"/a/path/to/a/file");
}

TEST_P(ModularFileSystemTest, TestCreateFile) {
  const std::string filepath = GetURIForPath("a_file");
  std::unique_ptr<WritableFile> new_file;
  Status status = env_->NewWritableFile(filepath, &new_file);
  EXPECT_PRED2(UnimplementedOrReturnsCode, status, Code::OK);
}

TEST_P(ModularFileSystemTest, TestCreateFileNonExisting) {

  EXPECT_EQ(*value, expected);
  TF_DeleteTensor(result_t);
}

// TODO(srbs): Add tests for captures.
// TODO(srbs): Add tests for polymorphism (different shapes and dtypes).
TEST_P(FunctionTest, Square) {
  // Construct a scalar.
  impl::TaggedValueTensor x = CreateScalarTensor<float, TF_FLOAT>(2.0f);
  FunctionDef fdef = SquareFunc();

  EXPECT_EQ(out_val[0], 5.0f);
}

TEST_P(RuntimeTest, CreateHostTensorIncompatibleShape) {
  Runtime runtime = RuntimeTest::GetParam()();
  EXPECT_THAT(runtime.CreateHostTensor<float>({2}, TF_FLOAT, {2.0f}),
              StatusIs(absl::StatusCode::kInvalidArgument,
                       HasSubstr("Mismatched shape and data size")));
}

TEST_P(RuntimeTest, CreateHostTensorNonFullyDefinedShapeRaises) {

  GradientRegistry registry_;
  Status status_;

 public:
  bool UseMlir() const { return strcmp(std::get<0>(GetParam()), "mlir") == 0; }
  bool UseFunction() const { return std::get<2>(GetParam()); }
};

TEST_P(CppGradients, TestAddGrad) {
  AbstractTensorHandlePtr x;
  {
    AbstractTensorHandle* x_raw = nullptr;
    status_ = TestScalarTensorHandle<float, TF_FLOAT>(
        immediate_execution_ctx_.get(), 2.0f, &x_raw);

  std::unique_ptr<StaticDeviceMgr> device_mgr_;
  EagerContextPtr ctx_;
};

// Sanity check that constructing a tensorflow::Variable from a SavedVariable
// 1. does not cause an error
// 2. preserves dtype and shape.
TEST_P(SavedVariableLoadingTest, LoadSavedVariableSuccessful) {
  auto& test_params = GetParam();
  DataType dtype = std::get<0>(test_params);
  TensorShape shape(std::get<1>(test_params));

  SavedVariable saved_variable;

  // You can implement all the usual class fixture members here.
};

// Then, use the TEST_P macro to define as many parameterized tests
// for this fixture as you want. The _P suffix is for "parameterized"
// or "pattern", whichever you prefer to think.

TEST_P(FooTest, DoesBlah) {
  // Inside a test, access the test parameter with the GetParam() method
  // of the TestWithParam<T> class:

  if (shape.dims() != 0) {
    return errors::InvalidArgument(
        "Tensor expected to have scalar shape found rank: ", shape.dims());
  }
  return absl::OkStatus();
}

TEST_P(UnifiedAPI, TestTensorShapeScalar) {
  if (UseFunction() && UseMlir()) {
    // TODO(b/173074167): Remove this.
    GTEST_SKIP() << "MlirTensor::Shape is not implemented yet.";
  }
  AbstractContextPtr ctx;
  {

  // You can implement all the usual class fixture members here.
};

// Then, use the TEST_P macro to define as many parameterized tests
// for this fixture as you want. The _P suffix is for "parameterized"
// or "pattern", whichever you prefer to think.

TEST_P(FooTest, DoesBlah) {
  // Inside a test, access the test parameter with the GetParam() method
  // of the TestWithParam<T> class:

    TF_SetTracingImplementation(std::get<0>(GetParam()), status.get());
    Status s = StatusFromTF_Status(status.get());
    CHECK_EQ(errors::OK, s.code()) << s.message();
  }
};

TEST_P(UnifiedCAPI, TestBasicEager) {
  std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
      TF_NewStatus(), TF_DeleteStatus);
  TFE_ContextOptions* opts = TFE_NewContextOptions();

// https://github.com/google/googletest/blob/dcc92d0ab6c4ce022162a23566d44f673251eee4/googletest/docs/advanced.md#value-parameterized-tests
class CSavedModelAPITest : public ::testing::TestWithParam<bool> {};

TEST_P(CSavedModelAPITest, LoadsSavedModelWithTags) {
  TF_Status* status = TF_NewStatus();
  TFE_ContextOptions* opts = TFE_NewContextOptions();
  bool use_tfrt = GetParam();
  if (use_tfrt) {
    TFE_DeleteContextOptions(opts);