class_docstr: A factory class that provides the document with its instance of
  `BaseTextRank`
      
class_docstr_tok: ["A","factory","class","that","provides","the","document","with","its","instance","of","`","BaseTextRank","`"]
docstr: Load a dictionary of
  [*stop words*](https://derwen.ai/docs/ptr/glossary/#stop-words)
  – i.e., tokens to be ignored when constructing the
  [*lemma graph*](https://derwen.ai/docs/ptr/glossary/#lemma-graph).
  
  Note: be cautious about the use of this feature, since it can get
  "greedy" and bias or otherwise distort the results.
  
      stopwords:
  optional dictionary of `lemma: [pos]` items to define the *stop words*, where each item has a key as a lemmatized token and a value as a list of POS tags; may be a file name (string) or a [`pathlib.Path`](https://docs.python.org/3/library/pathlib.html) for a JSON file; otherwise throws a `TypeError` exception
  
      returns:
  the *stop words* dictionary
          
code: def _load_stopwords(
      cls,
      stopwords: typing.Optional[StopWordsLike] = None,
  ) -> typing.Dict[str, typing.List[str]]:
      """
      Load a dictionary of
      [*stop words*](https://derwen.ai/docs/ptr/glossary/#stop-words)
      – i.e., tokens to be ignored when constructing the
      [*lemma graph*](https://derwen.ai/docs/ptr/glossary/#lemma-graph).
  
      Note: be cautious about the use of this feature, since it can get
      "greedy" and bias or otherwise distort the results.
  
          stopwords:
      optional dictionary of `lemma: [pos]` items to define the *stop words*, where each item has a key as a lemmatized token and a value as a list of POS tags; may be a file name (string) or a [`pathlib.Path`](https://docs.python.org/3/library/pathlib.html) for a JSON file; otherwise throws a `TypeError` exception
  
          returns:
      the *stop words* dictionary
      """
      if isinstance(stopwords, dict):
          return stopwords
  
      if isinstance(stopwords, pathlib.Path):
          path: pathlib.Path = stopwords
      else:
          path = pathlib.Path(str)  # type: ignore
  
      if path.exists():
          with open(path, "r") as f:
              data = json.load(f)
  
              if data:
                  return data.items()
  
      raise TypeError(
          "cannot parse the stopwords source as a dictionary"
      )
  
imports: from collections import Counter, defaultdict, OrderedDict
  from dataclasses import dataclass
  import json
  import math
  import pathlib
  import time
  import typing
  from icecream import ic
  from spacy.tokens import Doc, Span, Token
  import graphviz
  import networkx
  from .util import groupby_apply, default_scrubber
label_desc: Use image node_id 2 for calling the BaseTextRankFactory obj's underlying member method code with example usage: obj._load_stopwords(cls, stopwords) and returns: stopwords, data