code: def embeddings_to_dict(filename):
      """
      :param filename: the file name of the word embeddings | file is assumed
      to follow this format: "word[tab]dimension 1[space]dimension 2[space]...[space]dimension 50"
      :return: a dictionary with keys that are words and values that are the embedding of a word
      """
      with io.open(filename, "r", encoding="utf-8") as f:
          word_vecs = {}
          for line in f:
              line = line.strip("\n").split()
              word_vecs[line[0]] = np.array(
                  [float(s) for s in line[1:]]
              )
  
      return word_vecs
  
file_path: smooth-gelu/twitter_pos.py
imports: import numpy
  import tensorflow
  import pickle
  import sys
  import io
  import os
label_desc: Use image node_id 1 for calling a global function with example usage: embeddings_to_dict(filename) and returns: word_vecs