}

  // Populate return_nodes
  DCHECK(tf_results->return_nodes.empty());
  tf_results->return_nodes.resize(results.return_nodes.size());
  for (int i = 0; i < results.return_nodes.size(); ++i) {
    tf_results->return_nodes[i] = ToOperation(results.return_nodes[i]);
  }

  // Populate missing unused map keys
  DCHECK(tf_results->missing_unused_key_names.empty());

    float grad_data[1];
    memcpy(&grad_data[0], TF_TensorData(grad_tensor),
           TF_TensorByteSize(grad_tensor));
    TF_DeleteTensor(grad_tensor);
    dtheta_approx[i] = grad_data[0];
  }

  // Populate *numerical_grad with the data from dtheta_approx.
  TF_RETURN_IF_ERROR(TestTensorHandleWithDims<float, TF_FLOAT>(
      ctx, dtheta_approx.data(), theta_dims.data(), num_dims, numerical_grad));
  TF_DeleteTensor(theta_tensor);

  // Consider getting rid of this and making the behavior between number types
  // and string consistent.
  forward_op_.attrs.BuildNodeDef();
  // TODO(b/170307493): Populate skip_input_indices here.
  std::unique_ptr<GradientFunction> backward_fn;
  TF_RETURN_IF_ERROR(registry_.Lookup(forward_op_, &backward_fn));
  tape_->RecordOperation(forward_op_.inputs, forward_op_.outputs,

  // Finalize the function by providing the returned values.
  TF_AbstractFunction* func;
  {
    // We want to return the output of both add operations, create a new list
    // and populate it.
    TF_OutputList* func_outputs = TF_NewOutputList();
    TF_OutputListPushBack(func_outputs, add_output1, s);
    ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);