class_docstr: Class for training and inference examples for BERT.
  
  Attributes:
      editing_task: The EditingTask from which this example was created. Needed
          when realizing labels predicted for this example.
      features: Feature dictionary.
class_docstr_tok: ["Class","for","training","and","inference","examples","for","BERT",".","Attributes",":","editing_task",":","The","EditingTask","from","which","this","example","was","created",".","Needed","when","realizing","labels","predicted","for","this","example",".","features",":","Feature","dictionary","."]
docstr: Returns labels/tags for the original tokens, not for wordpieces.
docstr_tok: ["Returns","labels\/tags","for","the","original","tokens",",","not","for","wordpieces","."]
code: def get_token_labels(self, features_key: str) -> List[int]:
      """Returns labels/tags for the original tokens, not for wordpieces."""
      labels = []
      for idx in self._token_start_indices:
          # For unmasked and untruncated tokens, use the label in the features, and
          # for the truncated tokens, use the default label.
          if (
              idx < len(self.features[features_key])
              and self.features["labels_mask"][idx]
          ):
              labels.append(self.features[features_key][idx])
          else:
              labels.append(self._default_label)
      return labels
  
imports: import logging
  from collections import OrderedDict
  from os import path
  from typing import Dict, List, Optional, Tuple, Union
  from transformers import PreTrainedTokenizerBase
  from nemo.collections.nlp.data.text_normalization_as_tagging.tagging import EditingTask, Tag
  from nemo.collections.nlp.data.text_normalization_as_tagging.utils import yield_sources_and_targets
label_desc: Use image node_id 3 for calling the BertExample obj's underlying member method code with example usage: obj.get_token_labels(features_key) and returns: labels