#
#   Note: if you interrupt a bazel command on docker (ctrl-c), you
#   will need to run `docker exec tf pkill bazel` to quit bazel.
#
#   Note: new files created from the container are owned by "root".
#   You can run e.g. `docker exec tf chown -R $(id -u):$(id -g) build_output`
#   to transfer ownership to your user.
#
# Docker is enabled by default on Linux. You may disable it if you prefer:

      : owned_executor(new tensorflow::EagerExecutor(
            async, enable_streaming_enqueue, in_flight_nodes_limit)) {}

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

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

  Status Name(StringPiece* result) const override;

 private:
  std::string filename_;
  std::unique_ptr<TF_RandomAccessFile> file_;
  const TF_RandomAccessFileOps* ops_;  // not owned
  ModularRandomAccessFile(const ModularRandomAccessFile&) = delete;
  void operator=(const ModularRandomAccessFile&) = delete;
};

class ModularWritableFile final : public WritableFile {
 public:

      opts->session_options.options,
      static_cast<tensorflow::ContextDevicePlacementPolicy>(
          opts->device_placement_policy),
      opts->async, device_mgr.release(),
      /*device_mgr_owned*/ true, std::move(r),
      /*cluster_flr=*/nullptr,
      /*collective_executor_mgr=*/nullptr,
      /*run_eager_op_as_function=*/opts->run_eager_op_as_function,
      /*jit_compile_rewrite=*/opts->jit_compile_rewrite);

namespace tf_writable_file {
typedef struct GCSFile {
  const std::string bucket;
  const std::string object;
  gcs::Client* gcs_client;  // not owned
  TempFile outfile;
  bool sync_need;
  // `offset` tells us how many bytes of this file are already uploaded to
  // server. If `offset == -1`, we always upload the entire temporary file.
  int64_t offset;

// "fifo_queue_enqueue_<tensor_id>", to be executed by this API call. It reads
// from a placeholder node "arg_tensor_enqueue_<tensor_id>".
//
// `tensor` is still owned by the caller. This call will be blocked if the queue
// has reached its capacity, and will be unblocked when the queued tensors again
// drop below the capacity due to dequeuing.
//

      const std::vector<Gradient*>& in_grads, absl::Span<Gradient*> out_grads);

  // Maps from tensor IDs to corresponding JVPs.
  std::unordered_map<int64_t, Gradient*> accumulated_gradients_;
  // Not owned; provides operations on Tensors which are currently only
  // available in language bindings (e.g. Python).
  const VSpace<Gradient, BackwardFunction, TapeTensor>& vspace_;

  // Decides if tangents are vectorized or not

  if (input_arg.number_attr().empty()) {
    status->status = tensorflow::errors::NotFound(
        op_name, " does not have number_attr() defined.");
    return nullptr;
  }

  // The returned string is owned by OpRegistry, so liveness is not a concern.
  return input_arg.number_attr().c_str();
}

int TF_OpIsStateful(const char* op_type, TF_Status* status) {
  const tensorflow::OpRegistrationData* op_reg_data;

// Fetches the return outputs requested via
// TF_ImportGraphDefOptionsAddReturnOutput(). The number of fetched outputs is
// returned in `num_outputs`. The array of return outputs is returned in
// `outputs`. `*outputs` is owned by and has the lifetime of `results`.
TF_CAPI_EXPORT extern void TF_ImportGraphDefResultsReturnOutputs(
    TF_ImportGraphDefResults* results, int* num_outputs, TF_Output** outputs);

  // Notifies a StartExecute that the previous Join has finished.
  tensorflow::condition_variable finished_join_;

  // Temporary state between `StartExecute` and `Join`.
  //
  //   Inputs; pointers are to objects not owned by the DeviceThread, but which
  //   are expected to live at least until `Join` finishes:
  TFE_Context* context_ TF_GUARDED_BY(execution_mutex_);
  const char* operation_name_ TF_GUARDED_BY(execution_mutex_);