code: def _get_pretrain_model():
      # Train and test on English part of ConLL data (WSJ part of Penn Treebank)
      # Train: section 2-11
      # Test : section 23
      tagger = PerceptronTagger()
      training = _load_data_conll_format("english_ptb_train.conll")
      testing = _load_data_conll_format("english_ptb_test.conll")
      print(
          "Size of training and testing (sentence)",
          len(training),
          len(testing),
      )
      # Train and save the model
      tagger.train(training, PICKLE)
      print("Accuracy : ", tagger.accuracy(testing))
  
imports: import logging
  import pickle
  import random
  from collections import defaultdict
  from nltk import jsontags
  from nltk.data import find, load
  from nltk.tag.api import TaggerI
label_desc: Use image node_id 3 for calling a global function with example usage: _get_pretrain_model() without return types