TF_DeleteStatus(status);

  // TODO(b/136478427): Figure out how to correctly shut the server down.
  worker_server1.release();
  worker_server2.release();

  // Disable the test graph pass so it does not affect other test cases.
  GraphErrorInjectionPass::enabled_ = false;
}

string VariableAddFunctionWithGraphError() {
  string signature = VariableAddFunctionSignature();

      } else {
        const auto num_dims = tensor_shape.dim_size();
        std::unique_ptr<int64_t[]> dims(new int64_t[num_dims]);
        for (int i = 0; i < num_dims; ++i) {
          dims[i] = tensor_shape.dim(i).size();
        }
        TFE_OpSetAttrShape(op, attr_name, dims.get(), num_dims, status);
      }
    } break;
    case tensorflow::AttrValue::kFunc: {

                       TF_Status* status) {
  if (!undeleted_files || !undeleted_dirs)
    return TF_SetStatus(
        status, TF_INTERNAL,
        "'undeleted_files' and 'undeleted_dirs' cannot be nullptr.");
  *undeleted_files = 0;
  *undeleted_dirs = 0;
  if (!IsDirectory(filesystem, path, status)) {
    *undeleted_dirs = 1;
    return;
  }

  constexpr int64_t dims[] = {100, 100};
  constexpr int num_elements = dims[0] * dims[1];
  float data[num_elements];
  for (int i = 0; i < num_elements; ++i) {
    data[i] = 1.0f;
  }
  TF_Status* status = TF_NewStatus();
  TF_Tensor* t = TFE_AllocateHostTensor(ctx, TF_FLOAT, &dims[0],
                                        sizeof(dims) / sizeof(int64_t), status);

    DCHECK(shape != nullptr);
    TF_Status status;
    int num_dims = TF_GraphGetTensorNumDims(graph_, output_, &status);
    DCHECK_GE(num_dims, -1);
    TF_RETURN_IF_ERROR(StatusFromTF_Status(&status));
    if (num_dims == kUnknownRank) {
      return absl::OkStatus();
    }

    std::vector<int64_t> dims(num_dims, kUnknownDim);
    TF_GraphGetTensorShape(graph_, output_,

    int num_dims = output_shapes->items[0].num_dims;
    int64_t* dims = output_shapes->items[0].dims;

    if (!expected_shape.has_value()) {
      EXPECT_EQ(num_dims, -1);
      EXPECT_EQ(dims, nullptr);
      return;
    }

    EXPECT_EQ(num_dims, expected_shape->size());
    for (size_t i = 0; i < num_dims; ++i) {
      EXPECT_EQ(dims[i], (*expected_shape)[i]);
    }

  PartialTensorShape shape;
  TF_RETURN_IF_ERROR(inputs[0]->Shape(&shape));
  if (shape.dims() != 2) {
    return errors::InvalidArgument(
        "Tensor expected to have rank 2 found rank: ", shape.dims());
  }
  int64_t dim_sizes[] = {2, 4};
  for (int i = 0; i < shape.dims(); i++) {
    if (shape.dim_size(i) != dim_sizes[i]) {
      return errors::InvalidArgument("Dim ", i, " expected to be of size ",

};

TEST_P(GradientCheckerTest, TestMatMul) {
  float A_vals[] = {1.0f, 2.0f, 3.0f, 4.0f};
  int64_t A_dims[] = {2, 2};
  AbstractTensorHandlePtr A;
  {
    AbstractTensorHandle* A_raw;
    Status s = TestTensorHandleWithDims<float, TF_FLOAT>(ctx_.get(), A_vals,
                                                         A_dims, 2, &A_raw);
    ASSERT_EQ(errors::OK, s.code()) << s.message();
    A.reset(A_raw);
  }

}

TF_Tensor* TFE_AllocateHostTensor(TFE_Context* ctx, TF_DataType dtype,
                                  const int64_t* dims, int num_dims,
                                  TF_Status* status) {
  std::vector<int64_t> dimvec(num_dims);
  for (int i = 0; i < num_dims; ++i) {
    dimvec[i] = static_cast<int64_t>(dims[i]);
  }

  if (ctx == nullptr) {
    status->status = tensorflow::errors::InvalidArgument("Invalid Context");

  tensorflow::AsyncValueTensor* av_tensor =
      tensorflow::AsyncValueTensor::FromTensor(tensor);
  if (av_tensor == nullptr || av_tensor->GetBuffer() == nullptr) {
    return absl::InternalError("Input tensor does not have PjRtBuffer.");
  }
  auto* c_api_buffer =
      dynamic_cast<xla::PjRtCApiBuffer*>(av_tensor->GetBuffer().get());
  if (c_api_buffer == nullptr) {
    return absl::InternalError(