auto allocator = std::make_unique<AsyncValueAllocator>();
  tensorflow::Tensor tensor(allocator.get(), DT_FLOAT, {1});
  TF_ASSERT_OK_AND_ASSIGN(
      auto pjrt_client,
      xla::GetTfrtCpuClient(/*asynchronous=*/true, /*cpu_device_count=*/1));
  std::vector<int32_t> data(1, 0);
  xla::Shape shape = xla::ShapeUtil::MakeShape(xla::S32, {1});
  TF_ASSERT_OK_AND_ASSIGN(
      auto buffer,
      pjrt_client->BufferFromHostBuffer(

        `tf.data.experimental.OptimizationOptions`. If it is set to
        `True`,`tf.data` will now automatically add a `prefetch` transformation
        to datasets that end in synchronous transformations. This enables data
        generation to be overlapped with data consumption. This may cause a
        small increase in memory usage due to buffering. To enable this