TFE_TensorHandle* var_handle1 = TestVariable(ctx, 2.0, dev1_name);
  EXPECT_NE(var_handle1, nullptr);

  TFE_TensorHandle* value_handle = nullptr;
  ReadVariable(ctx, var_handle1, &value_handle);
  CheckTFE_TensorHandleHasFloats(value_handle, {2});
  TFE_DeleteTensorHandle(value_handle);

  // Start a new worker to replace task:1
  ReplaceTaskInServerDef(&server_def, 1);
  server_def.set_task_index(1);

void TF_AttrBuilderSetType(TF_AttrBuilder* builder, const char* attr_name,
                           TF_DataType value) {
  auto iter = builder->attr_names.insert(attr_name).first;
  builder->Set(*iter, static_cast<tensorflow::DataType>(value));
}

void TF_AttrBuilderSetTypeList(TF_AttrBuilder* builder, const char* attr_name,

                   ForwardOperation* forward_op_) {
  forward_op_->attrs.Set(attr_name, value);
  return op_->SetAttrBool(attr_name, value);
}
Status SetAttrType(AbstractOperation* op_, const char* attr_name,
                   DataType value, ForwardOperation* forward_op_) {
  forward_op_->attrs.Set(attr_name, value);
  return op_->SetAttrType(attr_name, value);
}

    return false;
  }
  bool found_dtype = false;
  bool found_value = false;
  for (const auto& attr : node_def.attr()) {
    if (attr.first == "dtype") {
      if (attr.second.type() == tensorflow::DT_INT32) {
        found_dtype = true;
      } else {
        return false;
      }
    } else if (attr.first == "value") {
      if (attr.second.has_tensor() &&

  DLTensor* dl_tensor = &dlmt->dl_tensor;
  absl::optional<std::string> device_name =
      DeviceNameFromDlContext(dl_tensor->device, status);
  if (!device_name.has_value()) {
    status->status =
        tensorflow::errors::InvalidArgument("Unsupported Device Type");
    return nullptr;
  }
  TF_DataType dtype;
  Status s = TfDataTypeFormDlDataType(dl_tensor->dtype, &dtype);

  // Create a tensor on the first parallel device
  TensorHandlePtr value_one(FloatTensorHandle(1., status.get()));
  TensorHandlePtr value_two(FloatTensorHandle(2., status.get()));
  ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());
  std::array<TFE_TensorHandle*, 2> components{value_one.get(), value_two.get()};
  TensorHandlePtr first_combined_value = CreatePerDeviceValues(

  {
    TensorHandlePtr value_one(FloatTensorHandle(3., status.get()));
    TensorHandlePtr value_two(FloatTensorHandle(-2., status.get()));
    std::array<TFE_TensorHandle*, 2> components{value_one.get(),
                                                value_two.get()};
    TensorHandlePtr combined_value =
        CreatePerDeviceValues(context, components, device_name, status.get());

      "      attr {"
      "        key: 'dtype'"
      "        value {"
      "          type: DT_FLOAT"
      "        }"
      "      }"
      "    }"
      "    node_def {"
      "      name: 'add1'"
      "      op: 'Add'"
      "      input: 'read0:value:0'"
      "      input: 'read1:value:0'"
      "      attr {"
      "        key: 'T'"
      "        value {"
      "          type: DT_FLOAT"
      "        }"

    status->status = fdef_or.status();
    return;
  }

  (*(fdef_or.value()->mutable_attr()))[string(attr_name)] = attr_value;

  status->status = absl::OkStatus();
}

void TF_FunctionGetAttrValueProto(TF_Function* func, const char* attr_name,
                                  TF_Buffer* output_attr_value,
                                  TF_Status* status) {

  if (TF_GetCode(status) != TF_OK) return;
  if (!maybe_typed_outputs.has_value()) {
    TF_SetStatus(status, TF_INTERNAL, "OK status but no value was returned.");
    return;
  }

  std::vector<MaybeParallelTensorOwned> typed_outputs(
      std::move(maybe_typed_outputs.value()));

  if (typed_outputs.size() > *num_outputs) {
    TF_SetStatus(status, TF_INTERNAL,