code: def load_glove_matrix(path_emb, word_dict, word_embedding_dim):
      """Load pretrained embedding metrics of words in word_dict
  
      Args:
          path_emb (string): Folder path of downloaded glove file
          word_dict (dict): word dictionary
          word_embedding_dim: dimention of word embedding vectors
  
      Returns:
          numpy.ndarray, list: pretrained word embedding metrics, words can be found in glove files
      """
  
      embedding_matrix = np.zeros(
          (len(word_dict) + 1, word_embedding_dim)
      )
      exist_word = []
  
      with open(
          os.path.join(path_emb, f"glove.6B.{word_embedding_dim}d.txt"),
          "rb",
      ) as f:
          for l in tqdm(f):  # noqa: E741 ambiguous variable name 'l'
              l = l.split()  # noqa: E741 ambiguous variable name 'l'
              word = l[0].decode()
              if len(word) != 0:
                  if word in word_dict:
                      wordvec = [float(x) for x in l[1:]]
                      index = word_dict[word]
                      embedding_matrix[index] = np.array(wordvec)
                      exist_word.append(word)
  
      return embedding_matrix, exist_word
  
imports: import os
  import random
  import logging
  import json
  import numpy
  import re
  from tqdm import tqdm
  from nltk.tokenize import RegexpTokenizer
  from recommenders.datasets.download_utils import maybe_download, download_path, unzip_file
label_desc: Use image node_id 12 for calling a global function with example usage: load_glove_matrix(path_emb, word_dict, word_embedding_dim) and returns: embedding_matrix,  exist_word