code: def test_fit_and_predict(self):
      classification_kwargs = {
          "use_differentiable_head": self.use_differentiable_head,
          "multi_label": self.multi_label,
      }
      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()
      clf.fit(train_set)
      if self.use_differentiable_head:
          self.assertTrue(check_is_fitted(clf.model.model_head))
  
      test_set = twenty_news_text(
          30, num_classes=self.num_classes, multi_label=self.multi_label
      )
      y_pred = clf.predict(test_set)
  
      if self.multi_label:
          self.assertEqual((30, self.num_classes), y_pred.shape)
          self.assertTrue(issparse(y_pred))
          self.assertEqual(y_pred.dtype, np.int64)
          self.assertTrue(
              np.logical_or(
                  y_pred.indices.all() >= 0, y_pred.indices.all() <= 3
              )
          )
      else:
          self.assertEqual((30,), y_pred.shape)
          self.assertTrue(isinstance(y_pred, np.ndarray))
          self.assertTrue(
              np.all([isinstance(y, np.int64) for y in y_pred])
          )
          self.assertTrue(
              np.logical_or(y_pred.all() >= 0, y_pred.all() <= 3)
          )
  
      y_pred_proba = clf.predict_proba(test_set)
      self.assertEqual((30, self.num_classes), y_pred_proba.shape)
      self.assertTrue(isinstance(y_pred_proba, np.ndarray))
      self.assertTrue(
          np.all(
              [
                  isinstance(y, np.float64)
                  for row in y_pred_proba
                  for y in row
              ]
          )
      )
      self.assertTrue(
          np.logical_or(
              y_pred_proba.all() >= 0.0, y_pred_proba.all() <= 1.0
          )
      )
  
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 2 for calling the _ClassificationTest obj's underlying member method code with example usage: obj.test_fit_and_predict() without return types