docstr: Checks if the given word starting at the given index is in the list of stopwords.
  
  Parameters
  ----------
  tokenized_text
      Tokenized text.
  start_idx
      Starting index of a word.
  stopwords:
      List of stop words. The words in this list should be lowercase.
  punctuation
      Punctuation to be considered. See :py:meth:`alibi.utils.lang_model.LanguageModel.select_entire_word`.
  
  Returns
  -------
  ``True`` if the `token` is in the `stopwords` list. ``False`` otherwise.
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code: def is_stop_word(
      self,
      tokenized_text: List[str],
      start_idx: int,
      punctuation: str,
      stopwords: Optional[List[str]],
  ) -> bool:
      """
      Checks if the given word starting at the given index is in the list of stopwords.
  
      Parameters
      ----------
      tokenized_text
          Tokenized text.
      start_idx
          Starting index of a word.
      stopwords:
          List of stop words. The words in this list should be lowercase.
      punctuation
          Punctuation to be considered. See :py:meth:`alibi.utils.lang_model.LanguageModel.select_entire_word`.
  
      Returns
      -------
      ``True`` if the `token` is in the `stopwords` list. ``False`` otherwise.
      """
      if not stopwords:
          return False
  
      if self.is_subword_prefix(tokenized_text[start_idx]):
          return False
  
      word = self.select_word(
          tokenized_text, start_idx=start_idx, punctuation=punctuation
      ).strip()
      return word.strip().lower() in stopwords
  
imports: import abc
  import numpy
  from pathlib import Path
  from typing import Any, Callable, List, Optional, Tuple, Union
  import tensorflow
  import transformers
  from transformers import TFAutoModelForMaskedLM, AutoTokenizer
label_desc: Use image node_id 6 for calling the LanguageModel obj's underlying member method code with example usage: obj.is_stop_word(tokenized_text, start_idx, punctuation, stopwords) and returns: unknown, False, False