code: def test_evaluate_with_pred_dict_output(self):
      y = (
          torch.rand(ModelDictOutputTest.evaluate_dataset_len, 1),
          torch.rand(ModelDictOutputTest.evaluate_dataset_len, 1),
      )
      x = torch.rand(ModelDictOutputTest.evaluate_dataset_len, 1)
      # We also test the unpacking.
      _, pred_y = self.model.evaluate(
          x,
          y,
          batch_size=ModelDictOutputTest.batch_size,
          return_pred=True,
      )
      for pred in pred_y.values():
          self.assertEqual(
              pred.shape, (ModelDictOutputTest.evaluate_dataset_len, 1)
          )
  
file_path: poutyne/tests/framework/model/test_dict_output.py
imports: import numpy
  import torch
  import torch.nn
  from poutyne import Model
  from tests.framework.model.base import ModelFittingTestCase
label_desc: Use image node_id 4 for calling the ModelDictOutputTest obj's underlying member method code with example usage: obj.test_evaluate_with_pred_dict_output() without return types