old_contents: import array
  import numpy as np
  import tensorflow as tf
  from collections import defaultdict
  
  def load_vocab(filename):
    vocab = None
    with open(filename) as f:
      vocab = f.read().splitlines()
    dct = defaultdict(int)
    for idx, word in enumerate(vocab):
      dct[word] = idx
    return [vocab, dct]
  
  def load_glove_vectors(filename, vocab):
    """
    Load glove vectors from a .txt file.
    Optionally limit the vocabulary to save memory. `vocab` should be a set.
    """
    dct = {}
    vectors = array.array('d')
    current_idx = 0
    with open(filename, "r", encoding="utf-8") as f:
      for _, line in enumerate(f):
        tokens = line.split(" ")
        word = tokens[0]
        entries = tokens[1:]
        if not vocab or word in vocab:
          dct[word] = current_idx
          vectors.extend(float(x) for x in entries)
          current_idx += 1
      word_dim = len(entries)
      num_vectors = len(dct)
      tf.logging.info("Found {} out of {} vectors in Glove".format(num_vectors, len(vocab)))
      return [np.array(vectors).reshape(num_vectors, word_dim), dct]
  
  
  def build_initial_embedding_matrix(vocab_dict, glove_dict, glove_vectors, embedding_dim):
    initial_embeddings = np.random.uniform(-0.25, 0.25, (len(vocab_dict), embedding_dim)).astype("float32")
    for word, glove_word_idx in glove_dict.items():
      word_idx = vocab_dict.get(word)
      initial_embeddings[word_idx, :] = glove_vectors[glove_word_idx]
new_contents: import array
  import numpy as np
  import tensorflow as tf
  from collections import defaultdict
  
  def load_vocab(filename):
    vocab = None
    with open(filename) as f:
      vocab = f.read().splitlines()
    dct = defaultdict(int)
    for idx, word in enumerate(vocab):
      dct[word] = idx
    return [vocab, dct]
  
  def load_glove_vectors(filename, vocab):
    """
    Load glove vectors from a .txt file.
    Optionally limit the vocabulary to save memory. `vocab` should be a set.
    """
    dct = {}
    vectors = array.array('d')
    current_idx = 0
    with open(filename, "r", encoding="utf-8") as f:
      for _, line in enumerate(f):
        tokens = line.split(" ")
        word = tokens[0]
        entries = tokens[1:]
        if not vocab or word in vocab:
          dct[word] = current_idx
          vectors.extend(float(x) for x in entries)
          current_idx += 1
      word_dim = len(entries)
      num_vectors = len(dct)
      tf.logging.info("Found {} out of {} vectors in Glove".format(num_vectors, len(vocab)))
      return [np.array(vectors).reshape(num_vectors, word_dim), dct]
  
  
  def build_initial_embedding_matrix(vocab_dict, glove_dict, glove_vectors, embedding_dim):
    initial_embeddings = np.random.uniform(-0.25, 0.25, (len(vocab_dict), embedding_dim)).astype("float32")
    for word, glove_word_idx in glove_dict.items():
      word_idx = vocab_dict.get(word)
      initial_embeddings[word_idx, :] = glove_vectors[glove_word_idx]
    return initial_embeddings
  
subject: Update helpers.py