code: def test_fit_and_predict_proba_dropout_sampling(
      self, num_classes=4, dropout_sampling=3
  ):
      model_args = TransformerModelArguments(
          "sshleifer/tiny-distilroberta-base"
      )
      clf = TransformerBasedClassification(
          model_args,
          num_classes,
          multi_label=self.multi_label,
          class_weight="balanced",
          num_epochs=1,
      )
  
      train_set = self._get_dataset(num_samples=20)
      test_set = self._get_dataset(num_samples=10)
  
      clf.fit(train_set)
  
      y_pred_proba = clf.predict_proba(test_set)
      self.assertSequenceEqual(
          (len(test_set), num_classes), y_pred_proba.shape
      )
      self.assertTrue(isinstance(y_pred_proba, np.ndarray))
      self.assertTrue(
          np.all(
              [
                  isinstance(p, np.float64)
                  for pred in y_pred_proba
                  for p in pred
              ]
          )
      )
  
      y_pred_proba = clf.predict_proba(
          test_set, dropout_sampling=dropout_sampling
      )
      self.assertSequenceEqual(
          (len(test_set), dropout_sampling, num_classes),
          y_pred_proba.shape,
      )
      self.assertTrue(isinstance(y_pred_proba, np.ndarray))
      self.assertTrue(
          np.all(
              [
                  isinstance(p, np.float64)
                  for pred in y_pred_proba
                  for sample in pred
                  for p in sample
              ]
          )
      )
  
imports: import unittest
  import pytest
  import warnings
  import numpy
  from unittest import mock
  from unittest.mock import patch, Mock
  from parameterized import parameterized_class
  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
  from tests.utils.datasets import twenty_news_transformers
  from tests.utils.testing import assert_array_not_equal
label_desc: Use image node_id 8 for calling the _TransformerBasedClassificationTest obj's underlying member method code with example usage: obj.test_fit_and_predict_proba_dropout_sampling(num_classes, dropout_sampling) without return types