code: def test_evaluate_with_pred_multi_output(self):
      y = (
          torch.rand(ModelMultiOutputTest.evaluate_dataset_len, 1),
          torch.rand(ModelMultiOutputTest.evaluate_dataset_len, 1),
      )
      x = torch.rand(ModelMultiOutputTest.evaluate_dataset_len, 1)
      # We also test the unpacking.
      _, pred_y = self.model.evaluate(
          x,
          y,
          batch_size=ModelMultiOutputTest.batch_size,
          return_pred=True,
      )
      for pred in pred_y:
          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 6 for calling the ModelMultiOutputTest obj's underlying member method code with example usage: obj.test_evaluate_with_pred_multi_output() without return types