TFE_CustomDevice* custom_device = new TFE_CustomDevice;
  custom_device->copy_tensor_to_device = &CopyToLoggingDevice;
  custom_device->copy_tensor_from_device = &CopyTensorFromLoggingDevice;
  custom_device->delete_device = &DeleteLoggingDevice;
  custom_device->execute = &LoggingDeviceExecute;
  *device = custom_device;
  LoggingDevice* logging_device = new LoggingDevice;
  logging_device->arrived_flag = arrived_flag;

      : status_(TF_NewStatus()),
        // If the context's default exector is set to async, re-using that in
        // each thread would cause collectives to deadlock. For consistency we
        // create a new sync executor for every thread.
        //
        // TODO(allenl): We should have an async API that works with the
        // parallel device.
        device_(device),
        executor_(

  TFE_TensorHandle* var_handle = nullptr;
  int num_retvals = 1;
  TFE_Execute(op.get(), &var_handle, &num_retvals, status);
  if (TF_GetCode(status) != TF_OK) return nullptr;
  return new Variable(var_handle, type);
}

void Variable::Destroy(TFE_Context* context, TF_Status* status) {
  // Free the backing buffer for the variable.
  std::unique_ptr<TFE_Op, decltype(&TFE_DeleteOp)> op(

      /*byte_strides=*/std::nullopt,
      xla::PjRtClient::HostBufferSemantics::kImmutableOnlyDuringCall, nullptr,
      c_api_client->pjrt_c_client()->client->addressable_devices()[0]);
  CHECK_OK(buffer.status());

  return new PJRT_Buffer{std::move(*buffer), c_api_client->pjrt_c_client()};
}

TEST(TensorPjRtBufferUtilTest, GetPjRtCBufferFromTensorNoBuffer) {
  auto allocator = std::make_unique<AsyncValueAllocator>();

       ++device_index) {
    underlying_devices_vector.push_back(underlying_devices[device_index]);
  }
  std::unique_ptr<ParallelDevice> parallel_device(
      new ParallelDevice(underlying_devices_vector));
  *device_info =
      new NamedParallelDevice{device_name, std::move(parallel_device)};
}
}  // namespace parallel_device

  const std::string filepath = GetURIForPath("a_file");
  std::unique_ptr<WritableFile> new_file;
  Status status = env_->NewWritableFile(filepath, &new_file);
  if (!status.ok())
    GTEST_SKIP() << "NewWritableFile() not supported: " << status;

  const std::string new_filepath = GetURIForPath("a_new_file");
  status = env_->RenameFile(filepath, new_filepath);
  EXPECT_PRED2(UnimplementedOrReturnsCode, status, Code::OK);

TFE_MonitoringCounter0* TFE_MonitoringNewCounter0(const char* name,
                                                  TF_Status* status,
                                                  const char* description) {
  auto* result = new TFE_MonitoringCounter0({name, description});
  tsl::Set_TF_Status_from_Status(status, result->counter->GetStatus());
  if (!result->counter->GetStatus().ok()) {
    delete result;
    return nullptr;
  }
  return result;

  return new SqrtGradientFunction(op.outputs[0]);
}

GradientFunction* NegRegisterer(const ForwardOperation& op) {
  return new NegGradientFunction;
}

GradientFunction* SubRegisterer(const ForwardOperation& op) {
  return new SubGradientFunction;
}

GradientFunction* MulRegisterer(const ForwardOperation& op) {
  return new MulGradientFunction(op.inputs);
}

 public:
  explicit GraphContext(const char* name)
      : TracingContext(kGraph),
        graph_(new TF_Graph(), TF_DeleteGraph),
        name_(name) {}

  void Release() override { delete this; }

  TracingOperation* CreateOperation() override {
    return new GraphOperation(graph_.get());
  }

  Status AddParameter(DataType dtype, const PartialTensorShape& shape,

                  TF_Status* status) {
  tensorflow::ImmediateExecutionOperation* new_op =
      tensorflow::unwrap(ctx)->CreateOperation();
  status->status = new_op->Reset(op_or_function_name, nullptr);
  if (!status->status.ok()) {
    new_op->Release();
    new_op = nullptr;
  }
  return tensorflow::wrap(new_op);
}

void TFE_DeleteOp(TFE_Op* op) {
  if (op == nullptr) {
    return;
  }