code: def build_embedding(embed_file, targ_vocab, dim_vec):
      vocab_size = len(targ_vocab)
      emb = np.zeros((vocab_size, dim_vec))
      w2id = {w: i for i, w in enumerate(targ_vocab)}
      with open(embed_file) as f:
          for line in f:
              elems = line.split()
              token = normalize_text("".join(elems[0:-wv_dim]))
              if token in w2id:
                  emb[w2id[token]] = [float(v) for v in elems[-wv_dim:]]
      return emb
  
imports: import re
  import json
  import spacy
  import msgpack
  import unicodedata
  import numpy
  import pandas
  import argparse
  import collections
  import multiprocessing
  from concurrent.futures import ProcessPoolExecutor
  import logging
label_desc: Use image node_id 10 for calling a global function with example usage: build_embedding(embed_file, targ_vocab, dim_vec) and returns: emb