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 __init__(
      self,
      model,
      word_vocab,
      char_vocab,
      chunk_vocab,
      batch_size: int = 32,
  ):
      self.model = model
      self.bs = batch_size
      self.word_vocab = word_vocab
      self.char_vocab = char_vocab
      self.chunk_vocab = chunk_vocab
      Doc.set_extension("noun_phrases", default=[], force=True)
  
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 1 to create a new NPAnnotator object from inherited base classes: object with example: obj = NPAnnotator(model, word_vocab, char_vocab, chunk_vocab, batch_size)