result = session->PRun(handle, input_pairs, output_tensor_names, &outputs);
  }
  if (!result.ok()) {
    status->status = result;
    return;
  }

  // Store results in c_outputs[]
  for (int i = 0; i < noutputs; ++i) {
    const Tensor& src = outputs[i];
    if (!src.IsInitialized() || src.NumElements() == 0) {
      c_outputs[i] =

         TF_TensorByteSize(theta_tensor));

  // Initialize space for the numerical gradient.
  vector<float> dtheta_approx(num_elems);

  // Get theta shape and store in theta_dims.
  int num_dims = TF_NumDims(theta_tensor);
  vector<int64_t> theta_dims(num_dims);
  GetDims(theta_tensor, theta_dims.data());

  // Initialize auxilary data structures.

  if (UseFunction()) {
    // TODO(b/168850692): Enable this.
    GTEST_SKIP() << "Can't take gradient of "
                    "SparseSoftmaxCrossEntropyWithLogits in tracing mode.";
  }

  // Score
  float X_vals[] = {1.0f, 2.0f, 3.0f, -5.0f, -4.0f, -3.0f, 2.0f, 0.0f, -1.0f};
  int64_t X_dims[] = {3, 3};
  AbstractTensorHandlePtr X;
  {
    AbstractTensorHandle* X_raw;

    EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
    TFE_DeleteTensorHandle(n);
    n = n_gpu;
  }

  TFE_TensorHandle* m = TestMatrixTensorHandle100x100(ctx);

  // Store pointer to raw buffer for validation of forwarding behaviour.
  TF_Tensor* orig = TFE_TensorHandleResolve(n, status);
  void* orig_ptr = TF_TensorData(orig);
  TF_DeleteTensor(orig);

  TFE_Op* add_op = AddOp(ctx, n, m);