class_docstr: Spacy pipeline wrapper which prompts user for model download authorization.
  
  Args:
      model (str, optional): spacy model name (default: english small model)
      disable (list of string, optional): pipeline annotators to disable
          (default: [])
      display_prompt (bool, optional): flag to display/skip license prompt
      n_jobs (int, optional): maximum number of concurrent Python worker processes.
          If -1 all CPUs are used.
      batch_size (int, optional): number of docs per batch.
      spacy_doc (bool, optional): if True, parser outputs `spacy.tokens.doc`
          instead of `CoreNLPDoc`
      show_tok (bool, optional): include token text in `CoreNLPDoc` output
      show_dok (bool, optional): include document text in `CoreNLPDoc` output
      ptb_pos (bool, optional): convert spacy POS tags to Penn Treebank tags
class_docstr_tok: ["Spacy","pipeline","wrapper","which","prompts","user","for","model","download","authorization",".","Args",":","model","(","str",",","optional",")",":","spacy","model","name","(","default",":","english","small","model",")","disable","(","list","of","string",",","optional",")",":","pipeline","annotators","to","disable","(","default",":","[","]",")","display_prompt","(","bool",",","optional",")",":","flag","to","display\/skip","license","prompt","n_jobs","(","int",",","optional",")",":","maximum","number","of","concurrent","Python","worker","processes",".","If","-1","all","CPUs","are","used",".","batch_size","(","int",",","optional",")",":","number","of","docs","per","batch",".","spacy_doc","(","bool",",","optional",")",":","if","True",",","parser","outputs","`","spacy.tokens.doc","`","instead","of","`","CoreNLPDoc","`","show_tok","(","bool",",","optional",")",":","include","token","text","in","`","CoreNLPDoc","`","output","show_dok","(","bool",",","optional",")",":","include","document","text","in","`","CoreNLPDoc","`","output","ptb_pos","(","bool",",","optional",")",":","convert","spacy","POS","tags","to","Penn","Treebank","tags"]
code: def __init__(
      self,
      model="en",
      disable=None,
      display_prompt=True,
      n_jobs=8,
      batch_size=1500,
      spacy_doc=False,
      show_tok=True,
      show_doc=True,
      ptb_pos=False,
  ):
      if disable is None:
          disable = []
      try:
          self._parser = spacy.load(model, disable=disable)
      except OSError:
          url = "https://spacy.io/models"
          if (
              display_prompt
              and license_prompt("Spacy {} model".format(model), url)
              is False
          ):
              sys.exit(0)
          spacy_download(model)
          print("Spacy model installed, please rerun your command.")
          sys.exit(0)
      self.n_jobs = n_jobs
      self.batch_size = batch_size
      self.spacy_doc = spacy_doc
      self.show_tok = show_tok
      self.show_doc = show_doc
      self.ptb_pos = ptb_pos
  
imports: import re
  import string
  import sys
  from os import path
  from typing import List, Tuple
  import spacy
  from nltk import WordNetLemmatizer
  from nltk.stem.snowball import EnglishStemmer
  from spacy.cli.download import download
  from spacy.lang.en import LEMMA_EXC, LEMMA_INDEX, LEMMA_RULES
  from spacy.lemmatizer import Lemmatizer
  from nlp_architect.utils.generic import license_prompt
  from joblib import Parallel, delayed
  from functools import partial
  from spacy.util import minibatch
  from nlp_architect.common.core_nlp_doc import CoreNLPDoc
  from pathlib import Path
label_desc: Use image node_id 1 to create a new SpacyInstance object  with example: obj = SpacyInstance(model, disable, display_prompt, n_jobs, batch_size, spacy_doc, show_tok, show_doc, ptb_pos)