code: def _dataset(self, num_labels=3):
      assert self.NUM_SAMPLES > self.NUM_SAMPLES_VIEW
      self.multi_label = False
  
      dataset = random_transformer_dataset(
          num_samples=self.NUM_SAMPLES,
          multi_label=self.multi_label,
          num_classes=num_labels,
          target_labels=self.target_labels,
      )
  
      return dataset
  
imports: import unittest
  import pytest
  import numpy
  from scipy.sparse import csr_matrix
  from numpy.testing import assert_array_equal
  from parameterized import parameterized_class
  from small_text.base import LABEL_UNLABELED
  from small_text.integrations.pytorch.datasets import PytorchDatasetView
  from small_text.integrations.pytorch.exceptions import PytorchNotFoundError
  from tests.utils.misc import increase_dense_labels_safe
  from tests.utils.testing import assert_array_not_equal, assert_csr_matrix_equal, assert_csr_matrix_not_equal, assert_list_of_tensors_equal, assert_list_of_tensors_not_equal
label_desc: Use image node_id 2 for calling the TransformersDatasetViewSingleLabelExplicitTest obj's underlying member method code with example usage: obj._dataset(num_labels) and returns: dataset