ASSERT_EQ(absl::OkStatus(),
            model_api->GetVariable("sub_module.uninitialized_variable",
                                   &uninitialized_variable));
  ASSERT_EQ(tensorflow::DT_INT64, uninitialized_variable->dtype());

  TF_DeleteSavedModel(saved_model);
  TF_DeleteStatus(status);
  TFE_DeleteContext(ctx);
}

TEST_P(CSavedModelAPITest, LoadSavedModelWithWhileLoop) {

}

TEST_F(ArrayGradTest, GatherNdGrad_SliceIndexing_Int64) {
  TensorShape shape({2, 2});
  auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape));
  auto indices = Cast(scope_, Const(scope_, {{1}, {0}}), DT_INT64);
  auto y = GatherNd(scope_, x, indices);
  RunTest(x, shape, y, shape);
}

TEST_F(ArrayGradTest, CheckNumericsGrad) {
  TensorShape shape({5, 2});

    case DT_INT16:
    case DT_QINT16:
      return PrintArray(num_elts, t.flat<int16>().data());
    case DT_INT8:
    case DT_QINT8:
      return PrintArray(num_elts, t.flat<int8>().data());
    case DT_INT64:
      return PrintArray(num_elts, t.flat<int64_t>().data());
    case DT_BOOL:
      return PrintArray(num_elts, t.flat<bool>().data());
    case DT_STRING: {
      string ret;

  // so it's fine to convert it back to int32 regardless of truncation.
  auto params = op.input(0);
  auto colocate_scope = scope.ColocateWith(params);
  Shape::Attrs shape_attrs;
  shape_attrs.out_type_ = DT_INT64;
  auto params_shape64 = Shape(colocate_scope, params, shape_attrs);
  Output params_shape = Cast(colocate_scope, params_shape64, DT_INT32);

  auto indices = op.input(1);

  EXPECT_EQ(TF_COMPLEX64, static_cast<TF_DataType>(tensorflow::DT_COMPLEX64));
  EXPECT_EQ(TF_COMPLEX, TF_COMPLEX64);
  EXPECT_EQ(TF_INT64, static_cast<TF_DataType>(tensorflow::DT_INT64));
  EXPECT_EQ(TF_BOOL, static_cast<TF_DataType>(tensorflow::DT_BOOL));
  EXPECT_EQ(TF_QINT8, static_cast<TF_DataType>(tensorflow::DT_QINT8));
  EXPECT_EQ(TF_QUINT8, static_cast<TF_DataType>(tensorflow::DT_QUINT8));

    if (status.ok()) {
      // We can only leave the transpose op on host if its dtype is supported on
      // host.
      if (dtype == tensorflow::DT_UINT64 || dtype == tensorflow::DT_INT64 ||
          dtype == tensorflow::DT_UINT32 || dtype == tensorflow::DT_INT32 ||
          dtype == tensorflow::DT_UINT16 || dtype == tensorflow::DT_INT16 ||