code: def test_evaluate_data_loader_multi_output(self):
      x = torch.rand(ModelMultiOutputTest.evaluate_dataset_len, 1)
      y1 = torch.rand(ModelMultiOutputTest.evaluate_dataset_len, 1)
      y2 = torch.rand(ModelMultiOutputTest.evaluate_dataset_len, 1)
      dataset = TensorDataset(x, (y1, y2))
      generator = DataLoader(dataset, ModelMultiOutputTest.batch_size)
      loss, pred_y = self.model.evaluate_generator(
          generator, return_pred=True
      )
      self.assertEqual(type(loss), float)
      for pred in pred_y:
          self.assertEqual(type(pred), np.ndarray)
          self.assertEqual(
              pred.shape, (ModelMultiOutputTest.evaluate_dataset_len, 1)
          )
  
imports: import numpy
  import torch
  import torch.nn
  from torch.utils.data import DataLoader
  from poutyne import Model, TensorDataset
  from tests.framework.model.base import ModelFittingTestCase, MultiIOModel
label_desc: Use image node_id 7 for calling the ModelMultiOutputTest obj's underlying member method code with example usage: obj.test_evaluate_data_loader_multi_output() without return types