class_docstr: A ``Process`` that takes a doc containing sentences of CLTK words
  and returns a dependency tree for each sentence.
  
  TODO: JS help to make this work, illustrate better.
  
  >>> from cltk import NLP
  >>> nlp = NLP(language="got", suppress_banner=True)
  >>> from cltk.dependency.processes import TreeBuilderProcess
  
  >>> nlp.pipeline.add_process(TreeBuilderProcess)  # doctest: +SKIP
  >>> from cltk.languages.example_texts import get_example_text  # doctest: +SKIP
  >>> doc = nlp.analyze(text=get_example_text("got"))  # doctest: +SKIP
  >>> len(doc.trees)  # doctest: +SKIP
  4
class_docstr_tok: ["A","``","Process","``","that","takes","a","doc","containing","sentences","of","CLTK","words","and","returns","a","dependency","tree","for","each","sentence",".","TODO",":","JS","help","to","make","this","work",",","illustrate","better",".",">",">",">","from","cltk","import","NLP",">",">",">","nlp","=","NLP","(","language=","''","got","''",",","suppress_banner=True",")",">",">",">","from","cltk.dependency.processes","import","TreeBuilderProcess",">",">",">","nlp.pipeline.add_process","(","TreeBuilderProcess",")","#","doctest",":","+SKIP",">",">",">","from","cltk.languages.example_texts","import","get_example_text","#","doctest",":","+SKIP",">",">",">","doc","=","nlp.analyze","(","text=get_example_text","(","``","got","''",")",")","#","doctest",":","+SKIP",">",">",">","len","(","doc.trees",")","#","doctest",":","+SKIP","4"]
code: def algorithm(self, doc):
      doc.trees = [
          DependencyTree.to_tree(sentence) for sentence in doc.sentences
      ]
      return doc
  
imports: from copy import deepcopy
  from dataclasses import dataclass
  from typing import Any, Dict, List, Literal, Optional, Tuple
  import spacy
  import stanza
  from boltons.cacheutils import cachedproperty
  from cltk.core.data_types import Doc, MorphosyntacticFeature, Process, Word
  from cltk.dependency.spacy_wrapper import SpacyWrapper
  from cltk.dependency.stanza_wrapper import StanzaWrapper
  from cltk.dependency.tree import DependencyTree
  from cltk.morphology.morphosyntax import MorphosyntacticFeatureBundle, from_ud, to_categorial
label_desc: Use image node_id 1 for calling the TreeBuilderProcess obj's underlying member method code with example usage: obj.algorithm(doc) and returns: doc