code: def main():
      # parse the command line arguments
      parser = NeonArgparser(__doc__)
      parser.add_argument(
          "--output_path",
          required=True,
          help="Output path used when training model",
      )
      parser.add_argument(
          "--w2v_path",
          required=False,
          default=None,
          help="Path to GoogleNews w2v file for voab expansion.",
      )
      parser.add_argument(
          "--eval_data_path",
          required=False,
          default="./SICK_data",
          help="Path to the SICK dataset for evaluating semantic relateness",
      )
      parser.add_argument(
          "--max_vocab_size",
          required=False,
          default=1000000,
          help="Limit the vocabulary expansion to fit in GPU memory",
      )
      parser.add_argument(
          "--subset_pct",
          required=False,
          default=100,
          help="subset of training dataset to use (use to retreive \
                          preprocessed data from training)",
      )
      args = parser.parse_args(gen_be=True)
  
      # load vocab file from training
      _, vocab_file = load_data(
          args.data_dir,
          output_path=args.output_path,
          subset_pct=float(args.subset_pct),
      )
      vocab, _, _ = load_obj(vocab_file)
  
      vocab_size = len(vocab)
      neon_logger.display(
          "\nVocab size from the dataset is: {}".format(vocab_size)
      )
  
      index_from = 2  # 0: padding 1: oov
      vocab_size_layer = vocab_size + index_from
      max_len = 30
  
      # load trained model
      model_dict = load_obj(args.model_file)
  
      # Vocabulary expansion trick needs to pass the correct vocab set to evaluate (for tokenization)
      if args.w2v_path:
          neon_logger.display(
              "Performing Vocabulary Expansion... Loading W2V..."
          )
          w2v_vocab, w2v_vocab_size = get_w2v_vocab(
              args.w2v_path, int(args.max_vocab_size), cache=True
          )
  
          vocab_size_layer = w2v_vocab_size + index_from
          model = load_sent_encoder(
              model_dict,
              expand_vocab=True,
              orig_vocab=vocab,
              w2v_vocab=w2v_vocab,
              w2v_path=args.w2v_path,
              use_recur_last=True,
          )
          vocab = w2v_vocab
      else:
          # otherwise stick with original vocab size used to train the model
          model = load_sent_encoder(model_dict, use_recur_last=True)
  
      model.initialize(dataset=(max_len, 1))
  
      evaluate(
          model,
          vocab=vocab,
          data_path=args.eval_data_path,
          evaltest=True,
          vocab_size_layer=vocab_size_layer,
      )
  
imports: from __future__ import division
  from __future__ import print_function
  import numpy
  import copy
  from scipy.stats import pearsonr
  from scipy.stats import spearmanr
  from neon.util.argparser import NeonArgparser
  from neon.util.persist import load_obj
  from neon.initializers import Gaussian
  from neon.layers import GeneralizedCost, Affine
  from neon.data import ArrayIterator
  from neon.models import Model
  from neon.optimizers import Adam
  from neon.transforms import Softmax, CrossEntropyMulti
  from neon.callbacks.callbacks import Callbacks
  from neon.data import SICK
  from neon import logger
  from data_iterator import SentenceEncode
  from data_loader import load_data
  from util import load_sent_encoder, tokenize, get_w2v_vocab
label_desc: Use image node_id 1 for calling a global function with example usage: main() without return types