ASSERT_EQ(errors::OK, s.code()) << s.message();
    auto num_elem_numerical = TF_TensorElementCount(numerical_tensor);

    TF_Tensor* analytical_tensor;
    s = GetValue(outputs[i], &analytical_tensor);
    ASSERT_EQ(errors::OK, s.code()) << s.message();
    auto num_elem_analytical = TF_TensorElementCount(analytical_tensor);

    ASSERT_EQ(num_elem_numerical, num_elem_analytical);

  // Convert from AbstractTensor to TF_Tensor.
  TF_Tensor* theta_tensor;
  TF_RETURN_IF_ERROR(GetValue(theta, &theta_tensor));

  // Get number of elements and fill data.
  int num_elems = TF_TensorElementCount(theta_tensor);
  vector<float> theta_data(num_elems);
  memcpy(theta_data.data(), TF_TensorData(theta_tensor),
         TF_TensorByteSize(theta_tensor));

  // Initialize space for the numerical gradient.

  }

  TF_Tensor* numerical_tensor;
  s = GetValue(numerical_grad.get(), &numerical_tensor);
  ASSERT_EQ(errors::OK, s.code()) << s.message();
  auto num_elem_numerical = TF_TensorElementCount(numerical_tensor);
  ASSERT_EQ(num_elem_numerical, num_grad);

  float* dnumerical = new float[num_elem_numerical]{0};
  memcpy(&dnumerical[0], TF_TensorData(numerical_tensor),

  TF_Tensor* b =
      TF_AllocateTensor(TF_UINT64, nullptr, 0, TF_DataTypeSize(TF_UINT64));
  EXPECT_NE(a, nullptr);
  EXPECT_NE(b, nullptr);

  EXPECT_EQ(6, TF_TensorElementCount(a));
  EXPECT_EQ(1, TF_TensorElementCount(b));
  EXPECT_EQ(6 * TF_DataTypeSize(TF_UINT64), TF_TensorByteSize(a));
  EXPECT_EQ(TF_DataTypeSize(TF_UINT64), TF_TensorByteSize(b));

  int64_t new_dims[] = {3, 2};