namespace {
class CustomDeviceAPI : public tensorflow::CustomDevice {
 public:
  CustomDeviceAPI(TFE_Context* context, TFE_CustomDevice device, void* info,
                  string name)
      : context_(context), device_(device), info_(info), name_(name) {}

  ~CustomDeviceAPI() override { device_.delete_device(info_); }

  const string& name() override { return name_; }

  tensorflow::Status CopyTensorToDevice(

        "Unable to find function with name: ", function_name);
    return;
  }

  tensorflow::GraphDebugInfo debug_info =
      tensorflow::StackTracesMapToGraphDebugInfo(
          function_record->stack_traces());

  string str = debug_info.SerializeAsString();
  void* data = tensorflow::port::Malloc(str.length());
  str.copy(static_cast<char*>(data), str.length(), 0);
  buf->data = data;

    const string dev_name(TF_DeviceListName(devices, i, status.get()));
    CHECK_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());
    if (dev_type == device_type) {
      *device_name = dev_name;
      LOG(INFO) << "Found " << device_type << " device " << *device_name;
      TF_DeleteDeviceList(devices);
      return true;
    }
  }
  TF_DeleteDeviceList(devices);
  return false;
}

auto& kUnknownDim = shape_inference::InferenceContext::kUnknownDim;
auto& kUnknownRank = shape_inference::InferenceContext::kUnknownRank;

// GraphTensor wraps a `TF_Output`, i.e. a pointer to TF_Operation and the index
// into the list of outputs for the operation.
class GraphTensor : public TracingTensorHandle {
 public:
  explicit GraphTensor(TF_Output output, TF_Graph* graph)
      : TracingTensorHandle(kGraph), output_(output), graph_(graph) {}

// src is either:
// - input name (as it appears in FunctionDef)
// - name of output tensor (in nested "add:z:0" format)
// dst is either:
// - output name (as it appears in FunctionDef)
// - <name_of_node>:<index_of_this_input_into_node> (this looks the same as
//      output tensor naming, but it the index is actually an input index)
struct EdgeSpec : public std::pair<string, string> {
  typedef std::pair<string, string> Base;

    TFE_TensorDebugInfo* debug_info) {
  delete debug_info;
}

TF_CAPI_EXPORT extern int TFE_TensorDebugInfoOnDeviceNumDims(
    TFE_TensorDebugInfo* debug_info) {
  return debug_info->dev_dims.size();
}

TF_CAPI_EXPORT extern int64_t TFE_TensorDebugInfoOnDeviceDim(
    TFE_TensorDebugInfo* debug_info, int dim_index) {
  return debug_info->dev_dims[dim_index];
}

  // Step 3: Call `TF_InitPlugin`
  TF_FilesystemPluginInfo info;
  memset(&info, 0, sizeof(info));
  auto TF_InitPlugin =
      reinterpret_cast<int (*)(TF_FilesystemPluginInfo*)>(dso_symbol);
  TF_InitPlugin(&info);

  // Step 4: Do the actual registration
  return filesystem_registration::RegisterFilesystemPluginImpl(&info);
}

    TF_SetStatus(status, TF_OK, "");
    return 0;
  }
  if (!IsCacheEnabled() || (n > max_bytes_)) {
    // The cache is effectively disabled, so we pass the read through to the
    // fetcher without breaking it up into blocks.
    return block_fetcher_(filename, offset, n, buffer, status);
  }
  // Calculate the block-aligned start and end of the read.
  size_t start = block_size_ * (offset / block_size_);

  LOG(INFO) << "There are " << num_devices << " devices.";
  for (int i = 0; i < num_devices; ++i) {
    const char* device_name = TF_DeviceListName(device_list, i, s);
    CHECK_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
    const char* device_type = TF_DeviceListType(device_list, i, s);
    CHECK_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
    LOG(INFO) << "Device " << i << " has name " << device_name << ", type "

  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());
  TF_Tensor* result_tensor = TFE_TensorHandleResolve(result_t, status.get());

  // Copy Tensor data into an array.
  float result_data[4] = {0};
  memcpy(&result_data[0], TF_TensorData(result_tensor),
         TF_TensorByteSize(result_tensor));

  int data_len = 4;  // length of result_data