code: def build_embedding_weights(word_index, embeddings_index):
      
      logger.info('Loading embeddings for all words in the corpus')
      embedding_dim = list(embeddings_index.values())[0].shape[-1]
  
      # setting special tokens such as UNK and PAD to 0
      # all other words are also set to 0.
      embedding_weights = np.zeros((len(word_index), embedding_dim))
  
      for word, i in word_index.items():
          word_vector = embeddings_index.get(word)
          if word_vector is not None:
              embedding_weights[i] = word_vector
  
      return embedding_weights
docs: Builds an embedding matrix for all words in vocab using embeddings_index