 3, 3, 2]` in \"symmetric\" mode.\n * If `mode` is \"reflect\" then both `paddings[D, 0]` and `paddings[D, 1]`\n * must be no greater than `x.shape[D] - 1`. If mode is \"symmetric\"\n * then both `paddings[D, 0]` and `paddings[D, 1]` must be no greater than\n * `x.shape[D]`\n * @param mode String to specify padding mode. Can be `'reflect' | 'symmetric'`\n */\n/** @doc {heading: 'Tensors', subheading: 'Transformations'} */\nfunction mirrorPad_(x, paddings, mode) {\n assert(mode === 'reflect' || mode === 'symmetric', () => `Invalid mode. Mode must be either reflect or symmetric. ` +\n `Got ${mode}.`);\n const $x = convertToTensor(x, 'x', 'mirrorPad');\n if ($x.rank === 0) {\n throw new Error('mirrorPad(scalar) is not defined. ' +\n 'Pass non-scalar to mirrorPad');\n }\n assert(paddings.length === $x.rank, () => `Padding doesn't match input. Must be ${$x.rank}. ` +\n `Got ${paddings.length}.`);\n const shapeOffset = mode === 'reflect' ? 1 : 0;\n for (let i = 0; i < $x.rank; i++) {\n assert(paddings[i].length === 2, () => `Invalid number of paddings. Must be length of 2 each.`);\n assert(paddings[i][0] >= 0 && paddings[i][0] <= $x.shape[i] - shapeOffset &&\n paddings[i][1] >= 0 && paddings[i][1] <= $x.shape[i] - shapeOffset, () => `Padding in dimension ${i} cannot be greater than or equal ` +\n `to ${$x.shape[i] - shapeOffset} or less than 0 for input of ` +\n `shape ${$x.shape}`);\n }\n const attrs = { paddings, mode };\n const inputs = { x: $x };\n return ENGINE.runKernel(MirrorPad, inputs, attrs);\n}\nconst mirrorPad = op({ mirrorPad_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Returns the mod of a and b element-wise.\n * `floor(x / y) * y + mod(x, y) = x`\n * Supports broadcasting.\n *\n * We also expose `tf.modStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.mod(b).print(); // or tf.mod(a, b)\n * ```\n *\n * ```js\n * // Broadcast a mod b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.mod(b).print(); // or tf.mod(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction mod_(a, b) {\n let $a = convertToTensor(a, 'a', 'mod');\n let $b = convertToTensor(b, 'b', 'mod');\n [$a, $b] = makeTypesMatch($a, $b);\n const inputs = { a: $a, b: $b };\n return ENGINE.runKernel(Mod, inputs);\n}\nconst mod = op({ mod_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Calculates the mean and variance of `x`. The mean and variance are\n * calculated by aggregating the contents of `x` across `axes`. If `x` is\n * 1-D and `axes = [0]` this is just the mean and variance of a vector.\n *\n * @param x The input tensor.\n * @param axis The dimension(s) along with to compute mean and\n * variance. By default it reduces all dimensions.\n * @param keepDims If true, the moments have the same dimensionality as the\n * input.\n * @return An object with two keys: `mean` and `variance`.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction moments_(x, axis = null, keepDims = false) {\n x = convertToTensor(x, 'x', 'moments');\n const axes = parseAxisParam(axis, x.shape);\n const xMean = mean(x, axes, keepDims);\n let keepDimsShape = xMean.shape;\n if (!keepDims) {\n keepDimsShape = expandShapeToKeepDim(xMean.shape, axes);\n }\n const devSquared = square(sub(cast(x, 'float32'), reshape(xMean, keepDimsShape)));\n const variance = mean(devSquared, axes, keepDims);\n return { mean: xMean, variance };\n}\nconst moments = op({ moments_ });\n\n/**\n * Computes the next states and outputs of a stack of LSTMCells.\n *\n * Each cell output is used as input to the next cell.\n *\n * Returns `[cellState, cellOutput]`.\n *\n * Derived from tf.contrib.rn.MultiRNNCell.\n *\n * @param lstmCells Array of LSTMCell functions.\n * @param data The input to the cell.\n * @param c Array of previous cell states.\n * @param h Array of previous cell outputs.\n *\n * @doc {heading: 'Operations', subheading: 'RNN'}\n */\nfunction multiRNNCell_(lstmCells, data, c, h) {\n const $data = convertToTensor(data, 'data', 'multiRNNCell');\n const $c = convertToTensorArray(c, 'c', 'multiRNNCell');\n const $h = convertToTensorArray(h, 'h', 'multiRNNCell');\n let input = $data;\n const newStates = [];\n for (let i = 0; i < lstmCells.length; i++) {\n const output = lstmCells[i](input, $c[i], $h[i]);\n newStates.push(output[0]);\n newStates.push(output[1]);\n input = output[1];\n }\n const newC = [];\n const newH = [];\n for (let i = 0; i < newStates.length; i += 2) {\n newC.push(newStates[i]);\n newH.push(newStates[i + 1]);\n }\n return [newC, newH];\n}\nconst multiRNNCell = op({ multiRNNCell_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Creates a `tf.Tensor` with values drawn from a multinomial distribution.\n *\n * ```js\n * const probs = tf.tensor([.75, .25]);\n * tf.multinomial(probs, 3).print();\n * ```\n *\n * @param logits 1D array with unnormalized log-probabilities, or\n * 2D array of shape `[batchSize, numOutcomes]`. See the `normalized`\n * parameter.\n * @param numSamples Number of samples to draw for each row slice.\n * @param seed The seed number.\n * @param normalized Whether the provided `logits` are normalized true\n * probabilities (sum to 1). Defaults to false.\n * @return 1D array of shape `[numSamples]`, or 2D array of shape\n * `[batchSize, numSamples]`, depending on the rank of the input.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction multinomial_(logits, numSamples, seed, normalized = false) {\n const $logits = convertToTensor(logits, 'logits', 'multinomial');\n const numOutcomes = $logits.size;\n const origRank = $logits.rank;\n if (numOutcomes < 2) {\n throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ` +\n `${numOutcomes}.`);\n }\n if (origRank > 2) {\n throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`);\n }\n // TODO(lina128): Investigate correct seed behavior. The code seems not allow\n // setting see to 0.\n seed = seed || Math.random();\n // The kernel only accepts (and returns) rank 2 tensors.\n const logits2D = origRank === 1 ? reshape($logits, [1, -1]) : $logits;\n const inputs = { logits: logits2D };\n const attrs = { numSamples, seed, normalized };\n // tslint:disable-next-line: no-unnecessary-type-assertion\n const res = ENGINE.runKernel(Multinomial, inputs, attrs);\n // tslint:disable-next-line:no-unnecessary-type-assertion\n return origRank === 1 ? reshape(res, [res.size]) : res;\n}\nconst multinomial = op({ multinomial_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Returns the truth value of (a != b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([0, 2, 3]);\n *\n * a.notEqual(b).print();\n * ```\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction notEqual_(a, b) {\n let $a = convertToTensor(a, 'a', 'notEqual', 'string_or_numeric');\n let $b = convertToTensor(b, 'b', 'notEqual', 'string_or_numeric');\n [$a, $b] = makeTypesMatch($a, $b);\n assertAndGetBroadcastShape($a.shape, $b.shape);\n const inputs = { a: $a, b: $b };\n return ENGINE.runKernel(NotEqual, inputs);\n}\nconst notEqual = op({ notEqual_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Creates a one-hot `tf.Tensor`. The locations represented by `indices` take\n * value `onValue` (defaults to 1), while all other locations take value\n * `offValue` (defaults to 0). If `indices` is rank `R`, the output has rank\n * `R+1` with the last axis of size `depth`.\n * `indices` used to encode prediction class must start from 0. For example,\n * if you have 3 classes of data, class 1 should be encoded as 0, class 2\n * should be 1, and class 3 should be 2.\n *\n * ```js\n * tf.oneHot(tf.tensor1d([0, 1], 'int32'), 3).print();\n * ```\n *\n * @param indices `tf.Tensor` of indices with dtype `int32`. Indices must\n * start from 0.\n * @param depth The depth of the one hot dimension.\n * @param onValue A number used to fill in the output when the index matches\n * the location.\n * @param offValue A number used to fill in the output when the index does\n * not match the location.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction oneHot_(indices, depth, onValue = 1, offValue = 0) {\n if (depth < 2) {\n throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);\n }\n const $indices = convertToTensor(indices, 'indices', 'oneHot', 'int32');\n const inputs = { indices: $indices };\n const attrs = { depth, onValue, offValue };\n return ENGINE.runKernel(OneHot, inputs, attrs);\n}\nconst oneHot = op({ oneHot_ });\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Creates a `tf.Tensor` with all elements set to 1 with the same shape as the\n * given tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n * tf.onesLike(x).print();\n * ```\n * @param x A tensor.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction onesLike_(x) {\n const $x = convertToTensor(x, 'x', 'onesLike');\n const inputs = { x: $x };\n return ENGINE.runKernel(OnesLike, inputs);\n}\nconst onesLike = op({ onesLike_ });\n\n/**\n * Computes the outer product of two vectors, `v1` and `v2`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([3, 4, 5]);\n *\n * tf.outerProduct(a, b).print();\n * ```\n * @param v1 The first vector in the outer product operation.\n * @param v2 The second vector in the outer product operation.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction outerProduct_(v1, v2) {\n const $v1 = convertToTensor(v1, 'v1', 'outerProduct');\n const $v2 = convertToTensor(v2, 'v2', 'outerProduct');\n assert($v1.rank === 1 && $v2.rank === 1, () => `Error in outerProduct: inputs must be rank 1, but got ranks ` +\n `${$v1.rank} and ${$v2.rank}.`);\n const v12D = reshape($v1, [-1, 1]);\n const v22D = reshape($v2, [1, -1]);\n return matMul(v12D, v22D);\n}\nconst outerProduct = op({ outerProduct_ });\n\n/**\n * Pads a `tf.Tensor1D` with a given value and paddings. See `pad` for details.\n */\nfunction pad1d_(x, paddings, constantValue = 0) {\n assert(paddings.length === 2, () => 'Invalid number of paddings. Must be length of 2.');\n return pad(x, [paddings], constantValue);\n}\nconst pad1d = op({ pad1d_ });\n\n/**\n * Pads a `tf.Tensor2D` with a given value and paddings. See `pad` for details.\n */\nfunction pad2d_(x, paddings, constantValue = 0) {\n assert(paddings.length === 2 && paddings[0].length === 2 &&\n paddings[1].length === 2, () => 'Invalid number of paddings. Must be length of 2 each.');\n return pad(x, paddings, constantValue);\n}\nconst pad2d = op({ pad2d_ });\n\n/**\n * Pads a `tf.Tensor3D` with a given value and paddings. See `pad` for details.\n */\nfunction pad3d_(x, paddings, constantValue = 0) {\n assert(paddings.length === 3 && paddings[0].length === 2 &&\n paddings[1].length === 2 && paddings[2].length === 2, () => 'Invalid number of paddings. Must be length of 2 each.');\n return pad(x, paddings, constantValue);\n}\nconst pad3d = op({ pad3d_ });\n\n/**\n * Pads a `tf.Tensor4D` with a given value and paddings. See `pad` for details.\n */\nfunction pad4d_(x, paddings, constantValue = 0) {\n assert(paddings.length === 4 && paddings[0].length === 2 &&\n paddings[1].length === 2 && paddings[2].length === 2 &&\n paddings[3].length === 2, () => 'Invalid number of paddings. Must be length of 2 each.');\n return pad(x, paddings, constantValue);\n}\nconst pad4d = op({ pad4d_ });\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Performs an N-D pooling operation\n *\n * @param input The input tensor, of rank 4 or rank 3 of shape\n * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param windowShape The filter size: `[filterHeight, filterWidth]`. If\n * `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param poolingType The type of pooling, either 'max' or 'avg'.\n * @param pad The type of padding algorithm:\n * - `same` and stride 1: output will be of same size as input,\n * regardless of filter size.\n * - `valid`: output will be smaller than input if filter is larger\n * than 1x1.\n * - For more info, see this guide:\n * [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n * https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n * in which we sample input values across the height and width dimensions\n * in dilated pooling. Defaults to `[1, 1]`. If `dilationRate` is a single\n * number, then `dilationHeight == dilationWidth`. If it is greater than\n * 1, then all values of `strides` must be 1.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n * `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n * provided, it will default to truncate.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction pool_(input, windowShape, poolingType, pad, dilations, strides, dimRoundingMode) {\n if (dilations == null) {\n dilations = [1, 1];\n }\n if (strides == null) {\n strides = 1;\n }\n if (pad === 0) {\n pad = 'valid';\n }\n const $x = convertToTensor(input, 'x', 'maxPool');\n let x4D = $x;\n let reshapedTo4D = false;\n if ($x.rank === 3) {\n reshapedTo4D = true;\n x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n }\n assert(eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in pool: Either strides or dilations must be 1. ' +\n `Got strides ${strides} and dilations '${dilations}'`);\n const convInfo = computePool2DInfo(x4D.shape, windowShape, strides, dilations, pad);\n const dilation = [convInfo.dilationHeight, convInfo.dilationWidth];\n // The following implementation does batchToSpace(pool(spaceToBatch(x)))\n // whenever dilation > 1 since the TF kernels do not support dilation > 1.\n // tslint:disable-next-line:max-line-length\n // https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/nn_ops.py#L1037\n let basePadding;\n if (pad === 'same') {\n basePadding = withSpaceToBatchBasePaddings([convInfo.filterHeight, convInfo.filterWidth], dilation);\n }\n else {\n basePadding = [[0, 0], [0, 0]];\n }\n const isDilationOne = dilation[0] === 1 && dilation[1] === 1;\n const [adjustedPadding, adjustedCrops] = requiredSpaceToBatchPaddings([convInfo.inHeight, convInfo.inWidth], dilation, basePadding);\n const convertedPad = isDilationOne ? pad : 'valid';\n const convertedX = isDilationOne ? x4D : spaceToBatchND(x4D, dilation, adjustedPadding);\n const forwardOp = poolingType === 'avg' ?\n () => avgPool(convertedX, windowShape, strides, convertedPad, dimRoundingMode) :\n () => maxPool(convertedX, windowShape, strides, convertedPad, dimRoundingMode);\n const y = forwardOp();\n const res = isDilationOne ? y : batchToSpaceND(y, dilation, adjustedCrops);\n if (reshapedTo4D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n }\n return res;\n}\n// Helper function to compute crops and paddings for pool with dilation > 1.\n// tslint:disable-next-line:max-line-length\n// https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/array_ops.py#L2184\nfunction requiredSpaceToBatchPaddings(inputShape, blockShape, basePadding) {\n const padStart = basePadding.map(b => b[0]);\n const origPadEnd = basePadding.map(b => b[1]);\n const fullInputShape = inputShape.concat(padStart, origPadEnd);\n const padEndExtra = blockShape.map((b, i) => (b - fullInputShape[i] % b) % b);\n const padEnd = origPadEnd.map((s, i) => s + padEndExtra[i]);\n const paddings = blockShape.map((_, i) => [padStart[i], padEnd[i]]);\n const crops = blockShape.map((_, i) => [0, padEndExtra[i]]);\n return [paddings, crops];\n}\n// Helper function to compute base paddings for pool with dilation > 1.\n// tslint:disable-next-line:max-line-length\n// https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/nn_ops.py#L524\nfunction withSpaceToBatchBasePaddings(filterShape, dilation) {\n // Spatial dimensions of the filters and the upsampled filters in which we\n // introduce (rate - 1) zeros between consecutive filter values.\n const dilatedFilterShape = filterShape.map((s, i) => {\n return s + (s - 1) * (dilation[i] - 1);\n });\n const padExtraShape = dilatedFilterShape.map(s => s - 1);\n // When padding is odd, we pad more at end, following the same\n // convention as conv2d.\n const padExtraStart = padExtraShape.map(s => Math.floor(s / 2));\n const padExtraEnd = padExtraShape.map((s, i) => s - padExtraStart[i]);\n return padExtraShape.map((_, i) => {\n return [padExtraStart[i], padExtraEnd[i]];\n });\n}\nconst pool = op({ pool_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes leaky rectified linear element-wise with parametric alphas.\n *\n * `x < 0 ? alpha * x : f(x) = x`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n * const alpha = tf.scalar(0.1);\n *\n * x.prelu(alpha).print(); // or tf.prelu(x, alpha)\n * ```\n * @param x The input tensor.\n * @param alpha Scaling factor for negative values.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction prelu_(x, alpha) {\n const $x = convertToTensor(x, 'x', 'prelu');\n const $alpha = convertToTensor(alpha, 'alpha', 'prelu');\n const inputs = { x: $x, alpha: $alpha };\n return ENGINE.runKernel(Prelu, inputs);\n}\nconst prelu = op({ prelu_ });\n\n/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Prints information about the `tf.Tensor` including its data.\n *\n * ```js\n * const verbose = true;\n * tf.tensor2d([1, 2, 3, 4], [2, 2]).print(verbose);\n * ```\n * @param x The tensor to be printed.\n * @param verbose Whether to print verbose information about the ` Tensor`,\n * including dtype and size.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction print(x, verbose = false) {\n console.log(x.toString(verbose));\n}\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes the product of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and a\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.prod().print(); // or tf.prod(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.prod(axis).print(); // or tf.prod(x, axis)\n * ```\n *\n * @param x The input tensor to compute the product over. If the dtype is `bool`\n * it will be converted to `int32` and the output dtype will be `int32`.\n * @param axis The dimension(s) to reduce. By default it reduces\n * all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction prod_(x, axis = null, keepDims = false) {\n let $x = convertToTensor(x, 'x', 'prod');\n if ($x.dtype === 'bool') {\n // bool is not an allowed type for the underlying kernel.\n $x = cast($x, 'int32');\n }\n const inputs = { x: $x };\n const attrs = { axis, keepDims };\n return ENGINE.runKernel(Prod, inputs, attrs);\n}\nconst prod = op({ prod_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Creates a `tf.Tensor` with values sampled from a random number generator\n * function defined by the user.\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param randFunction A random number generator function which is called\n * for each element in the output tensor.\n * @param dtype The data type of the output tensor. Defaults to 'float32'.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction rand_(shape, randFunction, dtype) {\n const size = sizeFromShape(shape);\n let values = null;\n if (dtype == null || dtype === 'float32') {\n values = new Float32Array(size);\n }\n else if (dtype === 'int32') {\n values = new Int32Array(size);\n }\n else if (dtype === 'bool') {\n values = new Uint8Array(size);\n }\n else {\n throw new Error(`Unknown data type ${dtype}`);\n }\n for (let i = 0; i < size; i++) {\n values[i] = randFunction();\n }\n return ENGINE.makeTensor(values, shape, dtype);\n}\nconst rand = op({ rand_ });\n\nvar commonjsGlobal = typeof globalThis !== 'undefined' ? globalThis : typeof window !== 'undefined' ? window : typeof global !== 'undefined' ? global : typeof self !== 'undefined' ? self : {};\n\nfunction unwrapExports (x) {\n\treturn x && x.__esModule && Object.prototype.hasOwnProperty.call(x, 'default') ? x['default'] : x;\n}\n\nfunction createCommonjsModule(fn, module) {\n\treturn module = { exports: {} }, fn(module, module.exports), module.exports;\n}\n\nfunction getCjsExportFromNamespace (n) {\n\treturn n && n['default'] || n;\n}\n\nfunction commonjsRequire () {\n\tthrow new Error('Dynamic requires are not currently supported by @rollup/plugin-commonjs');\n}\n\nvar alea = createCommonjsModule(function (module) {\n// A port of an algorithm by Johannes Baagøe , 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baagøe \n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n var me = this, mash = Mash();\n\n me.next = function() {\n var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n me.s0 = me.s1;\n me.s1 = me.s2;\n return me.s2 = t - (me.c = t | 0);\n };\n\n // Apply the seeding algorithm from Baagoe.\n me.c = 1;\n me.s0 = mash(' ');\n me.s1 = mash(' ');\n me.s2 = mash(' ');\n me.s0 -= mash(seed);\n if (me.s0 < 0) { me.s0 += 1; }\n me.s1 -= mash(seed);\n if (me.s1 < 0) { me.s1 += 1; }\n me.s2 -= mash(seed);\n if (me.s2 < 0) { me.s2 += 1; }\n mash = null;\n}\n\nfunction copy(f, t) {\n t.c = f.c;\n t.s0 = f.s0;\n t.s1 = f.s1;\n t.s2 = f.s2;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new Alea(seed),\n state = opts && opts.state,\n prng = xg.next;\n prng.int32 = function() { return (xg.next() * 0x100000000) | 0; };\n prng.double = function() {\n return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n };\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); };\n }\n return prng;\n}\n\nfunction Mash() {\n var n = 0xefc8249d;\n\n var mash = function(data) {\n data = data.toString();\n for (var i = 0; i < data.length; i++) {\n n += data.charCodeAt(i);\n var h = 0.02519603282416938 * n;\n n = h >>> 0;\n h -= n;\n h *= n;\n n = h >>> 0;\n h -= n;\n n += h * 0x100000000; // 2^32\n }\n return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n };\n\n return mash;\n}\n\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.alea = impl;\n}\n\n})(\n commonjsGlobal,\n ('object') == 'object' && module, // present in node.js\n (typeof undefined) == 'function' && undefined // present with an AMD loader\n);\n});\n\nvar xor128 = createCommonjsModule(function (module) {\n// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n\n // Set up generator function.\n me.next = function() {\n var t = me.x ^ (me.x << 11);\n me.x = me.y;\n me.y = me.z;\n me.z = me.w;\n return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n };\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); };\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor128 = impl;\n}\n\n})(\n commonjsGlobal,\n ('object') == 'object' && module, // present in node.js\n (typeof undefined) == 'function' && undefined // present with an AMD loader\n);\n});\n\nvar xorwow = createCommonjsModule(function (module) {\n// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var t = (me.x ^ (me.x >>> 2));\n me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n return (me.d = (me.d + 362437 | 0)) +\n (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n };\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n me.v = 0;\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n if (k == strseed.length) {\n me.d = me.x << 10 ^ me.x >>> 4;\n }\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n t.v = f.v;\n t.d = f.d;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); };\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorwow = impl;\n}\n\n})(\n commonjsGlobal,\n ('object') == 'object' && module, // present in node.js\n (typeof undefined) == 'function' && undefined // present with an AMD loader\n);\n});\n\nvar xorshift7 = createCommonjsModule(function (module) {\n// A Javascript implementaion of the \"xorshift7\" algorithm by\n// François Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n // Update xor generator.\n var X = me.x, i = me.i, t, v, w;\n t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n X[i] = v;\n me.i = (i + 1) & 7;\n return v;\n };\n\n function init(me, seed) {\n var j, w, X = [];\n\n if (seed === (seed | 0)) {\n // Seed state array using a 32-bit integer.\n w = X[0] = seed;\n } else {\n // Seed state using a string.\n seed = '' + seed;\n for (j = 0; j < seed.length; ++j) {\n X[j & 7] = (X[j & 7] << 15) ^\n (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n }\n }\n // Enforce an array length of 8, not all zeroes.\n while (X.length < 8) X.push(0);\n for (j = 0; j < 8 && X[j] === 0; ++j);\n if (j == 8) w = X[7] = -1; else w = X[j];\n\n me.x = X;\n me.i = 0;\n\n // Discard an initial 256 values.\n for (j = 256; j > 0; --j) {\n me.next();\n }\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.x = f.x.slice();\n t.i = f.i;\n return t;\n}\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.x) copy(state, xg);\n prng.state = function() { return copy(xg, {}); };\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorshift7 = impl;\n}\n\n})(\n commonjsGlobal,\n ('object') == 'object' && module, // present in node.js\n (typeof undefined) == 'function' && undefined // present with an AMD loader\n);\n});\n\nvar xor4096 = createCommonjsModule(function (module) {\n// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated. For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1); // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897); // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C. This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n var w = me.w,\n X = me.X, i = me.i, t, v;\n // Update Weyl generator.\n me.w = w = (w + 0x61c88647) | 0;\n // Update xor generator.\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n // Update Xor generator array state.\n v = X[i] = v ^ t;\n me.i = i;\n // Result is the combination.\n return (v + (w ^ (w >>> 16))) | 0;\n };\n\n function init(me, seed) {\n var t, v, i, j, w, X = [], limit = 128;\n if (seed === (seed | 0)) {\n // Numeric seeds initialize v, which is used to generates X.\n v = seed;\n seed = null;\n } else {\n // String seeds are mixed into v and X one character at a time.\n seed = seed + '\\0';\n v = 0;\n limit = Math.max(limit, seed.length);\n }\n // Initialize circular array and weyl value.\n for (i = 0, j = -32; j < limit; ++j) {\n // Put the unicode characters into the array, and shuffle them.\n if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n // After 32 shuffles, take v as the starting w value.\n if (j === 0) w = v;\n v ^= v << 10;\n v ^= v >>> 15;\n v ^= v << 4;\n v ^= v >>> 13;\n if (j >= 0) {\n w = (w + 0x61c88647) | 0; // Weyl.\n t = (X[j & 127] ^= (v + w)); // Combine xor and weyl to init array.\n i = (0 == t) ? i + 1 : 0; // Count zeroes.\n }\n }\n // We have detected all zeroes; make the key nonzero.\n if (i >= 128) {\n X[(seed && seed.length || 0) & 127] = -1;\n }\n // Run the generator 512 times to further mix the state before using it.\n // Factoring this as a function slows the main generator, so it is just\n // unrolled here. The weyl generator is not advanced while warming up.\n i = 127;\n for (j = 4 * 128; j > 0; --j) {\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n X[i] = v ^ t;\n }\n // Storing state as object members is faster than using closure variables.\n me.w = w;\n me.X = X;\n me.i = i;\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.i = f.i;\n t.w = f.w;\n t.X = f.X.slice();\n return t;\n};\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.X) copy(state, xg);\n prng.state = function() { return copy(xg, {}); };\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor4096 = impl;\n}\n\n})(\n commonjsGlobal, // window object or global\n ('object') == 'object' && module, // present in node.js\n (typeof undefined) == 'function' && undefined // present with an AMD loader\n);\n});\n\nvar tychei = createCommonjsModule(function (module) {\n// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var b = me.b, c = me.c, d = me.d, a = me.a;\n b = (b << 25) ^ (b >>> 7) ^ c;\n c = (c - d) | 0;\n d = (d << 24) ^ (d >>> 8) ^ a;\n a = (a - b) | 0;\n me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n me.c = c = (c - d) | 0;\n me.d = (d << 16) ^ (c >>> 16) ^ a;\n return me.a = (a - b) | 0;\n };\n\n /* The following is non-inverted tyche, which has better internal\n * bit diffusion, but which is about 25% slower than tyche-i in JS.\n me.next = function() {\n var a = me.a, b = me.b, c = me.c, d = me.d;\n a = (me.a + me.b | 0) >>> 0;\n d = me.d ^ a; d = d << 16 ^ d >>> 16;\n c = me.c + d | 0;\n b = me.b ^ c; b = b << 12 ^ d >>> 20;\n me.a = a = a + b | 0;\n d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n me.c = c = c + d | 0;\n b = b ^ c;\n return me.b = (b << 7 ^ b >>> 25);\n }\n */\n\n me.a = 0;\n me.b = 0;\n me.c = 2654435769 | 0;\n me.d = 1367130551;\n\n if (seed === Math.floor(seed)) {\n // Integer seed.\n me.a = (seed / 0x100000000) | 0;\n me.b = seed | 0;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 20; k++) {\n me.b ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.a = f.a;\n t.b = f.b;\n t.c = f.c;\n t.d = f.d;\n return t;\n};\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); };\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.tychei = impl;\n}\n\n})(\n commonjsGlobal,\n ('object') == 'object' && module, // present in node.js\n (typeof undefined) == 'function' && undefined // present with an AMD loader\n);\n});\n\nvar seedrandom = createCommonjsModule(function (module) {\n/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n width = 256, // each RC4 output is 0 <= x < 256\n chunks = 6, // at least six RC4 outputs for each double\n digits = 52, // there are 52 significant digits in a double\n rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n startdenom = math.pow(width, chunks),\n significance = math.pow(2, digits),\n overflow = significance * 2,\n mask = width - 1,\n nodecrypto; // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n var key = [];\n options = (options == true) ? { entropy: true } : (options || {});\n\n // Flatten the seed string or build one from local entropy if needed.\n var shortseed = mixkey(flatten(\n options.entropy ? [seed, tostring(pool)] :\n (seed == null) ? autoseed() : seed, 3), key);\n\n // Use the seed to initialize an ARC4 generator.\n var arc4 = new ARC4(key);\n\n // This function returns a random double in [0, 1) that contains\n // randomness in every bit of the mantissa of the IEEE 754 value.\n var prng = function() {\n var n = arc4.g(chunks), // Start with a numerator n < 2 ^ 48\n d = startdenom, // and denominator d = 2 ^ 48.\n x = 0; // and no 'extra last byte'.\n while (n < significance) { // Fill up all significant digits by\n n = (n + x) * width; // shifting numerator and\n d *= width; // denominator and generating a\n x = arc4.g(1); // new least-significant-byte.\n }\n while (n >= overflow) { // To avoid rounding up, before adding\n n /= 2; // last byte, shift everything\n d /= 2; // right using integer math until\n x >>>= 1; // we have exactly the desired bits.\n }\n return (n + x) / d; // Form the number within [0, 1).\n };\n\n prng.int32 = function() { return arc4.g(4) | 0; };\n prng.quick = function() { return arc4.g(4) / 0x100000000; };\n prng.double = prng;\n\n // Mix the randomness into accumulated entropy.\n mixkey(tostring(arc4.S), pool);\n\n // Calling convention: what to return as a function of prng, seed, is_math.\n return (options.pass || callback ||\n function(prng, seed, is_math_call, state) {\n if (state) {\n // Load the arc4 state from the given state if it has an S array.\n if (state.S) { copy(state, arc4); }\n // Only provide the .state method if requested via options.state.\n prng.state = function() { return copy(arc4, {}); };\n }\n\n // If called as a method of Math (Math.seedrandom()), mutate\n // Math.random because that is how seedrandom.js has worked since v1.0.\n if (is_math_call) { math[rngname] = prng; return seed; }\n\n // Otherwise, it is a newer calling convention, so return the\n // prng directly.\n else return prng;\n })(\n prng,\n shortseed,\n 'global' in options ? options.global : (this == math),\n options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation. The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4. Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n var t, keylen = key.length,\n me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n // The empty key [] is treated as [0].\n if (!keylen) { key = [keylen++]; }\n\n // Set up S using the standard key scheduling algorithm.\n while (i < width) {\n s[i] = i++;\n }\n for (i = 0; i < width; i++) {\n s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n s[j] = t;\n }\n\n // The \"g\" method returns the next (count) outputs as one number.\n (me.g = function(count) {\n // Using instance members instead of closure state nearly doubles speed.\n var t, r = 0,\n i = me.i, j = me.j, s = me.S;\n while (count--) {\n t = s[i = mask & (i + 1)];\n r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n }\n me.i = i; me.j = j;\n return r;\n // For robust unpredictability, the function call below automatically\n // discards an initial batch of values. This is called RC4-drop[256].\n // See http://google.com/search?q=rsa+fluhrer+response&btnI\n })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n t.i = f.i;\n t.j = f.j;\n t.S = f.S.slice();\n return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n var result = [], typ = (typeof obj), prop;\n if (depth && typ == 'object') {\n for (prop in obj) {\n try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n }\n }\n return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n var stringseed = seed + '', smear, j = 0;\n while (j < stringseed.length) {\n key[mask & j] =\n mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n }\n return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n try {\n var out;\n if (nodecrypto && (out = nodecrypto.randomBytes)) {\n // The use of 'out' to remember randomBytes makes tight minified code.\n out = out(width);\n } else {\n out = new Uint8Array(width);\n (global.crypto || global.msCrypto).getRandomValues(out);\n }\n return tostring(out);\n } catch (e) {\n var browser = global.navigator,\n plugins = browser && browser.plugins;\n return [+new Date, global, plugins, global.screen, tostring(pool)];\n }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool. Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif (('object') == 'object' && module.exports) {\n module.exports = seedrandom;\n // When in node.js, try using crypto package for autoseeding.\n try {\n nodecrypto = require('crypto');\n } catch (ex) {}\n} else if ((typeof undefined) == 'function' && undefined.amd) {\n undefined(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n [], // pool: entropy pool starts empty\n Math // math: package containing random, pow, and seedrandom\n);\n});\n\n// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random(); // 0 <= x < 1. Every bit is random.\n// var x = random.quick(); // 0 <= x < 1. 32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baagøe.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\n\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\n\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\n\n\n// xorshift7, by François Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three. It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\n\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures. Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\n\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\n\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\n\n\nseedrandom.alea = alea;\nseedrandom.xor128 = xor128;\nseedrandom.xorwow = xorwow;\nseedrandom.xorshift7 = xorshift7;\nseedrandom.xor4096 = xor4096;\nseedrandom.tychei = tychei;\n\nvar seedrandom$1 = seedrandom;\nvar seedrandom_1 = seedrandom$1.alea;\n\n/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst TEST_EPSILON_FLOAT32 = 1e-3;\nconst TEST_EPSILON_FLOAT16 = 1e-1;\nfunction expectArraysClose(actual, expected, epsilon) {\n if (epsilon == null) {\n epsilon = testEpsilon();\n }\n return expectArraysPredicate(actual, expected, (a, b) => areClose(a, b, epsilon));\n}\nfunction testEpsilon() {\n return ENGINE.backend.floatPrecision() === 32 ? TEST_EPSILON_FLOAT32 :\n TEST_EPSILON_FLOAT16;\n}\nfunction expectArraysPredicate(actual, expected, predicate) {\n let checkClassType = true;\n if (isTypedArray(actual) || isTypedArray(expected)) {\n checkClassType = false;\n }\n if (isTypedArray(actual) && isTypedArray(expected)) {\n checkClassType = true;\n }\n if (checkClassType) {\n const aType = actual.constructor.name;\n const bType = expected.constructor.name;\n if (aType !== bType) {\n throw new Error(`Arrays are of different type. Actual: ${aType}. ` +\n `Expected: ${bType}`);\n }\n }\n if (Array.isArray(actual) && Array.isArray(expected)) {\n const actualShape = inferShape(actual);\n const expectedShape = inferShape(expected);\n if (!arraysEqual(actualShape, expectedShape)) {\n throw new Error(`Arrays have different shapes. ` +\n `Actual: [${actualShape}]. Expected: [${expectedShape}]`);\n }\n }\n const actualFlat = isTypedArray(actual) ? actual : flatten(actual);\n const expectedFlat = isTypedArray(expected) ?\n expected :\n flatten(expected);\n if (actualFlat.length !== expectedFlat.length) {\n throw new Error(`Arrays have different lengths actual: ${actualFlat.length} vs ` +\n `expected: ${expectedFlat.length}.\\n` +\n `Actual: ${actualFlat}.\\n` +\n `Expected: ${expectedFlat}.`);\n }\n for (let i = 0; i < expectedFlat.length; ++i) {\n const a = actualFlat[i];\n const e = expectedFlat[i];\n if (!predicate(a, e)) {\n throw new Error(`Arrays differ: actual[${i}] = ${a}, expected[${i}] = ${e}.\\n` +\n `Actual: ${actualFlat}.\\n` +\n `Expected: ${expectedFlat}.`);\n }\n }\n}\nfunction expectPromiseToFail(fn, done) {\n fn().then(() => done.fail(), () => done());\n}\nfunction expectArraysEqual(actual, expected) {\n const exp = typeof expected === 'string' || typeof expected === 'number' ||\n typeof expected === 'boolean' ?\n [expected] :\n expected;\n if (isString(actual) || isString(actual[0]) ||\n isString(expected) || isString(expected[0])) {\n // tslint:disable-next-line: triple-equals\n return expectArraysPredicate(actual, exp, (a, b) => a == b);\n }\n return expectArraysPredicate(actual, expected, (a, b) => areClose(a, b, 0));\n}\nfunction expectNumbersClose(a, e, epsilon) {\n if (epsilon == null) {\n epsilon = testEpsilon();\n }\n if (!areClose(a, e, epsilon)) {\n throw new Error(`Numbers differ: actual === ${a}, expected === ${e}`);\n }\n}\nfunction areClose(a, e, epsilon) {\n if (!isFinite(a) && !isFinite(e)) {\n return true;\n }\n if (isNaN(a) || isNaN(e) || Math.abs(a - e) > epsilon) {\n return false;\n }\n return true;\n}\nfunction expectValuesInRange(actual, low, high) {\n for (let i = 0; i < actual.length; i++) {\n if (actual[i] < low || actual[i] > high) {\n throw new Error(`Value out of range:${actual[i]} low: ${low}, high: ${high}`);\n }\n }\n}\nfunction expectArrayBuffersEqual(actual, expected) {\n // Safari does not like comparing ArrayBuffers directly. Wrapping in\n // a Float32Array solves this issue.\n const actualArray = new Float32Array(actual);\n const expectedArray = new Float32Array(expected);\n if (actualArray.length !== expectedArray.length) {\n throw new Error('Expected ArrayBuffer to be of length ' +\n `${expectedArray.length}, but it was ${actualArray.length}`);\n }\n for (let i = 0; i < expectedArray.length; i++) {\n if (actualArray[i] !== expectedArray[i]) {\n throw new Error(`Expected ArrayBuffer value at ${i} to be ` +\n `${expectedArray[i]} but got ${actualArray[i]} instead`);\n }\n }\n}\n/** Encodes strings into utf-8 bytes. */\nfunction encodeStrings(a) {\n for (let i = 0; i < a.length; i++) {\n const val = a[i];\n if (Array.isArray(val)) {\n encodeStrings(val);\n }\n else {\n a[i] = encodeString(val);\n }\n }\n return a;\n}\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// https://en.wikipedia.org/wiki/Marsaglia_polar_method\nclass MPRandGauss {\n constructor(mean, stdDeviation, dtype, truncated, seed) {\n this.mean = mean;\n this.stdDev = stdDeviation;\n this.dtype = dtype;\n this.nextVal = NaN;\n this.truncated = truncated;\n if (this.truncated) {\n this.upper = this.mean + this.stdDev * 2;\n this.lower = this.mean - this.stdDev * 2;\n }\n const seedValue = seed ? seed : Math.random();\n this.random = seedrandom_1(seedValue.toString());\n }\n /** Returns next sample from a Gaussian distribution. */\n nextValue() {\n if (!isNaN(this.nextVal)) {\n const value = this.nextVal;\n this.nextVal = NaN;\n return value;\n }\n let resultX, resultY;\n let isValid = false;\n while (!isValid) {\n let v1, v2, s;\n do {\n v1 = 2 * this.random() - 1;\n v2 = 2 * this.random() - 1;\n s = v1 * v1 + v2 * v2;\n } while (s >= 1 || s === 0);\n const mul = Math.sqrt(-2.0 * Math.log(s) / s);\n resultX = this.mean + this.stdDev * v1 * mul;\n resultY = this.mean + this.stdDev * v2 * mul;\n if (!this.truncated || this.isValidTruncated(resultX)) {\n isValid = true;\n }\n }\n if (!this.truncated || this.isValidTruncated(resultY)) {\n this.nextVal = this.convertValue(resultY);\n }\n return this.convertValue(resultX);\n }\n /** Handles proper rounding for non-floating-point numbers. */\n convertValue(value) {\n if (this.dtype == null || this.dtype === 'float32') {\n return value;\n }\n return Math.round(value);\n }\n /** Returns true if less than 2-standard-deviations from the mean. */\n isValidTruncated(value) {\n return value <= this.upper && value >= this.lower;\n }\n}\n// Marsaglia, George, and Wai Wan Tsang. 2000. \"A Simple Method for Generating\n// Gamma Variables.\"\nclass RandGamma {\n constructor(alpha, beta, dtype, seed) {\n this.alpha = alpha;\n this.beta = 1 / beta; // convert rate to scale parameter\n this.dtype = dtype;\n const seedValue = seed ? seed : Math.random();\n this.randu = seedrandom_1(seedValue.toString());\n this.randn = new MPRandGauss(0, 1, dtype, false, this.randu());\n if (alpha < 1) {\n this.d = alpha + (2 / 3);\n }\n else {\n this.d = alpha - (1 / 3);\n }\n this.c = 1 / Math.sqrt(9 * this.d);\n }\n /** Returns next sample from a gamma distribution. */\n nextValue() {\n let x2, v0, v1, x, u, v;\n while (true) {\n do {\n x = this.randn.nextValue();\n v = 1 + (this.c * x);\n } while (v <= 0);\n v *= v * v;\n x2 = x * x;\n v0 = 1 - (0.331 * x2 * x2);\n v1 = (0.5 * x2) + (this.d * (1 - v + Math.log(v)));\n u = this.randu();\n if (u < v0 || Math.log(u) < v1) {\n break;\n }\n }\n v = (1 / this.beta) * this.d * v;\n if (this.alpha < 1) {\n v *= Math.pow(this.randu(), 1 / this.alpha);\n }\n return this.convertValue(v);\n }\n /** Handles proper rounding for non-floating-point numbers. */\n convertValue(value) {\n if (this.dtype === 'float32') {\n return value;\n }\n return Math.round(value);\n }\n}\nclass UniformRandom {\n constructor(min = 0, max = 1, dtype, seed) {\n /** Handles proper rounding for non floating point numbers. */\n this.canReturnFloat = () => (this.dtype == null || this.dtype === 'float32');\n this.min = min;\n this.range = max - min;\n this.dtype = dtype;\n if (seed == null) {\n seed = Math.random();\n }\n if (typeof seed === 'number') {\n seed = seed.toString();\n }\n if (!this.canReturnFloat() && this.range <= 1) {\n throw new Error(`The difference between ${min} - ${max} <= 1 and dtype is not float`);\n }\n this.random = seedrandom_1(seed);\n }\n convertValue(value) {\n if (this.canReturnFloat()) {\n return value;\n }\n return Math.round(value);\n }\n nextValue() {\n return this.convertValue(this.min + this.range * this.random());\n }\n}\nfunction jarqueBeraNormalityTest(values) {\n // https://en.wikipedia.org/wiki/Jarque%E2%80%93Bera_test\n const n = values.length;\n const s = skewness(values);\n const k = kurtosis(values);\n const jb = n / 6 * (Math.pow(s, 2) + 0.25 * Math.pow(k - 3, 2));\n // JB test requires 2-degress of freedom from Chi-Square @ 0.95:\n // http://www.itl.nist.gov/div898/handbook/eda/section3/eda3674.htm\n const CHI_SQUARE_2DEG = 5.991;\n if (jb > CHI_SQUARE_2DEG) {\n throw new Error(`Invalid p-value for JB: ${jb}`);\n }\n}\nfunction expectArrayInMeanStdRange(actual, expectedMean, expectedStdDev, epsilon) {\n if (epsilon == null) {\n epsilon = testEpsilon();\n }\n const actualMean = mean$1(actual);\n expectNumbersClose(actualMean, expectedMean, epsilon);\n expectNumbersClose(standardDeviation(actual, actualMean), expectedStdDev, epsilon);\n}\nfunction mean$1(values) {\n let sum = 0;\n for (let i = 0; i < values.length; i++) {\n sum += values[i];\n }\n return sum / values.length;\n}\nfunction standardDeviation(values, mean) {\n let squareDiffSum = 0;\n for (let i = 0; i < values.length; i++) {\n const diff = values[i] - mean;\n squareDiffSum += diff * diff;\n }\n return Math.sqrt(squareDiffSum / values.length);\n}\nfunction kurtosis(values) {\n // https://en.wikipedia.org/wiki/Kurtosis\n const valuesMean = mean$1(values);\n const n = values.length;\n let sum2 = 0;\n let sum4 = 0;\n for (let i = 0; i < n; i++) {\n const v = values[i] - valuesMean;\n sum2 += Math.pow(v, 2);\n sum4 += Math.pow(v, 4);\n }\n return (1 / n) * sum4 / Math.pow((1 / n) * sum2, 2);\n}\nfunction skewness(values) {\n // https://en.wikipedia.org/wiki/Skewness\n const valuesMean = mean$1(values);\n const n = values.length;\n let sum2 = 0;\n let sum3 = 0;\n for (let i = 0; i < n; i++) {\n const v = values[i] - valuesMean;\n sum2 += Math.pow(v, 2);\n sum3 += Math.pow(v, 3);\n }\n return (1 / n) * sum3 / Math.pow((1 / (n - 1)) * sum2, 3 / 2);\n}\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Creates a `tf.Tensor` with values sampled from a gamma distribution.\n *\n * ```js\n * tf.randomGamma([2, 2], 1).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param alpha The shape parameter of the gamma distribution.\n * @param beta The inverse scale parameter of the gamma distribution. Defaults\n * to 1.\n * @param dtype The data type of the output. Defaults to float32.\n * @param seed The seed for the random number generator.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction randomGamma_(shape, alpha, beta = 1, dtype = 'float32', seed) {\n if (beta == null) {\n beta = 1;\n }\n if (dtype == null) {\n dtype = 'float32';\n }\n if (dtype !== 'float32' && dtype !== 'int32') {\n throw new Error(`Unsupported data type ${dtype}`);\n }\n const rgamma = new RandGamma(alpha, beta, dtype, seed);\n const res = buffer(shape, dtype);\n for (let i = 0; i < res.values.length; i++) {\n res.values[i] = rgamma.nextValue();\n }\n return res.toTensor();\n}\nconst randomGamma = op({ randomGamma_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Creates a `tf.Tensor` with values sampled from a normal distribution.\n *\n * ```js\n * tf.randomNormal([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param mean The mean of the normal distribution.\n * @param stdDev The standard deviation of the normal distribution.\n * @param dtype The data type of the output.\n * @param seed The seed for the random number generator.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction randomNormal_(shape, mean = 0, stdDev = 1, dtype, seed) {\n if (dtype != null && dtype === 'bool') {\n throw new Error(`Unsupported data type ${dtype}`);\n }\n const randGauss = new MPRandGauss(mean, stdDev, dtype, false /* truncated */, seed);\n const res = buffer(shape, dtype);\n for (let i = 0; i < res.values.length; i++) {\n res.values[i] = randGauss.nextValue();\n }\n return res.toTensor();\n}\nconst randomNormal = op({ randomNormal_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Creates a `tf.Tensor` with values sampled from a uniform distribution.\n *\n * The generated values follow a uniform distribution in the range [minval,\n * maxval). The lower bound minval is included in the range, while the upper\n * bound maxval is excluded.\n *\n * ```js\n * tf.randomUniform([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param minval The lower bound on the range of random values to generate.\n * Defaults to 0.\n * @param maxval The upper bound on the range of random values to generate.\n * Defaults to 1.\n * @param dtype The data type of the output tensor. Defaults to 'float32'.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction randomUniform_(shape, minval = 0, maxval = 1, dtype = 'float32', seed) {\n const res = buffer(shape, dtype);\n const random = new UniformRandom(minval, maxval, null, seed);\n for (let i = 0; i < res.values.length; i++) {\n res.values[i] = random.nextValue();\n }\n return res.toTensor();\n}\nconst randomUniform = op({ randomUniform_ });\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Creates a new `tf.Tensor1D` filled with the numbers in the range provided.\n *\n * The tensor is a is half-open interval meaning it includes start, but\n * excludes stop. Decrementing ranges and negative step values are also\n * supported.sv\n *\n *\n * ```js\n * tf.range(0, 9, 2).print();\n * ```\n *\n * @param start An integer start value\n * @param stop An integer stop value\n * @param step An integer increment (will default to 1 or -1)\n * @param dtype The data type of the output tensor. Defaults to 'float32'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction range(start, stop, step = 1, dtype = 'float32') {\n if (step === 0) {\n throw new Error('Cannot have a step of zero');\n }\n const attrs = { start, stop, step, dtype };\n return ENGINE.runKernel(Range, {} /* inputs */, attrs);\n}\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Returns the real part of a complex (or real) tensor.\n *\n * Given a tensor input, this operation returns a tensor of type float that is\n * the real part of each element in input considered as a complex number.\n *\n * If the input is real, it simply makes a clone.\n *\n * ```js\n * const x = tf.complex([-2.25, 3.25], [4.75, 5.75]);\n * tf.real(x).print();\n * ```\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction real_(input) {\n const $input = convertToTensor(input, 'input', 'real');\n const inputs = { input: $input };\n return ENGINE.runKernel(Real, inputs);\n}\nconst real = op({ real_ });\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes reciprocal of x element-wise: `1 / x`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, 2]);\n *\n * x.reciprocal().print(); // or tf.reciprocal(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction reciprocal_(x) {\n const $x = convertToTensor(x, 'x', 'reciprocal');\n const inputs = { x: $x };\n return ENGINE.runKernel(Reciprocal, inputs);\n}\nconst reciprocal = op({ reciprocal_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes rectified linear element-wise: `max(x, 0)`.\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.relu().print(); // or tf.relu(x)\n * ```\n * @param x The input tensor. If the dtype is `bool`, the output dtype will be\n * `int32'.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction relu_(x) {\n const $x = convertToTensor(x, 'x', 'relu');\n const inputs = { x: $x };\n return ENGINE.runKernel(Relu, inputs);\n}\nconst relu = op({ relu_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes rectified linear 6 element-wise: `min(max(x, 0), 6)`.\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 8]);\n *\n * x.relu6().print(); // or tf.relu6(x)\n * ```\n * @param x The input tensor. If the dtype is `bool`, the output dtype will be\n * `int32'.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction relu6_(x) {\n const $x = convertToTensor(x, 'x', 'relu6');\n const inputs = { x: $x };\n return ENGINE.runKernel(Relu6, inputs);\n}\nconst relu6 = op({ relu6_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Reverses a `tf.Tensor1D`.\n *\n * @param x The input tensor.\n */\nfunction reverse1d_(x) {\n const $x = convertToTensor(x, 'x', 'reverse');\n assert($x.rank === 1, () => `Error in reverse1D: x must be rank 1 but got rank ${$x.rank}.`);\n return reverse($x, 0);\n}\nconst reverse1d = op({ reverse1d_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Reverses a `tf.Tensor2D` along a specified axis.\n *\n * @param x The input tensor.\n * @param axis The set of dimensions to reverse. Must be in the\n * range [-rank(x), rank(x)). Defaults to all axes.\n */\nfunction reverse2d_(x, axis) {\n const $x = convertToTensor(x, 'x', 'reverse');\n assert($x.rank === 2, () => `Error in reverse2D: x must be rank 2 but got rank ${$x.rank}.`);\n return reverse($x, axis);\n}\nconst reverse2d = op({ reverse2d_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Reverses a `tf.Tensor3D` along a specified axis.\n *\n * @param x The input tensor.\n * @param axis The set of dimensions to reverse. Must be in the\n * range [-rank(x), rank(x)). Defaults to all axes.\n */\nfunction reverse3d_(x, axis) {\n const $x = convertToTensor(x, 'x', 'reverse');\n assert($x.rank === 3, () => `Error in reverse3D: x must be rank 3 but got rank ${$x.rank}.`);\n return reverse($x, axis);\n}\nconst reverse3d = op({ reverse3d_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Reverses a `tf.Tensor4D` along a specified axis.\n *\n * @param x The input tensor.\n * @param axis The set of dimensions to reverse. Must be in the\n * range [-rank(x), rank(x)). Defaults to all axes.\n */\nfunction reverse4d_(x, axis) {\n const $x = convertToTensor(x, 'x', 'reverse');\n assert($x.rank === 4, () => `Error in reverse4D: x must be rank 4 but got rank ${$x.rank}.`);\n return reverse($x, axis);\n}\nconst reverse4d = op({ reverse4d_ });\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes round of input `tf.Tensor` element-wise: `round(x)`.\n * It implements banker's rounding.\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3]);\n *\n * x.round().print(); // or tf.round(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction round_(x) {\n const $x = convertToTensor(x, 'x', 'round');\n const inputs = { x: $x };\n return ENGINE.runKernel(Round, inputs);\n}\nconst round$1 = op({ round_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes scaled exponential linear element-wise.\n *\n * `x < 0 ? scale * alpha * (exp(x) - 1) : x`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.selu().print(); // or tf.selu(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction selu_(x) {\n const $x = convertToTensor(x, 'x', 'selu');\n const inputs = { x: $x };\n return ENGINE.runKernel(Selu, inputs);\n}\nconst selu = op({ selu_ });\n\n/**\n * 2-D convolution with separable filters.\n *\n * Performs a depthwise convolution that acts separately on channels followed\n * by a pointwise convolution that mixes channels. Note that this is\n * separability between dimensions [1, 2] and 3, not spatial separability\n * between dimensions 1 and 2.\n *\n * See\n * [https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d](\n * https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d)\n * for more details.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param depthwiseFilter The depthwise filter tensor, rank 4, of shape\n * `[filterHeight, filterWidth, inChannels, channelMultiplier]`. This is\n * the filter used in the first step.\n * @param pointwiseFilter The pointwise filter tensor, rank 4, of shape\n * `[1, 1, inChannels * channelMultiplier, outChannels]`. This is\n * the filter used in the second step.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`. If strides is a single number, then `strideHeight ==\n * strideWidth`.\n * @param pad The type of padding algorithm.\n * - `same` and stride 1: output will be of same size as input,\n * regardless of filter size.\n * - `valid`: output will be smaller than input if filter is larger\n * than 1x1.\n * - For more info, see this guide:\n * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](\n * https://www.tensorflow.org/api_docs/python/tf/nn/convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n * in which we sample input values across the height and width dimensions\n * in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single\n * number, then `dilationHeight == dilationWidth`. If it is greater than\n * 1, then all values of `strides` must be 1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n * \"NHWC\". Specify the data format of the input and output data. With the\n * default format \"NHWC\", the data is stored in the order of: [batch,\n * height, width, channels]. Only \"NHWC\" is currently supported.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction separableConv2d_(x, depthwiseFilter, pointwiseFilter, strides, pad, dilation = [1, 1], dataFormat = 'NHWC') {\n const $x = convertToTensor(x, 'x', 'separableConv2d');\n const $depthwiseFilter = convertToTensor(depthwiseFilter, 'depthwiseFilter', 'separableConv2d');\n const $pointwiseFilter = convertToTensor(pointwiseFilter, 'pointwiseFilter', 'separableConv2d');\n let x4D = $x;\n let reshapedTo4D = false;\n if ($x.rank === 3) {\n reshapedTo4D = true;\n x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n }\n if (dataFormat === 'NCHW') {\n throw new Error('separableConv2d currently does not support dataFormat NCHW; only ' +\n 'NHWC is supported');\n }\n assert(x4D.rank === 4, () => `Error in separableConv2d: input must be rank 4, but got ` +\n `rank ${x4D.rank}.`);\n assert($depthwiseFilter.rank === 4, () => `Error in separableConv2d: depthwise filter must be rank 4, but ` +\n `got rank ${$depthwiseFilter.rank}.`);\n assert($pointwiseFilter.rank === 4, () => `Error in separableConv2d: pointwise filter must be rank 4, but ` +\n `got rank ${$depthwiseFilter.rank}.`);\n assert($pointwiseFilter.shape[0] === 1, () => `Error in separableConv2d: the first dimension of pointwise filter ` +\n ` must be 1, but got ${$pointwiseFilter.shape[0]}.`);\n assert($pointwiseFilter.shape[1] === 1, () => `Error in separableConv2d: the second dimension of pointwise ` +\n `filter must be 1, but got ${$pointwiseFilter.shape[1]}.`);\n const inChannels = $depthwiseFilter.shape[2];\n const channelMultiplier = $depthwiseFilter.shape[3];\n assert($pointwiseFilter.shape[2] === inChannels * channelMultiplier, () => `Error in separableConv2d: the third dimension of pointwise filter ` +\n `must be ${inChannels * channelMultiplier}, ` +\n `but got ${$pointwiseFilter.shape[2]}.`);\n const depthwise = depthwiseConv2d(x4D, $depthwiseFilter, strides, pad, dataFormat, dilation);\n const pointwiseStride = 1;\n const res = conv2d(depthwise, $pointwiseFilter, pointwiseStride, 'valid', dataFormat);\n if (reshapedTo4D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n }\n return res;\n}\nconst separableConv2d = op({ separableConv2d_ });\n\n/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes the difference between two lists of numbers.\n *\n * Given a Tensor `x` and a Tensor `y`, this operation returns a Tensor `out`\n * that represents all values that are in `x` but not in `y`. The returned\n * Tensor `out` is sorted in the same order that the numbers appear in `x`\n * (duplicates are preserved). This operation also returns a Tensor indices that\n * represents the position of each out element in `x`. In other words:\n *\n * `out[i] = x[idx[i]] for i in [0, 1, ..., out.length - 1]`\n *\n * ```js\n * const x = [1, 2, 3, 4, 5, 6];\n * const y = [1, 3, 5];\n *\n * const [out, indices] = await tf.setdiff1dAsync(x, y);\n * out.print(); // [2, 4, 6]\n * indices.print(); // [1, 3, 5]\n * ```\n *\n * @param x 1-D Tensor. Values to keep.\n * @param y 1-D Tensor. Must have the same type as x. Values to exclude in the\n * output.\n * @returns Promise of Tensor tuple [out, indices].\n * out: Tensor with the same type as x.\n * indices: A Tensor of type int32.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nasync function setdiff1dAsync_(x, y) {\n const $x = convertToTensor(x, 'x', 'setdiff1d');\n const $y = convertToTensor(y, 'y', 'setdiff1d');\n assert($x.dtype === $y.dtype, () => `x and y should have the same dtype, but got x (${$x.dtype}) and y (${$y.dtype}).`);\n assert($x.rank === 1, () => `x should be 1D tensor, but got x (${$x.shape}).`);\n assert($y.rank === 1, () => `y should be 1D tensor, but got y (${$y.shape}).`);\n const xVals = await $x.data();\n const yVals = await $y.data();\n const ySet = new Set(yVals);\n let outputSize = 0;\n for (let i = 0; i < xVals.length; i++) {\n if (!ySet.has(xVals[i])) {\n outputSize++;\n }\n }\n const buffer = new TensorBuffer([outputSize], $x.dtype);\n const indices = new TensorBuffer([outputSize], 'int32');\n for (let i = 0, p = 0; i < xVals.length; i++) {\n if (!ySet.has(xVals[i])) {\n buffer.values[p] = xVals[i];\n indices.values[p] = i;\n p++;\n }\n }\n return [buffer.toTensor(), indices.toTensor()];\n}\nconst setdiff1dAsync = setdiff1dAsync_;\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Returns an element-wise indication of the sign of a number.\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3, NaN, 0]);\n *\n * x.sign().print(); // or tf.sign(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sign_(x) {\n const $x = convertToTensor(x, 'x', 'sign');\n const inputs = { x: $x };\n return ENGINE.runKernel(Sign, inputs);\n}\nconst sign = op({ sign_ });\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Extracts a 1D slice from 1D array starting at coordinates `begin` and is\n * of length `size`. See `slice` for details.\n */\nfunction slice1d_(x, begin, size) {\n const $x = convertToTensor(x, 'x', 'slice1d');\n assert($x.rank === 1, () => `slice1d expects a rank-1 tensor, but got a rank-${$x.rank} tensor`);\n return slice($x, [begin], [size]);\n}\nconst slice1d = op({ slice1d_ });\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Extracts a 2D slice from a 2D array starting at coordinates `begin` and\n * is of size `size`. See `slice` for details.\n */\nfunction slice2d_(x, begin, size) {\n const $x = convertToTensor(x, 'x', 'slice2d');\n assert($x.rank === 2, () => `slice2d expects a rank-2 tensor, but got a rank-${$x.rank} tensor`);\n return slice($x, begin, size);\n}\nconst slice2d = op({ slice2d_ });\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Extracts a 3D slice from a 3D array starting at coordinates `begin` and\n * is of size `size`. See `slice` for details.\n */\nfunction slice3d_(x, begin, size) {\n const $x = convertToTensor(x, 'x', 'slice3d');\n assert($x.rank === 3, () => `slice3d expects a rank-3 tensor, but got a rank-${$x.rank} tensor`);\n return slice($x, begin, size);\n}\nconst slice3d = op({ slice3d_ });\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Extracts a 4D slice from a 4D array starting at coordinates `begin` and\n * is of size `size`. See `slice` for details.\n */\nfunction slice4d_(x, begin, size) {\n const $x = convertToTensor(x, 'x', 'slice4d');\n assert($x.rank === 4, () => `slice4d expects a rank-4 tensor, but got a rank-${$x.rank} tensor`);\n return slice($x, begin, size);\n}\nconst slice4d = op({ slice4d_ });\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes the softmax normalized vector given the logits.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n *\n * a.softmax().print(); // or tf.softmax(a)\n * ```\n *\n * ```js\n * const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]);\n *\n * a.softmax().print(); // or tf.softmax(a)\n * ```\n *\n * @param logits The logits array.\n * @param dim The dimension softmax would be performed on. Defaults to `-1`\n * which indicates the last dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction softmax_(logits, dim = -1) {\n const $logits = convertToTensor(logits, 'logits', 'softmax', 'float32');\n if (dim === -1) {\n dim = $logits.rank - 1;\n }\n if (dim !== $logits.rank - 1) {\n throw Error('Softmax along a non-last dimension is not yet supported. ' +\n `Logits was rank ${$logits.rank} and dim was ${dim}`);\n }\n const inputs = { logits: $logits };\n const attrs = { dim };\n return ENGINE.runKernel(Softmax, inputs, attrs);\n}\nconst softmax = op({ softmax_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Fast Fourier transform.\n *\n * Computes the 1-dimensional discrete Fourier transform over the inner-most\n * dimension of input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n * const imag = tf.tensor1d([1, 2, 3]);\n * const x = tf.complex(real, imag);\n *\n * x.fft().print(); // tf.spectral.fft(x).print();\n * ```\n * @param input The complex input to compute an fft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction fft_(input) {\n assert(input.dtype === 'complex64', () => `The dtype for tf.spectral.fft() must be complex64 ` +\n `but got ${input.dtype}.`);\n const inputs = { input };\n return ENGINE.runKernel(FFT, inputs);\n}\nconst fft = op({ fft_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Inverse fast Fourier transform.\n *\n * Computes the inverse 1-dimensional discrete Fourier transform over the\n * inner-most dimension of input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n * const imag = tf.tensor1d([1, 2, 3]);\n * const x = tf.complex(real, imag);\n *\n * x.ifft().print(); // tf.spectral.ifft(x).print();\n * ```\n * @param input The complex input to compute an ifft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction ifft_(input) {\n assert(input.dtype === 'complex64', () => `The dtype for tf.spectral.ifft() must be complex64 ` +\n `but got ${input.dtype}.`);\n const inputs = { input };\n return ENGINE.runKernel(IFFT, inputs);\n}\nconst ifft = op({ ifft_ });\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Inversed real value input fast Fourier transform.\n *\n * Computes the 1-dimensional inversed discrete Fourier transform over the\n * inner-most dimension of the real input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n * const imag = tf.tensor1d([0, 0, 0]);\n * const x = tf.complex(real, imag);\n *\n * x.irfft().print();\n * ```\n * @param input The real value input to compute an irfft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction irfft_(input) {\n const innerDimensionSize = input.shape[input.shape.length - 1];\n const batch = input.size / innerDimensionSize;\n let ret;\n if (innerDimensionSize <= 2) {\n const complexInput = reshape(input, [batch, innerDimensionSize]);\n ret = ifft(complexInput);\n }\n else {\n // The length of unique components of the DFT of a real-valued signal\n // is 2 * (input_len - 1)\n const outputShape = [batch, 2 * (innerDimensionSize - 1)];\n const realInput = reshape(real(input), [batch, innerDimensionSize]);\n const imagInput = reshape(imag(input), [batch, innerDimensionSize]);\n const realConjugate = reverse(slice(realInput, [0, 1], [batch, innerDimensionSize - 2]), 1);\n const imagConjugate = mul(reverse(slice(imagInput, [0, 1], [batch, innerDimensionSize - 2]), 1), scalar(-1));\n const r = concat([realInput, realConjugate], 1);\n const i = concat([imagInput, imagConjugate], 1);\n const complexInput = reshape(complex(r, i), [outputShape[0], outputShape[1]]);\n ret = ifft(complexInput);\n }\n ret = real(ret);\n // reshape the result if the input is 3D tensor.\n if (input.rank === 3 && input.shape[0] !== 0) {\n const temp = ret;\n const batch = input.shape[0];\n ret = reshape(ret, [batch, ret.shape[0] / batch, ret.shape[1]]);\n temp.dispose();\n }\n return ret;\n}\nconst irfft = op({ irfft_ });\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Real value input fast Fourier transform.\n *\n * Computes the 1-dimensional discrete Fourier transform over the\n * inner-most dimension of the real input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n *\n * real.rfft().print();\n * ```\n * @param input The real value input to compute an rfft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction rfft_(input, fftLength) {\n assert(input.dtype === 'float32', () => `The dtype for rfft() must be real value but got ${input.dtype}`);\n let innerDimensionSize = input.shape[input.shape.length - 1];\n const batch = input.size / innerDimensionSize;\n let adjustedInput;\n if (fftLength != null && fftLength < innerDimensionSize) {\n // Need to crop\n const begin = input.shape.map(v => 0);\n const size = input.shape.map(v => v);\n size[input.shape.length - 1] = fftLength;\n adjustedInput = slice(input, begin, size);\n innerDimensionSize = fftLength;\n }\n else if (fftLength != null && fftLength > innerDimensionSize) {\n // Need to pad with zeros\n const zerosShape = input.shape.map(v => v);\n zerosShape[input.shape.length - 1] = fftLength - innerDimensionSize;\n adjustedInput = concat([input, zeros(zerosShape)], input.shape.length - 1);\n innerDimensionSize = fftLength;\n }\n else {\n adjustedInput = input;\n }\n // Complement the input with zero imaginary numbers.\n const zerosInput = zerosLike(adjustedInput);\n const complexInput = reshape(complex(adjustedInput, zerosInput), [batch, innerDimensionSize]);\n const ret = fft(complexInput);\n // Exclude complex conjugations. These conjugations are put symmetrically.\n const half = Math.floor(innerDimensionSize / 2) + 1;\n const realValues = real(ret);\n const imagValues = imag(ret);\n const realComplexConjugate = split(realValues, [half, innerDimensionSize - half], realValues.shape.length - 1);\n const imagComplexConjugate = split(imagValues, [half, innerDimensionSize - half], imagValues.shape.length - 1);\n const outputShape = adjustedInput.shape.slice();\n outputShape[adjustedInput.shape.length - 1] = half;\n return reshape(complex(realComplexConjugate[0], imagComplexConjugate[0]), outputShape);\n}\nconst rfft = op({ rfft_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Returns (a - b) * (a - b) element-wise.\n * Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.squaredDifference(b).print(); // or tf.squaredDifference(a, b)\n * ```\n *\n * ```js\n * // Broadcast squared difference a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.squaredDifference(b).print(); // or tf.squaredDifference(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction squaredDifference_(a, b) {\n let $a = convertToTensor(a, 'a', 'squaredDifference');\n let $b = convertToTensor(b, 'b', 'squaredDifference');\n [$a, $b] = makeTypesMatch($a, $b);\n assertAndGetBroadcastShape($a.shape, $b.shape);\n const inputs = { a: $a, b: $b };\n const attrs = {};\n return ENGINE.runKernel(SquaredDifference, inputs, attrs);\n}\nconst squaredDifference = op({ squaredDifference_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Removes dimensions of size 1 from the shape of a `tf.Tensor`.\n *\n * ```js\n * const x = tf.tensor([1, 2, 3, 4], [1, 1, 4]);\n * x.squeeze().print();\n * ```\n *\n * @param x The input tensor to be squeezed.\n * @param axis An optional list of numbers. If specified, only\n * squeezes the dimensions listed. The dimension index starts at 0. It\n * is an error to squeeze a dimension that is not 1.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction squeeze_(x, axis) {\n const $x = convertToTensor(x, 'x', 'squeeze');\n return reshape($x, squeezeShape($x.shape, axis).newShape);\n}\nconst squeeze = op({ squeeze_ });\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Extracts a strided slice of a tensor.\n *\n * Roughly speaking, this op extracts a slice of size (end-begin)/stride from\n * the given input tensor (x). Starting at the location specified by begin the\n * slice continues by adding stride to the index until all dimensions are not\n * less than end. Note that a stride can be negative, which causes a reverse\n * slice.\n *\n * ```js\n * const t = tf.tensor3d([1, 1, 1 ,2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6],\n * [3, 2, 3]);\n * t.stridedSlice([1, 0, 0], [2, 1, 3], [1, 1, 1]).print() // [[[3, 3, 3]]]\n * t.stridedSlice([1, 0, 0], [2, 2, 3], [1, 1, 1]).print() // [[[3, 3, 3],\n * // [4, 4, 4]]]\n * t.stridedSlice([1, -1, 0], [2, -3, 3], [1, -1, 1]).print() // [[[4, 4, 4],\n * // [3, 3, 3]]]\n * ```\n *\n * @param x The tensor to stride slice.\n * @param begin The coordinates to start the slice from.\n * @param end: The coordinates to end the slice at.\n * @param strides: The size of the slice.\n * @param beginMask: If the ith bit of beginMask is set, begin[i] is ignored\n * and the fullest possible range in that dimension is used instead.\n * @param endMask: If the ith bit of endMask is set, end[i] is ignored\n * and the fullest possible range in that dimension is used instead.\n * @param shrinkAxisMask: a bitmask where bit i implies that\n * the ith specification should shrink the dimensionality. begin and end must\n * imply a slice of size 1 in the dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}\n */\nfunction stridedSlice_(x, begin, end, strides, beginMask = 0, endMask = 0, ellipsisMask = 0, newAxisMask = 0, shrinkAxisMask = 0) {\n const $x = convertToTensor(x, 'x', 'stridedSlice', 'string_or_numeric');\n const inputs = { x: $x };\n const attrs = {\n begin,\n end,\n strides,\n beginMask,\n endMask,\n ellipsisMask,\n newAxisMask,\n shrinkAxisMask\n };\n return ENGINE.runKernel(StridedSlice, inputs, attrs);\n}\nconst stridedSlice = op({ stridedSlice_ });\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes tan of the input `tf.Tensor` element-wise, `tan(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);\n *\n * x.tan().print(); // or tf.tan(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction tan_(x) {\n const $x = convertToTensor(x, 'x', 'tan', 'float32');\n const inputs = { x: $x };\n return ENGINE.runKernel(Tan, inputs);\n}\nconst tan = op({ tan_ });\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Creates a `tf.Tensor` with the provided values, shape and dtype.\n *\n * ```js\n * // Pass an array of values to create a vector.\n * tf.tensor([1, 2, 3, 4]).print();\n * ```\n *\n * ```js\n * // Pass a nested array of values to make a matrix or a higher\n * // dimensional tensor.\n * tf.tensor([[1, 2], [3, 4]]).print();\n * ```\n *\n * ```js\n * // Pass a flat array and specify a shape yourself.\n * tf.tensor([1, 2, 3, 4], [2, 2]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n * or a flat array, or a `TypedArray`. If the values are strings,\n * they will be encoded as utf-8 and kept as `Uint8Array[]`.\n * @param shape The shape of the tensor. Optional. If not provided,\n * it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction tensor(values, shape, dtype) {\n const inferredShape = inferShape(values, dtype);\n return makeTensor(values, shape, inferredShape, dtype);\n}\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Creates rank-1 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor1d` as it makes the code more readable.\n *\n * ```js\n * tf.tensor1d([1, 2, 3]).print();\n * ```\n *\n * @param values The values of the tensor. Can be array of numbers,\n * or a `TypedArray`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction tensor1d(values, dtype) {\n assertNonNull(values);\n const inferredShape = inferShape(values, dtype);\n if (inferredShape.length !== 1) {\n throw new Error('tensor1d() requires values to be a flat/TypedArray');\n }\n const shape = null;\n return makeTensor(values, shape, inferredShape, dtype);\n}\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Creates rank-2 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor2d` as it makes the code more readable.\n *\n * ```js\n * // Pass a nested array.\n * tf.tensor2d([[1, 2], [3, 4]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor2d([1, 2, 3, 4], [2, 2]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n * or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. If not provided, it is inferred from\n * `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction tensor2d(values, shape, dtype) {\n assertNonNull(values);\n if (shape != null && shape.length !== 2) {\n throw new Error('tensor2d() requires shape to have two numbers');\n }\n const inferredShape = inferShape(values, dtype);\n if (inferredShape.length !== 2 && inferredShape.length !== 1) {\n throw new Error('tensor2d() requires values to be number[][] or flat/TypedArray');\n }\n if (inferredShape.length === 1 && shape == null) {\n throw new Error('tensor2d() requires shape to be provided when `values` ' +\n 'are a flat/TypedArray');\n }\n return makeTensor(values, shape, inferredShape, dtype);\n}\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Creates rank-3 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor3d` as it makes the code more readable.\n *\n * ```js\n * // Pass a nested array.\n * tf.tensor3d([[[1], [2]], [[3], [4]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor3d([1, 2, 3, 4], [2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n * or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. If not provided, it is inferred from\n * `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction tensor3d(values, shape, dtype) {\n assertNonNull(values);\n if (shape != null && shape.length !== 3) {\n throw new Error('tensor3d() requires shape to have three numbers');\n }\n const inferredShape = inferShape(values, dtype);\n if (inferredShape.length !== 3 && inferredShape.length !== 1) {\n throw new Error('tensor3d() requires values to be number[][][] or flat/TypedArray');\n }\n if (inferredShape.length === 1 && shape == null) {\n throw new Error('tensor3d() requires shape to be provided when `values` ' +\n 'are a flat array');\n }\n return makeTensor(values, shape, inferredShape, dtype);\n}\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Creates rank-4 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor4d` as it makes the code more readable.\n *\n * ```js\n * // Pass a nested array.\n * tf.tensor4d([[[[1], [2]], [[3], [4]]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n * or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n * it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction tensor4d(values, shape, dtype) {\n assertNonNull(values);\n if (shape != null && shape.length !== 4) {\n throw new Error('tensor4d() requires shape to have four numbers');\n }\n const inferredShape = inferShape(values, dtype);\n if (inferredShape.length !== 4 && inferredShape.length !== 1) {\n throw new Error('tensor4d() requires values to be number[][][][] or flat/TypedArray');\n }\n if (inferredShape.length === 1 && shape == null) {\n throw new Error('tensor4d() requires shape to be provided when `values` ' +\n 'are a flat array');\n }\n return makeTensor(values, shape, inferredShape, dtype);\n}\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Creates rank-5 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor5d` as it makes the code more readable.\n *\n * ```js\n * // Pass a nested array.\n * tf.tensor5d([[[[[1],[2]],[[3],[4]]],[[[5],[6]],[[7],[8]]]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n * or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n * it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction tensor5d(values, shape, dtype) {\n assertNonNull(values);\n if (shape != null && shape.length !== 5) {\n throw new Error('tensor5d() requires shape to have five numbers');\n }\n const inferredShape = inferShape(values, dtype);\n if (inferredShape.length !== 5 && inferredShape.length !== 1) {\n throw new Error('tensor5d() requires values to be ' +\n 'number[][][][][] or flat/TypedArray');\n }\n if (inferredShape.length === 1 && shape == null) {\n throw new Error('tensor5d() requires shape to be provided when `values` ' +\n 'are a flat array');\n }\n return makeTensor(values, shape, inferredShape, dtype);\n}\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Creates rank-6 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor6d` as it makes the code more readable.\n *\n * ```js\n * // Pass a nested array.\n * tf.tensor6d([[[[[[1],[2]],[[3],[4]]],[[[5],[6]],[[7],[8]]]]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor6d([1, 2, 3, 4, 5, 6, 7, 8], [1, 1, 2, 2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n * or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n * it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction tensor6d(values, shape, dtype) {\n assertNonNull(values);\n if (shape != null && shape.length !== 6) {\n throw new Error('tensor6d() requires shape to have six numbers');\n }\n const inferredShape = inferShape(values, dtype);\n if (inferredShape.length !== 6 && inferredShape.length !== 1) {\n throw new Error('tensor6d() requires values to be number[][][][][][] or ' +\n 'flat/TypedArray');\n }\n if (inferredShape.length === 1 && shape == null) {\n throw new Error('tensor6d() requires shape to be provided when `values` ' +\n 'are a flat array');\n }\n shape = shape ||\n inferredShape;\n return makeTensor(values, shape, inferredShape, dtype);\n}\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Finds the values and indices of the `k` largest entries along the last\n * dimension.\n *\n * If the input is a vector (rank=1), finds the k largest entries in the vector\n * and outputs their values and indices as vectors. Thus values[j] is the j-th\n * largest entry in input, and its index is indices[j].\n * For higher rank inputs, computes the top k entries along the last dimension.\n *\n * If two elements are equal, the lower-index element appears first.\n *\n * ```js\n * const a = tf.tensor2d([[1, 5], [4, 3]]);\n * const {values, indices} = tf.topk(a);\n * values.print();\n * indices.print();\n * ```\n * @param x 1-D or higher `tf.Tensor` with last dimension being at least `k`.\n * @param k Number of top elements to look for along the last dimension.\n * @param sorted If true, the resulting `k` elements will be sorted by the\n * values in descending order.\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nfunction topk_(x, k = 1, sorted = true) {\n const $x = convertToTensor(x, 'x', 'topk');\n if ($x.rank === 0) {\n throw new Error('topk() expects the input to be of rank 1 or higher');\n }\n const lastDim = $x.shape[$x.shape.length - 1];\n if (k < 0) {\n throw new Error(`'k' passed to topk() must be >= 0 but got ${k}`);\n }\n if (k > lastDim) {\n throw new Error(`'k' passed to topk() must be <= the last dimension (${lastDim}) ` +\n `but got ${k}`);\n }\n const inputs = { x: $x };\n const attrs = { k, sorted };\n const [values, indices] = ENGINE.runKernel(TopK, inputs, attrs);\n return { values, indices };\n}\nconst topk = op({ topk_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Creates a `tf.Tensor` with values sampled from a truncated normal\n * distribution.\n *\n * ```js\n * tf.truncatedNormal([2, 2]).print();\n * ```\n *\n * The generated values follow a normal distribution with specified mean and\n * standard deviation, except that values whose magnitude is more than 2\n * standard deviations from the mean are dropped and re-picked.\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param mean The mean of the normal distribution.\n * @param stdDev The standard deviation of the normal distribution.\n * @param dtype The data type of the output tensor.\n * @param seed The seed for the random number generator.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction truncatedNormal_(shape, mean = 0, stdDev = 1, dtype, seed) {\n if (dtype != null && dtype === 'bool') {\n throw new Error(`Unsupported data type $ { dtype }`);\n }\n const randGauss = new MPRandGauss(mean, stdDev, dtype, true /* truncated */, seed);\n const res = buffer(shape, dtype);\n for (let i = 0; i < res.values.length; i++) {\n res.values[i] = randGauss.nextValue();\n }\n return res.toTensor();\n}\nconst truncatedNormal = op({ truncatedNormal_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Finds unique elements along an axis of a tensor.\n *\n * It returns a tensor `values` containing all of the unique elements along the\n * `axis` of the given tensor `x` in the same order that they occur along the\n * `axis` in `x`; `x` does not need to be sorted. It also returns a tensor\n * `indices` the same size as the number of the elements in `x` along the `axis`\n * dimension. It contains the index in the unique output `values`.\n *\n * ```js\n * // A 1-D tensor\n * const a = tf.tensor1d([1, 1, 2, 4, 4, 4, 7, 8, 8]);\n * const {values, indices} = tf.unique(a);\n * values.print(); // [1, 2, 4, 7, 8,]\n * indices.print(); // [0, 0, 1, 2, 2, 2, 3, 4, 4]\n * ```\n *\n * ```js\n * // A 2-D tensor with axis=0\n * //\n * // 'a' is: [[1, 0, 0],\n * // [1, 0, 0],\n * // [2, 0, 0]]\n * const a = tf.tensor2d([[1, 0, 0], [1, 0, 0], [2, 0, 0]]);\n * const {values, indices} = tf.unique(a, 0)\n * values.print(); // [[1, 0, 0],\n * // [2, 0, 0]]\n * indices.print(); // [0, 0, 1]\n * ```\n *\n * ```js\n * // A 2-D tensor with axis=1\n * //\n * // 'a' is: [[1, 0, 0],\n * // [1, 0, 0],\n * // [2, 0, 0]]\n * const a = tf.tensor2d([[1, 0, 0], [1, 0, 0], [2, 0, 0]]);\n * const {values, indices} = tf.unique(a, 1)\n * values.print(); // [[1, 0],\n * // [1, 0],\n * // [2, 0]]\n * indices.print(); // [0, 1, 1]\n * ```\n * @param x A tensor (int32, string, bool).\n * @param axis The axis of the tensor to find the unique elements.\n * @returns [uniqueElements, indices] (see above for details)\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nfunction unique_(x, axis = 0) {\n const $x = convertToTensor(x, 'x', 'unique', 'string_or_numeric');\n assert($x.rank > 0, () => 'The input tensor must be at least 1D');\n const inputs = { x: $x };\n const attrs = { axis };\n const [values, indices] = ENGINE.runKernel(Unique, inputs, attrs);\n return { values, indices };\n}\nconst unique = op({ unique_ });\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Creates a new variable with the provided initial value.\n * ```js\n * const x = tf.variable(tf.tensor([1, 2, 3]));\n * x.assign(tf.tensor([4, 5, 6]));\n *\n * x.print();\n * ```\n *\n * @param initialValue Initial value for the tensor.\n * @param trainable If true, optimizers are allowed to update it.\n * @param name Name of the variable. Defaults to a unique id.\n * @param dtype If set, initialValue will be converted to the given type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction variable(initialValue, trainable = true, name, dtype) {\n return ENGINE.makeVariable(initialValue, trainable, name, dtype);\n}\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nfunction whereImpl(condShape, condVals) {\n const indices = [];\n for (let i = 0; i < condVals.length; i++) {\n if (condVals[i]) {\n indices.push(i);\n }\n }\n const inBuffer = buffer(condShape, 'int32');\n const out = buffer([indices.length, condShape.length], 'int32');\n for (let i = 0; i < indices.length; i++) {\n const loc = inBuffer.indexToLoc(indices[i]);\n const offset = i * condShape.length;\n out.values.set(loc, offset);\n }\n return out.toTensor();\n}\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Returns the coordinates of true elements of condition.\n *\n * The coordinates are returned in a 2-D tensor where the first dimension (rows)\n * represents the number of true elements, and the second dimension (columns)\n * represents the coordinates of the true elements. Keep in mind, the shape of\n * the output tensor can vary depending on how many true values there are in\n * input. Indices are output in row-major order. The resulting tensor has the\n * shape `[numTrueElems, condition.rank]`.\n *\n * This is analogous to calling the python `tf.where(cond)` without an x or y.\n *\n * ```js\n * const cond = tf.tensor1d([false, false, true], 'bool');\n * const result = await tf.whereAsync(cond);\n * result.print();\n * ```\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nasync function whereAsync_(condition) {\n const $condition = convertToTensor(condition, 'condition', 'whereAsync', 'bool');\n const vals = await $condition.data();\n const res = whereImpl($condition.shape, vals);\n if (condition !== $condition) {\n $condition.dispose();\n }\n return res;\n}\nconst whereAsync = whereAsync_;\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Apply boolean mask to tensor.\n *\n * ```js\n * const tensor = tf.tensor2d([1, 2, 3, 4, 5, 6], [3, 2]);\n * const mask = tf.tensor1d([1, 0, 1], 'bool');\n * const result = await tf.booleanMaskAsync(tensor, mask);\n * result.print();\n * ```\n *\n * @param tensor N-D tensor.\n * @param mask K-D boolean tensor, K <= N and K must be known statically.\n * @param axis A 0-D int Tensor representing the axis in tensor to mask from.\n * By default, axis is 0 which will mask from the first dimension.\n * Otherwise K + axis <= N.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nasync function booleanMaskAsync_(tensor, mask, axis) {\n const $tensor = convertToTensor(tensor, 'tensor', 'boolMask');\n const $mask = convertToTensor(mask, 'mask', 'boolMask', 'bool');\n const axisFrom = axis == null ? 0 : axis;\n const maskDim = $mask.rank;\n const tensorShape = $tensor.shape;\n assert(maskDim > 0, () => 'mask cannot be scalar');\n assertShapesMatch(tensorShape.slice(axisFrom, axisFrom + maskDim), $mask.shape, `mask's shape must match the first K dimensions of tensor's shape,`);\n let leadingSize = 1;\n for (let i = axisFrom; i < axisFrom + maskDim; i++) {\n leadingSize *= tensorShape[i];\n }\n const targetTensorShape = tensorShape.slice(0, axisFrom)\n .concat([leadingSize], tensorShape.slice(axisFrom + maskDim));\n const reshapedTensor = reshape($tensor, targetTensorShape);\n const reshapedMask = reshape($mask, [-1]);\n const positivePositions = await whereAsync(reshapedMask);\n const indices = squeeze(positivePositions, [1]);\n const res = gather(reshapedTensor, indices, axisFrom);\n // Ensure no memory leak.\n if (tensor !== $tensor) {\n $tensor.dispose();\n }\n if (mask !== $mask) {\n $mask.dispose();\n }\n indices.dispose();\n reshapedTensor.dispose();\n reshapedMask.dispose();\n positivePositions.dispose();\n return res;\n}\nconst booleanMaskAsync = booleanMaskAsync_;\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes the norm of scalar, vectors, and matrices.\n * This function can compute several different vector norms (the 1-norm, the\n * Euclidean or 2-norm, the inf-norm, and in general the p-norm for p > 0)\n * and matrix norms (Frobenius, 1-norm, and inf-norm).\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * x.norm().print(); // or tf.norm(x)\n * ```\n *\n * @param x The input array.\n * @param ord Optional. Order of the norm. Supported norm types are\n * following:\n *\n * | ord | norm for matrices | norm for vectors\n * |------------|---------------------------|---------------------\n * |'euclidean' |Frobenius norm |2-norm\n * |'fro' |Frobenius norm\t |\n * |Infinity |max(sum(abs(x), axis=1)) |max(abs(x))\n * |-Infinity |min(sum(abs(x), axis=1)) |min(abs(x))\n * |1 |max(sum(abs(x), axis=0)) |sum(abs(x))\n * |2 | |sum(abs(x)^2)^1/2*\n *\n * @param axis Optional. If axis is null (the default), the input is\n * considered a vector and a single vector norm is computed over the entire\n * set of values in the Tensor, i.e. norm(x, ord) is equivalent\n * to norm(x.reshape([-1]), ord). If axis is a integer, the input\n * is considered a batch of vectors, and axis determines the axis in x\n * over which to compute vector norms. If axis is a 2-tuple of integer it is\n * considered a batch of matrices and axis determines the axes in NDArray\n * over which to compute a matrix norm.\n * @param keepDims Optional. If true, the norm have the same dimensionality\n * as the input.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction norm_(x, ord = 'euclidean', axis = null, keepDims = false) {\n x = convertToTensor(x, 'x', 'norm');\n const norm = normImpl(x, ord, axis);\n let keepDimsShape = norm.shape;\n if (keepDims) {\n const axes = parseAxisParam(axis, x.shape);\n keepDimsShape = expandShapeToKeepDim(norm.shape, axes);\n }\n return reshape(norm, keepDimsShape);\n}\nfunction normImpl(x, p, axis = null) {\n if (x.rank === 0) {\n return abs(x);\n }\n // consider vector when no axis is specified\n if (x.rank !== 1 && axis === null) {\n return normImpl(reshape(x, [-1]), p, axis);\n }\n // vector\n if (x.rank === 1 || typeof axis === 'number' ||\n Array.isArray(axis) && axis.length === 1) {\n if (p === 1) {\n return sum$1(abs(x), axis);\n }\n if (p === Infinity) {\n return max(abs(x), axis);\n }\n if (p === -Infinity) {\n return min(abs(x), axis);\n }\n if (p === 'euclidean' || p === 2) {\n // norm(x, 2) = sum(abs(xi) ^ 2) ^ 1/2\n return sqrt(sum$1(pow(abs(x), scalar(2, 'int32')), axis));\n }\n throw new Error(`Error in norm: invalid ord value: ${p}`);\n }\n // matrix (assumption axis[0] < axis[1])\n if (Array.isArray(axis) && axis.length === 2) {\n if (p === 1) {\n return max(sum$1(abs(x), axis[0]), axis[1] - 1);\n }\n if (p === Infinity) {\n return max(sum$1(abs(x), axis[1]), axis[0]);\n }\n if (p === -Infinity) {\n return min(sum$1(abs(x), axis[1]), axis[0]);\n }\n if (p === 'fro' || p === 'euclidean') {\n // norm(x) = sqrt(sum(pow(x, 2)))\n return sqrt(sum$1(square(x), axis));\n }\n throw new Error(`Error in norm: invalid ord value: ${p}`);\n }\n throw new Error(`Error in norm: invalid axis: ${axis}`);\n}\nconst norm = op({ norm_ });\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Compute the moving average of a variable.\n *\n * Without zeroDebias, the moving average operation is defined by:\n * `v += delta`\n * where\n * `delta = (1 - decay) * (x - v)`\n *\n * With zeroDebias (default), the `delta` term is scaled to debias the\n * effect of the (assumed) zero-initialization of `v`.\n * `delta /= (1 - decay ^ step)`\n *\n * For more details on the zero-debiasing algorithm, see:\n * https://arxiv.org/abs/1412.6980\n *\n * Note that this function is completely stateless and does not keep track of\n * step count. The step count needs to be maintained by the caller and passed\n * in as `step`.\n *\n * @param v The current moving average value.\n * @param x New input value, must have the same shape and dtype as `v`.\n * @param decay The decay factor. Typical values are 0.95 and 0.99.\n * @param step Step count.\n * @param zeroDebias: Whether zeroDebias is to be performed (default: `true`).\n * @returns The new moving average value.\n *\n * @doc {heading: 'Operations', subheading: 'Moving Average'}\n */\nfunction movingAverage_(v, x, decay, step, zeroDebias = true) {\n const $v = convertToTensor(v, 'v', 'movingAverage');\n const $x = convertToTensor(x, 'x', 'movingAverage');\n const $decay = convertToTensor(decay, 'decay', 'movingAverage');\n assertTypesMatch($v, $x);\n assert(arraysEqual($v.shape, $x.shape), () => 'Shape mismatch in v and x');\n const one = scalar(1);\n const oneMinusDecay = sub(one, $decay);\n let update = mul(sub($x, $v), oneMinusDecay);\n if (zeroDebias) {\n assert(step != null, () => 'When using zeroDebias: true, step is required.');\n const $step = convertToTensor(step, 'step', 'movingAverage');\n update = div(update, sub(one, pow($decay, $step)));\n }\n return add$1($v, update);\n}\nconst movingAverage = op({ movingAverage_ });\n\n/**\n * Check whether updates.shape = indices.shape[:batchDim] +\n * shape[sliceDim:]\n *\n * @param x The input tensor.\n */\nfunction validateUpdateShape(shape, indices, updates) {\n const sliceDim = (indices.rank > 1) ? indices.shape[indices.rank - 1] : 1;\n const batchDim = (indices.rank > 1) ? indices.rank - 1 : 1;\n const shapeError = 'Must have updates.shape = indices.shape[:batchDim] + ' +\n `shape[sliceDim:], got updates.shape: ${updates.shape}` +\n `, indices.shape: ${indices.shape}, shape: ${shape}` +\n `, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`;\n if (updates.rank < batchDim) {\n throw new Error(shapeError + ` update.rank < ${batchDim}. `);\n }\n if (shape.length < sliceDim + (updates.rank - batchDim)) {\n throw new Error(shapeError +\n ` Output shape length < ${sliceDim + (updates.rank - batchDim)}`);\n }\n if (updates.rank !== batchDim + shape.length - sliceDim) {\n throw new Error(shapeError + ` update.rank != ${batchDim + shape.length - sliceDim}`);\n }\n for (let d = 0; d < batchDim; ++d) {\n if (updates.shape[d] !== indices.shape[d]) {\n throw new Error(shapeError +\n ` updates.shape[${d}] (${updates.shape[d]}) != indices.shape[${d}] (${indices.shape[d]}).`);\n }\n }\n for (let d = 0; d < updates.rank - batchDim; ++d) {\n if (updates.shape[d + batchDim] !== shape[d + sliceDim]) {\n throw new Error(shapeError +\n ` updates.shape[${d + batchDim}] (${updates.shape[d + batchDim]}) != shape[${d + batchDim}] (${shape[d + batchDim]})`);\n }\n }\n}\n/**\n * Validate scatter nd inputs.\n *\n * @param update The tensor contains the update values.\n * @param indices The tensor contains the indices for the update values.\n * @param shape The shape of the output tensor.\n */\nfunction validateInput(updates, indices, shape) {\n if (indices.rank < 1) {\n throw new Error('tf.scatterND() expects the indices to be rank 1 or higher,' +\n ` but the rank was ${indices.rank}.`);\n }\n if (updates.rank < 1) {\n throw new Error('tf.scatterND() expects the updates to be rank 1 or higher,' +\n ` but the rank was ${updates.rank}.`);\n }\n if (indices.dtype !== 'int32') {\n throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${indices.dtype}`);\n }\n if (shape.length < 1) {\n throw new Error(`Output rank must be greater or equal to 1, but got shape: ${shape}`);\n }\n if (shape.length === 0) {\n if (indices.size === 0) {\n throw new Error(`Indices specified for empty output. indices shape: ${indices.shape}`);\n }\n if (updates.size === 0) {\n throw new Error(`Updates specified for empty output. updates shape: ${updates.shape}`);\n }\n }\n validateUpdateShape(shape, indices, updates);\n}\n/**\n * Calculate the shape information for the output.\n *\n * @param update The tensor contains the update values.\n * @param indices The tensor contains the indices for the update values.\n * @param shape The shape of the output tensor.\n *\n * @returns ScatterShapeInfo\n */\nfunction calculateShapes(updates, indices, shape) {\n // Calculate the number of dimensions in indices\n const indicesRank = indices.shape.length;\n const sliceRank = (indicesRank > 1) ? indices.shape[indicesRank - 1] : 1;\n // Calculate the number of elements that make up each slice of our updated\n // tensor. This allows us to work with flattened tensors and copy over whole\n // slices at a time.\n const totalNd = shape.length;\n let sliceSize = 1;\n for (let i = sliceRank; i < totalNd; ++i) {\n sliceSize *= shape[i];\n }\n const safeSliceDim = (sliceRank < 1) ? 1 : sliceRank;\n const numUpdates = sizeFromShape(indices.shape) / safeSliceDim;\n const strides = [...computeStrides(shape.slice(0, sliceRank)), 1];\n const outputSize = sizeFromShape(shape);\n return { sliceRank, numUpdates, sliceSize, strides, outputSize };\n}\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Creates a new tensor by applying sparse updates to individual\n * values or slices within a zero tensor of the given shape tensor according to\n * indices. This operator is the inverse of the `tf.gatherND` operator which\n * extracts values or slices from a given tensor.\n *\n * ```js\n * const indices = tf.tensor2d([4, 3, 1, 7], [4, 1], 'int32');\n * const updates = tf.tensor1d([9, 10, 11, 12]);\n * const shape = [8];\n * tf.scatterND(indices, updates, shape).print() //[0, 11, 0, 10, 9, 0, 0, 12]\n * ```\n *\n * @param indices The tensor contains the indices into the output tensor.\n * @param updates The tensor contains the value for the indices.\n * @param shape: The shape of the output tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}\n */\nfunction scatterND_(indices, updates, shape) {\n const $indices = convertToTensor(indices, 'indices', 'scatterND', 'int32');\n const $updates = convertToTensor(updates, 'updates', 'scatterND');\n validateInput($updates, $indices, shape);\n const inputs = { indices: $indices, updates: $updates };\n const attrs = { shape };\n // tslint:disable-next-line: no-unnecessary-type-assertion\n return ENGINE.runKernel(ScatterNd, inputs, attrs);\n}\nconst scatterND = op({ scatterND_ });\n\n/**\n * Validate sparseToDense inputs.\n *\n * @param sparseIndices A 0-D, 1-D, or 2-D Tensor of type int32.\n * sparseIndices[i] contains the complete index where sparseValues[i] will be\n * placed.\n * @param sparseValues A 0-D or 1-D Tensor. Values\n * corresponding to each row of sparseIndices, or a scalar value to be used for\n * all sparse indices.\n * @param outputShape number[]. Shape of the dense output tensor.\n * @param validateIndices boolean. indice validation is not supported, error\n * will be thrown if it is set.\n */\nfunction validateInput$1(sparseIndices, sparseValues, outputShape, defaultValues) {\n if (sparseIndices.dtype !== 'int32') {\n throw new Error('tf.sparseToDense() expects the indices to be int32 type,' +\n ` but the dtype was ${sparseIndices.dtype}.`);\n }\n if (sparseIndices.rank > 2) {\n throw new Error('sparseIndices should be a scalar, vector, or matrix,' +\n ` but got shape ${sparseIndices.shape}.`);\n }\n const numElems = sparseIndices.rank > 0 ? sparseIndices.shape[0] : 1;\n const numDims = sparseIndices.rank > 1 ? sparseIndices.shape[1] : 1;\n if (outputShape.length !== numDims) {\n throw new Error('outputShape has incorrect number of elements:,' +\n ` ${outputShape.length}, should be: ${numDims}.`);\n }\n const numValues = sparseValues.size;\n if (!(sparseValues.rank === 0 ||\n sparseValues.rank === 1 && numValues === numElems)) {\n throw new Error('sparseValues has incorrect shape ' +\n `${sparseValues.shape}, should be [] or [${numElems}]`);\n }\n if (sparseValues.dtype !== defaultValues.dtype) {\n throw new Error('sparseValues.dtype must match defaultValues.dtype');\n }\n}\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Converts a sparse representation into a dense tensor.\n *\n * Builds an array dense with shape outputShape such that:\n *\n * // If sparseIndices is scalar\n * dense[i] = (i == sparseIndices ? sparseValues : defaultValue)\n *\n * // If sparseIndices is a vector, then for each i\n * dense[sparseIndices[i]] = sparseValues[i]\n *\n * // If sparseIndices is an n by d matrix, then for each i in [0, n)\n * dense[sparseIndices[i][0], ..., sparseIndices[i][d-1]] = sparseValues[i]\n * All other values in dense are set to defaultValue. If sparseValues is a\n * scalar, all sparse indices are set to this single value.\n *\n * If indices are repeated the final value is summed over all values for those\n * indices.\n *\n * ```js\n * const indices = tf.tensor1d([4, 5, 6, 1, 2, 3], 'int32');\n * const values = tf.tensor1d([10, 11, 12, 13, 14, 15], 'float32');\n * const shape = [8];\n * tf.sparseToDense(indices, values, shape).print();\n * ```\n *\n * @param sparseIndices A 0-D, 1-D, or 2-D Tensor of type int32.\n * sparseIndices[i] contains the complete index where sparseValues[i] will be\n * placed.\n * @param sparseValues A 0-D or 1-D Tensor. Values\n * corresponding to each row of sparseIndices, or a scalar value to be used for\n * all sparse indices.\n * @param outputShape Shape of the dense output tensor. the type is inferred.\n * @param defaultValue Scalar. Value to set for indices not specified in\n * sparseIndices. Defaults to zero.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction sparseToDense_(sparseIndices, sparseValues, outputShape, defaultValue = 0) {\n const $sparseIndices = convertToTensor(sparseIndices, 'sparseIndices', 'sparseToDense', 'int32');\n const $sparseValues = convertToTensor(sparseValues, 'sparseValues', 'sparseToDense');\n const $defaultValue = convertToTensor(defaultValue, 'defaultValue', 'sparseToDense', $sparseValues.dtype);\n validateInput$1($sparseIndices, $sparseValues, outputShape, $defaultValue);\n const inputs = {\n sparseIndices: $sparseIndices,\n sparseValues: $sparseValues,\n defaultValue: $defaultValue\n };\n const attrs = { outputShape };\n return ENGINE.runKernel(SparseToDense, inputs, attrs);\n}\nconst sparseToDense = op({ sparseToDense_ });\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Gather slices from input tensor into a Tensor with shape specified by\n * `indices`.\n *\n * `indices` is an K-dimensional integer tensor, best thought of as a\n * (K-1)-dimensional tensor of indices into input, where each element defines a\n * slice of input:\n * output[\\\\(i_0, ..., i_{K-2}\\\\)] = input[indices[\\\\(i_0, ..., i_{K-2}\\\\)]]\n *\n * Whereas in `tf.gather`, `indices` defines slices into the first dimension of\n * input, in `tf.gatherND`, `indices` defines slices into the first N dimensions\n * of input, where N = indices.shape[-1].\n *\n * The last dimension of indices can be at most the rank of input:\n * indices.shape[-1] <= input.rank\n *\n * The last dimension of `indices` corresponds to elements\n * (if indices.shape[-1] == input.rank) or slices\n * (if indices.shape[-1] < input.rank) along dimension indices.shape[-1] of\n * input.\n * The output tensor has shape\n * indices.shape[:-1] + input.shape[indices.shape[-1]:]\n *\n * Note that on CPU, if an out of bound index is found, an error is returned. On\n * GPU, if an out of bound index is found, a 0 is stored in the corresponding\n * output value.\n *\n * ```js\n * const indices = tf.tensor2d([0, 1, 1, 0], [2,2], 'int32');\n * const input = tf.tensor2d([9, 10, 11, 12], [2, 2]);\n * tf.gatherND(input, indices).print() // [10, 11]\n * ```\n *\n * @param x The tensor from which to gather values.\n * @param indices Index tensor, must be of type int32.\n *\n * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}\n */\nfunction gatherND_(x, indices) {\n const $indices = convertToTensor(indices, 'indices', 'gatherND', 'int32');\n const $x = convertToTensor(x, 'x', 'gatherND', 'string_or_numeric');\n const inputs = { params: $x, indices: $indices };\n return ENGINE.runKernel(GatherNd, inputs);\n}\nconst gatherND = op({ gatherND_ });\n\n/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Normalize noise shape based on provided tensor and noise shape.\n *\n * @param x Tensor.\n * @param noiseShape The shape for the randomly generated keep/drop flags, as\n * an array of numbers. Optional.\n * @returns Normalized noise shape.\n */\nfunction getNoiseShape(x, noiseShape) {\n if (noiseShape == null) {\n return x.shape.slice();\n }\n if (arraysEqual(x.shape, noiseShape)) {\n return noiseShape;\n }\n if (x.shape.length === noiseShape.length) {\n const newDimension = [];\n for (let i = 0; i < x.shape.length; i++) {\n if (noiseShape[i] == null && x.shape[i] != null) {\n newDimension.push(x.shape[i]);\n }\n else {\n newDimension.push(noiseShape[i]);\n }\n }\n return newDimension;\n }\n return noiseShape;\n}\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes dropout.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 2, 1]);\n * const rate = 0.75;\n * const output = tf.dropout(x, rate);\n * output.print();\n * ```\n *\n * @param x A floating point Tensor or TensorLike.\n * @param rate A float in the range [0, 1). The probability that each element\n * of x is discarded.\n * @param noiseShape An array of numbers of type int32, representing the\n * shape for randomly generated keep/drop flags. If the noiseShape has null\n * value, it will be automatically replaced with the x's relative dimension\n * size. Optional.\n * @param seed Used to create random seeds. Optional.\n * @returns A Tensor of the same shape of x.\n *\n * @doc {heading: 'Operations', subheading: 'Dropout'}\n */\nfunction dropout_(x, rate, noiseShape, seed) {\n const $x = convertToTensor(x, 'x', 'dropout');\n assert($x.dtype === 'float32', () => `x has to be a floating point tensor since it's going to be ` +\n `scaled, but got a ${$x.dtype} tensor instead.`);\n assert(rate >= 0 && rate < 1, () => `rate must be a float in the range [0, 1), but got ${rate}.`);\n if (rate === 0) {\n return x instanceof Tensor ? $x.clone() : $x;\n }\n const $noiseShape = getNoiseShape($x, noiseShape);\n const keepProb = 1 - rate;\n const multiplier = div(floor(add$1(randomUniform($noiseShape, 0, 1, 'float32', seed), keepProb)), keepProb);\n return mul($x, multiplier);\n}\nconst dropout = op({ dropout_ });\n\n/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nfunction enclosingPowerOfTwo(value) {\n // Return 2**N for integer N such that 2**N >= value.\n return Math.floor(Math.pow(2, Math.ceil(Math.log(value) / Math.log(2.0))));\n}\nfunction cosineWindow(windowLength, a, b) {\n const even = 1 - windowLength % 2;\n const newValues = new Float32Array(windowLength);\n for (let i = 0; i < windowLength; ++i) {\n const cosArg = (2.0 * Math.PI * i) / (windowLength + even - 1);\n newValues[i] = a - b * Math.cos(cosArg);\n }\n return tensor1d(newValues, 'float32');\n}\n\n/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Returns whether the targets are in the top K predictions.\n *\n * ```js\n * const predictions = tf.tensor2d([[20, 10, 40, 30], [30, 50, -20, 10]]);\n * const targets = tf.tensor1d([2, 0]);\n * const precision = await tf.inTopKAsync(predictions, targets);\n * precision.print();\n * ```\n * @param predictions 2-D or higher `tf.Tensor` with last dimension being\n * at least `k`.\n * @param targets 1-D or higher `tf.Tensor`.\n * @param k Optional Number of top elements to look at for computing precision,\n * default to 1.\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nasync function inTopKAsync_(predictions, targets, k = 1) {\n const $predictions = convertToTensor(predictions, 'predictions', 'inTopK');\n const $targets = convertToTensor(targets, 'targets', 'inTopK');\n assert($predictions.rank > 1, () => 'inTopK() expects the predictions to be of rank 2 or higher, ' +\n `but got ${$predictions.rank}`);\n assert($predictions.rank - 1 === $targets.rank, () => `predictions rank should be 1 larger than ` +\n `targets rank, but got predictions rank ` +\n `${$predictions.rank} and targets rank ${$targets.rank}`);\n assertShapesMatch($predictions.shape.slice(0, $predictions.shape.length - 1), $targets.shape, `predictions's shape should be align with the targets' shape, ` +\n 'except the last dimension.');\n const lastDim = $predictions.shape[$predictions.shape.length - 1];\n assert(k > 0 && k <= lastDim, () => `'k' passed to inTopK() must be > 0 && <= the predictions last ` +\n `dimension (${lastDim}), but got ${k}`);\n const predictionsVals = await $predictions.data();\n const targetsVals = await $targets.data();\n // Reshape predictionsVals into a 2d tensor [batch, lastDim]\n // and look up topK along lastDim.\n const [batch, size] = [predictionsVals.length / lastDim, lastDim];\n const precision = getTypedArrayFromDType('bool', batch);\n for (let b = 0; b < batch; b++) {\n const offset = b * size;\n const vals = predictionsVals.subarray(offset, offset + size);\n const valAndInd = [];\n for (let i = 0; i < vals.length; i++) {\n valAndInd.push({ value: vals[i], index: i });\n }\n valAndInd.sort((a, b) => b.value - a.value);\n precision[b] = 0;\n for (let i = 0; i < k; i++) {\n if (valAndInd[i].index === targetsVals[b]) {\n precision[b] = 1;\n break;\n }\n }\n }\n if (predictions !== $predictions) {\n $predictions.dispose();\n }\n if (targets !== $targets) {\n $targets.dispose();\n }\n // Output precision has the same shape as targets.\n return tensor(precision, $targets.shape, 'bool');\n}\nconst inTopKAsync = inTopKAsync_;\n\n/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Returns gradient for fused activation.\nfunction getFusedDyActivation(dy, y, activation) {\n if (activation == null || activation === 'linear') {\n return dy;\n }\n if (activation === 'relu') {\n return mul(dy, step(y));\n }\n throw new Error(`Cannot compute gradient for fused activation ${activation}.`);\n}\n// Returns gradient for fused bias.\nfunction getFusedBiasGradient(bias, dyActivation) {\n let res = dyActivation;\n const reduceAxes = getReductionAxes(bias.shape, dyActivation.shape);\n if (reduceAxes.length > 0) {\n res = sum$1(res, reduceAxes);\n }\n return reshape(res, bias.shape);\n}\nfunction applyActivation(x, activation, preluActivationWeights, leakyreluAlpha) {\n if (activation === 'linear') {\n return x;\n }\n else if (activation === 'relu') {\n return relu(x);\n }\n else if (activation === 'elu') {\n return elu(x);\n }\n else if (activation === 'relu6') {\n return relu6(x);\n }\n else if (activation === 'prelu') {\n return prelu(x, preluActivationWeights);\n }\n else if (activation === 'leakyrelu') {\n return leakyRelu(x, leakyreluAlpha);\n }\n else if (activation === 'sigmoid') {\n return sigmoid(x);\n }\n throw new Error(`Unknown fused activation ${activation}.`);\n}\n// Whether we should call fused ops.\nconst shouldFuse = (gradientDepth, activation) => {\n const gradientMode = gradientDepth > 0;\n return !gradientMode || activation === 'linear';\n};\n\n/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes a 2D convolution over the input x, optionally fused with adding a\n * bias and applying an activation.\n *\n * ```js\n * const inputDepth = 2;\n * const inShape = [2, 2, 2, inputDepth];\n * const outputDepth = 2;\n * const fSize = 1;\n * const pad = 0;\n * const strides = 1;\n *\n * const x = tf.tensor4d( [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,\n * 16], inShape);\n * const w = tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth,\n * outputDepth]);\n *\n * tf.fused.conv2d({ x, filter: w, strides, pad, dataFormat: 'NHWC',\n * dilations: [1, 1], bias: tf.scalar(5), activation: 'relu' }).print();\n * ```\n *\n * @param obj An object with the following properties:\n * @param x The input tensor, of rank 4 or rank 3, of shape\n * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter, rank 4, of shape\n * `[filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n * - `same` and stride 1: output will be of same size as input,\n * regardless of filter size.\n * - `valid` output will be smaller than input if filter is larger\n * than 1x1.\n * - For more info, see this guide:\n * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](\n * https://www.tensorflow.org/api_docs/python/tf/nn/convolution)\n * @param dataFormat An optional string from: \"NHWC\", \"NCHW\". Defaults to\n * \"NHWC\". Specify the data format of the input and output data. With the\n * default format \"NHWC\", the data is stored in the order of: [batch,\n * height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n * in which we sample input values across the height and width dimensions\n * in atrous convolution. Defaults to `[1, 1]`. If `dilations` is a single\n * number, then `dilationHeight == dilationWidth`. If it is greater than\n * 1, then all values of `strides` must be 1.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n * provided, it will default to truncate.\n * @param bias Tensor to be added to the result.\n * @param activation Name of activation kernel (defaults to `linear`) to be\n * applied\n * after biasAdd.\n * @param preluActivationWeights Tensor of prelu weights to be applied as part\n * of a `prelu` activation, typically the same shape as `x`.\n * @param leakyreluAlpha Optional. Alpha to be applied as part of a `leakyrelu`\n * activation.\n */\nfunction fusedConv2d_({ x, filter, strides, pad, dataFormat = 'NHWC', dilations = [1, 1], dimRoundingMode, bias, activation = 'linear', preluActivationWeights, leakyreluAlpha }) {\n activation = activation || 'linear';\n if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {\n let result = conv2d(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n if (bias != null) {\n result = add$1(result, bias);\n }\n return applyActivation(result, activation, preluActivationWeights, leakyreluAlpha);\n }\n const $x = convertToTensor(x, 'x', 'conv2d', 'float32');\n const $filter = convertToTensor(filter, 'filter', 'conv2d', 'float32');\n let x4D = $x;\n let reshapedTo4D = false;\n if ($x.rank === 3) {\n reshapedTo4D = true;\n x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n }\n assert(x4D.rank === 4, () => `Error in fused conv2d: input must be rank 4, but got rank ` +\n `${x4D.rank}.`);\n assert($filter.rank === 4, () => `Error in fused conv2d: filter must be rank 4, but got rank ` +\n `${$filter.rank}.`);\n checkPadOnDimRoundingMode('fused conv2d', pad, dimRoundingMode);\n assert(x4D.shape[3] === $filter.shape[2], () => `Error in conv2d: depth of input (${x4D.shape[3]}) must match ` +\n `input depth for filter ${$filter.shape[2]}.`);\n assert(eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in conv2D: Either strides or dilations must be 1. ' +\n `Got strides ${strides} and dilations '${dilations}'`);\n assert(dataFormat === 'NHWC', () => `Error in conv2d: got dataFormat of ${dataFormat} but only NHWC is currently supported.`);\n const convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode);\n let $bias;\n if (bias != null) {\n $bias = convertToTensor(bias, 'bias', 'fused conv2d');\n [$bias] = makeTypesMatch($bias, $x);\n assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);\n }\n let $preluActivationWeights;\n if (preluActivationWeights != null) {\n $preluActivationWeights = convertToTensor(preluActivationWeights, 'prelu weights', 'fused conv2d');\n }\n const grad = (dy, saved) => {\n const [$filter, x4D, y, $bias] = saved;\n const dyActivation = getFusedDyActivation(dy, y, activation);\n assert(tupleValuesAreOne(dilations), () => 'Error in gradient of fused conv2D: ' +\n `dilation rates greater than 1 ` +\n `are not yet supported in gradients. Got dilations '${dilations}'`);\n const xDer = conv2DBackpropInput(x4D.shape, dyActivation, $filter, strides, pad);\n const filterDer = conv2DBackpropFilter(x4D, dyActivation, $filter.shape, strides, pad);\n const der = [xDer, filterDer];\n if ($bias != null) {\n const biasDer = getFusedBiasGradient($bias, dyActivation);\n der.push(biasDer);\n }\n return der;\n };\n const inputs = {\n x: x4D,\n filter: $filter,\n bias: $bias,\n preluActivationWeights: $preluActivationWeights\n };\n const attrs = {\n strides,\n pad,\n dataFormat,\n dilations,\n dimRoundingMode,\n activation,\n leakyreluAlpha\n };\n // Depending on the the params passed in we will have different number of\n // inputs and thus a a different number of elements in the gradient.\n if (bias == null) {\n const customOp = customGrad((x4D, filter, save) => {\n let res = \n // tslint:disable-next-line: no-unnecessary-type-assertion\n ENGINE.runKernel(FusedConv2D, inputs, attrs);\n save([filter, x4D, res]);\n if (reshapedTo4D) {\n // tslint:disable-next-line: no-unnecessary-type-assertion\n res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n }\n return { value: res, gradFunc: grad };\n });\n return customOp(x4D, $filter);\n }\n else {\n const customOpWithBias = customGrad((x4D, filter, bias, save) => {\n let res = ENGINE.runKernel(FusedConv2D, inputs, attrs);\n save([filter, x4D, res, bias]);\n if (reshapedTo4D) {\n // tslint:disable-next-line: no-unnecessary-type-assertion\n res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n }\n return { value: res, gradFunc: grad };\n });\n return customOpWithBias(x4D, $filter, $bias);\n }\n}\nconst conv2d$1 = op({ fusedConv2d_ });\n\n/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes depthwise 2D convolution, optionally fused with adding a\n * bias and applying an activation.\n *\n * Given a 4D `input` array and a `filter` array of shape\n * `[filterHeight, filterWidth, inChannels, channelMultiplier]` containing\n * `inChannels` convolutional filters of depth 1, this op applies a\n * different filter to each input channel (expanding from 1 channel to\n * `channelMultiplier` channels for each), then concatenates the results\n * together. The output has `inChannels * channelMultiplier` channels.\n *\n * See\n * [https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d](\n * https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d)\n * for more details.\n *\n * @param obj An object with the following properties:\n * @param x The input tensor, of rank 4 or rank 3, of shape\n * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter tensor, rank 4, of shape\n * `[filterHeight, filterWidth, inChannels, channelMultiplier]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`. If strides is a single number, then `strideHeight ==\n * strideWidth`.\n * @param pad The type of padding algorithm.\n * - `same` and stride 1: output will be of same size as input,\n * regardless of filter size.\n * - `valid`: output will be smaller than input if filter is larger\n * than 1x1.\n * - For more info, see this guide:\n * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](\n * https://www.tensorflow.org/api_docs/python/tf/nn/convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n * in which we sample input values across the height and width dimensions\n * in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single\n * number, then `dilationHeight == dilationWidth`. If it is greater than\n * 1, then all values of `strides` must be 1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n * \"NHWC\". Specify the data format of the input and output data. With the\n * default format \"NHWC\", the data is stored in the order of: [batch,\n * height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n * provided, it will default to truncate.\n * @param bias Tensor to be added to the result.\n * @param activation Name of activation kernel (defaults to `linear`).\n * @param preluActivationWeights Tensor of prelu weights to be applied as part\n * of a `prelu` activation, typically the same shape as `x`.\n * @param leakyreluAlpha Optional. Alpha to be applied as part of a `leakyrelu`\n * activation.\n */\nfunction fusedDepthwiseConv2d_({ x, filter, strides, pad, dataFormat = 'NHWC', dilations = [1, 1], dimRoundingMode, bias, activation = 'linear', preluActivationWeights, leakyreluAlpha }) {\n if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {\n let result = depthwiseConv2d(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n if (bias != null) {\n result = add$1(result, bias);\n }\n return applyActivation(result, activation, preluActivationWeights, leakyreluAlpha);\n }\n const $x = convertToTensor(x, 'x', 'depthwiseConv2d', 'float32');\n const $filter = convertToTensor(filter, 'filter', 'depthwiseConv2d', 'float32');\n let x4D = $x;\n let reshapedTo4D = false;\n if ($x.rank === 3) {\n reshapedTo4D = true;\n x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n }\n assert(x4D.rank === 4, () => `Error in fused depthwiseConv2d: input must be rank 4, but got ` +\n `rank ${x4D.rank}.`);\n assert($filter.rank === 4, () => `Error in fused depthwiseConv2d: filter must be rank 4, ` +\n `but got rank ${$filter.rank}.`);\n assert(x4D.shape[3] === $filter.shape[2], () => `Error in fused depthwiseConv2d: number of input channels ` +\n `(${x4D.shape[3]}) must match the inChannels dimension in ` +\n `filter ${$filter.shape[2]}.`);\n if (dilations == null) {\n dilations = [1, 1];\n }\n assert(eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in fused depthwiseConv2d: Either strides or dilations must ' +\n `be 1. Got strides ${strides} and dilations '${dilations}'`);\n checkPadOnDimRoundingMode('fused depthwiseConv2d', pad, dimRoundingMode);\n const convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode, true /* depthwise */);\n let $bias;\n if (bias != null) {\n $bias = convertToTensor(bias, 'bias', 'fused conv2d');\n [$bias] = makeTypesMatch($bias, $x);\n assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);\n }\n let $preluActivationWeights;\n if (preluActivationWeights != null) {\n $preluActivationWeights = convertToTensor(preluActivationWeights, 'prelu weights', 'fused depthwiseConv2d');\n }\n const grad = (dy, saved) => {\n assert(tupleValuesAreOne(dilations), () => 'Error in gradient of fused depthwiseConv2d: dilation rates ' +\n `greater than 1 are not yet supported. Got dilations ` +\n `'${dilations}'`);\n const [$filter, x4D, y, bias] = saved;\n const dyActivation = getFusedDyActivation(dy, y, activation);\n const xDer = depthwiseConv2dNativeBackpropInput(x4D.shape, dyActivation, $filter, strides, pad, dilations, dimRoundingMode);\n const filterDer = depthwiseConv2dNativeBackpropFilter(x4D, dyActivation, $filter.shape, strides, pad, dilations, dimRoundingMode);\n if (bias != null) {\n const biasDer = getFusedBiasGradient($bias, dyActivation);\n return [xDer, filterDer, biasDer];\n }\n return [xDer, filterDer];\n };\n const inputs = {\n x: x4D,\n filter: $filter,\n bias: $bias,\n preluActivationWeights: $preluActivationWeights\n };\n const attrs = {\n strides,\n pad,\n dataFormat,\n dilations,\n dimRoundingMode,\n activation,\n leakyreluAlpha\n };\n // Depending on the the params passed in we will have different number of\n // inputs and thus a a different number of elements in the gradient.\n if (bias == null) {\n const customOp = customGrad((x4D, filter, save) => {\n // tslint:disable-next-line: no-unnecessary-type-assertion\n let res = ENGINE.runKernel(FusedDepthwiseConv2D, inputs, attrs);\n save([filter, x4D, res]);\n if (reshapedTo4D) {\n // tslint:disable-next-line: no-unnecessary-type-assertion\n res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n }\n return { value: res, gradFunc: grad };\n });\n return customOp(x4D, $filter);\n }\n else {\n const customOpWithBias = customGrad((x4D, filter, bias, save) => {\n // tslint:disable-next-line: no-unnecessary-type-assertion\n let res = ENGINE.runKernel(FusedDepthwiseConv2D, inputs, attrs);\n save([filter, x4D, res, bias]);\n if (reshapedTo4D) {\n // tslint:disable-next-line: no-unnecessary-type-assertion\n res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n }\n return { value: res, gradFunc: grad };\n });\n return customOpWithBias(x4D, $filter, $bias);\n }\n}\nconst depthwiseConv2d$1 = op({ fusedDepthwiseConv2d_ });\n\n/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes the dot product of two matrices with optional activation and bias.\n *\n * ```js\n * const a = tf.tensor2d([-1, -2], [1, 2]);\n * const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const bias = tf.tensor2d([1, 2], [1, 2]);\n *\n * tf.fused.matMul({a, b, bias, activation: 'relu'}).print();\n * ```\n *\n * @param obj An object with the following properties:\n * - `a` First matrix in dot product operation.\n * - `b` Second matrix in dot product operation.\n * - `transposeA` If true, `a` is transposed before multiplication.\n * - `transposeB` If true, `b` is transposed before multiplication.\n * - `bias` Matrix to be added to the result.\n * - `activation` Name of activation kernel (defaults to `linear`).\n * - `preluActivationWeights` Tensor of prelu weights.\n * - `leakyreluAlpha` Alpha of leakyrelu.\n */\nfunction fusedMatMul_({ a, b, transposeA = false, transposeB = false, bias, activation = 'linear', preluActivationWeights, leakyreluAlpha, }) {\n if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {\n let result = matMul(a, b, transposeA, transposeB);\n if (bias != null) {\n result = add$1(result, bias);\n }\n return applyActivation(result, activation, preluActivationWeights, leakyreluAlpha);\n }\n let $a = convertToTensor(a, 'a', 'fused matMul');\n let $b = convertToTensor(b, 'b', 'fused matMul');\n [$a, $b] = makeTypesMatch($a, $b);\n const innerShapeA = transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];\n const innerShapeB = transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];\n const outerShapeA = transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];\n const outerShapeB = transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];\n const outerDimsA = $a.shape.slice(0, -2);\n const outerDimsB = $b.shape.slice(0, -2);\n const batchDimA = sizeFromShape(outerDimsA);\n const batchDimB = sizeFromShape(outerDimsB);\n assert(innerShapeA === innerShapeB, () => `Error in fused matMul: inner shapes (${innerShapeA}) and (` +\n `${innerShapeB}) of Tensors with shapes ${$a.shape} and ` +\n `${$b.shape} and transposeA=${transposeA}` +\n ` and transposeB=${transposeB} must match.`);\n const outShapeOuterDims = assertAndGetBroadcastShape($a.shape.slice(0, -2), $b.shape.slice(0, -2));\n const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);\n const a3D = transposeA ?\n reshape($a, [batchDimA, innerShapeA, outerShapeA]) :\n reshape($a, [batchDimA, outerShapeA, innerShapeA]);\n const b3D = transposeB ?\n reshape($b, [batchDimB, outerShapeB, innerShapeB]) :\n reshape($b, [batchDimB, innerShapeB, outerShapeB]);\n let $bias;\n if (bias != null) {\n $bias = convertToTensor(bias, 'bias', 'fused matMul');\n [$bias] = makeTypesMatch($bias, $a);\n assertAndGetBroadcastShape(outShape, $bias.shape);\n }\n let $preluActivationWeights;\n if (preluActivationWeights != null) {\n $preluActivationWeights = convertToTensor(preluActivationWeights, 'prelu weights', 'fused matMul');\n }\n const grad = (dy, saved) => {\n const [a3D, b3D, y, $bias] = saved;\n // we reshape dy because the result of the forward is not\n // necessarily going to be a 3d tensor due to a reshape done at the end of\n // the customOp.\n const dyActivation = getFusedDyActivation(reshape(dy, y.shape), y, activation);\n let aDer;\n let bDer;\n if (!transposeA && !transposeB) {\n aDer = matMul(dyActivation, b3D, false, true);\n bDer = matMul(a3D, dyActivation, true, false);\n }\n else if (!transposeA && transposeB) {\n aDer = matMul(dyActivation, b3D, false, false);\n bDer = matMul(dyActivation, a3D, true, false);\n }\n else if (transposeA && !transposeB) {\n aDer = matMul(b3D, dyActivation, false, true);\n bDer = matMul(a3D, dyActivation, false, false);\n }\n else {\n aDer = matMul(b3D, dyActivation, true, true);\n bDer = matMul(dyActivation, a3D, true, true);\n }\n if (bias != null) {\n const biasDer = getFusedBiasGradient($bias, dyActivation);\n return [aDer, bDer, biasDer];\n }\n else {\n return [aDer, bDer];\n }\n };\n const inputs = {\n a: a3D,\n b: b3D,\n bias: $bias,\n preluActivationWeights: $preluActivationWeights\n };\n const attrs = { transposeA, transposeB, activation, leakyreluAlpha };\n // Depending on the the params passed in we will have different number of\n // inputs and thus a a different number of elements in the gradient.\n if (bias == null) {\n const customOp = customGrad((a3D, b3D, save) => {\n const res = \n // tslint:disable-next-line: no-unnecessary-type-assertion\n ENGINE.runKernel(_FusedMatMul, inputs, attrs);\n save([a3D, b3D, res]);\n return { value: reshape(res, outShape), gradFunc: grad };\n });\n return customOp(a3D, b3D);\n }\n else {\n const customOpWithBias = customGrad((a3D, b3D, $bias, save) => {\n const res = \n // tslint:disable-next-line: no-unnecessary-type-assertion\n ENGINE.runKernel(_FusedMatMul, inputs, attrs);\n save([a3D, b3D, res, $bias]);\n return { value: reshape(res, outShape), gradFunc: grad };\n });\n return customOpWithBias(a3D, b3D, $bias);\n }\n}\nconst matMul$1 = op({ fusedMatMul_ });\n\n/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Generate a hamming window.\n *\n * See: https://en.wikipedia.org/wiki/Window_function#Hann_and_Hamming_windows\n *\n * ```js\n * tf.signal.hammingWindow(10).print();\n * ```\n * @param The length of window\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction hammingWindow_(windowLength) {\n return cosineWindow(windowLength, 0.54, 0.46);\n}\nconst hammingWindow = op({ hammingWindow_ });\n\n/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Generate a Hann window.\n *\n * See: https://en.wikipedia.org/wiki/Window_function#Hann_and_Hamming_windows\n *\n * ```js\n * tf.signal.hannWindow(10).print();\n * ```\n * @param The length of window\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction hannWindow_(windowLength) {\n return cosineWindow(windowLength, 0.5, 0.5);\n}\nconst hannWindow = op({ hannWindow_ });\n\n/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Expands input into frames of frameLength.\n * Slides a window size with frameStep.\n *\n * ```js\n * tf.signal.frame([1, 2, 3], 2, 1).print();\n * ```\n * @param signal The input tensor to be expanded\n * @param frameLength Length of each frame\n * @param frameStep The frame hop size in samples.\n * @param padEnd Whether to pad the end of signal with padValue.\n * @param padValue An number to use where the input signal does\n * not exist when padEnd is True.\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction frame_(signal, frameLength, frameStep, padEnd = false, padValue = 0) {\n let start = 0;\n const output = [];\n while (start + frameLength <= signal.size) {\n output.push(slice(signal, start, frameLength));\n start += frameStep;\n }\n if (padEnd) {\n while (start < signal.size) {\n const padLen = (start + frameLength) - signal.size;\n const pad = concat([\n slice(signal, start, frameLength - padLen), fill([padLen], padValue)\n ]);\n output.push(pad);\n start += frameStep;\n }\n }\n if (output.length === 0) {\n return tensor2d([], [0, frameLength]);\n }\n return reshape(concat(output), [output.length, frameLength]);\n}\nconst frame = op({ frame_ });\n\n/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes the Short-time Fourier Transform of signals\n * See: https://en.wikipedia.org/wiki/Short-time_Fourier_transform\n *\n * ```js\n * const input = tf.tensor1d([1, 1, 1, 1, 1])\n * tf.signal.stft(input, 3, 1).print();\n * ```\n * @param signal 1-dimensional real value tensor.\n * @param frameLength The window length of samples.\n * @param frameStep The number of samples to step.\n * @param fftLength The size of the FFT to apply.\n * @param windowFn A callable that takes a window length and returns 1-d tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction stft_(signal, frameLength, frameStep, fftLength, windowFn = hannWindow) {\n if (fftLength == null) {\n fftLength = enclosingPowerOfTwo(frameLength);\n }\n const framedSignal = frame(signal, frameLength, frameStep);\n const windowedSignal = mul(framedSignal, windowFn(frameLength));\n return rfft(windowedSignal, fftLength);\n}\nconst stft = op({ stft_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Extracts crops from the input image tensor and resizes them using bilinear\n * sampling or nearest neighbor sampling (possibly with aspect ratio change)\n * to a common output size specified by cropSize.\n *\n * @param image 4d tensor of shape `[batch,imageHeight,imageWidth, depth]`,\n * where imageHeight and imageWidth must be positive, specifying the\n * batch of images from which to take crops\n * @param boxes 2d float32 tensor of shape `[numBoxes, 4]`. Each entry is\n * `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the normalized\n * coordinates of the box in the boxInd[i]'th image in the batch\n * @param boxInd 1d int32 tensor of shape `[numBoxes]` with values in range\n * `[0, batch)` that specifies the image that the `i`-th box refers to.\n * @param cropSize 1d int32 tensor of 2 elements `[cropHeigh, cropWidth]`\n * specifying the size to which all crops are resized to.\n * @param method Optional string from `'bilinear' | 'nearest'`,\n * defaults to bilinear, which specifies the sampling method for resizing\n * @param extrapolationValue A threshold for deciding when to remove boxes based\n * on score. Defaults to 0.\n * @return A 4D tensor of the shape `[numBoxes,cropHeight,cropWidth,depth]`\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction cropAndResize_(image, boxes, boxInd, cropSize, method = 'bilinear', extrapolationValue = 0) {\n const $image = convertToTensor(image, 'image', 'cropAndResize');\n const $boxes = convertToTensor(boxes, 'boxes', 'cropAndResize', 'float32');\n const $boxInd = convertToTensor(boxInd, 'boxInd', 'cropAndResize', 'int32');\n const numBoxes = $boxes.shape[0];\n assert($image.rank === 4, () => 'Error in cropAndResize: image must be rank 4,' +\n `but got rank ${$image.rank}.`);\n assert($boxes.rank === 2 && $boxes.shape[1] === 4, () => `Error in cropAndResize: boxes must be have size [${numBoxes},4] ` +\n `but had shape ${$boxes.shape}.`);\n assert($boxInd.rank === 1 && $boxInd.shape[0] === numBoxes, () => `Error in cropAndResize: boxInd must be have size [${numBoxes}] ` +\n `but had shape ${$boxes.shape}.`);\n assert(cropSize.length === 2, () => `Error in cropAndResize: cropSize must be of length 2, but got ` +\n `length ${cropSize.length}.`);\n assert(cropSize[0] >= 1 && cropSize[1] >= 1, () => `cropSize must be atleast [1,1], but was ${cropSize}`);\n assert(method === 'bilinear' || method === 'nearest', () => `method must be bilinear or nearest, but was ${method}`);\n const inputs = { image: $image, boxes: $boxes, boxInd: $boxInd };\n const attrs = { method, extrapolationValue, cropSize };\n const res = ENGINE.runKernel(CropAndResize, inputs, attrs);\n return res;\n}\nconst cropAndResize = op({ cropAndResize_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Flips the image left to right. Currently available in the CPU, WebGL, and\n * WASM backends.\n *\n * @param image 4d tensor of shape `[batch, imageHeight, imageWidth, depth]`.\n */\n/** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */\nfunction flipLeftRight_(image) {\n const $image = convertToTensor(image, 'image', 'flipLeftRight', 'float32');\n assert($image.rank === 4, () => 'Error in flipLeftRight: image must be rank 4,' +\n `but got rank ${$image.rank}.`);\n const inputs = { image: $image };\n const res = ENGINE.runKernel(FlipLeftRight, inputs, {});\n return res;\n}\nconst flipLeftRight = op({ flipLeftRight_ });\n\n/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Converts images from grayscale to RGB format.\n *\n * @param image A grayscale tensor to convert. The `image`'s last dimension must\n * be size 1 with at least a two-dimensional shape.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction grayscaleToRGB_(image) {\n const $image = convertToTensor(image, 'image', 'grayscaleToRGB');\n const lastDimsIdx = $image.rank - 1;\n const lastDims = $image.shape[lastDimsIdx];\n assert($image.rank >= 2, () => 'Error in grayscaleToRGB: images must be at least rank 2, ' +\n `but got rank ${$image.rank}.`);\n assert(lastDims === 1, () => 'Error in grayscaleToRGB: last dimension of a grayscale image ' +\n `should be size 1, but got size ${lastDims}.`);\n const reps = new Array($image.rank);\n reps.fill(1, 0, lastDimsIdx);\n reps[lastDimsIdx] = 3;\n return tile($image, reps);\n}\nconst grayscaleToRGB = op({ grayscaleToRGB_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Rotates the input image tensor counter-clockwise with an optional offset\n * center of rotation. Currently available in the CPU, WebGL, and WASM backends.\n *\n * @param image 4d tensor of shape `[batch, imageHeight, imageWidth, depth]`.\n * @param radians The amount of rotation.\n * @param fillValue The value to fill in the empty space leftover\n * after rotation. Can be either a single grayscale value (0-255), or an\n * array of three numbers `[red, green, blue]` specifying the red, green,\n * and blue channels. Defaults to `0` (black).\n * @param center The center of rotation. Can be either a single value (0-1), or\n * an array of two numbers `[centerX, centerY]`. Defaults to `0.5` (rotates\n * the image around its center).\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction rotateWithOffset_(image, radians, fillValue = 0, center = 0.5) {\n const $image = convertToTensor(image, 'image', 'rotateWithOffset', 'float32');\n assert($image.rank === 4, () => 'Error in rotateWithOffset: image must be rank 4,' +\n `but got rank ${$image.rank}.`);\n const inputs = { image: $image };\n const attrs = { radians, fillValue, center };\n const res = ENGINE.runKernel(RotateWithOffset, inputs, attrs);\n return res;\n}\nconst rotateWithOffset = op({ rotateWithOffset_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nfunction nonMaxSuppSanityCheck(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {\n if (iouThreshold == null) {\n iouThreshold = 0.5;\n }\n if (scoreThreshold == null) {\n scoreThreshold = Number.NEGATIVE_INFINITY;\n }\n if (softNmsSigma == null) {\n softNmsSigma = 0.0;\n }\n const numBoxes = boxes.shape[0];\n maxOutputSize = Math.min(maxOutputSize, numBoxes);\n assert(0 <= iouThreshold && iouThreshold <= 1, () => `iouThreshold must be in [0, 1], but was '${iouThreshold}'`);\n assert(boxes.rank === 2, () => `boxes must be a 2D tensor, but was of rank '${boxes.rank}'`);\n assert(boxes.shape[1] === 4, () => `boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`);\n assert(scores.rank === 1, () => 'scores must be a 1D tensor');\n assert(scores.shape[0] === numBoxes, () => `scores has incompatible shape with boxes. Expected ${numBoxes}, ` +\n `but was ${scores.shape[0]}`);\n assert(0 <= softNmsSigma && softNmsSigma <= 1, () => `softNmsSigma must be in [0, 1], but was '${softNmsSigma}'`);\n return { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma };\n}\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Performs non maximum suppression of bounding boxes based on\n * iou (intersection over union).\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n * `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n * the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n * boxes overlap too much with respect to IOU. Must be between [0, 1].\n * Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n * on score. Defaults to -inf, which means any score is accepted.\n * @return A 1D tensor with the selected box indices.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction nonMaxSuppression_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) {\n const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression', 'float32');\n const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression', 'float32');\n const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);\n maxOutputSize = inputs.maxOutputSize;\n iouThreshold = inputs.iouThreshold;\n scoreThreshold = inputs.scoreThreshold;\n const attrs = { maxOutputSize, iouThreshold, scoreThreshold };\n return ENGINE.runKernel(NonMaxSuppressionV3, { boxes: $boxes, scores: $scores }, attrs);\n}\nconst nonMaxSuppression = op({ nonMaxSuppression_ });\n\n/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Inserts a value into a sorted array. This method allows duplicate, meaning it\n * allows inserting duplicate value, in which case, the element will be inserted\n * at the lowest index of the value.\n * @param arr The array to modify.\n * @param element The element to insert.\n * @param comparator Optional. If no comparator is specified, elements are\n * compared using array_util.defaultComparator, which is suitable for Strings\n * and Numbers in ascending arrays. If the array contains multiple instances of\n * the target value, the left-most instance will be returned. To provide a\n * comparator, it should take 2 arguments to compare and return a negative,\n * zero, or a positive number.\n */\nfunction binaryInsert(arr, element, comparator) {\n const index = binarySearch(arr, element, comparator);\n const insertionPoint = index < 0 ? -(index + 1) : index;\n arr.splice(insertionPoint, 0, element);\n}\n/**\n * Searches the array for the target using binary search, returns the index\n * of the found element, or position to insert if element not found. If no\n * comparator is specified, elements are compared using array_\n * util.defaultComparator, which is suitable for Strings and Numbers in\n * ascending arrays. If the array contains multiple instances of the target\n * value, the left-most instance will be returned.\n * @param arr The array to be searched in.\n * @param target The target to be searched for.\n * @param comparator Should take 2 arguments to compare and return a negative,\n * zero, or a positive number.\n * @return Lowest index of the target value if found, otherwise the insertion\n * point where the target should be inserted, in the form of\n * (-insertionPoint - 1).\n */\nfunction binarySearch(arr, target, comparator) {\n return binarySearch_(arr, target, comparator || defaultComparator);\n}\n/**\n * Compares its two arguments for order.\n * @param a The first element to be compared.\n * @param b The second element to be compared.\n * @return A negative number, zero, or a positive number as the first\n * argument is less than, equal to, or greater than the second.\n */\nfunction defaultComparator(a, b) {\n return a > b ? 1 : a < b ? -1 : 0;\n}\nfunction binarySearch_(arr, target, comparator) {\n let left = 0;\n let right = arr.length;\n let middle = 0;\n let found = false;\n while (left < right) {\n middle = left + ((right - left) >>> 1);\n const compareResult = comparator(target, arr[middle]);\n if (compareResult > 0) {\n left = middle + 1;\n }\n else {\n right = middle;\n // If compareResult is 0, the value is found. We record it is found,\n // and then keep looking because there may be duplicate.\n found = !compareResult;\n }\n }\n return found ? left : -left - 1;\n}\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nfunction nonMaxSuppressionV3Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {\n return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, 0 /* softNmsSigma */);\n}\nfunction nonMaxSuppressionV4Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize) {\n return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, 0 /* softNmsSigma */, false /* returnScoresTensor */, padToMaxOutputSize /* padToMaxOutputSize */, true\n /* returnValidOutputs */ );\n}\nfunction nonMaxSuppressionV5Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {\n return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, true /* returnScoresTensor */);\n}\nfunction nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, returnScoresTensor = false, padToMaxOutputSize = false, returnValidOutputs = false) {\n // The list is sorted in ascending order, so that we can always pop the\n // candidate with the largest score in O(1) time.\n const candidates = [];\n for (let i = 0; i < scores.length; i++) {\n if (scores[i] > scoreThreshold) {\n candidates.push({ score: scores[i], boxIndex: i, suppressBeginIndex: 0 });\n }\n }\n candidates.sort(ascendingComparator);\n // If softNmsSigma is 0, the outcome of this algorithm is exactly same as\n // before.\n const scale = softNmsSigma > 0 ? (-0.5 / softNmsSigma) : 0.0;\n const selectedIndices = [];\n const selectedScores = [];\n while (selectedIndices.length < maxOutputSize && candidates.length > 0) {\n const candidate = candidates.pop();\n const { score: originalScore, boxIndex, suppressBeginIndex } = candidate;\n if (originalScore < scoreThreshold) {\n break;\n }\n // Overlapping boxes are likely to have similar scores, therefore we\n // iterate through the previously selected boxes backwards in order to\n // see if candidate's score should be suppressed. We use\n // suppressBeginIndex to track and ensure a candidate can be suppressed\n // by a selected box no more than once. Also, if the overlap exceeds\n // iouThreshold, we simply ignore the candidate.\n let ignoreCandidate = false;\n for (let j = selectedIndices.length - 1; j >= suppressBeginIndex; --j) {\n const iou = intersectionOverUnion(boxes, boxIndex, selectedIndices[j]);\n if (iou >= iouThreshold) {\n ignoreCandidate = true;\n break;\n }\n candidate.score =\n candidate.score * suppressWeight(iouThreshold, scale, iou);\n if (candidate.score <= scoreThreshold) {\n break;\n }\n }\n // At this point, if `candidate.score` has not dropped below\n // `scoreThreshold`, then we know that we went through all of the\n // previous selections and can safely update `suppressBeginIndex` to the\n // end of the selected array. Then we can re-insert the candidate with\n // the updated score and suppressBeginIndex back in the candidate list.\n // If on the other hand, `candidate.score` has dropped below the score\n // threshold, we will not add it back to the candidates list.\n candidate.suppressBeginIndex = selectedIndices.length;\n if (!ignoreCandidate) {\n // Candidate has passed all the tests, and is not suppressed, so\n // select the candidate.\n if (candidate.score === originalScore) {\n selectedIndices.push(boxIndex);\n selectedScores.push(candidate.score);\n }\n else if (candidate.score > scoreThreshold) {\n // Candidate's score is suppressed but is still high enough to be\n // considered, so add back to the candidates list.\n binaryInsert(candidates, candidate, ascendingComparator);\n }\n }\n }\n // NonMaxSuppressionV4 feature: padding output to maxOutputSize.\n const validOutputs = selectedIndices.length;\n const elemsToPad = maxOutputSize - validOutputs;\n if (padToMaxOutputSize && elemsToPad > 0) {\n selectedIndices.push(...new Array(elemsToPad).fill(0));\n selectedScores.push(...new Array(elemsToPad).fill(0.0));\n }\n const result = { selectedIndices };\n if (returnScoresTensor) {\n result['selectedScores'] = selectedScores;\n }\n if (returnValidOutputs) {\n result['validOutputs'] = validOutputs;\n }\n return result;\n}\nfunction intersectionOverUnion(boxes, i, j) {\n const iCoord = boxes.subarray(i * 4, i * 4 + 4);\n const jCoord = boxes.subarray(j * 4, j * 4 + 4);\n const yminI = Math.min(iCoord[0], iCoord[2]);\n const xminI = Math.min(iCoord[1], iCoord[3]);\n const ymaxI = Math.max(iCoord[0], iCoord[2]);\n const xmaxI = Math.max(iCoord[1], iCoord[3]);\n const yminJ = Math.min(jCoord[0], jCoord[2]);\n const xminJ = Math.min(jCoord[1], jCoord[3]);\n const ymaxJ = Math.max(jCoord[0], jCoord[2]);\n const xmaxJ = Math.max(jCoord[1], jCoord[3]);\n const areaI = (ymaxI - yminI) * (xmaxI - xminI);\n const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);\n if (areaI <= 0 || areaJ <= 0) {\n return 0.0;\n }\n const intersectionYmin = Math.max(yminI, yminJ);\n const intersectionXmin = Math.max(xminI, xminJ);\n const intersectionYmax = Math.min(ymaxI, ymaxJ);\n const intersectionXmax = Math.min(xmaxI, xmaxJ);\n const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0) *\n Math.max(intersectionXmax - intersectionXmin, 0.0);\n return intersectionArea / (areaI + areaJ - intersectionArea);\n}\n// A Gaussian penalty function, this method always returns values in [0, 1].\n// The weight is a function of similarity, the more overlap two boxes are, the\n// smaller the weight is, meaning highly overlapping boxe will be significantly\n// penalized. On the other hand, a non-overlapping box will not be penalized.\nfunction suppressWeight(iouThreshold, scale, iou) {\n const weight = Math.exp(scale * iou * iou);\n return iou <= iouThreshold ? weight : 0.0;\n}\nfunction ascendingComparator(c1, c2) {\n // For objects with same scores, we make the object with the larger index go\n // first. In an array that pops from the end, this means that the object with\n // the smaller index will be popped first. This ensures the same output as\n // the TensorFlow python version.\n return (c1.score - c2.score) ||\n ((c1.score === c2.score) && (c2.boxIndex - c1.boxIndex));\n}\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Performs non maximum suppression of bounding boxes based on\n * iou (intersection over union).\n *\n * This is the async version of `nonMaxSuppression`\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n * `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n * the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n * boxes overlap too much with respect to IOU. Must be between [0, 1].\n * Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n * on score. Defaults to -inf, which means any score is accepted.\n * @return A 1D tensor with the selected box indices.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nasync function nonMaxSuppressionAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) {\n const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');\n const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');\n const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);\n maxOutputSize = inputs.maxOutputSize;\n iouThreshold = inputs.iouThreshold;\n scoreThreshold = inputs.scoreThreshold;\n const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);\n const boxesVals = boxesAndScores[0];\n const scoresVals = boxesAndScores[1];\n // We call a cpu based impl directly with the typedarray data here rather\n // than a kernel because all kernels are synchronous (and thus cannot await\n // .data()).\n const { selectedIndices } = nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);\n if ($boxes !== boxes) {\n $boxes.dispose();\n }\n if ($scores !== scores) {\n $scores.dispose();\n }\n return tensor1d(selectedIndices, 'int32');\n}\nconst nonMaxSuppressionAsync = nonMaxSuppressionAsync_;\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Performs non maximum suppression of bounding boxes based on\n * iou (intersection over union).\n *\n * This op also supports a Soft-NMS mode (c.f.\n * Bodla et al, https://arxiv.org/abs/1704.04503) where boxes reduce the score\n * of other overlapping boxes, therefore favoring different regions of the image\n * with high scores. To enable this Soft-NMS mode, set the `softNmsSigma`\n * parameter to be larger than 0.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n * `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n * the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n * boxes overlap too much with respect to IOU. Must be between [0, 1].\n * Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n * on score. Defaults to -inf, which means any score is accepted.\n * @param softNmsSigma A float representing the sigma parameter for Soft NMS.\n * When sigma is 0, it falls back to nonMaxSuppression.\n * @return A map with the following properties:\n * - selectedIndices: A 1D tensor with the selected box indices.\n * - selectedScores: A 1D tensor with the corresponding scores for each\n * selected box.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction nonMaxSuppressionWithScore_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0.0) {\n const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');\n const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');\n const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n maxOutputSize = params.maxOutputSize;\n iouThreshold = params.iouThreshold;\n scoreThreshold = params.scoreThreshold;\n softNmsSigma = params.softNmsSigma;\n const inputs = { boxes: $boxes, scores: $scores };\n const attrs = { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma };\n // tslint:disable-next-line: no-unnecessary-type-assertion\n const result = ENGINE.runKernel(NonMaxSuppressionV5, inputs, attrs);\n return { selectedIndices: result[0], selectedScores: result[1] };\n}\nconst nonMaxSuppressionWithScore = op({ nonMaxSuppressionWithScore_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Asynchronously performs non maximum suppression of bounding boxes based on\n * iou (intersection over union).\n *\n * This op also supports a Soft-NMS mode (c.f.\n * Bodla et al, https://arxiv.org/abs/1704.04503) where boxes reduce the score\n * of other overlapping boxes, therefore favoring different regions of the image\n * with high scores. To enable this Soft-NMS mode, set the `softNmsSigma`\n * parameter to be larger than 0.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n * `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n * the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n * boxes overlap too much with respect to IOU. Must be between [0, 1].\n * Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n * on score. Defaults to -inf, which means any score is accepted.\n * @param softNmsSigma A float representing the sigma parameter for Soft NMS.\n * When sigma is 0, it falls back to nonMaxSuppression.\n * @return A map with the following properties:\n * - selectedIndices: A 1D tensor with the selected box indices.\n * - selectedScores: A 1D tensor with the corresponding scores for each\n * selected box.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nasync function nonMaxSuppressionWithScoreAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0.0) {\n const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');\n const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');\n const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n maxOutputSize = params.maxOutputSize;\n iouThreshold = params.iouThreshold;\n scoreThreshold = params.scoreThreshold;\n softNmsSigma = params.softNmsSigma;\n const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);\n const boxesVals = boxesAndScores[0];\n const scoresVals = boxesAndScores[1];\n // We call a cpu based impl directly with the typedarray data here rather\n // than a kernel because all kernels are synchronous (and thus cannot await\n // .data()).\n const { selectedIndices, selectedScores } = nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n if ($boxes !== boxes) {\n $boxes.dispose();\n }\n if ($scores !== scores) {\n $scores.dispose();\n }\n return {\n selectedIndices: tensor1d(selectedIndices, 'int32'),\n selectedScores: tensor1d(selectedScores)\n };\n}\nconst nonMaxSuppressionWithScoreAsync = nonMaxSuppressionWithScoreAsync_;\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Asynchronously performs non maximum suppression of bounding boxes based on\n * iou (intersection over union), with an option to pad results.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n * `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n * the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n * boxes overlap too much with respect to IOU. Must be between [0, 1].\n * Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n * on score. Defaults to -inf, which means any score is accepted.\n * @param padToMaxOutputSize Defalts to false. If true, size of output\n * `selectedIndices` is padded to maxOutputSize.\n * @return A map with the following properties:\n * - selectedIndices: A 1D tensor with the selected box indices.\n * - validOutputs: A scalar denoting how many elements in `selectedIndices`\n * are valid. Valid elements occur first, then padding.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction nonMaxSuppressionPadded_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) {\n const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');\n const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');\n const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null /* softNmsSigma */);\n const $maxOutputSize = params.maxOutputSize;\n const $iouThreshold = params.iouThreshold;\n const $scoreThreshold = params.scoreThreshold;\n const inputs = { boxes: $boxes, scores: $scores };\n const attrs = {\n maxOutputSize: $maxOutputSize,\n iouThreshold: $iouThreshold,\n scoreThreshold: $scoreThreshold,\n padToMaxOutputSize\n };\n // tslint:disable-next-line: no-unnecessary-type-assertion\n const result = ENGINE.runKernel(NonMaxSuppressionV4, inputs, attrs);\n return { selectedIndices: result[0], validOutputs: result[1] };\n}\nconst nonMaxSuppressionPadded = op({ nonMaxSuppressionPadded_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Asynchronously performs non maximum suppression of bounding boxes based on\n * iou (intersection over union), with an option to pad results.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n * `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n * the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n * boxes overlap too much with respect to IOU. Must be between [0, 1].\n * Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n * on score. Defaults to -inf, which means any score is accepted.\n * @param padToMaxOutputSize Defalts to false. If true, size of output\n * `selectedIndices` is padded to maxOutputSize.\n * @return A map with the following properties:\n * - selectedIndices: A 1D tensor with the selected box indices.\n * - validOutputs: A scalar denoting how many elements in `selectedIndices`\n * are valid. Valid elements occur first, then padding.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nasync function nonMaxSuppressionPaddedAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) {\n const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');\n const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');\n const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null /* softNmsSigma */);\n const $maxOutputSize = params.maxOutputSize;\n const $iouThreshold = params.iouThreshold;\n const $scoreThreshold = params.scoreThreshold;\n const [boxesVals, scoresVals] = await Promise.all([$boxes.data(), $scores.data()]);\n // We call a cpu based impl directly with the typedarray data here rather\n // than a kernel because all kernels are synchronous (and thus cannot await\n // .data()).\n const { selectedIndices, validOutputs } = nonMaxSuppressionV4Impl(boxesVals, scoresVals, $maxOutputSize, $iouThreshold, $scoreThreshold, padToMaxOutputSize);\n if ($boxes !== boxes) {\n $boxes.dispose();\n }\n if ($scores !== scores) {\n $scores.dispose();\n }\n return {\n selectedIndices: tensor1d(selectedIndices, 'int32'),\n validOutputs: scalar(validOutputs, 'int32')\n };\n}\nconst nonMaxSuppressionPaddedAsync = nonMaxSuppressionPaddedAsync_;\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Bilinear resize a single 3D image or a batch of 3D images to a new shape.\n *\n * @param images The images, of rank 4 or rank 3, of shape\n * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param size The new shape `[newHeight, newWidth]` to resize the\n * images to. Each channel is resized individually.\n * @param alignCorners Defaults to `false`. If true, rescale\n * input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4\n * corners of images and resized images. If false, rescale by\n * `new_height / height`. Treat similarly the width dimension.\n * @param halfPixelCenters Defaults to `false`. Whether to assume pixel centers\n * are at 0.5, which would make the floating point coordinates of the top\n * left pixel 0.5, 0.5.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction resizeBilinear_(images, size, alignCorners = false, halfPixelCenters = false) {\n const $images = convertToTensor(images, 'images', 'resizeBilinear');\n assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeBilinear: x must be rank 3 or 4, but got ` +\n `rank ${$images.rank}.`);\n assert(size.length === 2, () => `Error in resizeBilinear: new shape must 2D, but got shape ` +\n `${size}.`);\n assert(halfPixelCenters === false || alignCorners === false, () => `Error in resizeBilinear: If halfPixelCenters is true, ` +\n `alignCorners must be false.`);\n let batchImages = $images;\n let reshapedTo4D = false;\n if ($images.rank === 3) {\n reshapedTo4D = true;\n batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);\n }\n const [] = size;\n const inputs = { images: batchImages };\n const attrs = { alignCorners, halfPixelCenters, size };\n // tslint:disable-next-line: no-unnecessary-type-assertion\n const res = ENGINE.runKernel(ResizeBilinear, inputs, attrs);\n if (reshapedTo4D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n }\n return res;\n}\nconst resizeBilinear = op({ resizeBilinear_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * NearestNeighbor resize a batch of 3D images to a new shape.\n *\n * @param images The images, of rank 4 or rank 3, of shape\n * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param size The new shape `[newHeight, newWidth]` to resize the\n * images to. Each channel is resized individually.\n * @param alignCorners Defaults to False. If true, rescale\n * input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4\n * corners of images and resized images. If false, rescale by\n * `new_height / height`. Treat similarly the width dimension.\n * @param halfPixelCenters Defaults to `false`. Whether to assumes pixels are of\n * half the actual dimensions, and yields more accurate resizes. This flag\n * would also make the floating point coordinates of the top left pixel\n * 0.5, 0.5.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction resizeNearestNeighbor_(images, size, alignCorners = false, halfPixelCenters = false) {\n const $images = convertToTensor(images, 'images', 'resizeNearestNeighbor');\n assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeNearestNeighbor: x must be rank 3 or 4, but got ` +\n `rank ${$images.rank}.`);\n assert(size.length === 2, () => `Error in resizeNearestNeighbor: new shape must 2D, but got shape ` +\n `${size}.`);\n assert($images.dtype === 'float32' || $images.dtype === 'int32', () => '`images` must have `int32` or `float32` as dtype');\n assert(halfPixelCenters === false || alignCorners === false, () => `Error in resizeNearestNeighbor: If halfPixelCenters is true, ` +\n `alignCorners must be false.`);\n let batchImages = $images;\n let reshapedTo4D = false;\n if ($images.rank === 3) {\n reshapedTo4D = true;\n batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);\n }\n const [] = size;\n const inputs = { images: batchImages };\n const attrs = { alignCorners, halfPixelCenters, size };\n // tslint:disable-next-line: no-unnecessary-type-assertion\n const res = ENGINE.runKernel(ResizeNearestNeighbor, inputs, attrs);\n if (reshapedTo4D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n }\n return res;\n}\nconst resizeNearestNeighbor = op({ resizeNearestNeighbor_ });\n\n/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * https://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Performs image binarization with corresponding threshold\n * (depends on the method)value, which creates a binary image from a grayscale.\n * @param image 3d tensor of shape [imageHeight,imageWidth, depth],\n * where imageHeight and imageWidth must be positive.The image color\n * range should be [0, 255].\n * @param method Optional string from `'binary' | 'otsu'`\n * which specifies the method for thresholding. Defaults to 'binary'.\n * @param inverted Optional boolean whichspecifies\n * if colours should be inverted. Defaults to false.\n * @param threshValue Optional number which defines threshold value from 0 to 1.\n * Defaults to 0.5.\n * @return A 3d tensor of shape [imageHeight,imageWidth, depth], which\n * contains binarized image.\n */\nfunction threshold_(image, method = 'binary', inverted = false, threshValue = 0.5) {\n const $image = convertToTensor(image, 'image', 'threshold');\n /* 0.2989, 0.5870, 0.1140 are represent luma coefficients in CCIR601.\n Reference for converting between RGB and grayscale: https://en.wikipedia.org/wiki/Luma_%28video%29 */\n const RED_INTENCITY_COEF = 0.2989;\n const GREEN_INTENCITY_COEF = 0.5870;\n const BLUE_INTENCITY_COEF = 0.1140;\n const totalPixelsInImage = $image.shape[0] * $image.shape[1];\n let $threshold = mul(tensor1d([threshValue]), 255);\n let r, g, b, grayscale;\n assert($image.rank === 3, () => 'Error in threshold: image must be rank 3,' +\n `but got rank ${$image.rank}.`);\n assert($image.shape[2] === 3 || $image.shape[2] === 1, () => 'Error in threshold: ' +\n 'image color channel must be equal to 3 or 1' +\n `but got ${$image.shape[2]}.`);\n assert($image.dtype === 'int32' || $image.dtype === 'float32', () => 'Error in dtype: image dtype must be int32 or float32,' +\n `but got dtype ${$image.dtype}.`);\n assert(method === 'otsu' || method === 'binary', () => `Method must be binary or otsu, but was ${method}`);\n if ($image.shape[2] === 3) {\n [r, g, b] = split($image, [1, 1, 1], -1);\n const $r = mul(r, RED_INTENCITY_COEF);\n const $g = mul(g, GREEN_INTENCITY_COEF);\n const $b = mul(b, BLUE_INTENCITY_COEF);\n grayscale = add$1(add$1($r, $g), $b);\n }\n else {\n grayscale = image;\n }\n if (method === 'otsu') {\n const $histogram = bincount(cast(round$1(grayscale), 'int32'), tensor([]), 256);\n $threshold = otsu($histogram, totalPixelsInImage);\n }\n const invCondition = inverted ?\n lessEqual(grayscale, $threshold) : greater(grayscale, $threshold);\n const result = cast(mul(invCondition, 255), 'int32');\n return result;\n}\nfunction otsu(histogram, total) {\n let bestThresh = tensor1d([-1]);\n let bestInBetVar = tensor1d([0]);\n let cInBetVar = tensor1d([0]);\n let classFirst, classSecond, meanFirst, meanSec, weightForeground, weightBack;\n for (let index = 0; index < histogram.size - 1; index++) {\n classFirst = slice(histogram, 0, index + 1);\n classSecond = slice(histogram, index + 1);\n weightForeground = div(sum$1(classFirst), total);\n weightBack = div(sum$1(classSecond), total);\n const meanFirstDivA = sum$1(mul(classFirst, range(0, classFirst.size)));\n meanFirst = div(meanFirstDivA, sum$1(classFirst));\n const meanSecFill = fill(classSecond.shape, classFirst.size);\n const meanSecAdd = add$1(range(0, classSecond.size), meanSecFill);\n const meanSecMul = mul(classSecond, (meanSecAdd));\n meanSec = div(sum$1(meanSecMul), sum$1(classSecond));\n const cInBetVarSubA = sub(meanFirst, meanSec);\n const cInBetVarSubB = sub(meanFirst, meanSec);\n const cInBetVarMul = mul(weightForeground, weightBack);\n cInBetVar = mul(mul(cInBetVarMul, cInBetVarSubA), cInBetVarSubB);\n const condition = greater(cInBetVar, bestInBetVar);\n bestInBetVar = where(condition, cInBetVar, bestInBetVar);\n bestThresh = where(condition, tensor1d([index]), bestThresh);\n }\n return bestThresh;\n}\nconst threshold = op({ threshold_ });\n\n/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Applies the given transform(s) to the image(s).\n *\n * @param image 4d tensor of shape `[batch, imageHeight, imageWidth, depth]`.\n * @param transforms Projective transform matrix/matrices. A tensor1d of length\n * 8 or tensor of size N x 8. If one row of transforms is [a0, a1, a2, b0\n * b1, b2, c0, c1], then it maps the output point (x, y) to a transformed\n * input point (x', y') = ((a0 x + a1 y + a2) / k, (b0 x + b1 y + b2) / k),\n * where k = c0 x + c1 y + 1. The transforms are inverted compared to the\n * transform mapping input points to output points.\n * @param interpolation Interpolation mode.\n * Supported values: 'nearest', 'bilinear'. Default to 'nearest'.\n * @param fillMode Points outside the boundaries of the input are filled\n * according to the given mode, one of 'constant', 'reflect', 'wrap',\n * 'nearest'. Default to 'constant'.\n * 'reflect': (d c b a | a b c d | d c b a ) The input is extended by\n * reflecting about the edge of the last pixel.\n * 'constant': (k k k k | a b c d | k k k k) The input is extended by\n * filling all values beyond the edge with the same constant value k.\n * 'wrap': (a b c d | a b c d | a b c d) The input is extended by\n * wrapping around to the opposite edge.\n * 'nearest': (a a a a | a b c d | d d d d) The input is extended by\n * the nearest pixel.\n * @param fillValue A float represents the value to be filled outside the\n * boundaries when fillMode is 'constant'.\n * @param Output dimension after the transform, [height, width]. If undefined,\n * output is the same size as input image.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction transform_(image, transforms, interpolation = 'nearest', fillMode = 'constant', fillValue = 0, outputShape) {\n const $image = convertToTensor(image, 'image', 'transform', 'float32');\n const $transforms = convertToTensor(transforms, 'transforms', 'transform', 'float32');\n assert($image.rank === 4, () => 'Error in transform: image must be rank 4,' +\n `but got rank ${$image.rank}.`);\n assert($transforms.rank === 2 &&\n ($transforms.shape[0] === $image.shape[0] ||\n $transforms.shape[0] === 1) &&\n $transforms.shape[1] === 8, () => `Error in transform: Input transform should be batch x 8 or 1 x 8`);\n assert(outputShape == null || outputShape.length === 2, () => 'Error in transform: outputShape must be [height, width] or null, ' +\n `but got ${outputShape}.`);\n const inputs = { image: $image, transforms: $transforms };\n const attrs = { interpolation, fillMode, fillValue, outputShape };\n return ENGINE.runKernel(Transform, inputs, attrs);\n}\nconst transform = op({ transform_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Copy a tensor setting everything outside a central band in each innermost\n * matrix to zero.\n *\n * The band part is computed as follows: Assume input has `k` dimensions\n * `[I, J, K, ..., M, N]`, then the output is a tensor with the same shape where\n * `band[i, j, k, ..., m, n] = in_band(m, n) * input[i, j, k, ..., m, n]`.\n * The indicator function\n * `in_band(m, n) = (num_lower < 0 || (m-n) <= num_lower))`\n * `&& (num_upper < 0 || (n-m) <= num_upper)`\n *\n * ```js\n * const x = tf.tensor2d([[ 0, 1, 2, 3],\n * [-1, 0, 1, 2],\n * [-2, -1, 0, 1],\n * [-3, -2, -1, 0]]);\n * let y = tf.linalg.bandPart(x, 1, -1);\n * y.print(); // [[ 0, 1, 2, 3],\n * // [-1, 0, 1, 2],\n * // [ 0, -1, 0, 1],\n * // [ 0, 0 , -1, 0]]\n * let z = tf.linalg.bandPart(x, 2, 1);\n * z.print(); // [[ 0, 1, 0, 0],\n * // [-1, 0, 1, 0],\n * // [-2, -1, 0, 1],\n * // [ 0, -2, -1, 0]]\n * ```\n *\n * @param x Rank `k` tensor\n * @param numLower Number of subdiagonals to keep.\n * If negative, keep entire lower triangle.\n * @param numUpper Number of subdiagonals to keep.\n * If negative, keep entire upper triangle.\n * @returns Rank `k` tensor of the same shape as input.\n * The extracted banded tensor.\n *\n * @doc {heading:'Operations', subheading:'Linear Algebra', namespace:'linalg'}\n */\nfunction bandPart_(a, numLower, numUpper) {\n assert(numLower % 1 === 0, () => `bandPart(): numLower must be an integer, got ${numLower}.`);\n assert(numUpper % 1 === 0, () => `bandPart(): numUpper must be an integer, got ${numUpper}.`);\n const $a = convertToTensor(a, 'a', 'bandPart');\n assert($a.rank >= 2, () => `bandPart(): Rank must be at least 2, got ${$a.rank}.`);\n const shape = $a.shape;\n const [M, N] = $a.shape.slice(-2);\n if (!(numLower <= M)) {\n throw new Error(`bandPart(): numLower (${numLower})` +\n ` must not be greater than the number of rows (${M}).`);\n }\n if (!(numUpper <= N)) {\n throw new Error(`bandPart(): numUpper (${numUpper})` +\n ` must not be greater than the number of columns (${N}).`);\n }\n if (numLower < 0) {\n numLower = M;\n }\n if (numUpper < 0) {\n numUpper = N;\n }\n const i = reshape(range(0, M, 1, 'int32'), [-1, 1]);\n const j = range(0, N, 1, 'int32');\n const ij = sub(i, j);\n const inBand = logicalAnd(lessEqual(ij, scalar(+numLower, 'int32')), greaterEqual(ij, scalar(-numUpper, 'int32')));\n const zero = zeros([M, N], $a.dtype);\n return reshape(stack(unstack(reshape($a, [-1, M, N]))\n .map(mat => where(inBand, mat, zero))), shape);\n}\nconst bandPart = op({ bandPart_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Gram-Schmidt orthogonalization.\n *\n * ```js\n * const x = tf.tensor2d([[1, 2], [3, 4]]);\n * let y = tf.linalg.gramSchmidt(x);\n * y.print();\n * console.log('Othogonalized:');\n * y.dot(y.transpose()).print(); // should be nearly the identity matrix.\n * console.log('First row direction maintained:');\n * const data = await y.array();\n * console.log(data[0][1] / data[0][0]); // should be nearly 2.\n * ```\n *\n * @param xs The vectors to be orthogonalized, in one of the two following\n * formats:\n * - An Array of `tf.Tensor1D`.\n * - A `tf.Tensor2D`, i.e., a matrix, in which case the vectors are the rows\n * of `xs`.\n * In each case, all the vectors must have the same length and the length\n * must be greater than or equal to the number of vectors.\n * @returns The orthogonalized and normalized vectors or matrix.\n * Orthogonalization means that the vectors or the rows of the matrix\n * are orthogonal (zero inner products). Normalization means that each\n * vector or each row of the matrix has an L2 norm that equals `1`.\n *\n * @doc {heading:'Operations', subheading:'Linear Algebra', namespace:'linalg'}\n */\nfunction gramSchmidt_(xs) {\n let inputIsTensor2D;\n if (Array.isArray(xs)) {\n inputIsTensor2D = false;\n assert(xs != null && xs.length > 0, () => 'Gram-Schmidt process: input must not be null, undefined, or ' +\n 'empty');\n const dim = xs[0].shape[0];\n for (let i = 1; i < xs.length; ++i) {\n assert(xs[i].shape[0] === dim, () => 'Gram-Schmidt: Non-unique lengths found in the input vectors: ' +\n `(${xs[i].shape[0]} vs. ${dim})`);\n }\n }\n else {\n inputIsTensor2D = true;\n xs = split(xs, xs.shape[0], 0).map(x => squeeze(x, [0]));\n }\n assert(xs.length <= xs[0].shape[0], () => `Gram-Schmidt: Number of vectors (${xs.length}) exceeds ` +\n `number of dimensions (${xs[0].shape[0]}).`);\n const ys = [];\n const xs1d = xs;\n for (let i = 0; i < xs.length; ++i) {\n ys.push(ENGINE.tidy(() => {\n let x = xs1d[i];\n if (i > 0) {\n for (let j = 0; j < i; ++j) {\n const proj = mul(sum$1(mul(ys[j], x)), ys[j]);\n x = sub(x, proj);\n }\n }\n return div(x, norm(x, 'euclidean'));\n }));\n }\n if (inputIsTensor2D) {\n return stack(ys, 0);\n }\n else {\n return ys;\n }\n}\nconst gramSchmidt = op({ gramSchmidt_ });\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Enables production mode which disables correctness checks in favor of\n * performance.\n *\n * @doc {heading: 'Environment'}\n */\nfunction enableProdMode() {\n env().set('PROD', true);\n}\n/**\n * Enables debug mode which will log information about all executed kernels:\n * the elapsed time of the kernel execution, as well as the rank, shape, and\n * size of the output tensor.\n *\n * Debug mode will significantly slow down your application as it will\n * download the result of every operation to the CPU. This should not be used in\n * production. Debug mode does not affect the timing information of the kernel\n * execution as we do not measure download time in the kernel execution time.\n *\n * See also: `tf.profile`, `tf.memory`.\n *\n * @doc {heading: 'Environment'}\n */\nfunction enableDebugMode() {\n env().set('DEBUG', true);\n}\n/** Globally disables deprecation warnings */\nfunction disableDeprecationWarnings() {\n env().set('DEPRECATION_WARNINGS_ENABLED', false);\n console.warn(`TensorFlow.js deprecation warnings have been disabled.`);\n}\n/** Warn users about deprecated functionality. */\nfunction deprecationWarn(msg) {\n if (env().getBool('DEPRECATION_WARNINGS_ENABLED')) {\n console.warn(msg + ' You can disable deprecation warnings with ' +\n 'tf.disableDeprecationWarnings().');\n }\n}\nsetDeprecationWarningFn(deprecationWarn);\n/**\n * Dispose all variables kept in backend engine.\n *\n * @doc {heading: 'Environment'}\n */\nfunction disposeVariables() {\n ENGINE.disposeVariables();\n}\n/**\n * It returns the global engine that keeps track of all tensors and backends.\n *\n * @doc {heading: 'Environment'}\n */\nfunction engine() {\n return ENGINE;\n}\n/**\n * Returns memory info at the current time in the program. The result is an\n * object with the following properties:\n *\n * - `numBytes`: Number of bytes allocated (undisposed) at this time.\n * - `numTensors`: Number of unique tensors allocated.\n * - `numDataBuffers`: Number of unique data buffers allocated\n * (undisposed) at this time, which is ≤ the number of tensors\n * (e.g. `a.reshape(newShape)` makes a new Tensor that shares the same\n * data buffer with `a`).\n * - `unreliable`: True if the memory usage is unreliable. See `reasons` when\n * `unreliable` is true.\n * - `reasons`: `string[]`, reasons why the memory is unreliable, present if\n * `unreliable` is true.\n *\n * WebGL Properties:\n * - `numBytesInGPU`: Number of bytes allocated (undisposed) in the GPU only at\n * this time.\n *\n * @doc {heading: 'Performance', subheading: 'Memory'}\n */\nfunction memory() {\n return ENGINE.memory();\n}\n/**\n * Executes the provided function `f()` and returns a promise that resolves\n * with information about the function's memory use:\n * - `newBytes`: the number of new bytes allocated\n * - `newTensors`: the number of new tensors created\n * - `peakBytes`: the peak number of bytes allocated\n * - `kernels`: an array of objects for each kernel involved that reports\n * their input and output shapes, number of bytes used, and number of new\n * tensors created.\n * - `kernelNames`: an array of unique strings with just the names of the\n * kernels in the `kernels` array.\n *\n * ```js\n * const profile = await tf.profile(() => {\n * const x = tf.tensor1d([1, 2, 3]);\n * let x2 = x.square();\n * x2.dispose();\n * x2 = x.square();\n * x2.dispose();\n * return x;\n * });\n *\n * console.log(`newBytes: ${profile.newBytes}`);\n * console.log(`newTensors: ${profile.newTensors}`);\n * console.log(`byte usage over all kernels: ${profile.kernels.map(k =>\n * k.totalBytesSnapshot)}`);\n * ```\n *\n *\n * @doc {heading: 'Performance', subheading: 'Profile'}\n */\nfunction profile(f) {\n return ENGINE.profile(f);\n}\n/**\n * Executes the provided function `fn` and after it is executed, cleans up all\n * intermediate tensors allocated by `fn` except those returned by `fn`.\n * `fn` must not return a Promise (async functions not allowed). The returned\n * result can be a complex object.\n *\n * Using this method helps avoid memory leaks. In general, wrap calls to\n * operations in `tf.tidy` for automatic memory cleanup.\n *\n * NOTE: Variables do *not* get cleaned up when inside a tidy(). If you want to\n * dispose variables, please use `tf.disposeVariables` or call dispose()\n * directly on variables.\n *\n * ```js\n * // y = 2 ^ 2 + 1\n * const y = tf.tidy(() => {\n * // a, b, and one will be cleaned up when the tidy ends.\n * const one = tf.scalar(1);\n * const a = tf.scalar(2);\n * const b = a.square();\n *\n * console.log('numTensors (in tidy): ' + tf.memory().numTensors);\n *\n * // The value returned inside the tidy function will return\n * // through the tidy, in this case to the variable y.\n * return b.add(one);\n * });\n *\n * console.log('numTensors (outside tidy): ' + tf.memory().numTensors);\n * y.print();\n * ```\n *\n * @param nameOrFn The name of the closure, or the function to execute.\n * If a name is provided, the 2nd argument should be the function.\n * If debug mode is on, the timing and the memory usage of the function\n * will be tracked and displayed on the console using the provided name.\n * @param fn The function to execute.\n *\n * @doc {heading: 'Performance', subheading: 'Memory'}\n */\nfunction tidy(nameOrFn, fn) {\n return ENGINE.tidy(nameOrFn, fn);\n}\n/**\n * Disposes any `tf.Tensor`s found within the provided object.\n *\n * @param container an object that may be a `tf.Tensor` or may directly\n * contain `tf.Tensor`s, such as a `Tensor[]` or `{key: Tensor, ...}`. If\n * the object is not a `tf.Tensor` or does not contain `Tensors`, nothing\n * happens. In general it is safe to pass any object here, except that\n * `Promise`s are not supported.\n *\n * @doc {heading: 'Performance', subheading: 'Memory'}\n */\nfunction dispose(container) {\n const tensors = getTensorsInContainer(container);\n tensors.forEach(tensor => tensor.dispose());\n}\n/**\n * Keeps a `tf.Tensor` generated inside a `tf.tidy` from being disposed\n * automatically.\n *\n * ```js\n * let b;\n * const y = tf.tidy(() => {\n * const one = tf.scalar(1);\n * const a = tf.scalar(2);\n *\n * // b will not be cleaned up by the tidy. a and one will be cleaned up\n * // when the tidy ends.\n * b = tf.keep(a.square());\n *\n * console.log('numTensors (in tidy): ' + tf.memory().numTensors);\n *\n * // The value returned inside the tidy function will return\n * // through the tidy, in this case to the variable y.\n * return b.add(one);\n * });\n *\n * console.log('numTensors (outside tidy): ' + tf.memory().numTensors);\n * console.log('y:');\n * y.print();\n * console.log('b:');\n * b.print();\n * ```\n *\n * @param result The tensor to keep from being disposed.\n *\n * @doc {heading: 'Performance', subheading: 'Memory'}\n */\nfunction keep(result) {\n return ENGINE.keep(result);\n}\n/**\n * Executes `f()` and returns a promise that resolves with timing\n * information.\n *\n * The result is an object with the following properties:\n *\n * - `wallMs`: Wall execution time.\n * - `kernelMs`: Kernel execution time, ignoring data transfer. If using the\n * WebGL backend and the query timer extension is not available, this will\n * return an error object.\n * - On `WebGL` The following additional properties exist:\n * - `uploadWaitMs`: CPU blocking time on texture uploads.\n * - `downloadWaitMs`: CPU blocking time on texture downloads (readPixels).\n *\n * ```js\n * const x = tf.randomNormal([20, 20]);\n * const time = await tf.time(() => x.matMul(x));\n *\n * console.log(`kernelMs: ${time.kernelMs}, wallTimeMs: ${time.wallMs}`);\n * ```\n *\n * @param f The function to execute and time.\n *\n * @doc {heading: 'Performance', subheading: 'Timing'}\n */\nfunction time(f) {\n return ENGINE.time(f);\n}\n/**\n * Sets the backend (cpu, webgl, wasm, etc) responsible for creating tensors and\n * executing operations on those tensors. Returns a promise that resolves\n * to a boolean if the backend initialization was successful.\n *\n * Note this disposes the current backend, if any, as well as any tensors\n * associated with it. A new backend is initialized, even if it is of the\n * same type as the previous one.\n *\n * @param backendName The name of the backend. Currently supports\n * `'webgl'|'cpu'` in the browser, `'tensorflow'` under node.js\n * (requires tfjs-node), and `'wasm'` (requires tfjs-backend-wasm).\n *\n * @doc {heading: 'Backends'}\n */\nfunction setBackend(backendName) {\n return ENGINE.setBackend(backendName);\n}\n/**\n * Returns a promise that resolves when the currently selected backend (or the\n * highest priority one) has initialized. Await this promise when you are using\n * a backend that has async initialization.\n *\n * @doc {heading: 'Backends'}\n */\nfunction ready() {\n return ENGINE.ready();\n}\n/**\n * Returns the current backend name (cpu, webgl, etc). The backend is\n * responsible for creating tensors and executing operations on those tensors.\n *\n * @doc {heading: 'Backends'}\n */\nfunction getBackend() {\n return ENGINE.backendName;\n}\n/**\n * Removes a backend and the registered factory.\n *\n * @doc {heading: 'Backends'}\n */\nfunction removeBackend(name) {\n ENGINE.removeBackend(name);\n}\n/**\n * Finds the backend registered under the provided name. Returns null if the\n * name is not in the registry, or the registration hasn't finished yet.\n */\nfunction findBackend(name) {\n return ENGINE.findBackend(name);\n}\n/**\n * Finds the backend factory registered under the provided name. Returns a\n * function that produces a new backend when called. Returns null if the name\n * is not in the registry.\n */\nfunction findBackendFactory(name) {\n return ENGINE.findBackendFactory(name);\n}\n/**\n * Registers a global backend. The registration should happen when importing\n * a module file (e.g. when importing `backend_webgl.ts`), and is used for\n * modular builds (e.g. custom tfjs bundle with only webgl support).\n *\n * @param factory The backend factory function. When called, it should\n * return a backend instance, or a promise of an instance.\n * @param priority The priority of the backend (higher = more important).\n * In case multiple backends are registered, the priority is used to find\n * the best backend. Defaults to 1.\n * @return False if there is already a registered backend under this name, true\n * if not.\n *\n * @doc {heading: 'Backends'}\n */\nfunction registerBackend(name, factory, priority = 1) {\n return ENGINE.registerBackend(name, factory, priority);\n}\n/**\n * Gets the current backend. If no backends have been initialized, this will\n * attempt to initialize the best backend. Will throw an error if the highest\n * priority backend has async initialization, in which case, you should call\n * 'await tf.ready()' before running other code.\n *\n * @doc {heading: 'Backends'}\n */\nfunction backend() {\n return ENGINE.backend;\n}\n/**\n * Sets the global platform.\n *\n * @param platformName The name of this platform.\n * @param platform A platform implementation.\n */\nfunction setPlatform(platformName, platform) {\n env().setPlatform(platformName, platform);\n}\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Compute QR decomposition of m-by-n matrix using Householder transformation.\n *\n * Implementation based on\n * [http://www.cs.cornell.edu/~bindel/class/cs6210-f09/lec18.pdf]\n * (http://www.cs.cornell.edu/~bindel/class/cs6210-f09/lec18.pdf)\n *\n * ```js\n * const a = tf.tensor2d([[1, 2], [3, 4]]);\n * let [q, r] = tf.linalg.qr(a);\n * console.log('Q');\n * q.print();\n * console.log('R');\n * r.print();\n * console.log('Orthogonalized');\n * q.dot(q.transpose()).print() // should be nearly the identity matrix.\n * console.log('Reconstructed');\n * q.dot(r).print(); // should be nearly [[1, 2], [3, 4]];\n * ```\n *\n * @param x The `tf.Tensor` to be QR-decomposed. Must have rank >= 2. Suppose\n * it has the shape `[..., M, N]`.\n * @param fullMatrices An optional boolean parameter. Defaults to `false`.\n * If `true`, compute full-sized `Q`. If `false` (the default),\n * compute only the leading N columns of `Q` and `R`.\n * @returns An `Array` of two `tf.Tensor`s: `[Q, R]`. `Q` is a unitary matrix,\n * i.e., its columns all have unit norm and are mutually orthogonal.\n * If `M >= N`,\n * If `fullMatrices` is `false` (default),\n * - `Q` has a shape of `[..., M, N]`,\n * - `R` has a shape of `[..., N, N]`.\n * If `fullMatrices` is `true` (default),\n * - `Q` has a shape of `[..., M, M]`,\n * - `R` has a shape of `[..., M, N]`.\n * If `M < N`,\n * - `Q` has a shape of `[..., M, M]`,\n * - `R` has a shape of `[..., M, N]`.\n * @throws If the rank of `x` is less than 2.\n *\n * @doc {heading:'Operations',\n * subheading:'Linear Algebra',\n * namespace:'linalg'}\n */\nfunction qr_(x, fullMatrices = false) {\n assert(x.rank >= 2, () => `qr() requires input tensor to have a rank >= 2, but got rank ${x.rank}`);\n if (x.rank === 2) {\n return qr2d(x, fullMatrices);\n }\n else {\n // Rank > 2.\n // TODO(cais): Below we split the input into individual 2D tensors,\n // perform QR decomposition on them and then stack the results back\n // together. We should explore whether this can be parallelized.\n const outerDimsProd = x.shape.slice(0, x.shape.length - 2)\n .reduce((value, prev) => value * prev);\n const x2ds = unstack(reshape(x, [\n outerDimsProd, x.shape[x.shape.length - 2],\n x.shape[x.shape.length - 1]\n ]), 0);\n const q2ds = [];\n const r2ds = [];\n x2ds.forEach(x2d => {\n const [q2d, r2d] = qr2d(x2d, fullMatrices);\n q2ds.push(q2d);\n r2ds.push(r2d);\n });\n const q = reshape(stack(q2ds, 0), x.shape);\n const r = reshape(stack(r2ds, 0), x.shape);\n return [q, r];\n }\n}\nfunction qr2d(x, fullMatrices = false) {\n return ENGINE.tidy(() => {\n assert(x.shape.length === 2, () => `qr2d() requires a 2D Tensor, but got a ${x.shape.length}D Tensor.`);\n const m = x.shape[0];\n const n = x.shape[1];\n let q = eye(m); // Orthogonal transform so far.\n let r = clone(x); // Transformed matrix so far.\n const one2D = tensor2d([[1]], [1, 1]);\n let w = clone(one2D);\n const iters = m >= n ? n : m;\n for (let j = 0; j < iters; ++j) {\n // This tidy within the for-loop ensures we clean up temporary\n // tensors as soon as they are no longer needed.\n const rTemp = r;\n const wTemp = w;\n const qTemp = q;\n [w, r, q] = ENGINE.tidy(() => {\n // Find H = I - tau * w * w', to put zeros below R(j, j).\n const rjEnd1 = slice(r, [j, j], [m - j, 1]);\n const normX = norm(rjEnd1);\n const rjj = slice(r, [j, j], [1, 1]);\n // The sign() function returns 0 on 0, which causes division by zero.\n const s = where(greater(rjj, 0), tensor2d([[-1]]), tensor2d([[1]]));\n const u1 = sub(rjj, mul(s, normX));\n const wPre = div(rjEnd1, u1);\n if (wPre.shape[0] === 1) {\n w = clone(one2D);\n }\n else {\n w = concat([\n one2D,\n slice(wPre, [1, 0], [wPre.shape[0] - 1, wPre.shape[1]])\n ], 0);\n }\n const tau = neg(div(matMul(s, u1), normX));\n // -- R := HR, Q := QH.\n const rjEndAll = slice(r, [j, 0], [m - j, n]);\n const tauTimesW = mul(tau, w);\n const wT = transpose(w);\n if (j === 0) {\n r = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));\n }\n else {\n const rTimesTau = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));\n r = concat([slice(r, [0, 0], [j, n]), rTimesTau], 0);\n }\n const tawTimesWT = transpose(tauTimesW);\n const qAllJEnd = slice(q, [0, j], [m, q.shape[1] - j]);\n if (j === 0) {\n q = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));\n }\n else {\n const qTimesTau = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));\n q = concat([slice(q, [0, 0], [m, j]), qTimesTau], 1);\n }\n return [w, r, q];\n });\n dispose([rTemp, wTemp, qTemp]);\n }\n if (!fullMatrices && m > n) {\n q = slice(q, [0, 0], [m, n]);\n r = slice(r, [0, 0], [n, n]);\n }\n return [q, r];\n });\n}\nconst qr = op({ qr_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nvar Reduction;\n(function (Reduction) {\n Reduction[Reduction[\"NONE\"] = 0] = \"NONE\";\n Reduction[Reduction[\"MEAN\"] = 1] = \"MEAN\";\n Reduction[Reduction[\"SUM\"] = 2] = \"SUM\";\n Reduction[Reduction[\"SUM_BY_NONZERO_WEIGHTS\"] = 3] = \"SUM_BY_NONZERO_WEIGHTS\";\n})(Reduction || (Reduction = {}));\n\n/**\n * Computes the weighted loss between two tensors.\n *\n * @param losses Tensor of shape `[batch_size, d1, ... dN]`.\n * @param weights Tensor whose rank is either 0, or the same rank as\n * `losses`, and must be broadcastable to `losses` (i.e., all\n * dimensions must be either `1`, or the same as the corresponding\n * `losses` dimension).\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction computeWeightedLoss_(losses, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n const $losses = convertToTensor(losses, 'losses', 'computeWeightedLoss');\n let $weights = null;\n if (weights != null) {\n $weights = convertToTensor(weights, 'weights', 'computeWeightedLoss');\n }\n const weightedLoss = ($weights == null) ? $losses : mul($losses, $weights);\n if (reduction === Reduction.NONE) {\n return weightedLoss;\n }\n if (reduction === Reduction.SUM) {\n return sum$1(weightedLoss);\n }\n if (reduction === Reduction.MEAN) {\n if ($weights == null) {\n return mean(weightedLoss);\n }\n else {\n const broadcastFactor = $losses.size / $weights.size;\n const result = div(sum$1(weightedLoss), sum$1($weights));\n return broadcastFactor > 1 ? div(result, scalar(broadcastFactor)) :\n result;\n }\n }\n if (reduction === Reduction.SUM_BY_NONZERO_WEIGHTS) {\n if ($weights == null) {\n return div(sum$1(weightedLoss), scalar($losses.size));\n }\n else {\n const broadcastedWeights = mul($weights, ones$1($losses.shape));\n const numNonZeros = cast(sum$1(notEqual(broadcastedWeights, scalar(0))), 'float32');\n return div(sum$1(weightedLoss), numNonZeros);\n }\n }\n throw Error(`Unknown reduction: ${reduction}`);\n}\nconst computeWeightedLoss = op({ computeWeightedLoss_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes the absolute difference loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n * 'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n * `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n * must be either `1`, or the same as the corresponding `losses`\n * dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n * `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction absoluteDifference_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n const $labels = convertToTensor(labels, 'labels', 'absoluteDifference');\n const $predictions = convertToTensor(predictions, 'predictions', 'absoluteDifference');\n let $weights = null;\n if (weights != null) {\n $weights = convertToTensor(weights, 'weights', 'absoluteDifference');\n }\n assertShapesMatch($labels.shape, $predictions.shape, 'Error in absoluteDifference: ');\n const losses = abs(sub($labels, $predictions));\n return computeWeightedLoss(losses, $weights, reduction);\n}\nconst absoluteDifference = op({ absoluteDifference_ });\n\n/**\n * Computes the cosine distance loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n * 'predictions'.\n * @param predictions The predicted outputs.\n * @param axis The dimension along which the cosine distance is computed.\n * @param weights Tensor whose rank is either 0, or the same rank as\n * `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n * must be either `1`, or the same as the corresponding `losses`\n * dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n * `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction cosineDistance_(labels, predictions, axis, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n const $labels = convertToTensor(labels, 'labels', 'cosineDistance');\n const $predictions = convertToTensor(predictions, 'predictions', 'cosineDistance');\n let $weights = null;\n if (weights != null) {\n $weights = convertToTensor(weights, 'weights', 'cosineDistance');\n }\n assertShapesMatch($labels.shape, $predictions.shape, 'Error in cosineDistance: ');\n const one = scalar(1);\n const losses = sub(one, sum$1(mul($labels, $predictions), axis, true));\n return computeWeightedLoss(losses, $weights, reduction);\n}\nconst cosineDistance = op({ cosineDistance_ });\n\n/**\n * Computes the Hinge loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n * 'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n * `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n * must be either `1`, or the same as the corresponding `losses`\n * dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n * `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction hingeLoss_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n let $labels = convertToTensor(labels, 'labels', 'hingeLoss');\n const $predictions = convertToTensor(predictions, 'predictions', 'hingeLoss');\n let $weights = null;\n if (weights != null) {\n $weights = convertToTensor(weights, 'weights', 'hingeLoss');\n }\n assertShapesMatch($labels.shape, $predictions.shape, 'Error in hingeLoss: ');\n const one = scalar(1);\n // Convert binary labels to (-1, 1)\n $labels = sub(mul(scalar(2), $labels), one);\n const losses = relu(sub(one, mul($labels, $predictions)));\n return computeWeightedLoss(losses, $weights, reduction);\n}\nconst hingeLoss = op({ hingeLoss_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes the huber loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n * 'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n * `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n * must be either `1`, or the same as the corresponding `losses`\n * dimension).\n * @param delta Point where huber loss changes from quadratic to linear.\n * @param reduction Type of reduction to apply to loss. Should be of type\n * `Reduction`.\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction huberLoss_(labels, predictions, weights, delta = 1.0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n const $labels = convertToTensor(labels, 'labels', 'huberLoss');\n const $predictions = convertToTensor(predictions, 'predictions', 'huberLoss');\n let $weights = null;\n if (weights != null) {\n $weights = convertToTensor(weights, 'weights', 'huberLoss');\n }\n assertShapesMatch($labels.shape, $predictions.shape, 'Error in huberLoss: ');\n const deltaScalar = scalar(delta);\n const error = abs(sub($predictions, $labels));\n const quadratic = minimum(error, deltaScalar);\n const linear = sub(error, quadratic);\n const losses = add$1(mul(scalar(0.5), square(quadratic)), mul(deltaScalar, linear));\n return computeWeightedLoss(losses, $weights, reduction);\n}\nconst huberLoss = op({ huberLoss_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes the log loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n * 'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n * `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n * must be either `1`, or the same as the corresponding `losses`\n * dimension).\n * @param epsilon A small increment to avoid taking log of zero\n * @param reduction Type of reduction to apply to loss. Should be of type\n * `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction logLoss_(labels, predictions, weights, epsilon = 1e-7, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n const $labels = convertToTensor(labels, 'labels', 'logLoss');\n const $predictions = convertToTensor(predictions, 'predictions', 'logLoss');\n let $weights = null;\n if (weights != null) {\n $weights = convertToTensor(weights, 'weights', 'logLoss');\n }\n assertShapesMatch($labels.shape, $predictions.shape, 'Error in logLoss: ');\n const one = scalar(1);\n const epsilonScalar = scalar(epsilon);\n const l1 = neg(mul($labels, log$1(add$1($predictions, epsilonScalar))));\n const l2 = mul(sub(one, $labels), log$1(add$1(sub(one, $predictions), epsilonScalar)));\n const losses = sub(l1, l2);\n return computeWeightedLoss(losses, $weights, reduction);\n}\nconst logLoss = op({ logLoss_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes the mean squared error between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n * 'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n * `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n * must be either `1`, or the same as the corresponding `losses`\n * dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n * `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction meanSquaredError_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n const $labels = convertToTensor(labels, 'labels', 'meanSquaredError');\n const $predictions = convertToTensor(predictions, 'predictions', 'meanSquaredError');\n let $weights = null;\n if (weights != null) {\n $weights = convertToTensor(weights, 'weights', 'meanSquaredError');\n }\n assertShapesMatch($labels.shape, $predictions.shape, 'Error in meanSquaredError: ');\n const losses = squaredDifference($labels, $predictions);\n return computeWeightedLoss(losses, $weights, reduction);\n}\nconst meanSquaredError = op({ meanSquaredError_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nfunction sigmoidCrossEntropyWithLogits_(labels, logits) {\n const $labels = convertToTensor(labels, 'labels', 'sigmoidCrossEntropyWithLogits');\n const $logits = convertToTensor(logits, 'logits', 'sigmoidCrossEntropyWithLogits');\n assertShapesMatch($labels.shape, $logits.shape, 'Error in sigmoidCrossEntropyWithLogits: ');\n /**\n * Implementation Details:\n *\n * For brevity, let `x = logits`, `z = labels`. The logistic loss is\n * z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))\n * = z * -log(1 / (1 + exp(-x))) + (1 - z) * -log(exp(-x) / (1 + exp(-x)))\n * = z * log(1 + exp(-x)) + (1 - z) * (-log(exp(-x)) + log(1 + exp(-x)))\n * = z * log(1 + exp(-x)) + (1 - z) * (x + log(1 + exp(-x))\n * = (1 - z) * x + log(1 + exp(-x))\n * = x - x * z + log(1 + exp(-x))\n *\n * For x < 0, to avoid overflow in exp(-x), we reformulate the above\n * x - x * z + log(1 + exp(-x))\n * = log(exp(x)) - x * z + log(1 + exp(-x))\n * = - x * z + log(1 + exp(x))\n *\n * Hence, to ensure stability and avoid overflow, the implementation uses\n * this equivalent formulation:\n * max(x, 0) - x * z + log(1 + exp(-abs(x)))\n */\n const maxOutput = relu($logits);\n const outputXTarget = mul($logits, $labels);\n const sigmoidOutput = log1p(exp(neg(abs($logits))));\n return add$1(sub(maxOutput, outputXTarget), sigmoidOutput);\n}\n/**\n * Computes the sigmoid cross entropy loss between two tensors.\n *\n * If labelSmoothing is nonzero, smooth the labels towards 1/2:\n *\n * newMulticlassLabels = multiclassLabels * (1 - labelSmoothing)\n * + 0.5 * labelSmoothing\n *\n * @param multiClassLabels The ground truth output tensor of shape\n * [batch_size, num_classes], same dimensions as 'predictions'.\n * @param logits The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n * `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n * must be either `1`, or the same as the corresponding `losses`\n * dimension).\n * @param labelSmoothing If greater than 0, then smooth the labels.\n * @param reduction Type of reduction to apply to loss. Should be of type\n * `Reduction`\n *\n * @doc { heading: 'Training', subheading: 'Losses', namespace: 'losses' }\n */\nfunction sigmoidCrossEntropy_(multiClassLabels, logits, weights, labelSmoothing = 0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n let $multiClassLabels = convertToTensor(multiClassLabels, 'multiClassLabels', 'sigmoidCrossEntropy');\n const $logits = convertToTensor(logits, 'logits', 'sigmoidCrossEntropy');\n let $weights = null;\n if (weights != null) {\n $weights = convertToTensor(weights, 'weights', 'sigmoidCrossEntropy');\n }\n assertShapesMatch($multiClassLabels.shape, $logits.shape, 'Error in sigmoidCrossEntropy: ');\n if (labelSmoothing > 0) {\n const labelSmoothingScalar = scalar(labelSmoothing);\n const one = scalar(1);\n const half = scalar(0.5);\n $multiClassLabels =\n add$1(mul($multiClassLabels, sub(one, labelSmoothingScalar)), mul(half, labelSmoothingScalar));\n }\n const losses = sigmoidCrossEntropyWithLogits_($multiClassLabels, $logits);\n return computeWeightedLoss(losses, $weights, reduction);\n}\nconst sigmoidCrossEntropy = op({ sigmoidCrossEntropy_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes softmax cross entropy between logits and labels.\n *\n * Measures the probability error in discrete classification tasks in which\n * the classes are mutually exclusive (each entry is in exactly one class).\n * For example, each CIFAR-10 image is labeled with one and only one label: an\n * image can be a dog or a truck, but not both.\n *\n * `NOTE`: While the classes are mutually exclusive, their probabilities need\n * not be. All that is required is that each row of labels is a valid\n * probability distribution. If they are not, the computation of the gradient\n * will be incorrect.\n *\n * `WARNING`: This op expects unscaled logits, since it performs a softmax on\n * logits internally for efficiency. Do not call this op with the output of\n * softmax, as it will produce incorrect results.\n *\n * logits and labels must have the same shape, e.g. [batch_size, num_classes]\n * and the same dtype.\n * @param labels The labels array.\n * @param logits The logits array.\n * @param dim The dimension softmax would be performed on. Defaults to `-1`\n * which indicates the last dimension.\n */\nfunction softmaxCrossEntropyWithLogits_(labels, logits, dim = -1) {\n if (dim === -1) {\n dim = logits.rank - 1;\n }\n if (dim !== logits.rank - 1) {\n throw Error(`Softmax cross entropy along a non-last dimension is not yet ` +\n `supported. Labels / logits was rank ${logits.rank} ` +\n `and dim was ${dim}`);\n }\n // Use a custom gradient for numerical stability.\n const customOp = customGrad((labels, logits, save) => {\n // Reference:\n // 1. http://cs231n.github.io/linear-classify/#softmax\n // 2. https://blog.feedly.com/tricks-of-the-trade-logsumexp/\n const keepDims = true;\n const lse = logSumExp(logits, [dim], keepDims);\n const logResult = sub(cast(logits, 'float32'), lse);\n save([labels, logResult]);\n const costVector = neg(mul(logResult, labels));\n const value = sum$1(costVector, [dim]);\n const gradFunc = (dy, saved) => {\n const [labels, logResult] = saved;\n const dyShape = expandShapeToKeepDim(dy.shape, [dim]);\n return [\n mul(reshape(dy, dyShape), sub(cast(labels, 'float32'), exp(logResult))),\n mul(reshape(dy, dyShape), sub(exp(logResult), cast(labels, 'float32'))),\n ];\n };\n return { value, gradFunc };\n });\n return customOp(labels, logits);\n}\n/**\n * Computes the softmax cross entropy loss between two tensors.\n *\n * If labelSmoothing is nonzero, smooth the labels towards 1/2:\n *\n * newOnehotLabels = onehotLabels * (1 - labelSmoothing)\n * + labelSmoothing / numClasses\n *\n * @param onehotLabels One hot encoded labels\n * [batch_size, num_classes], same dimensions as 'predictions'.\n * @param logits The predicted outputs.\n * @param weights Tensor whose rank is either 0, or 1, and must be\n * broadcastable to `loss` of shape [batch_size]\n * @param labelSmoothing If greater than 0, then smooth the labels.\n * @param reduction Type of reduction to apply to loss. Should be of type\n * `Reduction`\n *\n * @doc { heading: 'Training', subheading: 'Losses', namespace: 'losses' }\n */\nfunction softmaxCrossEntropy_(onehotLabels, logits, weights, labelSmoothing = 0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n let $onehotLabels = convertToTensor(onehotLabels, 'onehotLabels', 'softmaxCrossEntropy');\n const $logits = convertToTensor(logits, 'logits', 'softmaxCrossEntropy');\n let $weights = null;\n if (weights != null) {\n $weights = convertToTensor(weights, 'weights', 'softmaxCrossEntropy');\n }\n assertShapesMatch($onehotLabels.shape, $logits.shape, 'Error in softmaxCrossEntropy: ');\n if (labelSmoothing > 0) {\n const labelSmoothingScalar = scalar(labelSmoothing);\n const one = scalar(1);\n const numClasses = scalar($onehotLabels.shape[1]);\n $onehotLabels =\n add$1(mul($onehotLabels, sub(one, labelSmoothingScalar)), div(labelSmoothingScalar, numClasses));\n }\n const losses = softmaxCrossEntropyWithLogits_($onehotLabels, $logits);\n return computeWeightedLoss(losses, $weights, reduction);\n}\nconst softmaxCrossEntropy = op({ softmaxCrossEntropy_ });\n\n/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * The input SparseTensor is represented via the map of inputs {`indices`,\n * `values`, `denseShape`}. The output SparseTensor has the same `denseShape`\n * but with indices `outputIndices` and values `outputValues`. This op inserts a\n * single entry for every row that doesn't have any values. The index is created\n * as `[row, 0, ..., 0]` and the inserted value is `defaultValue`.\n *\n * For example, suppose `spInput` has shape [5, 6] and non-empty values:\n * [0, 1]: a\n * [0, 3]: b\n * [2, 0]: c\n * [3, 1]: d\n *\n * Rows 1 and 4 are empty, so the output will be of shape [5, 6] with values:\n * [0, 1]: a\n * [0, 3]: b\n * [1, 0]: `defaultValue`\n * [2, 0]: c\n * [3, 1]: d\n * [4, 0]: `defaultValue`\n *\n * The output SparseTensor will be in row-major order and will have the same\n * shape as the input.\n *\n * This op also returns an indicator vector shaped [dense_shape[0]] such that\n * emptyRowIndicator[i] = True iff row i was an empty row.\n *\n * And a reverse index map vector shaped [indices.shape[0]] that is used during\n * backpropagation, reverseIndexMap[i] = outi s.t. indices[i, j] ==\n * outputIndices[outi, j] for all j\n *\n * ```js\n * const result = tf.sparse.sparseFillEmptyRows(\n * [[0, 0], [1, 0], [1, 3], [1, 4], [3, 2], [3, 3]],\n * [0, 10, 13, 14, 32, 33], [5, 6], -1);\n * console.log(result);\n * result['outputIndices'].print(); // [[0, 0], [1, 0], [1, 3], [1, 4],\n * // [2, 0], [3, 2], [3, 3], [4, 0]]\n * result['outputValues'].print(); // [0, 10, 13, 14,-1, 32, 33, -1]\n * result['emptyRowIndicator'].print(); // [false, false, true, false, true]\n * result['reverseIndexMap'].print(); // [0, 1, 2, 3, 5, 6]\n * ```\n * @param indices: 2-D. the indices of the sparse tensor.\n * @param values: 1-D. the values of the sparse tensor.\n * @param denseShape: 1-D. the shape of the sparse tensor.\n * @param defaultValue: 0-D. default value to insert into location [row, 0, ...,\n * 0] for rows missing from the input sparse tensor.\n * @return A map with the following properties:\n * - outputIndices\n * - outputValues: 1-D. the values of the filled sparse tensor.\n * - emptyRowIndicator: 1-D. whether the dense row was missing in the input\n * sparse tensor.\n * - reverseIndexMap: 1-D. a map from the input indices to the output\n * indices.\n * @doc {heading: 'Operations', subheading: 'Sparse'}\n */\nfunction sparseFillEmptyRows_(indices, values, denseShape, defaultValue) {\n const $indices = convertToTensor(indices, 'indices', 'sparseFillEmptyRows', 'int32');\n const $values = convertToTensor(values, 'values', 'sparseFillEmptyRows');\n const $denseShape = convertToTensor(denseShape, 'denseShape', 'sparseFillEmptyRows', 'int32');\n const $defaultValue = convertToTensor(defaultValue, 'defaultValue', 'sparseFillEmptyRows', $values.dtype);\n if ($indices.rank !== 2) {\n throw new Error(`Indices should be Tensor2D but received shape\n ${$indices.shape}`);\n }\n if ($values.rank !== 1) {\n throw new Error(`Values should be Tensor1D but received shape ${$values.shape}`);\n }\n if ($denseShape.rank !== 1) {\n throw new Error(`Dense shape should be Tensor1D but received shape ${$denseShape.shape}`);\n }\n if ($defaultValue.rank !== 0) {\n throw new Error(`Default value should be a scalar but received shape ${$defaultValue.shape}`);\n }\n const inputs = {\n indices: $indices,\n values: $values,\n denseShape: $denseShape,\n defaultValue: $defaultValue\n };\n const result = ENGINE.runKernel(SparseFillEmptyRows, inputs);\n return {\n outputIndices: result[0],\n outputValues: result[1],\n emptyRowIndicator: result[2],\n reverseIndexMap: result[3]\n };\n}\nconst sparseFillEmptyRows = op({ sparseFillEmptyRows_ });\n\n/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * This operation has the same semantics as reshape on the represented dense\n * tensor. The `inputIndices` are recomputed based on the requested `newShape`.\n * If one component of `newShape` is the special value -1, the size of that\n * dimension is computed so that the total dense size remains constant. At most\n * one component of `newShape` can be -1. The number of dense elements implied\n * by `newShape` must be the same as the number of dense elements originally\n * implied by `inputShape`. Reshaping does not affect the order of values in the\n * SparseTensor. If the input tensor has rank R_in and N non-empty values, and\n * `newShape` has length R_out, then `inputIndices` has shape [N, R_in],\n * `inputShape` has length R_in, `outputIndices` has shape [N, R_out], and\n * `outputShape` has length R_out.\n *\n * ```js\n * const result = tf.sparse.sparseReshape(\n * [[0, 0, 0], [0, 0, 1], [0, 1, 0], [1, 0, 0], [1, 2, 3]],\n * [2, 3, 6], [9, -1]);\n * console.log(result);\n * result['outputIndices'].print(); //[[0, 0], [0, 1], [1, 2], [4, 2], [8, 1]]\n * result['outputShape'].print(); // [9, 4]\n * ```\n * @param inputIndices: 2-D. N x R_in matrix with the indices of non-empty\n * values in a SparseTensor.\n * @param inputShape: 1-D. R_in Tensor1D with the input SparseTensor's dense\n * shape.\n * @param newShape: 1-D. R_out Tensor1D with the requested new dense shape.\n * @return A map with the following properties:\n * - outputIndices: 2-D. N x R_out matrix with the updated indices of\n * non-empty values in the output SparseTensor.\n * - outputShape: 1-D. R_out vector with the full dense shape of the output\n * SparseTensor. This is the same as newShape but with any -1 dimensions\n * filled in.\n * @doc {heading: 'Operations', subheading: 'Sparse'}\n */\nfunction sparseReshape_(inputIndices, inputShape, newShape) {\n const $inputIndices = convertToTensor(inputIndices, 'inputIndices', 'sparseReshape', 'int32');\n const $inputShape = convertToTensor(inputShape, 'inputShape', 'sparseReshape', 'int32');\n const $newShape = convertToTensor(newShape, 'newShape', 'sparseReshape', 'int32');\n if ($inputIndices.rank !== 2) {\n throw new Error(`Input indices should be Tensor2D but received shape\n ${$inputIndices.shape}`);\n }\n if ($inputShape.rank !== 1) {\n throw new Error(`Input shape should be Tensor1D but received shape ${$inputShape.shape}`);\n }\n if ($newShape.rank !== 1) {\n throw new Error(`New shape should be Tensor1D but received shape ${$newShape.shape}`);\n }\n const inputs = {\n inputIndices: $inputIndices,\n inputShape: $inputShape,\n newShape: $newShape\n };\n const result = ENGINE.runKernel(SparseReshape, inputs);\n return { outputIndices: result[0], outputShape: result[1] };\n}\nconst sparseReshape = op({ sparseReshape_ });\n\n/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes the mean along sparse segments of a tensor.\n *\n * ```js\n * const c = tf.tensor2d([[1,2,3,4], [-1,-2,-3,-4], [6,7,8,9]]);\n * // Select two rows, one segment.\n * const result1 = tf.sparse.sparseSegmentMean(c,\n * tf.tensor1d([0, 1], 'int32'),\n * tf.tensor1d([0, 0], 'int32'));\n * result1.print(); // [[0, 0, 0, 0]]\n *\n * // Select two rows, two segments.\n * const result2 = tf.sparse.sparseSegmentMean(c,\n * tf.tensor1d([0, 1], 'int32'),\n * tf.tensor1d([0, 1], 'int32'));\n * result2.print(); // [[1, 2, 3, 4], [-1, -2, -3, -4]]\n *\n * // Select all rows, two segments.\n * const result3 = tf.sparse.sparseSegmentMean(c,\n * tf.tensor1d([0, 1, 2], 'int32'),\n * tf.tensor1d([0, 1, 1], 'int32'));\n * result3.print(); // [[1.0, 2.0, 3.0, 4.0], [2.5, 2.5, 2.5, 2.5]]\n * ```\n * @param data: A Tensor of at least one dimension with data that will be\n * assembled in the output.\n * @param indices: A 1-D Tensor with indices into data. Has same rank as\n * segmentIds.\n * @param segmentIds: A 1-D Tensor with indices into the output Tensor. Values\n * should be sorted and can be repeated.\n * @return Has same shape as data, except for dimension 0 which has equal to\n * the number of segments.\n *\n * @doc {heading: 'Operations', subheading: 'Sparse'}\n */\nfunction sparseSegmentMean_(data, indices, segmentIds) {\n const $data = convertToTensor(data, 'data', 'sparseSegmentMean');\n const $indices = convertToTensor(indices, 'indices', 'sparseSegmentMean', 'int32');\n const $segmentIds = convertToTensor(segmentIds, 'segmentIds', 'sparseSegmentMean', 'int32');\n if ($data.rank < 1) {\n throw new Error(`Data should be at least 1 dimensional but received scalar`);\n }\n if ($indices.rank !== 1) {\n throw new Error(`Indices should be Tensor1D but received shape\n ${$indices.shape}`);\n }\n if ($segmentIds.rank !== 1) {\n throw new Error(`Segment ids should be Tensor1D but received shape\n ${$segmentIds.shape}`);\n }\n const inputs = {\n data: $data,\n indices: $indices,\n segmentIds: $segmentIds\n };\n return ENGINE.runKernel(SparseSegmentMean, inputs);\n}\nconst sparseSegmentMean = op({ sparseSegmentMean_ });\n\n/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Computes the sum along sparse segments of a tensor.\n *\n * ```js\n * const c = tf.tensor2d([[1,2,3,4], [-1,-2,-3,-4], [5,6,7,8]]);\n * // Select two rows, one segment.\n * const result1 = tf.sparse.sparseSegmentSum(c,\n * tf.tensor1d([0, 1], 'int32'),\n * tf.tensor1d([0, 0], 'int32'));\n * result1.print(); // [[0, 0, 0, 0]]\n *\n * // Select two rows, two segment.\n * const result2 = tf.sparse.sparseSegmentSum(c,\n * tf.tensor1d([0, 1], 'int32'),\n * tf.tensor1d([0, 1], 'int32'));\n * result2.print(); // [[1, 2, 3, 4], [-1, -2, -3, -4]]\n *\n * // Select all rows, two segments.\n * const result3 = tf.sparse.sparseSegmentSum(c,\n * tf.tensor1d([0, 1, 2], 'int32'),\n * tf.tensor1d([0, 0, 1], 'int32'));\n * result3.print(); // [[0, 0, 0, 0], [5, 6, 7, 8]]\n * ```\n * @param data: A Tensor of at least one dimension with data that will be\n * assembled in the output.\n * @param indices: A 1-D Tensor with indices into data. Has same rank as\n * segmentIds.\n * @param segmentIds: A 1-D Tensor with indices into the output Tensor. Values\n * should be sorted and can be repeated.\n * @return Has same shape as data, except for dimension 0 which has equal to\n * the number of segments.\n *\n * @doc {heading: 'Operations', subheading: 'Sparse'}\n */\nfunction sparseSegmentSum_(data, indices, segmentIds) {\n const $data = convertToTensor(data, 'data', 'sparseSegmentSum');\n const $indices = convertToTensor(indices, 'indices', 'sparseSegmentSum', 'int32');\n const $segmentIds = convertToTensor(segmentIds, 'segmentIds', 'sparseSegmentSum', 'int32');\n if ($data.rank < 1) {\n throw new Error(`Data should be at least 1 dimensional but received scalar`);\n }\n if ($indices.rank !== 1) {\n throw new Error(`Indices should be Tensor1D but received shape\n ${$indices.shape}`);\n }\n if ($segmentIds.rank !== 1) {\n throw new Error(`Segment ids should be Tensor1D but received shape\n ${$segmentIds.shape}`);\n }\n const inputs = {\n data: $data,\n indices: $indices,\n segmentIds: $segmentIds\n };\n return ENGINE.runKernel(SparseSegmentSum, inputs);\n}\nconst sparseSegmentSum = op({ sparseSegmentSum_ });\n\n/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Creates ngrams from ragged string data.\n *\n * This op accepts a ragged tensor with 1 ragged dimension containing only\n * strings and outputs a ragged tensor with 1 ragged dimension containing ngrams\n * of that string, joined along the innermost axis.\n *\n * ```js\n * const result = tf.string.stringNGrams(\n * ['a', 'b', 'c', 'd'], tf.tensor1d([0, 2, 4], 'int32'),\n * '|', [1, 2], 'LP', 'RP', -1, false);\n * result['nGrams'].print(); // ['a', 'b', 'LP|a', 'a|b', 'b|RP',\n * // 'c', 'd', 'LP|c', 'c|d', 'd|RP']\n * result['nGramsSplits'].print(); // [0, 5, 10]\n * ```\n * @param data: The values tensor of the ragged string tensor to make ngrams out\n * of. Must be a 1D string tensor.\n * @param dataSplits: The splits tensor of the ragged string tensor to make\n * ngrams out of.\n * @param separator: The string to append between elements of the token. Use \"\"\n * for no separator.\n * @param nGramWidths: The sizes of the ngrams to create.\n * @param leftPad: The string to use to pad the left side of the ngram sequence.\n * Only used if pad_width !== 0.\n * @param rightPad: The string to use to pad the right side of the ngram\n * sequence. Only used if pad_width !== 0.\n * @param padWidth: The number of padding elements to add to each side of each\n * sequence. Note that padding will never be greater than `nGramWidths`-1\n * regardless of this value. If `padWidth`=-1 , then add max(`nGramWidths)-1\n * elements.\n * @param preserveShortSequences: If true, then ensure that at least one ngram\n * is generated for each input sequence. In particular, if an input sequence\n * is shorter than min(ngramWidth) + 2*padWidth, then generate a single\n * ngram containing the entire sequence. If false, then no ngrams are\n * generated for these short input sequences.\n * @return A map with the following properties:\n * - nGrams: The values tensor of the output ngrams ragged tensor.\n * - nGramsSplits: The splits tensor of the output ngrams ragged tensor.\n *\n * @doc {heading: 'Operations', subheading: 'String'}\n */\nfunction stringNGrams_(data, dataSplits, separator, nGramWidths, leftPad, rightPad, padWidth, preserveShortSequences) {\n const $data = convertToTensor(data, 'data', 'stringNGrams', 'string');\n if ($data.dtype !== 'string') {\n throw new Error('Data must be of datatype string');\n }\n if ($data.shape.length !== 1) {\n throw new Error(`Data must be a vector, saw: ${$data.shape}`);\n }\n const $dataSplits = convertToTensor(dataSplits, 'dataSplits', 'stringNGrams');\n if ($dataSplits.dtype !== 'int32') {\n throw new Error('Data splits must be of datatype int32');\n }\n const attrs = {\n separator,\n nGramWidths,\n leftPad,\n rightPad,\n padWidth,\n preserveShortSequences\n };\n const inputs = { data: $data, dataSplits: $dataSplits };\n const result = ENGINE.runKernel(StringNGrams, inputs, attrs);\n return { nGrams: result[0], nGramsSplits: result[1] };\n}\nconst stringNGrams = op({ stringNGrams_ });\n\n/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Split elements of `input` based on `delimiter` into a SparseTensor .\n *\n * Let N be the size of source (typically N will be the batch size). Split each\n * element of `input` based on `delimiter` and return a SparseTensor containing\n * the splitted tokens. Empty tokens are ignored if `skipEmpty` is set to True.\n *\n * `delimiter` can be empty, or a string of split characters. If `delimiter` is\n * an empty string, each element of `input` is split into individual\n * character strings. Otherwise every character of `delimiter` is a potential\n * split point.\n *\n * ```js\n * const result = tf.string.stringSplit(['hello world', 'a b c'], ' ');\n * result['indices'].print(); // [[0, 0], [0, 1], [1, 0], [1, 1], [1, 2]]\n * result['values'].print(); // ['hello', 'world', 'a', 'b', 'c']\n * result['shape'].print(); // [2, 3]\n * ```\n * @param input: 1-D. Strings to split.\n * @param delimiter: 0-D. Delimiter characters, or empty string.\n * @param skipEmpty: Optional. If true, skip the empty strings from the result.\n * Defaults to true.\n * @return A map with the following properties:\n * - indices: A dense matrix of int32 representing the indices of the sparse\n * tensor.\n * - values: A vector of strings corresponding to the splited values.\n * - shape: a length-2 vector of int32 representing the shape of the sparse\n * tensor, where the first value is N and the second value is the maximum number\n * of tokens in a single input entry.\n *\n * @doc {heading: 'Operations', subheading: 'String'}\n */\nfunction stringSplit_(input, delimiter, skipEmpty = true) {\n const $input = convertToTensor(input, 'input', 'stringSplit', 'string');\n const $delimiter = convertToTensor(delimiter, 'delimiter', 'stringSplit', 'string');\n if ($input.rank !== 1) {\n throw new Error(`Input should be Tensor1D but received shape ${$input.shape}`);\n }\n if ($delimiter.rank !== 0) {\n throw new Error(`Delimiter should be a scalar but received shape ${$delimiter.shape}`);\n }\n const attrs = { skipEmpty };\n const inputs = { input: $input, delimiter: $delimiter };\n const result = ENGINE.runKernel(StringSplit, inputs, attrs);\n return { indices: result[0], values: result[1], shape: result[2] };\n}\nconst stringSplit = op({ stringSplit_ });\n\n/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Converts each string in the input Tensor to its hash mod by a number of\n * buckets.\n *\n * The hash function is deterministic on the content of the string within the\n * process and will never change. However, it is not suitable for cryptography.\n * This function may be used when CPU time is scarce and inputs are trusted or\n * unimportant. There is a risk of adversaries constructing inputs that all hash\n * to the same bucket.\n *\n * ```js\n * const result = tf.string.stringToHashBucketFast(\n * ['Hello', 'TensorFlow', '2.x'], 3);\n * result.print(); // [0, 2, 2]\n * ```\n * @param input: The strings to assign a hash bucket.\n * @param numBuckets: The number of buckets.\n * @return A Tensor of the same shape as the input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'String'}\n */\nfunction stringToHashBucketFast_(input, numBuckets) {\n const $input = convertToTensor(input, 'input', 'stringToHashBucketFast', 'string');\n const attrs = { numBuckets };\n if (numBuckets <= 0) {\n throw new Error(`Number of buckets must be at least 1`);\n }\n const inputs = { input: $input };\n return ENGINE.runKernel(StringToHashBucketFast, inputs, attrs);\n}\nconst stringToHashBucketFast = op({ stringToHashBucketFast_ });\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst spectral = {\n fft,\n ifft,\n rfft,\n irfft\n};\nconst signal = {\n hammingWindow,\n hannWindow,\n frame,\n stft,\n};\nconst image = {\n flipLeftRight,\n grayscaleToRGB,\n resizeNearestNeighbor,\n resizeBilinear,\n rotateWithOffset,\n cropAndResize,\n nonMaxSuppression,\n nonMaxSuppressionAsync,\n nonMaxSuppressionWithScore,\n nonMaxSuppressionWithScoreAsync,\n nonMaxSuppressionPadded,\n nonMaxSuppressionPaddedAsync,\n threshold,\n transform\n};\nconst linalg = {\n bandPart,\n gramSchmidt,\n qr\n};\nconst losses = {\n absoluteDifference,\n computeWeightedLoss,\n cosineDistance,\n hingeLoss,\n huberLoss,\n logLoss,\n meanSquaredError,\n sigmoidCrossEntropy,\n softmaxCrossEntropy\n};\nconst sparse = {\n sparseFillEmptyRows,\n sparseReshape,\n sparseSegmentMean,\n sparseSegmentSum\n};\n// tslint:disable-next-line:variable-name\nconst string = {\n stringNGrams,\n stringSplit,\n stringToHashBucketFast\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.abs = function () {\n this.throwIfDisposed();\n return abs(this);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.acos = function () {\n this.throwIfDisposed();\n return acos(this);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.acosh = function () {\n this.throwIfDisposed();\n return acosh(this);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.add = function (b) {\n this.throwIfDisposed();\n return add$1(this, b);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.all = function (axis, keepDims) {\n this.throwIfDisposed();\n return all(this, axis, keepDims);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.any = function (axis, keepDims) {\n this.throwIfDisposed();\n return any(this, axis, keepDims);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.argMax = function (axis) {\n this.throwIfDisposed();\n return argMax(this, axis);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.argMin = function (axis) {\n this.throwIfDisposed();\n return argMin(this, axis);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Converts a size-1 `tf.Tensor` to a `tf.Scalar`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\ngetGlobalTensorClass().prototype.asScalar = function () {\n this.throwIfDisposed();\n assert(this.size === 1, () => 'The array must have only 1 element.');\n return reshape(this, []);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Casts a `tf.Tensor` to a specified dtype.\n *\n * @param dtype Data-type to cast the tensor to.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\ngetGlobalTensorClass().prototype.asType = function (dtype) {\n this.throwIfDisposed();\n return cast(this, dtype);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Converts a `tf.Tensor` to a `tf.Tensor1D`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\ngetGlobalTensorClass().prototype.as1D = function () {\n this.throwIfDisposed();\n return reshape(this, [this.size]);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Converts a `tf.Tensor` to a `tf.Tensor2D`.\n *\n * @param rows Number of rows in `tf.Tensor2D`.\n * @param columns Number of columns in `tf.Tensor2D`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\ngetGlobalTensorClass().prototype.as2D = function (rows, columns) {\n this.throwIfDisposed();\n return reshape(this, [rows, columns]);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Converts a `tf.Tensor` to a `tf.Tensor3D`.\n *\n * @param rows Number of rows in `tf.Tensor3D`.\n * @param columns Number of columns in `tf.Tensor3D`.\n * @param depth Depth of `tf.Tensor3D`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\ngetGlobalTensorClass().prototype.as3D = function (rows, columns, depth) {\n this.throwIfDisposed();\n return reshape(this, [rows, columns, depth]);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Converts a `tf.Tensor` to a `tf.Tensor4D`.\n *\n * @param rows Number of rows in `tf.Tensor4D`.\n * @param columns Number of columns in `tf.Tensor4D`.\n * @param depth Depth of `tf.Tensor4D`.\n * @param depth2 4th dimension of `tf.Tensor4D`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\ngetGlobalTensorClass().prototype.as4D = function (rows, columns, depth, depth2) {\n this.throwIfDisposed();\n return reshape(this, [rows, columns, depth, depth2]);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Converts a `tf.Tensor` to a `tf.Tensor5D`.\n *\n * @param rows Number of rows in `tf.Tensor5D`.\n * @param columns Number of columns in `tf.Tensor5D`.\n * @param depth Depth of `tf.Tensor5D`.\n * @param depth2 4th dimension of `tf.Tensor5D`.\n * @param depth3 5th dimension of 'tf.Tensor5D'\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\ngetGlobalTensorClass().prototype.as5D = function (rows, columns, depth, depth2, depth3) {\n this.throwIfDisposed();\n return reshape(this, [rows, columns, depth, depth2, depth3]);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.asin = function () {\n this.throwIfDisposed();\n return asin(this);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.asinh = function () {\n this.throwIfDisposed();\n return asinh(this);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.atan = function () {\n this.throwIfDisposed();\n return atan(this);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.atan2 = function (b) {\n this.throwIfDisposed();\n return atan2(this, b);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.atanh = function () {\n this.throwIfDisposed();\n return atanh(this);\n};\n\ngetGlobalTensorClass().prototype.avgPool =\n function (filterSize, strides, pad, dimRoundingMode) {\n this.throwIfDisposed();\n return avgPool(this, filterSize, strides, pad, dimRoundingMode);\n };\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.batchToSpaceND = function (blockShape, crops) {\n this.throwIfDisposed();\n return batchToSpaceND(this, blockShape, crops);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.batchNorm = function (mean, variance, offset, scale, varianceEpsilon) {\n this.throwIfDisposed();\n return batchNorm(this, mean, variance, offset, scale, varianceEpsilon);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.broadcastTo = function (shape) {\n this.throwIfDisposed();\n return broadcastTo(this, shape);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.cast = function (dtype) {\n this.throwIfDisposed();\n return cast(this, dtype);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.ceil = function () {\n this.throwIfDisposed();\n return ceil(this);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.clipByValue = function (min, max) {\n this.throwIfDisposed();\n return clipByValue(this, min, max);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.concat = function (x, axis) {\n this.throwIfDisposed();\n if (x instanceof Tensor) {\n x = [x];\n }\n return concat([this, ...x], axis);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.conv1d = function (filter, stride, pad, dataFormat, dilation, dimRoundingMode) {\n this.throwIfDisposed();\n return conv1d(this, filter, stride, pad, dataFormat, dilation, dimRoundingMode);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.conv2dTranspose =\n function (filter, outputShape, strides, pad, dimRoundingMode) {\n this.throwIfDisposed();\n return conv2dTranspose(this, filter, outputShape, strides, pad, dimRoundingMode);\n };\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.conv2d = function (filter, strides, pad, dataFormat, dilations, dimRoundingMode) {\n this.throwIfDisposed();\n return conv2d(this, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.cos = function () {\n this.throwIfDisposed();\n return cos(this);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.cosh = function () {\n this.throwIfDisposed();\n return cosh(this);\n};\n\n/**\n * @license\n * Copyright 2022 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the 'License');\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an 'AS IS' BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.cumprod = function (axis, exclusive, reverse) {\n this.throwIfDisposed();\n return cumprod(this, axis, exclusive, reverse);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.cumsum = function (axis, exclusive, reverse) {\n this.throwIfDisposed();\n return cumsum(this, axis, exclusive, reverse);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.depthToSpace = function (blockSize, dataFormat) {\n this.throwIfDisposed();\n return depthToSpace(this, blockSize, dataFormat);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.depthwiseConv2d =\n function (filter, strides, pad, dataFormat, dilations, dimRoundingMode) {\n this.throwIfDisposed();\n return depthwiseConv2d(this, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n };\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.dilation2d =\n function (filter, strides, pad, dilations, dataFormat) {\n this.throwIfDisposed();\n return dilation2d(this, filter, strides, pad, dilations, dataFormat);\n };\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.divNoNan = function (b) {\n this.throwIfDisposed();\n return divNoNan(this, b);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.div = function (b) {\n this.throwIfDisposed();\n return div(this, b);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.dot = function (b) {\n this.throwIfDisposed();\n return dot(this, b);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.elu = function () {\n this.throwIfDisposed();\n return elu(this);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.equal = function (b) {\n this.throwIfDisposed();\n return equal(this, b);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.erf = function () {\n this.throwIfDisposed();\n return erf(this);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.exp = function () {\n this.throwIfDisposed();\n return exp(this);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.expandDims = function (axis) {\n this.throwIfDisposed();\n return expandDims(this, axis);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.expm1 = function () {\n this.throwIfDisposed();\n return expm1(this);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.fft = function () {\n this.throwIfDisposed();\n return fft(this);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Flatten a Tensor to a 1D array.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\ngetGlobalTensorClass().prototype.flatten = function () {\n this.throwIfDisposed();\n return reshape(this, [this.size]);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.floor = function () {\n this.throwIfDisposed();\n return floor(this);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.floorDiv = function (b) {\n this.throwIfDisposed();\n return floorDiv(this, b);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.gather = function (indices, axis) {\n this.throwIfDisposed();\n return gather(this, indices, axis);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.greaterEqual = function (b) {\n this.throwIfDisposed();\n return greaterEqual(this, b);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.greater = function (b) {\n this.throwIfDisposed();\n return greater(this, b);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.ifft = function () {\n this.throwIfDisposed();\n return ifft(this);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.irfft = function () {\n this.throwIfDisposed();\n return irfft(this);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.isFinite = function () {\n this.throwIfDisposed();\n return isFinite$1(this);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.isInf = function () {\n this.throwIfDisposed();\n return isInf(this);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.isNaN = function () {\n this.throwIfDisposed();\n return isNaN$1(this);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.leakyRelu = function (alpha) {\n this.throwIfDisposed();\n return leakyRelu(this, alpha);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.lessEqual = function (b) {\n this.throwIfDisposed();\n return lessEqual(this, b);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.less = function (b) {\n this.throwIfDisposed();\n return less(this, b);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.localResponseNormalization =\n function (depthRadius, bias, alpha, beta) {\n this.throwIfDisposed();\n return localResponseNormalization(this, depthRadius, bias, alpha, beta);\n };\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.logSigmoid = function () {\n this.throwIfDisposed();\n return logSigmoid(this);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.logSoftmax = function (axis) {\n this.throwIfDisposed();\n return logSoftmax(this, axis);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.logSumExp = function (axis, keepDims) {\n this.throwIfDisposed();\n return logSumExp(this, axis, keepDims);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.log = function () {\n this.throwIfDisposed();\n return log$1(this);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.log1p = function () {\n this.throwIfDisposed();\n return log1p(this);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.logicalAnd = function (b) {\n this.throwIfDisposed();\n return logicalAnd(this, b);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.logicalNot = function () {\n this.throwIfDisposed();\n return logicalNot(this);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.logicalOr = function (b) {\n this.throwIfDisposed();\n return logicalOr(this, b);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.logicalXor = function (b) {\n this.throwIfDisposed();\n return logicalXor(this, b);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.matMul = function (b, transposeA, transposeB) {\n this.throwIfDisposed();\n return matMul(this, b, transposeA, transposeB);\n};\n\ngetGlobalTensorClass().prototype.maxPool =\n function (filterSize, strides, pad, dimRoundingMode) {\n this.throwIfDisposed();\n return maxPool(this, filterSize, strides, pad, dimRoundingMode);\n };\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.max = function (axis, keepDims) {\n this.throwIfDisposed();\n return max(this, axis, keepDims);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.maximum = function (b) {\n this.throwIfDisposed();\n return maximum(this, b);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.mean = function (axis, keepDims) {\n this.throwIfDisposed();\n return mean(this, axis, keepDims);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.min = function (axis, keepDims) {\n this.throwIfDisposed();\n return min(this, axis, keepDims);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.minimum = function (b) {\n this.throwIfDisposed();\n return minimum(this, b);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.mirrorPad = function (paddings, mode) {\n this.throwIfDisposed();\n return mirrorPad(this, paddings, mode);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.mod = function (b) {\n this.throwIfDisposed();\n return mod(this, b);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.mul = function (b) {\n this.throwIfDisposed();\n return mul(this, b);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.neg = function () {\n this.throwIfDisposed();\n return neg(this);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.norm = function (ord, axis, keepDims) {\n this.throwIfDisposed();\n return norm(this, ord, axis, keepDims);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.notEqual = function (b) {\n this.throwIfDisposed();\n return notEqual(this, b);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.oneHot = function (depth, onValue = 1, offValue = 0) {\n this.throwIfDisposed();\n return oneHot(this, depth, onValue, offValue);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.onesLike = function () {\n this.throwIfDisposed();\n return onesLike(this);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.pad = function (paddings, constantValue) {\n this.throwIfDisposed();\n return pad(this, paddings, constantValue);\n};\n\ngetGlobalTensorClass().prototype.pool = function (windowShape, poolingType, padding, dilationRate, strides, dimRoundingMode) {\n this.throwIfDisposed();\n return pool(this, windowShape, poolingType, padding, dilationRate, strides, dimRoundingMode);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.pow = function (exp) {\n this.throwIfDisposed();\n return pow(this, exp);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.prelu = function (alpha) {\n this.throwIfDisposed();\n return prelu(this, alpha);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.prod = function (axis, keepDims) {\n this.throwIfDisposed();\n return prod(this, axis, keepDims);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.reciprocal = function () {\n this.throwIfDisposed();\n return reciprocal(this);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.relu = function () {\n this.throwIfDisposed();\n return relu(this);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.relu6 = function () {\n this.throwIfDisposed();\n return relu6(this);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Reshapes the tensor into the shape of the provided tensor.\n *\n * @param x The tensor of required shape.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\ngetGlobalTensorClass().prototype.reshapeAs = function (x) {\n this.throwIfDisposed();\n return reshape(this, x.shape);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.reshape = function (shape) {\n this.throwIfDisposed();\n return reshape(this, shape);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.resizeBilinear =\n function (newShape2D, alignCorners, halfPixelCenters) {\n this.throwIfDisposed();\n return resizeBilinear(this, newShape2D, alignCorners, halfPixelCenters);\n };\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.resizeNearestNeighbor =\n function (newShape2D, alignCorners, halfFloatCenters) {\n this.throwIfDisposed();\n return resizeNearestNeighbor(this, newShape2D, alignCorners, halfFloatCenters);\n };\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.reverse = function (axis) {\n this.throwIfDisposed();\n return reverse(this, axis);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.rfft = function () {\n this.throwIfDisposed();\n return rfft(this);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.round = function () {\n this.throwIfDisposed();\n return round$1(this);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.rsqrt = function () {\n this.throwIfDisposed();\n return rsqrt(this);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.selu = function () {\n this.throwIfDisposed();\n return selu(this);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.separableConv2d =\n function (depthwiseFilter, pointwiseFilter, strides, pad, dilation, dataFormat) {\n this.throwIfDisposed();\n return separableConv2d(this, depthwiseFilter, pointwiseFilter, strides, pad, dilation, dataFormat);\n };\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.sigmoid = function () {\n this.throwIfDisposed();\n return sigmoid(this);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.sign = function () {\n this.throwIfDisposed();\n return sign(this);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.sin = function () {\n this.throwIfDisposed();\n return sin(this);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.sinh = function () {\n this.throwIfDisposed();\n return sinh(this);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.slice = function (begin, size) {\n this.throwIfDisposed();\n return slice(this, begin, size);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.softmax = function (dim) {\n this.throwIfDisposed();\n return softmax(this, dim);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.softplus = function () {\n this.throwIfDisposed();\n return softplus(this);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.spaceToBatchND = function (blockShape, paddings) {\n this.throwIfDisposed();\n return spaceToBatchND(this, blockShape, paddings);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.split = function (numOrSizeSplits, axis) {\n this.throwIfDisposed();\n return split(this, numOrSizeSplits, axis);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.sqrt = function () {\n this.throwIfDisposed();\n return sqrt(this);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.square = function () {\n this.throwIfDisposed();\n return square(this);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.squaredDifference = function (b) {\n this.throwIfDisposed();\n return squaredDifference(this, b);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.squeeze = function (axis) {\n this.throwIfDisposed();\n return squeeze(this, axis);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.stack = function (x, axis) {\n this.throwIfDisposed();\n const tensorsToBeStacked = x instanceof Tensor ? [this, x] : [this, ...x];\n return stack(tensorsToBeStacked, axis);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.step = function (alpha) {\n this.throwIfDisposed();\n return step(this, alpha);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.stridedSlice = function (begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask) {\n this.throwIfDisposed();\n return stridedSlice(this, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.sub = function (b) {\n this.throwIfDisposed();\n return sub(this, b);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.sum = function (axis, keepDims) {\n this.throwIfDisposed();\n return sum$1(this, axis, keepDims);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.tan = function () {\n this.throwIfDisposed();\n return tan(this);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.tanh = function () {\n this.throwIfDisposed();\n return tanh$1(this);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.tile = function (reps) {\n this.throwIfDisposed();\n return tile(this, reps);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Casts the array to type `bool`\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\ngetGlobalTensorClass().prototype.toBool = function () {\n this.throwIfDisposed();\n return cast(this, 'bool');\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Casts the array to type `float32`\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\ngetGlobalTensorClass().prototype.toFloat = function () {\n this.throwIfDisposed();\n return cast(this, 'float32');\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Casts the array to type `int32`\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\ngetGlobalTensorClass().prototype.toInt = function () {\n this.throwIfDisposed();\n return cast(this, 'int32');\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.topk = function (k, sorted) {\n this.throwIfDisposed();\n return topk(this, k, sorted);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.transpose = function (perm) {\n this.throwIfDisposed();\n return transpose(this, perm);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.unique = function (axis) {\n this.throwIfDisposed();\n return unique(this, axis);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.unsortedSegmentSum =\n function (segmentIds, numSegments) {\n this.throwIfDisposed();\n return unsortedSegmentSum(this, segmentIds, numSegments);\n };\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.unstack = function (axis) {\n this.throwIfDisposed();\n return unstack(this, axis);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.where = function (condition, x) {\n this.throwIfDisposed();\n return where(condition, this, x);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\ngetGlobalTensorClass().prototype.zerosLike = function () {\n this.throwIfDisposed();\n return zerosLike(this);\n};\n\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nvar version = '3.15.0';\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nvar version$1 = {\n 'tfjs-core': tfjsCore.version_core,\n 'tfjs-backend-cpu': tfjsBackendCpu.version_cpu,\n 'tfjs-backend-webgl': tfjsBackendWebgl.version_webgl,\n 'tfjs-data': tfjsData.version_data,\n 'tfjs-layers': tfjsLayers.version_layers,\n 'tfjs-converter': tfjsConverter.version_converter,\n 'tfjs': version\n};\n\nObject.keys(tfjsCore).forEach(function (k) {\n if (k !== 'default') Object.defineProperty(exports, k, {\n enumerable: true,\n get: function () {\n return tfjsCore[k];\n }\n });\n});\nObject.keys(tfjsLayers).forEach(function (k) {\n if (k !== 'default') Object.defineProperty(exports, k, {\n enumerable: true,\n get: function () {\n return tfjsLayers[k];\n }\n });\n});\nObject.keys(tfjsConverter).forEach(function (k) {\n if (k !== 'default') Object.defineProperty(exports, k, {\n enumerable: true,\n get: function () {\n return tfjsConverter[k];\n }\n });\n});\nexports.data = tfjsData;\nexports.version = version$1;\n//# sourceMappingURL=tf.node.js.map\n","'use strict'\n\nexports.byteLength = byteLength\nexports.toByteArray = toByteArray\nexports.fromByteArray = fromByteArray\n\nvar lookup = []\nvar revLookup = []\nvar Arr = typeof Uint8Array !== 'undefined' ? Uint8Array : Array\n\nvar code = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'\nfor (var i = 0, len = code.length; i < len; ++i) {\n lookup[i] = code[i]\n revLookup[code.charCodeAt(i)] = i\n}\n\n// Support decoding URL-safe base64 strings, as Node.js does.\n// See: https://en.wikipedia.org/wiki/Base64#URL_applications\nrevLookup['-'.charCodeAt(0)] = 62\nrevLookup['_'.charCodeAt(0)] = 63\n\nfunction getLens (b64) {\n var len = b64.length\n\n if (len % 4 > 0) {\n throw new Error('Invalid string. Length must be a multiple of 4')\n }\n\n // Trim off extra bytes after placeholder bytes are found\n // See: https://github.com/beatgammit/base64-js/issues/42\n var validLen = b64.indexOf('=')\n if (validLen === -1) validLen = len\n\n var placeHoldersLen = validLen === len\n ? 0\n : 4 - (validLen % 4)\n\n return [validLen, placeHoldersLen]\n}\n\n// base64 is 4/3 + up to two characters of the original data\nfunction byteLength (b64) {\n var lens = getLens(b64)\n var validLen = lens[0]\n var placeHoldersLen = lens[1]\n return ((validLen + placeHoldersLen) * 3 / 4) - placeHoldersLen\n}\n\nfunction _byteLength (b64, validLen, placeHoldersLen) {\n return ((validLen + placeHoldersLen) * 3 / 4) - placeHoldersLen\n}\n\nfunction toByteArray (b64) {\n var tmp\n var lens = getLens(b64)\n var validLen = lens[0]\n var placeHoldersLen = lens[1]\n\n var arr = new Arr(_byteLength(b64, validLen, placeHoldersLen))\n\n var curByte = 0\n\n // if there are placeholders, only get up to the last complete 4 chars\n var len = placeHoldersLen > 0\n ? validLen - 4\n : validLen\n\n var i\n for (i = 0; i < len; i += 4) {\n tmp =\n (revLookup[b64.charCodeAt(i)] << 18) |\n (revLookup[b64.charCodeAt(i + 1)] << 12) |\n (revLookup[b64.charCodeAt(i + 2)] << 6) |\n revLookup[b64.charCodeAt(i + 3)]\n arr[curByte++] = (tmp >> 16) & 0xFF\n arr[curByte++] = (tmp >> 8) & 0xFF\n arr[curByte++] = tmp & 0xFF\n }\n\n if (placeHoldersLen === 2) {\n tmp =\n (revLookup[b64.charCodeAt(i)] << 2) |\n (revLookup[b64.charCodeAt(i + 1)] >> 4)\n arr[curByte++] = tmp & 0xFF\n }\n\n if (placeHoldersLen === 1) {\n tmp =\n (revLookup[b64.charCodeAt(i)] << 10) |\n (revLookup[b64.charCodeAt(i + 1)] << 4) |\n (revLookup[b64.charCodeAt(i + 2)] >> 2)\n arr[curByte++] = (tmp >> 8) & 0xFF\n arr[curByte++] = tmp & 0xFF\n }\n\n return arr\n}\n\nfunction tripletToBase64 (num) {\n return lookup[num >> 18 & 0x3F] +\n lookup[num >> 12 & 0x3F] +\n lookup[num >> 6 & 0x3F] +\n lookup[num & 0x3F]\n}\n\nfunction encodeChunk (uint8, start, end) {\n var tmp\n var output = []\n for (var i = start; i < end; i += 3) {\n tmp =\n ((uint8[i] << 16) & 0xFF0000) +\n ((uint8[i + 1] << 8) & 0xFF00) +\n (uint8[i + 2] & 0xFF)\n output.push(tripletToBase64(tmp))\n }\n return output.join('')\n}\n\nfunction fromByteArray (uint8) {\n var tmp\n var len = uint8.length\n var extraBytes = len % 3 // if we have 1 byte left, pad 2 bytes\n var parts = []\n var maxChunkLength = 16383 // must be multiple of 3\n\n // go through the array every three bytes, we'll deal with trailing stuff later\n for (var i = 0, len2 = len - extraBytes; i < len2; i += maxChunkLength) {\n parts.push(encodeChunk(\n uint8, i, (i + maxChunkLength) > len2 ? len2 : (i + maxChunkLength)\n ))\n }\n\n // pad the end with zeros, but make sure to not forget the extra bytes\n if (extraBytes === 1) {\n tmp = uint8[len - 1]\n parts.push(\n lookup[tmp >> 2] +\n lookup[(tmp << 4) & 0x3F] +\n '=='\n )\n } else if (extraBytes === 2) {\n tmp = (uint8[len - 2] << 8) + uint8[len - 1]\n parts.push(\n lookup[tmp >> 10] +\n lookup[(tmp >> 4) & 0x3F] +\n lookup[(tmp << 2) & 0x3F] +\n '='\n )\n }\n\n return parts.join('')\n}\n","exports.read = function (buffer, offset, isLE, mLen, nBytes) {\n var e, m\n var eLen = (nBytes * 8) - mLen - 1\n var eMax = (1 << eLen) - 1\n var eBias = eMax >> 1\n var nBits = -7\n var i = isLE ? (nBytes - 1) : 0\n var d = isLE ? -1 : 1\n var s = buffer[offset + i]\n\n i += d\n\n e = s & ((1 << (-nBits)) - 1)\n s >>= (-nBits)\n nBits += eLen\n for (; nBits > 0; e = (e * 256) + buffer[offset + i], i += d, nBits -= 8) {}\n\n m = e & ((1 << (-nBits)) - 1)\n e >>= (-nBits)\n nBits += mLen\n for (; nBits > 0; m = (m * 256) + buffer[offset + i], i += d, nBits -= 8) {}\n\n if (e === 0) {\n e = 1 - eBias\n } else if (e === eMax) {\n return m ? NaN : ((s ? -1 : 1) * Infinity)\n } else {\n m = m + Math.pow(2, mLen)\n e = e - eBias\n }\n return (s ? -1 : 1) * m * Math.pow(2, e - mLen)\n}\n\nexports.write = function (buffer, value, offset, isLE, mLen, nBytes) {\n var e, m, c\n var eLen = (nBytes * 8) - mLen - 1\n var eMax = (1 << eLen) - 1\n var eBias = eMax >> 1\n var rt = (mLen === 23 ? Math.pow(2, -24) - Math.pow(2, -77) : 0)\n var i = isLE ? 0 : (nBytes - 1)\n var d = isLE ? 1 : -1\n var s = value < 0 || (value === 0 && 1 / value < 0) ? 1 : 0\n\n value = Math.abs(value)\n\n if (isNaN(value) || value === Infinity) {\n m = isNaN(value) ? 1 : 0\n e = eMax\n } else {\n e = Math.floor(Math.log(value) / Math.LN2)\n if (value * (c = Math.pow(2, -e)) < 1) {\n e--\n c *= 2\n }\n if (e + eBias >= 1) {\n value += rt / c\n } else {\n value += rt * Math.pow(2, 1 - eBias)\n }\n if (value * c >= 2) {\n e++\n c /= 2\n }\n\n if (e + eBias >= eMax) {\n m = 0\n e = eMax\n } else if (e + eBias >= 1) {\n m = ((value * c) - 1) * Math.pow(2, mLen)\n e = e + eBias\n } else {\n m = value * Math.pow(2, eBias - 1) * Math.pow(2, mLen)\n e = 0\n }\n }\n\n for (; mLen >= 8; buffer[offset + i] = m & 0xff, i += d, m /= 256, mLen -= 8) {}\n\n e = (e << mLen) | m\n eLen += mLen\n for (; eLen > 0; buffer[offset + i] = e & 0xff, i += d, e /= 256, eLen -= 8) {}\n\n buffer[offset + i - d] |= s * 128\n}\n","var toString = {}.toString;\n\nmodule.exports = Array.isArray || function (arr) {\n return toString.call(arr) == '[object Array]';\n};\n","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '../environment';\n// We are wrapping this within an object so it can be stubbed by Jasmine.\nexport const getNodeFetch = {\n // tslint:disable-next-line:no-require-imports\n importFetch: () => require('node-fetch')\n};\nlet systemFetch;\n// These getters and setters are for testing so we don't export a mutable\n// variable.\nexport function resetSystemFetch() {\n systemFetch = null;\n}\nexport function setSystemFetch(fetchFn) {\n systemFetch = fetchFn;\n}\nexport function getSystemFetch() {\n return systemFetch;\n}\nexport class PlatformNode {\n constructor() {\n // tslint:disable-next-line:no-require-imports\n this.util = require('util');\n // According to the spec, the built-in encoder can do only UTF-8 encoding.\n // https://developer.mozilla.org/en-US/docs/Web/API/TextEncoder/TextEncoder\n this.textEncoder = new this.util.TextEncoder();\n }\n fetch(path, requestInits) {\n if (env().global.fetch != null) {\n return env().global.fetch(path, requestInits);\n }\n if (systemFetch == null) {\n systemFetch = getNodeFetch.importFetch();\n }\n return systemFetch(path, requestInits);\n }\n now() {\n const time = process.hrtime();\n return time[0] * 1000 + time[1] / 1000000;\n }\n encode(text, encoding) {\n if (encoding !== 'utf-8' && encoding !== 'utf8') {\n throw new Error(`Node built-in encoder only supports utf-8, but got ${encoding}`);\n }\n return this.textEncoder.encode(text);\n }\n decode(bytes, encoding) {\n if (bytes.length === 0) {\n return '';\n }\n return new this.util.TextDecoder(encoding).decode(bytes);\n }\n}\nif (env().get('IS_NODE') && !env().get('IS_BROWSER')) {\n env().setPlatform('node', new PlatformNode());\n}\n//# sourceMappingURL=data:application/json;base64,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","/* (ignored) */","/* (ignored) */","// A port of an algorithm by Johannes Baagøe , 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baagøe \n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n var me = this, mash = Mash();\n\n me.next = function() {\n var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n me.s0 = me.s1;\n me.s1 = me.s2;\n return me.s2 = t - (me.c = t | 0);\n };\n\n // Apply the seeding algorithm from Baagoe.\n me.c = 1;\n me.s0 = mash(' ');\n me.s1 = mash(' ');\n me.s2 = mash(' ');\n me.s0 -= mash(seed);\n if (me.s0 < 0) { me.s0 += 1; }\n me.s1 -= mash(seed);\n if (me.s1 < 0) { me.s1 += 1; }\n me.s2 -= mash(seed);\n if (me.s2 < 0) { me.s2 += 1; }\n mash = null;\n}\n\nfunction copy(f, t) {\n t.c = f.c;\n t.s0 = f.s0;\n t.s1 = f.s1;\n t.s2 = f.s2;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new Alea(seed),\n state = opts && opts.state,\n prng = xg.next;\n prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n prng.double = function() {\n return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n };\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nfunction Mash() {\n var n = 0xefc8249d;\n\n var mash = function(data) {\n data = data.toString();\n for (var i = 0; i < data.length; i++) {\n n += data.charCodeAt(i);\n var h = 0.02519603282416938 * n;\n n = h >>> 0;\n h -= n;\n h *= n;\n n = h >>> 0;\n h -= n;\n n += h * 0x100000000; // 2^32\n }\n return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n };\n\n return mash;\n}\n\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.alea = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n","// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n\n // Set up generator function.\n me.next = function() {\n var t = me.x ^ (me.x << 11);\n me.x = me.y;\n me.y = me.z;\n me.z = me.w;\n return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n };\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor128 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n","// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var t = (me.x ^ (me.x >>> 2));\n me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n return (me.d = (me.d + 362437 | 0)) +\n (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n };\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n me.v = 0;\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n if (k == strseed.length) {\n me.d = me.x << 10 ^ me.x >>> 4;\n }\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n t.v = f.v;\n t.d = f.d;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorwow = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n","// A Javascript implementaion of the \"xorshift7\" algorithm by\n// François Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n // Update xor generator.\n var X = me.x, i = me.i, t, v, w;\n t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n X[i] = v;\n me.i = (i + 1) & 7;\n return v;\n };\n\n function init(me, seed) {\n var j, w, X = [];\n\n if (seed === (seed | 0)) {\n // Seed state array using a 32-bit integer.\n w = X[0] = seed;\n } else {\n // Seed state using a string.\n seed = '' + seed;\n for (j = 0; j < seed.length; ++j) {\n X[j & 7] = (X[j & 7] << 15) ^\n (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n }\n }\n // Enforce an array length of 8, not all zeroes.\n while (X.length < 8) X.push(0);\n for (j = 0; j < 8 && X[j] === 0; ++j);\n if (j == 8) w = X[7] = -1; else w = X[j];\n\n me.x = X;\n me.i = 0;\n\n // Discard an initial 256 values.\n for (j = 256; j > 0; --j) {\n me.next();\n }\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.x = f.x.slice();\n t.i = f.i;\n return t;\n}\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.x) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorshift7 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n","// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated. For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1); // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897); // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C. This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n var w = me.w,\n X = me.X, i = me.i, t, v;\n // Update Weyl generator.\n me.w = w = (w + 0x61c88647) | 0;\n // Update xor generator.\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n // Update Xor generator array state.\n v = X[i] = v ^ t;\n me.i = i;\n // Result is the combination.\n return (v + (w ^ (w >>> 16))) | 0;\n };\n\n function init(me, seed) {\n var t, v, i, j, w, X = [], limit = 128;\n if (seed === (seed | 0)) {\n // Numeric seeds initialize v, which is used to generates X.\n v = seed;\n seed = null;\n } else {\n // String seeds are mixed into v and X one character at a time.\n seed = seed + '\\0';\n v = 0;\n limit = Math.max(limit, seed.length);\n }\n // Initialize circular array and weyl value.\n for (i = 0, j = -32; j < limit; ++j) {\n // Put the unicode characters into the array, and shuffle them.\n if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n // After 32 shuffles, take v as the starting w value.\n if (j === 0) w = v;\n v ^= v << 10;\n v ^= v >>> 15;\n v ^= v << 4;\n v ^= v >>> 13;\n if (j >= 0) {\n w = (w + 0x61c88647) | 0; // Weyl.\n t = (X[j & 127] ^= (v + w)); // Combine xor and weyl to init array.\n i = (0 == t) ? i + 1 : 0; // Count zeroes.\n }\n }\n // We have detected all zeroes; make the key nonzero.\n if (i >= 128) {\n X[(seed && seed.length || 0) & 127] = -1;\n }\n // Run the generator 512 times to further mix the state before using it.\n // Factoring this as a function slows the main generator, so it is just\n // unrolled here. The weyl generator is not advanced while warming up.\n i = 127;\n for (j = 4 * 128; j > 0; --j) {\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n X[i] = v ^ t;\n }\n // Storing state as object members is faster than using closure variables.\n me.w = w;\n me.X = X;\n me.i = i;\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.i = f.i;\n t.w = f.w;\n t.X = f.X.slice();\n return t;\n};\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.X) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor4096 = impl;\n}\n\n})(\n this, // window object or global\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n","// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var b = me.b, c = me.c, d = me.d, a = me.a;\n b = (b << 25) ^ (b >>> 7) ^ c;\n c = (c - d) | 0;\n d = (d << 24) ^ (d >>> 8) ^ a;\n a = (a - b) | 0;\n me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n me.c = c = (c - d) | 0;\n me.d = (d << 16) ^ (c >>> 16) ^ a;\n return me.a = (a - b) | 0;\n };\n\n /* The following is non-inverted tyche, which has better internal\n * bit diffusion, but which is about 25% slower than tyche-i in JS.\n me.next = function() {\n var a = me.a, b = me.b, c = me.c, d = me.d;\n a = (me.a + me.b | 0) >>> 0;\n d = me.d ^ a; d = d << 16 ^ d >>> 16;\n c = me.c + d | 0;\n b = me.b ^ c; b = b << 12 ^ d >>> 20;\n me.a = a = a + b | 0;\n d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n me.c = c = c + d | 0;\n b = b ^ c;\n return me.b = (b << 7 ^ b >>> 25);\n }\n */\n\n me.a = 0;\n me.b = 0;\n me.c = 2654435769 | 0;\n me.d = 1367130551;\n\n if (seed === Math.floor(seed)) {\n // Integer seed.\n me.a = (seed / 0x100000000) | 0;\n me.b = seed | 0;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 20; k++) {\n me.b ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.a = f.a;\n t.b = f.b;\n t.c = f.c;\n t.d = f.d;\n return t;\n};\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.tychei = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n","/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n width = 256, // each RC4 output is 0 <= x < 256\n chunks = 6, // at least six RC4 outputs for each double\n digits = 52, // there are 52 significant digits in a double\n rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n startdenom = math.pow(width, chunks),\n significance = math.pow(2, digits),\n overflow = significance * 2,\n mask = width - 1,\n nodecrypto; // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n var key = [];\n options = (options == true) ? { entropy: true } : (options || {});\n\n // Flatten the seed string or build one from local entropy if needed.\n var shortseed = mixkey(flatten(\n options.entropy ? [seed, tostring(pool)] :\n (seed == null) ? autoseed() : seed, 3), key);\n\n // Use the seed to initialize an ARC4 generator.\n var arc4 = new ARC4(key);\n\n // This function returns a random double in [0, 1) that contains\n // randomness in every bit of the mantissa of the IEEE 754 value.\n var prng = function() {\n var n = arc4.g(chunks), // Start with a numerator n < 2 ^ 48\n d = startdenom, // and denominator d = 2 ^ 48.\n x = 0; // and no 'extra last byte'.\n while (n < significance) { // Fill up all significant digits by\n n = (n + x) * width; // shifting numerator and\n d *= width; // denominator and generating a\n x = arc4.g(1); // new least-significant-byte.\n }\n while (n >= overflow) { // To avoid rounding up, before adding\n n /= 2; // last byte, shift everything\n d /= 2; // right using integer math until\n x >>>= 1; // we have exactly the desired bits.\n }\n return (n + x) / d; // Form the number within [0, 1).\n };\n\n prng.int32 = function() { return arc4.g(4) | 0; }\n prng.quick = function() { return arc4.g(4) / 0x100000000; }\n prng.double = prng;\n\n // Mix the randomness into accumulated entropy.\n mixkey(tostring(arc4.S), pool);\n\n // Calling convention: what to return as a function of prng, seed, is_math.\n return (options.pass || callback ||\n function(prng, seed, is_math_call, state) {\n if (state) {\n // Load the arc4 state from the given state if it has an S array.\n if (state.S) { copy(state, arc4); }\n // Only provide the .state method if requested via options.state.\n prng.state = function() { return copy(arc4, {}); }\n }\n\n // If called as a method of Math (Math.seedrandom()), mutate\n // Math.random because that is how seedrandom.js has worked since v1.0.\n if (is_math_call) { math[rngname] = prng; return seed; }\n\n // Otherwise, it is a newer calling convention, so return the\n // prng directly.\n else return prng;\n })(\n prng,\n shortseed,\n 'global' in options ? options.global : (this == math),\n options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation. The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4. Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n var t, keylen = key.length,\n me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n // The empty key [] is treated as [0].\n if (!keylen) { key = [keylen++]; }\n\n // Set up S using the standard key scheduling algorithm.\n while (i < width) {\n s[i] = i++;\n }\n for (i = 0; i < width; i++) {\n s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n s[j] = t;\n }\n\n // The \"g\" method returns the next (count) outputs as one number.\n (me.g = function(count) {\n // Using instance members instead of closure state nearly doubles speed.\n var t, r = 0,\n i = me.i, j = me.j, s = me.S;\n while (count--) {\n t = s[i = mask & (i + 1)];\n r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n }\n me.i = i; me.j = j;\n return r;\n // For robust unpredictability, the function call below automatically\n // discards an initial batch of values. This is called RC4-drop[256].\n // See http://google.com/search?q=rsa+fluhrer+response&btnI\n })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n t.i = f.i;\n t.j = f.j;\n t.S = f.S.slice();\n return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n var result = [], typ = (typeof obj), prop;\n if (depth && typ == 'object') {\n for (prop in obj) {\n try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n }\n }\n return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n var stringseed = seed + '', smear, j = 0;\n while (j < stringseed.length) {\n key[mask & j] =\n mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n }\n return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n try {\n var out;\n if (nodecrypto && (out = nodecrypto.randomBytes)) {\n // The use of 'out' to remember randomBytes makes tight minified code.\n out = out(width);\n } else {\n out = new Uint8Array(width);\n (global.crypto || global.msCrypto).getRandomValues(out);\n }\n return tostring(out);\n } catch (e) {\n var browser = global.navigator,\n plugins = browser && browser.plugins;\n return [+new Date, global, plugins, global.screen, tostring(pool)];\n }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool. Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n module.exports = seedrandom;\n // When in node.js, try using crypto package for autoseeding.\n try {\n nodecrypto = require('crypto');\n } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n [], // pool: entropy pool starts empty\n Math // math: package containing random, pow, and seedrandom\n);\n","/* (ignored) */","var scope = (typeof global !== \"undefined\" && global) ||\n (typeof self !== \"undefined\" && self) ||\n window;\nvar apply = Function.prototype.apply;\n\n// DOM APIs, for completeness\n\nexports.setTimeout = function() {\n return new Timeout(apply.call(setTimeout, scope, arguments), clearTimeout);\n};\nexports.setInterval = function() {\n return new Timeout(apply.call(setInterval, scope, arguments), clearInterval);\n};\nexports.clearTimeout =\nexports.clearInterval = function(timeout) {\n if (timeout) {\n timeout.close();\n }\n};\n\nfunction Timeout(id, clearFn) {\n this._id = id;\n this._clearFn = clearFn;\n}\nTimeout.prototype.unref = Timeout.prototype.ref = function() {};\nTimeout.prototype.close = function() {\n this._clearFn.call(scope, this._id);\n};\n\n// Does not start the time, just sets up the members needed.\nexports.enroll = function(item, msecs) {\n clearTimeout(item._idleTimeoutId);\n item._idleTimeout = msecs;\n};\n\nexports.unenroll = function(item) {\n clearTimeout(item._idleTimeoutId);\n item._idleTimeout = -1;\n};\n\nexports._unrefActive = exports.active = function(item) {\n clearTimeout(item._idleTimeoutId);\n\n var msecs = item._idleTimeout;\n if (msecs >= 0) {\n item._idleTimeoutId = setTimeout(function onTimeout() {\n if (item._onTimeout)\n item._onTimeout();\n }, msecs);\n }\n};\n\n// setimmediate attaches itself to the global object\nrequire(\"setimmediate\");\n// On some exotic environments, it's not clear which object `setimmediate` was\n// able to install onto. Search each possibility in the same order as the\n// `setimmediate` library.\nexports.setImmediate = (typeof self !== \"undefined\" && self.setImmediate) ||\n (typeof global !== \"undefined\" && global.setImmediate) ||\n (this && this.setImmediate);\nexports.clearImmediate = (typeof self !== \"undefined\" && self.clearImmediate) ||\n (typeof global !== \"undefined\" && global.clearImmediate) ||\n (this && this.clearImmediate);\n","(function (global, undefined) {\n \"use strict\";\n\n if (global.setImmediate) {\n return;\n }\n\n var nextHandle = 1; // Spec says greater than zero\n var tasksByHandle = {};\n var currentlyRunningATask = false;\n var doc = global.document;\n var registerImmediate;\n\n function setImmediate(callback) {\n // Callback can either be a function or a string\n if (typeof callback !== \"function\") {\n callback = new Function(\"\" + callback);\n }\n // Copy function arguments\n var args = new Array(arguments.length - 1);\n for (var i = 0; i < args.length; i++) {\n args[i] = arguments[i + 1];\n }\n // Store and register the task\n var task = { callback: callback, args: args };\n tasksByHandle[nextHandle] = task;\n registerImmediate(nextHandle);\n return nextHandle++;\n }\n\n function clearImmediate(handle) {\n delete tasksByHandle[handle];\n }\n\n function run(task) {\n var callback = task.callback;\n var args = task.args;\n switch (args.length) {\n case 0:\n callback();\n break;\n case 1:\n callback(args[0]);\n break;\n case 2:\n callback(args[0], args[1]);\n break;\n case 3:\n callback(args[0], args[1], args[2]);\n break;\n default:\n callback.apply(undefined, args);\n break;\n }\n }\n\n function runIfPresent(handle) {\n // From the spec: \"Wait until any invocations of this algorithm started before this one have completed.\"\n // So if we're currently running a task, we'll need to delay this invocation.\n if (currentlyRunningATask) {\n // Delay by doing a setTimeout. setImmediate was tried instead, but in Firefox 7 it generated a\n // \"too much recursion\" error.\n setTimeout(runIfPresent, 0, handle);\n } else {\n var task = tasksByHandle[handle];\n if (task) {\n currentlyRunningATask = true;\n try {\n run(task);\n } finally {\n clearImmediate(handle);\n currentlyRunningATask = false;\n }\n }\n }\n }\n\n function installNextTickImplementation() {\n registerImmediate = function(handle) {\n process.nextTick(function () { runIfPresent(handle); });\n };\n }\n\n function canUsePostMessage() {\n // The test against `importScripts` prevents this implementation from being installed inside a web worker,\n // where `global.postMessage` means something completely different and can't be used for this purpose.\n if (global.postMessage && !global.importScripts) {\n var postMessageIsAsynchronous = true;\n var oldOnMessage = global.onmessage;\n global.onmessage = function() {\n postMessageIsAsynchronous = false;\n };\n global.postMessage(\"\", \"*\");\n global.onmessage = oldOnMessage;\n return postMessageIsAsynchronous;\n }\n }\n\n function installPostMessageImplementation() {\n // Installs an event handler on `global` for the `message` event: see\n // * https://developer.mozilla.org/en/DOM/window.postMessage\n // * http://www.whatwg.org/specs/web-apps/current-work/multipage/comms.html#crossDocumentMessages\n\n var messagePrefix = \"setImmediate$\" + Math.random() + \"$\";\n var onGlobalMessage = function(event) {\n if (event.source === global &&\n typeof event.data === \"string\" &&\n event.data.indexOf(messagePrefix) === 0) {\n runIfPresent(+event.data.slice(messagePrefix.length));\n }\n };\n\n if (global.addEventListener) {\n global.addEventListener(\"message\", onGlobalMessage, false);\n } else {\n global.attachEvent(\"onmessage\", onGlobalMessage);\n }\n\n registerImmediate = function(handle) {\n global.postMessage(messagePrefix + handle, \"*\");\n };\n }\n\n function installMessageChannelImplementation() {\n var channel = new MessageChannel();\n channel.port1.onmessage = function(event) {\n var handle = event.data;\n runIfPresent(handle);\n };\n\n registerImmediate = function(handle) {\n channel.port2.postMessage(handle);\n };\n }\n\n function installReadyStateChangeImplementation() {\n var html = doc.documentElement;\n registerImmediate = function(handle) {\n // Create a