code: def ne_chunk_sents(tagged_sentences, binary=False):
      """
      Use NLTK's currently recommended named entity chunker to chunk the
      given list of tagged sentences, each consisting of a list of tagged tokens.
      """
      if binary:
          chunker_pickle = _BINARY_NE_CHUNKER
      else:
          chunker_pickle = _MULTICLASS_NE_CHUNKER
      chunker = load(chunker_pickle)
      return chunker.parse_sents(tagged_sentences)
  
imports: from nltk.chunk.api import ChunkParserI
  from nltk.chunk.regexp import RegexpChunkParser, RegexpParser
  from nltk.chunk.util import ChunkScore, accuracy, conllstr2tree, conlltags2tree, ieerstr2tree, tagstr2tree, tree2conllstr, tree2conlltags
  from nltk.data import load
label_desc: Use image node_id 2 for calling a global function with example usage: ne_chunk_sents(tagged_sentences, binary) and returns: chunker