ExpectScalarEq<float>(components[1].get(), 20.);

    std::string first_device =
        TFE_TensorHandleBackingDeviceName(components[0].get(), status.get());
    ASSERT_EQ(underlying_devices[0], first_device);
    std::string second_device =
        TFE_TensorHandleBackingDeviceName(components[1].get(), status.get());
    ASSERT_EQ(underlying_devices[1], second_device);
  }

  // Verify that the mirrors are placed on the component devices.
  std::string first_device = TFE_TensorHandleBackingDeviceName(
      second_components[0].get(), status.get());
  ASSERT_EQ(second_underlying_devices[0], first_device);
  std::string second_device = TFE_TensorHandleBackingDeviceName(
      second_components[1].get(), status.get());
  ASSERT_EQ(second_underlying_devices[1], second_device);

    ASSERT_TRUE(remote_arg->HasLocalMirror(nullptr));
  }

  if (remote_func_outputs) {
    const string backing_device =
        TFE_TensorHandleBackingDeviceName(retvals[0], status);
    ASSERT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status);
    EXPECT_EQ(backing_device, task2_name);
  }

  auto* retval_task0 = TFE_TensorHandleCopyToDevice(

// a copy, returns the destination device of the copy. Note that the returned
// device name is not always the device holding the tensor handle's memory. If
// you want the latter, use TFE_TensorHandleBackingDeviceName. This function
// will block till the operation that produces `h` has completed.
TF_CAPI_EXPORT extern const char* TFE_TensorHandleDeviceName(
    TFE_TensorHandle* h, TF_Status* status);

  auto value_cleaner = tensorflow::gtl::MakeCleanup(
      [var_value]() { TFE_DeleteTensorHandle(var_value); });
  ASSERT_EQ(tensorflow::string(name),
            tensorflow::string(
                TFE_TensorHandleBackingDeviceName(var_value, status.get())));
  TFE_TensorHandle* var_value_unpacked =
      UnpackTensorHandle(var_value, status.get());
  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());

  const string composite_device_name =
      "/job:localhost/replica:0/task:0/device:COMPOSITE:0";
  EXPECT_EQ(TFE_TensorHandleDeviceName(packed_handle, status),
            composite_device_name);
  EXPECT_EQ(TFE_TensorHandleBackingDeviceName(packed_handle, status),
            composite_device_name);
  ASSERT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status);

  // Register and run a function which returns the sum of 3 variables.

  ASSERT_EQ(device_name, nullptr);
  ASSERT_EQ("Invalid handle", string(TF_Message(status.get())));

  TF_SetStatus(status.get(), TF_OK, "");

  device_name = TFE_TensorHandleBackingDeviceName(h, status.get());
  ASSERT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(status.get()));
  ASSERT_EQ(device_name, nullptr);
  ASSERT_EQ("Invalid handle", string(TF_Message(status.get())));

  if (h == nullptr) {
    status->status = tensorflow::errors::InvalidArgument("Invalid handle");
    return nullptr;
  }
  return tensorflow::unwrap(h)->DeviceName(&status->status);
}

const char* TFE_TensorHandleBackingDeviceName(TFE_TensorHandle* h,
                                              TF_Status* status) {
  if (h == nullptr) {
    status->status = tensorflow::errors::InvalidArgument("Invalid handle");
    return nullptr;