ClientSession::CallableHandle callable;
  TF_CHECK_OK(session.MakeCallable(options, &callable));
  TF_EXPECT_OK(session.RunCallable(
      callable, {test::AsTensor<int>({1}, {}), test::AsTensor<int>({41}, {})},
      &outputs, nullptr));
  test::ExpectTensorEqual<int>(outputs[0], test::AsTensor<int>({42}, {}));
  TF_EXPECT_OK(session.ReleaseCallable(callable));
}

TEST(ClientSessionTest, CallableWithCustomThreadPool) {

  test::GetTensor(root, m, &out);
  test::ExpectTensorEqual<int>(out, test::AsTensor<int>({42}, {1, 1}));
}

TEST(CCOpTest, Attrs) {
  Scope root = Scope::NewRootScope();
  auto m = MatMul(root, {{1}, {1}}, {{41}, {1}}, MatMul::TransposeA(true));
  TF_EXPECT_OK(root.status());
  Tensor out;
  test::GetTensor(root, m, &out);
  test::ExpectTensorEqual<int>(out, test::AsTensor<int>({42}, {1, 1}));
}

TEST(CCOpTest, SplitConcat) {

  test::ExpectTensorEqual<int>(
      outputs[0],
      test::AsTensor<int>({40, 41, 42, 43, 44, 45, 46, 47}, {4, 2}));
  test::ExpectTensorEqual<int>(
      outputs[1],
      test::AsTensor<int>({50, 51, 52, 53, 55, 55, 56, 57}, {4, 2}));
  test::ExpectTensorEqual<int>(
      outputs[2],
      test::AsTensor<int>({60, 61, 62, 63, 66, 66, 66, 67}, {4, 2}));
}

  // Avoid input values where Leaky ReLU gradient is not well defined (around
  // zero).
  Tensor x_init_value = test::AsTensor<float>(
      {2.3f, 1.9f, 1.5f, 1.1f, 0.7f, 0.3f, -0.1f, -0.5f, -0.9f, -1.3f}, {5, 2});
  Tensor features = test::AsTensor<float>(
      {-0.9f, -0.7f, -0.5f, -0.3f, -0.1f, 0.1f, 0.3f, 0.5f, 0.7f, 0.9f},
      {5, 2});
  auto y = ops::internal::LeakyReluGrad(scope_, x, features);

    Tensor input =
        test::AsTensor<tstring>(serialized_examples, TensorShape({4}));
    std::vector<Tensor> outputs;
    TF_ASSERT_OK(bundle.GetSession()->Run({{input_name, input}}, {output_name},
                                          {}, &outputs));
    ASSERT_EQ(outputs.size(), 1);
    test::ExpectTensorEqual<float>(
        outputs[0],
        test::AsTensor<float>({2, 2.5, 3, 3.5}, TensorShape({4, 1})));

  Tensor loaded_var_0{};
  EXPECT_TRUE(bundle_reader.Lookup("var_0", &loaded_var_0).ok());
  ExpectEqual(loaded_var_0, AsTensor<float>({1.0, 2.0}));

  Tensor loaded_var_1{};
  EXPECT_TRUE(bundle_reader.Lookup("var_1", &loaded_var_1).ok());
  ExpectEqual(loaded_var_1, AsTensor<int>({3, 4, 5, 6}));
}

TEST_F(SaveVariablesToCheckpointTest,
       NoVariablesSavedWhenNoInitializerFunction) {

  auto y = Min(scope_, x, {-1});
  // y's shape is the result of reducing x along axes -1 (= 1)
  TensorShape y_shape({2});
  Tensor x_init_value =
      test::AsTensor<float>({0.5f, 0.7f, 0.2f, 1.0f, 1.5f, -2.8f}, x_shape);
  RunTest(x, x_init_value, y, y_shape);
}

TEST_F(NaryGradTest, Max) {
  TensorShape x_shape({2, 3});

    Tensor input =
        test::AsTensor<tstring>(serialized_examples, TensorShape({4}));
    std::vector<Tensor> outputs;
    TF_ASSERT_OK(bundle.session->Run({{input_name, input}}, {output_name}, {},
                                     &outputs));
    ASSERT_EQ(outputs.size(), 1);
    test::ExpectTensorEqual<float>(
        outputs[0],
        test::AsTensor<float>({2, 2.5, 3, 3.5}, TensorShape({4, 1})));
  }
};

  TF_EXPECT_OK(GetNodeAttr(n->attrs(), "value", &tensor));
  DataType dtype;
  TF_EXPECT_OK(GetNodeAttr(n->attrs(), "dtype", &dtype));
  EXPECT_EQ(tensor.dtype(), dtype);
  test::ExpectTensorEqual<T>(tensor, test::AsTensor(values, shape));
}

void ExpectTypeAndShape(const Node* n, DataType expected_dtype,
                        TensorShape expected_shape) {
  EXPECT_TRUE(n->IsConstant());
  Tensor tensor;

    DCHECK_EQ(expected_output_values.size(), out_tensors.size());
    for (int i = 0; i < out_tensors.size(); ++i) {
      test::ExpectTensorEqual<T>(
          out_tensors[i], test::AsTensor<T>({expected_output_values[i]}, {}));
    }
  }

  Scope scope_;
  std::vector<Output> inputs_;
  std::vector<Output> outputs_;

  static const char* const kFrameName;
};