class_docstr: Dataset class for classifiers from Pytorch Integration.
      
code: def y(self):
      if self.multi_label:
          label_list = [
              d[self.INDEX_LABEL]
              if d[self.INDEX_LABEL] is not None
              else []
              for d in self._data
          ]
  
          if self.track_target_labels:
              # TODO: int() cast should not be necessary here
              num_classes = int(self.target_labels.max()) + 1
          else:
              num_classes = len(self.target_labels)
          return list_to_csr(
              label_list, shape=(len(self.data), num_classes)
          )
      else:
          return np.array(
              [
                  d[self.INDEX_LABEL]
                  if d[self.INDEX_LABEL] is not None
                  else LABEL_UNLABELED
                  for d in self._data
              ],
              dtype=int,
          )
  
imports: import torch
  import numpy
  from abc import ABC, abstractmethod
  from scipy.sparse import csr_matrix
  from small_text.base import LABEL_UNLABELED
  from small_text.data import DatasetView
  from small_text.data.datasets import check_size, check_target_labels, get_updated_target_labels
  from small_text.data.exceptions import UnsupportedOperationException
  from small_text.utils.annotations import experimental
  from small_text.utils.labels import csr_to_list, get_num_labels, list_to_csr
label_desc: Use image node_id 6 for calling the PytorchTextClassificationDataset obj's underlying member method code with example usage: obj.y() and returns: list_to_csr, np