code: def test_fit_with_model_selection_none(self):
      dataset = self._get_dataset(num_samples=20, num_classes=4)
  
      train_set = dataset[0:10]
      validation_set = dataset[10:]
  
      num_classes = 4
      embedding_matrix = torch.FloatTensor(np.random.rand(10, 100))
      classifier = KimCNNClassifier(
          num_classes,
          multi_label=self.multi_label,
          embedding_matrix=embedding_matrix,
          num_epochs=2,
      )
  
      with patch.object(
          classifier, "_fit_main", wraps=classifier._fit_main
      ) as fit_main_spy:
          classifier.fit(
              train_set,
              validation_set=validation_set,
              model_selection="none",
          )
  
          self.assertEqual(1, fit_main_spy.call_count)
          model_selection_arg = fit_main_spy.call_args_list[0].args[4]
          self.assertTrue(
              isinstance(model_selection_arg, NoopModelSelection)
          )
  
imports: import unittest
  import pytest
  import numpy
  from unittest.mock import patch
  from unittest import mock
  from unittest.mock import Mock
  from scipy.sparse import issparse
  from small_text.integrations.pytorch.exceptions import PytorchNotFoundError
  from small_text.training.early_stopping import EarlyStopping, NoopEarlyStopping
  from small_text.training.metrics import Metric
  from small_text.training.model_selection import ModelSelection, NoopModelSelection
label_desc: Use image node_id 11 for calling the _KimCNNClassifierTest obj's underlying member method code with example usage: obj.test_fit_with_model_selection_none() without return types