low/compiler/mlir/tfrt/transforms/mlrt,tensorflow/core/runtime_fallback,tensorflow/core/runtime_fallback/conversion,tensorflow/core/runtime_fallback/kernel,tensorflow/core/runtime_fallback/opdefs,tensorflow/core/runtime_fallback/runtime,tensorflow/core/runtime_fallback/util,tensorflow/core/runtime_fallback/test,tensorflow/core/runtime_fallback/test/gpu,tensorflow/core/runtime_fallback/test/saved_model,tensorflow/core/runtime_fallback/test/testdata,tensorflow/core/tfrt/stubs,tensorflow/core/tfrt/...

  cat > $BATS_TEST_TMPDIR/ignore_deps_for_these_packages <<EOF
//tensorflow/lite
//tensorflow/compiler/mlir/lite
//tensorflow/compiler/mlir/tfrt
//tensorflow/core/runtime_fallback
//tensorflow/core/tfrt
//tensorflow/python/kernel_tests/signal
//tensorflow/examples
//tensorflow/tools/android
//tensorflow/python/eager/benchmarks
EOF

*   Added `tf.keras.layers.SpectralNormalization` layer wrapper to perform spectral normalization on the weights of a target layer.

    TFE_CancellationManager*);
TF_CAPI_EXPORT extern bool TFE_CancellationManagerRegisterCallback(
    TFE_CancellationManager*, TFE_CancellationToken token,
    const TFE_CancelCallback* c_callback, const char* callback_name);
TF_CAPI_EXPORT extern bool TFE_CancellationManagerDeregisterCallback(
    TFE_CancellationManager*, TFE_CancellationToken token);
TF_CAPI_EXPORT extern bool TFE_CancellationManagerTryDeregisterCallback(

    const TFE_CancelCallback* c_callback, const char* callback_name) {
  tensorflow::CancelCallback callback = [callback = c_callback->callback,
                                         context = c_callback->context]() {
    callback(context);
  };
  return tensorflow::unwrap(cancellation_manager)
      ->RegisterCallbackWithErrorLogging(token, callback, callback_name);
}

  // threadpool, so that we avoid the possibility of running the runner_ in the
  // threadpool of GPU event mgr, as that can trigger more callbacks to be
  // scheduled on that same threadpool, causing a deadlock in cases where the
  // caller of event_mgr->ThenExecute() blocks on the completion of the callback
  // (as in the case of ConstOp kernel creation on GPU, which involves copying a
  // CPU tensor to GPU).

  // If false, all activations are deleted in the first call to ComputeGradient.
  // Else, only when this is destructed.
  bool persistent_;
};

// Describes a callback for special-cased and more efficient jvp computation.
//
// Could just be a simple typedef in ForwardAccumulator, but MSVC chokes on
// that.
template <typename Gradient>
class ForwardFunction