code: def test_evaluate_without_progress_output(self):
      x = torch.rand(ModelFittingTestCase.evaluate_dataset_len, 1)
      y = torch.rand(ModelFittingTestCase.evaluate_dataset_len, 1)
  
      _, _ = self.model.evaluate(
          x,
          y,
          batch_size=ModelFittingTestCase.batch_size,
          verbose=False,
      )
  
      self.assertStdoutNotContains(["[32m", "[35m", "[36m", "[94m"])
  
imports: from unittest import skipIf
  import torch
  import torch.nn
  from torch.utils.data import DataLoader
  from poutyne import Model, TensorDataset
  from tests.framework.model.base import ModelFittingTestCase
  from tests.framework.tools import SomeConstantMetric, SomeDataGeneratorUsingStopIteration, repeat_batch_metric, repeat_batch_metric_value, some_batch_metric_1, some_batch_metric_2, some_constant_metric_value, some_data_tensor_generator, some_metric_1_value, some_metric_2_value
label_desc: Use image node_id 18 for calling the ModelFittingTestCaseProgress obj's underlying member method code with example usage: obj.test_evaluate_without_progress_output() without return types