class_docstr: Spacy based NP annotator - uses models.SequenceChunker model for annotation
  
  Args:
      model (SequenceChunker): a chunker model
      word_vocab (Vocabulary): word-id vocabulary of the model
      char_vocab (Vocabulary): char id vocabulary of words of the model
      chunk_vocab (Vocabulary): chunk tag vocabulary of the model
      batch_size (int, optional): inference batch size
code: def _infer_chunks(self, input_vec, doc_lengths):
      tagged_sents = self.model.predict(
          input_vec, batch_size=self.bs
      ).argmax(2)
      sentence = []
      for c, l in zip(tagged_sents, doc_lengths):
          sentence.append(c[-l:])
      doc = np.concatenate(sentence)
      chunk_tags = [self.chunk_vocab.id_to_word(w) for w in doc]
      return extract_nps(chunk_tags)
  
imports: import pickle
  from os import path
  import numpy
  import spacy
  from spacy.tokens import Doc
  from spacy.tokens import Span
  from nlp_architect.models.chunker import SequenceChunker
  from nlp_architect.utils.generic import pad_sentences
  from nlp_architect.utils.io import validate_existing_filepath
  from nlp_architect.utils.text import extract_nps, Stopwords
label_desc: Use image node_id 3 for calling the NPAnnotator obj's underlying member method code with example usage: obj._infer_chunks(input_vec, doc_lengths) and returns: extract_nps