code: def extract_bert(imdb_path, out_path, group_id=0, n_groups=1):
      imdb = np.load(imdb_path)
  
      feat_extractor = BertFeatExtractor("bert-base-uncased")
  
      if group_id == 0:
          iterator_obj = tqdm(imdb[1:])
      else:
          iterator_obj = imdb[1:]
  
      for idx, el in enumerate(iterator_obj):
          if idx % n_groups != group_id:
              continue
          emb = feat_extractor.get_bert_embedding(el["question_str"])
          save_path = out_path + str(el["question_id"])
          np.save(save_path, emb.cpu().numpy())
  
imports: import argparse
  import numpy
  import torch
  from tqdm import tqdm
label_desc: Use image node_id 1 for calling a global function with example usage: extract_bert(imdb_path, out_path, group_id, n_groups) without return types