code: def get_bag_of_words_indices(
      bag_of_words_ids_or_paths: List[str], tokenizer
  ) -> List[List[List[int]]]:
      bow_indices = []
      for id_or_path in bag_of_words_ids_or_paths:
          if id_or_path in BAG_OF_WORDS_ARCHIVE_MAP:
              filepath = cached_path(
                  BAG_OF_WORDS_ARCHIVE_MAP[id_or_path]
              )
          else:
              filepath = id_or_path
          with open(filepath, "r") as f:
              words = f.read().strip().split("\n")
          bow_indices.append(
              [
                  tokenizer.encode(word.strip(), add_prefix_space=True)
                  for word in words
              ]
          )
      return bow_indices
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/examples/research_projects/pplm/run_pplm.py
imports: import argparse
  import json
  from operator import add
  from typing import List, Optional, Tuple, Union
  import numpy
  import torch
  from torch import nn
  from tqdm import trange
  from pplm_classification_head import ClassificationHead
  from transformers import GPT2LMHeadModel, GPT2Tokenizer
  from transformers.file_utils import cached_path
label_desc: Use image node_id 4 for calling a global function with example usage: get_bag_of_words_indices(bag_of_words_ids_or_paths, tokenizer) and returns: bow_indices