code: def test_evaluate_with_pred_multi_io(self):
      x = (
          torch.rand(ModelMultiIOTest.evaluate_dataset_len, 1),
          torch.rand(ModelMultiIOTest.evaluate_dataset_len, 1),
      )
      y = (
          torch.rand(ModelMultiIOTest.evaluate_dataset_len, 1),
          torch.rand(ModelMultiIOTest.evaluate_dataset_len, 1),
      )
      # We also test the unpacking.
      _, pred_y = self.model.evaluate(
          x, y, batch_size=ModelMultiIOTest.batch_size, return_pred=True
      )
      for pred in pred_y:
          self.assertEqual(
              pred.shape, (ModelMultiIOTest.evaluate_dataset_len, 1)
          )
  
file_path: poutyne/tests/framework/model/test_multi_io.py
imports: import numpy
  import torch
  import torch.nn
  from poutyne import Model
  from tests.framework.model.base import ModelFittingTestCase, MultiIOModel
label_desc: Use image node_id 6 for calling the ModelMultiIOTest obj's underlying member method code with example usage: obj.test_evaluate_with_pred_multi_io() without return types