code: def test_batch_metrics_with_multiple_names_returned_by_tensor_on_gpu(
      self,
  ):
      with torch.cuda.device(MetricsModelIntegrationTest.cuda_device):
          batch_metric = get_const_batch_metric(
              torch.tensor(self.metric_values).cuda()
          )
          model = Model(
              self.pytorch_network,
              self.optimizer,
              self.loss_function,
              batch_metrics=[(self.metric_names, batch_metric)],
          )
          model.cuda()
          self._test_history(
              model, self.metric_names, self.metric_values
          )
  
imports: import os
  import unittest
  from unittest import skipIf
  from unittest.mock import ANY
  import numpy
  import torch
  import torch.nn
  import torchmetrics
  from poutyne import Metric, Model, do_register_metric_class, rename_doubles, unregister_metric_class
label_desc: Use image node_id 10 for calling the MetricsModelIntegrationTest obj's underlying member method code with example usage: obj.test_batch_metrics_with_multiple_names_returned_by_tensor_on_gpu() without return types