: 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();
  }

                 std::unique_ptr<tensorflow::Tensor>* out_tensor,
                 TF_Status* out_status) const;

 private:
  // Uses "v2_reader_" to build "var name -> shape" and "var name -> data type"
  // maps; both owned by caller.
  // REQUIRES: "v2_reader_ != nullptr && v2_reader_.status().ok()".
  std::pair<std::unique_ptr<TensorSliceReader::VarToShapeMap>,
            std::unique_ptr<TensorSliceReader::VarToDataTypeMap> >
  BuildV2VarMaps();

    string key(v2_reader_->key());
    (*var_to_shape_map)[key] = TensorShape(entry.shape());
    (*var_to_data_type_map)[key] = DataType(entry.dtype());
  }
  // The returned pointers are owned by the caller.
  return std::make_pair(std::move(var_to_shape_map),
                        std::move(var_to_data_type_map));
}

}  // namespace checkpoint

  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:

};

struct TF_ImportGraphDefOptions {
  tensorflow::ImportGraphDefOptions opts;

  // Backing memory for TensorId fields in opts.
  // TODO(skyewm): it'd be better if ImportGraphDefOptions owned this.
  std::vector<tensorflow::string> tensor_id_data;
};

struct TF_ImportGraphDefResults {
  std::vector<TF_Output> return_tensors;
  std::vector<TF_Operation*> return_nodes;

#
#   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:

// "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);