class_docstr: The object returned to the user from the ``NLP()`` class.
  Contains overall attributes of submitted texts, plus most
  importantly the processed tokenized text ``words``,
  being a list of ``Word`` types.
  
  >>> from cltk import NLP
  >>> from cltk.languages.example_texts import get_example_text
  >>> cltk_nlp = NLP(language="lat", suppress_banner=True)
  >>> cltk_doc = cltk_nlp.analyze(text=get_example_text("lat"))
  >>> cltk_doc.raw[:38]
  'Gallia est omnis divisa in partes tres'
  >>> isinstance(cltk_doc.raw, str)
  True
  >>> cltk_doc.tokens[:10]
  ['Gallia', 'est', 'omnis', 'divisa', 'in', 'partes', 'tres', ',', 'quarum', 'unam']
  >>> cltk_doc.tokens_stops_filtered[:10]
  ['Gallia', 'omnis', 'divisa', 'partes', 'tres', ',', 'incolunt', 'Belgae', ',', 'aliam']
  >>> cltk_doc.pos[:3]
  ['NOUN', 'AUX', 'DET']
  >>> cltk_doc.morphosyntactic_features[:3]
  [{Case: [nominative], Gender: [feminine], InflClass: [ind_eur_a], Number: [singular]}, {InflClass: [lat_anom], Number: [singular], VerbForm: [finite]}, {Case: [nominative], Gender: [feminine], InflClass: [ind_eur_i], Number: [singular], PronominalType: [indefinite]}]
  >>> cltk_doc[0].gender
  [feminine]
  >>> cltk_doc[0]['Case']
  [nominative]
  >>> cltk_doc.lemmata[:5]
  ['Gallia', 'sum', 'omnis', 'divisa', 'in']
  >>> len(cltk_doc.sentences)
  9
  >>> len(cltk_doc.sentences[0])
  26
  >>> type(cltk_doc.sentences[0][2])
  <class 'cltk.core.data_types.Word'>
  >>> cltk_doc.sentences[0][2].string
  'omnis'
  >>> len(cltk_doc.sentences_tokens)
  9
  >>> len(cltk_doc.sentences_tokens[0])
  26
  >>> isinstance(cltk_doc.sentences_tokens[0][2], str)
  True
  >>> cltk_doc.sentences_tokens[0][2]
  'omnis'
  >>> len(cltk_doc.sentences_strings)
  9
  >>> len(cltk_doc.sentences_strings[0])
  150
  >>> isinstance(cltk_doc.sentences_strings[0], str)
  True
  >>> cltk_doc.sentences_strings[1]
  'Hi omnes lingua , institutis , legibus inter se differunt .'
  >>> import numpy as np
  >>> isinstance(cltk_doc.embeddings[1], np.ndarray)
  True
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docstr: Returns a list of string word tokens of all words in the
  doc, but with stopwords removed.
docstr_tok: ["Returns","a","list","of","string","word","tokens","of","all","words","in","the","doc",",","but","with","stopwords","removed","."]
code: def tokens_stops_filtered(
      self,
  ) -> List[str]:
      """Returns a list of string word tokens of all words in the
      doc, but with stopwords removed.
      """
      tokens = self._get_words_attribute("string")  # type: List[str]
      # create equal-length list of True & False/None values
      is_token_stop = self._get_words_attribute(
          "stop"
      )  # type: List[bool]
      # remove from the token list any who index in ``is_token_stop`` is True
      tokens_no_stops = [
          token
          for index, token in enumerate(tokens)
          if not is_token_stop[index]
      ]  # type: List[str]
      return tokens_no_stops
  
imports: import importlib
  from abc import ABC, abstractmethod
  from collections import defaultdict
  from dataclasses import dataclass, field
  from typing import Dict, List, Optional, Type, Union
  import numpy
  import stringcase
  from cltk.morphology.morphosyntax import MorphosyntacticFeatureBundle
  from cltk.morphology.universal_dependencies_features import MorphosyntacticFeature
label_desc: Use image node_id 6 for calling the Doc obj's underlying member method code with example usage: obj.tokens_stops_filtered() and returns: tokens_no_stops