code: def test_init_when_some_samples_are_unlabeled(self):
      ds = self._random_data(num_samples=self.NUM_SAMPLES)
      if self.multi_label:
          data_list = list(ds.data[2])
          data_list[TransformersDataset.INDEX_LABEL] = []
          ds.data[2] = tuple(data_list)
      else:
          data_list = list(ds.data[2])
          data_list[TransformersDataset.INDEX_LABEL] = LABEL_UNLABELED
          ds.data[2] = tuple(data_list)
  
      if self.target_labels not in ["explicit", "inferred"]:
          raise ValueError(
              "Invalid test parameter value for target_labels:"
              + self.target_labels
          )
  
      target_labels = (
          None if self.target_labels == "inferred" else ds.target_labels
      )
  
      # passes when no exeption is raised here
      TransformersDataset(
          ds.data,
          multi_label=self.multi_label,
          target_labels=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 4 for calling the TransformersDatasetTest obj's underlying member method code with example usage: obj.test_init_when_some_samples_are_unlabeled() without return types