ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  tensorflow::RunMetadata rm;
  EXPECT_TRUE(
      rm.ParseFromString({reinterpret_cast<const char*>(b->data), b->length}));
  TF_DeleteBuffer(b);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  ASSERT_EQ(1, num_retvals);

  TF_Tensor* t = TFE_TensorHandleResolve(retvals[0], status);
  TFE_DeleteTensorHandle(retvals[0]);

    if (!status->status.ok()) {
      // Delete everything allocated to far, the operation has failed.
      for (int j = 0; j <= i; ++j) {
        TF_DeleteBuffer(values[j]);
      }
      return;
    }
  }
}

void TF_OperationGetAttrTensor(TF_Operation* oper, const char* attr_name,
                               TF_Tensor** value, TF_Status* status) {

      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),

                                    TF_Status* status) {
  status->status = value.status();
  if (!value.ok()) {
    return nullptr;
  }
  // Caller is responsible to call `TF_DeleteBuffer` to release the buffer.
  TF_Buffer* result = TF_NewBuffer();
  const std::string& value_str = *value;
  void* data = malloc(value_str.length());
  value_str.copy(static_cast<char*>(data), value_str.length(), 0);

// Write out a serialized representation of `graph` (as a GraphDef protocol
// message) to `output_graph_def` (allocated by TF_NewBuffer()).
// `output_graph_def`'s underlying buffer will be freed when TF_DeleteBuffer()
// is called.
//
// May fail on very large graphs in the future.
TF_CAPI_EXPORT extern void TF_GraphToGraphDef(TF_Graph* graph,
                                              TF_Buffer* output_graph_def,