code: def clone(self):
      if self.is_multi_label:
          data = [
              (
                  torch.clone(d[TransformersDataset.INDEX_TEXT]),
                  torch.clone(d[TransformersDataset.INDEX_MASK]),
                  d[TransformersDataset.INDEX_LABEL].copy(),
              )
              for d in self.data
          ]
      else:
          data = [
              (
                  torch.clone(d[TransformersDataset.INDEX_TEXT]),
                  torch.clone(d[TransformersDataset.INDEX_MASK]),
                  np.copy(d[TransformersDataset.INDEX_LABEL]),
              )
              for d in self.data
          ]
  
      target_labels = (
          None
          if self.dataset.track_target_labels
          else np.copy(self.target_labels)
      )
      return TransformersDataset(
          data,
          multi_label=self.is_multi_label,
          target_labels=target_labels,
      )
  
imports: import numpy
  from scipy.sparse import csr_matrix
  from small_text.base import LABEL_UNLABELED
  from small_text.data.datasets import check_size, check_target_labels, get_updated_target_labels
  from small_text.integrations.pytorch.exceptions import PytorchNotFoundError
  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 1 for calling the TransformersDatasetView obj's underlying member method code with example usage: obj.clone() and returns: TransformersDataset