// names of wrapped devices.
//
// There are currently no graph semantics implemented for registered custom
// devices, so executing tf.functions which contain operations placed on the
// custom devices will fail.
//
// `device_name` must not name an existing physical or custom device. It must
// follow the format:
//
//    /job:<name>/replica:<replica>/task:<task>/device:<type>:<device_num>
//

// message) to `output_graph_def` (allocated by TF_NewBuffer()).
// `output_graph_def`'s underlying buffer will be freed when TF_DeleteBuffer()
// is called.
//
// May fail on very large graphs in the future.
TF_CAPI_EXPORT extern void TF_GraphToGraphDef(TF_Graph* graph,
                                              TF_Buffer* output_graph_def,

  auto pop_call_state = gtl::MakeCleanup([this] { this->call_state_.pop(); });

  std::vector<Gradient*> forward_grads;
  if (forward_function == nullptr) {
    // We have no special-cased forward gradient. Fall back to running the
    // backward function under a gradient tape.
    forward_grads.resize(output_tensors.size());
    TF_RETURN_IF_ERROR(ForwardpropFromTape(
        op_type, output_tensors, backward_function_getter,

class Device;

// LINT.IfChange
// Note: Keep in sync with exported copy of enum in eager/c_api.h.
enum ContextDevicePlacementPolicy {
  // Running operations with input tensors on the wrong device will fail.
  DEVICE_PLACEMENT_EXPLICIT = 0,
  // Copy the tensor to the right device but log a warning.
  DEVICE_PLACEMENT_WARN = 1,
  // Silently copy the tensor, which has a performance cost since the operation