auto dim_sizes = combined_shape.dim_sizes();
    shape_ = std::vector<int64_t>(dim_sizes.begin(), dim_sizes.end());
  }
  *shape = &*shape_;
  return absl::OkStatus();
}

absl::Status ParallelTensor::SummarizeValue(std::string& summary) {
  summary = "{";
  std::vector<std::string> summarized_devices = device_.SummarizeDeviceNames();
  for (int component_index = 0; component_index < tensors_.size();
       ++component_index) {

  TF_DataType dtype() const { return dtype_; }

  // Sets its output argument to a summary of the values of this tensor on every
  // component device.
  absl::Status SummarizeValue(std::string& summary);

  std::vector<TensorHandlePtr> release_tensors() { return std::move(tensors_); }

  std::vector<TFE_TensorHandle*> tensors() const {
    std::vector<TFE_TensorHandle*> result;

}

TF_Buffer* ParallelTensorSummarize(void* data, TF_Status* status) {
  ParallelTensor* parallel_tensor = reinterpret_cast<ParallelTensor*>(data);
  std::string summary;
  absl::Status cpp_status = parallel_tensor->SummarizeValue(summary);
  if (!cpp_status.ok()) {
    tsl::Set_TF_Status_from_Status(status, cpp_status);
    return nullptr;
  }
  return TF_NewBufferFromString(summary.data(), summary.size());
}

  }

  bool PreferCustomSummarizer() const override {
    return methods_.summarize != nullptr;
  }

  absl::Status SummarizeValue(std::string& summary) const override {
    if (methods_.summarize == nullptr) {
      return tensorflow::CustomDeviceTensorHandle::SummarizeValue(summary);
    }
    TF_Status c_status;
    std::unique_ptr<TF_Buffer, decltype(&TF_DeleteBuffer)> summary_buffer(

  ImmediateExecutionTensorHandle* unwrapped_handle =
      tensorflow::unwrap(device_value.get());
  std::string summarized;
  TF_ASSERT_OK(unwrapped_handle->SummarizeValue(summarized));
  EXPECT_THAT(summarized, HasSubstr("\"CPU:0\": 3"));
}

}  // namespace parallel_device