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 lemmata, indexed to the word tokens
  provided by `Doc.tokens`.
docstr_tok: ["Returns","a","list","of","lemmata",",","indexed","to","the","word","tokens","provided","by","`","Doc.tokens","`","."]
code: def lemmata(self) -> List[str]:
      """Returns a list of lemmata, indexed to the word tokens
      provided by `Doc.tokens`.
      """
      return self._get_words_attribute("lemma")
  
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 9 for calling the Doc obj's underlying member method code with example usage: obj.lemmata() and returns: self