code: def compute_vocab_expansion(
      orig_word_vectors, w2v_W, w2v_vocab, word_idict
  ):
      neon_logger.display(
          "Learning linear mapping from w2v -> rnn embedding..."
      )
      clf = train_regressor(orig_word_vectors, w2v_W, w2v_vocab)
      neon_logger.display("Constructing map...")
      init_embed = apply_regressor(
          clf, w2v_W, w2v_vocab, orig_word_vectors, word_idict
      )
      return init_embed
  
imports: import os
  import numpy
  from collections import defaultdict, OrderedDict
  from neon.layers import LookupTable, RecurrentSum, RecurrentLast, Linear, Bias, GeneralizedCost
  from neon.initializers import Gaussian, Constant
  from neon.data import ArrayIterator
  from neon.data.text_preprocessing import clean_string
  from neon.optimizers import GradientDescentMomentum
  from neon.transforms import SumSquared
  from neon.callbacks.callbacks import Callbacks
  from neon.models import Model
  from neon.util.persist import load_obj, save_obj
  from neon import logger
label_desc: Use image node_id 4 for calling a global function with example usage: compute_vocab_expansion(orig_word_vectors, w2v_W, w2v_vocab, word_idict) and returns: init_embed