explicit TFE_Executor(tensorflow::EagerExecutor* executor)
      : owned_executor(nullptr), unowned_executor(executor) {}

  tensorflow::EagerExecutor* executor() {
    return owned_executor == nullptr ? unowned_executor : owned_executor.get();
  }

  std::unique_ptr<tensorflow::EagerExecutor> owned_executor;
  tensorflow::EagerExecutor* unowned_executor;
};

# to make sure all Genrule targets are excuted locally.
build:rbe_cross_compile_base_xla --config=rbe_cross_compile_base
build:rbe_cross_compile_base_xla --strategy=Genrule=standalone

# Due to the above strategy, all Genrule commands are executed locally, but the
# following actions invoke tools (E.g `flatc`, `llvm-tblgen`, etc.) that are
# only executabe on the RBE (x86) machine, so the strategy_regexp options are

// `struct_size` is used for struct member compatibility check between core TF
// and plug-ins with the same C API minor version. More info here:
// https://github.com/tensorflow/community/blob/master/rfcs/20200612-stream-executor-c-api/C_API_versioning_strategy.md
#define TF_VALIDATE_STRUCT_SIZE(STRUCT_NAME, STRUCT_OBJ, SIZE_VALUE_NAME) \
  do {                                                                    \

        "//tensorflow:android": [
            "//tensorflow/c/experimental/stream_executor:stream_executor_hdrs",
            "//tensorflow/core:portable_tensorflow_lib_lite",
        ],
        "//conditions:default": [
            ":tf_tensor",
            "//tensorflow/c/experimental/stream_executor",
            "//tensorflow/c/experimental/stream_executor:stream_executor_internal",
            "//tensorflow/core:framework",

* Add an option `disable_eager_executer_streaming_enqueue` to `tensorflow.ConfigProto.Experimental` to control the eager runtime's behavior around parallel remote function invocations; when set to `True`, the eager runtime will be allowed to execute multiple function invocations in parallel.

* `tf.constant_initializer`

  virtual void EndStep() = 0;

  // Return the Eager Executor for current thread. Please note that Eager
  // Executor is only used in current TF but not in TFRT.
  virtual EagerExecutor& Executor() = 0;
  // Update the Eager Executor for current thread.
  virtual void SetExecutorForThread(EagerExecutor* executor) = 0;

  // Return a list of local tensorflow::Device*.

                                        TF_Status* status) {
  status->status = executor->executor()->WaitForAllPendingNodes();
}

void TFE_ExecutorClearError(TFE_Executor* executor) {
  executor->executor()->ClearError();
}

void TFE_ContextSetExecutorForThread(TFE_Context* ctx, TFE_Executor* executor) {
  tensorflow::unwrap(ctx)->SetExecutorForThread(executor->executor());
}

        otherwise = [
            "@local_xla//xla/stream_executor/cuda:all_runtime",
            "@local_xla//xla/stream_executor/cuda:cuda_driver",
            "@local_xla//xla/stream_executor/cuda:cuda_platform",
            "@local_xla//xla/stream_executor/cuda:cudnn_plugin",
            "@local_xla//xla/stream_executor/cuda:cufft_plugin",
            "@local_xla//xla/stream_executor:cuda_platform",
        ],
    ),

// a map (`tensorflow::eager::TensorTape`) from the wrapped tensor to the id of
// the op that produced it (or -1 if this tensor was watched using
// `GradientTape::Watch`.) The op_id is simply a unique index assigned to each
// op executed under the tape. A separate map (`tensorflow::eager::OpTape`)
// maintains the map from `op_id` to a `OpTapeEntry` which stores the `op_type`,
// inputs and outputs and the gradient function These data structures combined

      this License, without any additional terms or conditions.
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