code: def create_w2v_model(text_list_t, text_list_r):
      """
      Create a w2v model on the given corpora
  
      Args:
          text_list_t: A list of documents - target corpus (List[String])
          text_list_r: A list of documents - reference corpus (List[String])
      """
      unified_data = unify_corpora_from_texts(text_list_t, text_list_r)
      train_w2v_model(unified_data)
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import argparse
  import csv
  import logging
  import os
  import sys
  from multiprocessing import Pool
  from os import makedirs, path
  from nlp_architect import LIBRARY_OUT
  from nlp_architect.utils.embedding import FasttextEmbeddingsModel
  from nlp_architect.utils.io import validate_existing_directory
  from nlp_architect.utils.text import SpacyInstance
  from .np_scorer import NPScorer
label_desc: Use image node_id 10 for calling a global function with example usage: create_w2v_model(text_list_t, text_list_r) without return types