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chore: update changelog for version 2.19.0 #11912

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27 changes: 27 additions & 0 deletions CHANGELOG.md
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Changelogs for versions not listed here can be found at https://github.com/DataDog/dd-trace-py/releases

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## 2.19.0

### New Features

- azure_functions: This introduces support for Azure Functions.
- ASM: This introduces "Standalone SCA billing", opting out for APM billing and applying to only SCA. Enable this by setting these two environment variables: `DD_APPSEC_SCA_ENABLED` and `DD_EXPERIMENTAL_APPSEC_STANDALONE_ENABLED`
- profiling: Adds an experimental integration with the PyTorch profiler which can be enabled by setting `DD_PROFILING_PYTORCH_ENABLED=true`. This feature instruments the PyTorch profiler API (<https://pytorch.org/docs/stable/_modules/torch/profiler/profiler.html>) so that GPU profiling data can be sent to Datadog for visualization. This feature supports torch version \>= 1.8.1.
- Code Security: This introduces stack trace reports for Code Security.
### Upgrade Notes

- Makes the library compatible with Python 3.13
### Bug Fixes

- ASGI: This fix resolves an issue parsing response cookies in FastAPI and awsgi- lib-injection: Fix missing lib-injection telemetry for common abort scenarios.

- LLM Observability: This fix resolves an issue where `LLMObs.enable()` ignored global patch configurations, specifically
the `DD_TRACE_<INTEGRATION>_ENABLED` and `DD_PATCH_MODULES` environment variables.

- ASM: This fix resolves an issue where AppSec was using a patched request and builtins functions, creating telemetry errors.

- datastreams: Logs at warning level for Kinesis errors that break the Data Streams Monitoring map.

- library: Resolves deadlocks that could occur when sending instrumentation telemetry data after an unhandled exception is raised.


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## 2.18.1
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