code: def fill_embedding_mat(src_mat, src_lex, emb_lex, emb_size):
      """
      Creates a new matrix from given matrix of int words using the embedding
      model provided.
  
      Args:
          src_mat (numpy.ndarray): source matrix
          src_lex (dict): source matrix lexicon
          emb_lex (dict): embedding lexicon
          emb_size (int): embedding vector size
      """
      emb_mat = np.zeros((src_mat.shape[0], src_mat.shape[1], emb_size))
      for i, sen in enumerate(src_mat):
          for j, w in enumerate(sen):
              if w > 0:
                  w_emb = emb_lex.get(str(src_lex.get(w)).lower())
                  if w_emb is not None:
                      emb_mat[i][j] = w_emb
      return emb_mat
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import os
  import sys
  from typing import List
  import numpy
  from gensim.models import FastText
  from nlp_architect.utils.text import Vocabulary
label_desc: Use image node_id 2 for calling a global function with example usage: fill_embedding_mat(src_mat, src_lex, emb_lex, emb_size) and returns: emb_mat