code: def ngram_constructor(cls):
      def f(train_sents, backoff=None):
          if args.trace:
              print(
                  "training %s tagger with backoff %s" % (cls, backoff)
              )
          # TODO: args.cutoff option
          return cls(train_sents, backoff=backoff)
  
      return f
  
file_path: nltk-trainer/train_tagger.py
imports: import argparse, math, itertools, os.path
  import nltk.corpus, nltk.data
  import nltk_trainer.classification.args
  from nltk.classify import DecisionTreeClassifier, MaxentClassifier, NaiveBayesClassifier
  from nltk.corpus.reader import SwitchboardCorpusReader, NPSChatCorpusReader, IndianCorpusReader
  from nltk.corpus.util import LazyCorpusLoader
  from nltk.tag import ClassifierBasedPOSTagger
  from nltk_trainer import dump_object, load_corpus_reader, simplify_wsj_tag
  from nltk_trainer.tagging import readers
  from nltk_trainer.tagging.training import train_brill_tagger
  from nltk_trainer.tagging.taggers import PhoneticClassifierBasedPOSTagger
label_desc: Use image node_id 2 for calling a global function with example usage: ngram_constructor(cls) and returns: f, cls