TF_Status* status)) {
  auto* cc_builder = reinterpret_cast<OpDefBuilder*>(builder);
  cc_builder->SetShapeFn(
      [shape_inference_func](InferenceContext* ctx) -> tensorflow::Status {
        TF_Status* c_status = TF_NewStatus();
        auto c_ctx = reinterpret_cast<TF_ShapeInferenceContext*>(ctx);
        shape_inference_func(c_ctx, c_status);

  params.patch_version = GO_PATCH;
  params.optimizer = &optimizer;
  params.optimizer_configs = &optimizer_configs;

  OwnedTFStatus c_status(TF_NewStatus());
  init_fn(&params, c_status.get());
  TF_RETURN_IF_ERROR(tsl::StatusFromTF_Status(c_status.get()));
  TF_RETURN_IF_ERROR(ValidateTPOptimizerRegistrationParams(params));
  TF_RETURN_IF_ERROR(ValidateTPOptimizer(optimizer));

      ::tensorflow::Set_TF_Status_from_Status(C_STATUS, _s);      \
      TF_OpKernelContext_Failure(CTX, C_STATUS);                  \
      TF_DeleteStatus(C_STATUS);                                  \
      return;                                                     \
    }                                                             \
  } while (0)

#define TF_CLEANUP_AND_RETURN_IF_ERROR(C_STATUS, BUFFER, __VA_ARGS__) \

  int VisibleDeviceCount() const override {
    int visible_device_count = 0;
    tensorflow::TF_StatusPtr c_status(TF_NewStatus());
    platform_fns_.get_device_count(&platform_, &visible_device_count,
                                   c_status.get());
    if (TF_GetCode(c_status.get()) != TF_OK) {
      LOG(ERROR) << TF_Message(c_status.get());
      return 0;
    }
    return visible_device_count;
  }

      return tensorflow::CustomDeviceTensorHandle::SummarizeValue(summary);
    }
    TF_Status c_status;
    std::unique_ptr<TF_Buffer, decltype(&TF_DeleteBuffer)> summary_buffer(
        methods_.summarize(data_, &c_status), TF_DeleteBuffer);
    if (!c_status.status.ok()) {
      return c_status.status;
    }
    summary = std::string(reinterpret_cast<const char*>(summary_buffer->data),

static inline void TF_SetStatusFromGCSStatus(
    const google::cloud::Status& gcs_status, TF_Status* status) {
  TF_SetStatus(status, static_cast<TF_Code>(gcs_status.code()),
               gcs_status.message().c_str());
}

static void* plugin_memory_allocate(size_t size) { return calloc(1, size); }
static void plugin_memory_free(void* ptr) { free(ptr); }

        1 * c1.checkExpiration() >> { new DaemonExpirationResult(c1Status, "r1") }
        1 * c2.checkExpiration() >> { new DaemonExpirationResult(c2Status, "r2") }

        then:
        DaemonExpirationResult result = agg.checkExpiration()
        result.status == allStatus
        result.reason == "r1 and r2"

        where:
        c1Status         | c2Status         | allStatus

        when:
        1 * c1.checkExpiration() >> { new DaemonExpirationResult(c1Status, "r1") }
        1 * c2.checkExpiration() >> { new DaemonExpirationResult(c2Status, "r2") }

        then:
        DaemonExpirationResult result = agg.checkExpiration()
        result.status == anyStatus
        result.reason == reason

        where:
        c1Status         | c2Status         | anyStatus        | reason

Shinsuke Sugaya <******@****.***> 1477533782 +0900

  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());
}

TensorHandlePtr ParallelTensorToTensorHandle(
    const std::string& parallel_device_name, TFE_Context* context,
    std::unique_ptr<ParallelTensor> t, TF_Status* status) {