unsigned char value) {
  desc->node_builder.Attr(attr_name, static_cast<bool>(value));
}

void TF_SetAttrBoolList(TF_OperationDescription* desc, const char* attr_name,
                        const unsigned char* values, int num_values) {
  std::unique_ptr<bool[]> b(new bool[num_values]);
  for (int i = 0; i < num_values; ++i) {
    b[i] = values[i];
  }
  desc->node_builder.Attr(attr_name,

  TF_DeleteTensor(t);

  for (int i = 0; i < expected_values.size(); i++) {
    EXPECT_EQ(expected_values[i], actual_values[i])
        << "Mismatch in expected values at (zero-based) index " << i;
  }
}

void CheckRemoteMatMulExecutesOK(TFE_Context* ctx,
                                 const char* remote_device_name,
                                 const char* local_device_name) {

                           TF_DataType value) {
  auto iter = builder->attr_names.insert(attr_name).first;
  builder->Set(*iter, static_cast<tensorflow::DataType>(value));
}

void TF_AttrBuilderSetTypeList(TF_AttrBuilder* builder, const char* attr_name,
                               const TF_DataType* values, int num_values) {
  auto iter = builder->attr_names.insert(attr_name).first;

  ASSERT_EQ(errors::OK, s.code()) << s.message();
  auto result_value = static_cast<float*>(TF_TensorData(result_tensor));
  EXPECT_EQ(*result_value, 1.0);
  outputs[0]->Unref();
  TF_DeleteTensor(result_tensor);
  result_tensor = nullptr;
}

INSTANTIATE_TEST_SUITE_P(
    CustomGradientTest, CustomGradientTest,
    ::testing::Combine(::testing::Values("graphdef", "mlir"),

    ::testing::Combine(::testing::Values("graphdef", "mlir"),
                       /*tfrt*/ ::testing::Values(false),
                       /*use_function*/ ::testing::Values(true, false)));
#else
INSTANTIATE_TEST_SUITE_P(
    UnifiedCAPI, CppGradients,
    ::testing::Combine(::testing::Values("graphdef", "mlir"),
                       /*tfrt*/ ::testing::Values(false),

  const FunctionDef assert_and_collective = FunctionDefHelper::Define(
      // Name
      "AssertAndCollective",
      // Args
      {"x: float", "condition: bool"},
      // Return values
      {"y: float"},
      // Attr def
      {},
      // Nodes
      {
          {{"assert"},
           "Assert",
           {"condition", "x"},
           {{"T", std::vector<DataType>{DT_FLOAT}}}},

  }

  size_t storage_size = 0;
  int num_values = 0;
  TF_Status* status = TF_NewStatus();
  TF_GetNodesToPreserveListSize(c_item, &num_values, &storage_size, status);
  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  EXPECT_EQ(nodes_preserved.size(), num_values);
  EXPECT_EQ(list_total_size, storage_size);

  std::unique_ptr<char*[]> values(new char*[nodes_preserved.size()]);

    ::testing::Combine(::testing::Values("graphdef", "mlir"),
                       /*tfrt*/ ::testing::Values(false),
                       /*executing_eagerly*/ ::testing::Values(true, false)));
#else
INSTANTIATE_TEST_SUITE_P(
    UnifiedCAPI, CppGradients,
    ::testing::Combine(::testing::Values("graphdef", "mlir"),
                       /*tfrt*/ ::testing::Values(false),

  }

  // Add a parallel tensor with different values on each device to the variable.
  {
    TensorHandlePtr value_one(FloatTensorHandle(3., status.get()));
    TensorHandlePtr value_two(FloatTensorHandle(-2., status.get()));
    std::array<TFE_TensorHandle*, 2> components{value_one.get(),
                                                value_two.get()};
    TensorHandlePtr combined_value =

                                       const float* values, int num_values) {
  forward_op_.attrs.Set(attr_name,
                        gtl::ArraySlice<const float>(values, num_values));
  return parent_op_->SetAttrFloatList(attr_name, values, num_values);
}
Status TapeOperation::SetAttrIntList(const char* attr_name,
                                     const int64_t* values, int num_values) {
  forward_op_.attrs.Set(