docstr: Train the Chunker on the ConLL-2000 corpus.
docstr_tok: ["Train","the","Chunker","on","the","ConLL-2000","corpus","."]
code: def train(self):
      """Train the Chunker on the ConLL-2000 corpus."""
      train_data = [
          [(t, c) for _, t, c in nltk.chunk.tree2conlltags(sent)]
          for sent in nltk.corpus.conll2000.chunked_sents(
              "train.txt", chunk_types=["NP"]
          )
      ]
      unigram_tagger = nltk.UnigramTagger(train_data)
      self.tagger = nltk.BigramTagger(
          train_data, backoff=unigram_tagger
      )
      self._trained = True
  
imports: from __future__ import unicode_literals, absolute_import
  import nltk
  from textblob.taggers import PatternTagger
  from textblob.decorators import requires_nltk_corpus
  from textblob.utils import tree2str, filter_insignificant
  from textblob.base import BaseNPExtractor
label_desc: Use image node_id 2 for calling the ChunkParser obj's underlying member method code with example usage: obj.train() without return types