code: def construct_init_word_vecs(vocab, word_vecs, glove_dict):
      for word, index in vocab.items():
          if word in glove_dict:
              embedding = glove_dict[word]
          elif word.lower() in glove_dict:
              embedding = glove_dict[word.lower()]
          else:
              embedding = None
  
          if embedding is not None:
              word_vecs[index] = embedding
      return word_vecs
  
imports: import re
  from collections import defaultdict
  import numpy
  import tensorflow
label_desc: Use image node_id 5 for calling a global function with example usage: construct_init_word_vecs(vocab, word_vecs, glove_dict) and returns: word_vecs