code: def _random_data(
      self, num_samples=NUM_SAMPLES, num_classes=NUM_LABELS
  ):
      if self.target_labels not in ["explicit", "inferred"]:
          raise ValueError(
              "Invalid test parameter value for target_labels:"
              + self.target_labels
          )
  
      return random_transformer_dataset(
          num_samples=num_samples,
          multi_label=self.multi_label,
          num_classes=num_classes,
          target_labels=self.target_labels,
      )
  
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 1 for calling the TransformersDatasetTest obj's underlying member method code with example usage: obj._random_data(num_samples, num_classes) and returns: random_transformer_dataset