class_docstr: Dataset class for classifiers from Transformers Integration.
      
class_docstr_tok: ["Dataset","class","for","classifiers","from","Transformers","Integration","."]
code: def _infer_target_labels(self):
      if len(self._data) == 0:
          self.target_labels = np.array([0])
      else:
          unique_labels = np.unique(self._get_flattened_labels())
          if unique_labels.shape[0] > 0:
              max_label_id = unique_labels.max()
              self.target_labels = np.arange(max_label_id + 1)
          else:
              self.target_labels = np.array([0])
  
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 2 for calling the TransformersDataset obj's underlying member method code with example usage: obj._infer_target_labels() without return types