code: def test_fit_and_validate(self):
      device = "cuda:0"
      classification_kwargs = {
          "use_differentiable_head": self.use_differentiable_head,
          "multi_label": self.multi_label,
          "device": device,
      }
      setfit_model_args = SetFitModelArguments(
          "sentence-transformers/paraphrase-MiniLM-L3-v2"
      )
      clf_factory = SetFitClassificationFactory(
          setfit_model_args,
          self.num_classes,
          classification_kwargs=classification_kwargs,
      )
  
      train_set = twenty_news_text(
          30, num_classes=self.num_classes, multi_label=self.multi_label
      )
  
      clf = clf_factory.new()
      with patch.object(
          clf.model.model_body, "to", wraps=clf.model.model_body.to
      ) as to_spy:
          clf.fit(train_set)
  
          self.assertEqual(3, to_spy.call_count)
          # our call is the first
          self.assertEqual(1, len(to_spy.call_args_list[0].args))
          self.assertEqual(device, to_spy.call_args_list[0].args[0])
  
      valid_set = twenty_news_text(
          10, num_classes=self.num_classes, multi_label=self.multi_label
      )
  
      if self.use_differentiable_head:
          with self.assertRaises(NotImplementedError):
              clf.validate(valid_set)
      else:
          with self.assertRaises(UnsupportedOperationException):
              clf.validate(valid_set)
  
imports: import unittest
  import pytest
  import numpy
  from unittest.mock import patch
  from scipy.sparse import issparse
  from small_text.data.datasets import TextDataset
  from small_text.exceptions import UnsupportedOperationException
  from small_text.integrations.pytorch.exceptions import PytorchNotFoundError
  from sklearn.utils.validation import check_is_fitted
  from tests.utils.datasets import twenty_news_text
label_desc: Use image node_id 4 for calling the _ClassificationTest obj's underlying member method code with example usage: obj.test_fit_and_validate() without return types