code: def load_words(
      fname: Union[str, PathLike],
      tokenizer: Tokenizer,
      vocab: Optional[Vocabulary] = None,
      namespace: str = "tokens",
      all_cases: bool = True,
  ) -> List[torch.Tensor]:
      """
      This function loads a list of words from a file,
      tokenizes each word into subword tokens, and converts the
      tokens into IDs.
  
      # Parameters
  
      fname : `Union[str, PathLike]`
          Name of file containing list of words to load.
      tokenizer : `Tokenizer`
          Tokenizer to tokenize words in file.
      vocab : `Vocabulary`, optional (default=`None`)
          Vocabulary of tokenizer. If `None`, assumes tokenizer is of
          type `PreTrainedTokenizer` and uses tokenizer's `vocab` attribute.
      namespace : `str`
          Namespace of vocab to use when tokenizing.
      all_cases : `bool`, optional (default=`True`)
          Whether to tokenize lower, title, and upper cases of each word.
  
      # Returns
  
      word_ids : `List[torch.Tensor]`
          List of tensors containing the IDs of subword tokens for
          each word in the file.
      """
      word_ids = []
      with open(cached_path(fname)) as f:
          words = json.load(f)
          for w in words:
              word_ids.extend(
                  _convert_word_to_ids_tensor(
                      w, tokenizer, vocab, namespace, all_cases
                  )
              )
      return word_ids
  
file_path: allennlp/allennlp/fairness/bias_utils.py
imports: import torch
  import json
  from os import PathLike
  from typing import List, Tuple, Union, Optional
  from allennlp.common.file_utils import cached_path
  from allennlp.data import Vocabulary
  from allennlp.data.tokenizers.tokenizer import Tokenizer
label_desc: Use image node_id 2 for calling a global function with example usage: load_words(fname, tokenizer, vocab, namespace, all_cases) and returns: word_ids