code: def stem(
      text_docs_bow: Iterable[Iterable[str]],
  ) -> Iterable[Iterable[str]]:
      """Stems all words in documents.
  
      Stemming is grouping words together by taking the stems of their inflected forms
      so they can be analyzed as a single item.
  
      Args:
          text_docs_bow: A list of lists of words from a document
  
      Returns:
          A generator expression for all of the processed documents
      """
      stemmer = _compat["nltk"].stem.lancaster.LancasterStemmer()  # type: ignore
      return (
          (stemmer.stem(word) for word in doc) for doc in text_docs_bow
      )
  
imports: import re
  import string
  import warnings
  from types import GeneratorType
  from typing import List, Optional, Any, Iterable, TYPE_CHECKING
  import pandas
  from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
  from data_describe.text import text_preprocessing
  from data_describe.compat import _requires, _compat
label_desc: Use image node_id 8 for calling a global function with example usage: stem(text_docs_bow) and returns: unknown