code: def test_fit_with_scheduler_but_without_optimizer(self):
      ds = self._get_dataset()
  
      num_classes = 4
  
      embedding_matrix = torch.FloatTensor(np.random.rand(10, 100))
      clf = KimCNNClassifier(
          num_classes,
          multi_label=self.multi_label,
          embedding_matrix=embedding_matrix,
      )
  
      scheduler = Mock()
  
      with self.assertRaisesRegex(
          ValueError, "You must also pass an optimizer"
      ):
          clf.fit(ds, scheduler=scheduler)
  
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 2 for calling the _KimCNNClassifierTest obj's underlying member method code with example usage: obj.test_fit_with_scheduler_but_without_optimizer() without return types