code: def build_bows_one_hot_vectors(bow_indices, tokenizer, device="cuda"):
      if bow_indices is None:
          return None
  
      one_hot_bows_vectors = []
      for single_bow in bow_indices:
          single_bow = list(filter(lambda x: len(x) <= 1, single_bow))
          single_bow = torch.tensor(single_bow).to(device)
          num_words = single_bow.shape[0]
          one_hot_bow = torch.zeros(num_words, tokenizer.vocab_size).to(
              device
          )
          one_hot_bow.scatter_(1, single_bow, 1)
          one_hot_bows_vectors.append(one_hot_bow)
      return one_hot_bows_vectors
  
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 5 for calling a global function with example usage: build_bows_one_hot_vectors(bow_indices, tokenizer, device) and returns: one_hot_bows_vectors, None