code: def create_tfidf_matrix(
      text_docs: Iterable[str], **kwargs
  ) -> pd.DataFrame:
      """Creates a Term Frequency-Inverse Document Frequency matrix.
  
      Args:
          text_docs: A list of strings of text documents
          **kwargs: Other arguments to be passed to sklearn.feature_extraction.text.TfidfVectorizer
  
      Returns:
          matrix_df: Pandas DataFrame of TF-IDF matrix with documents as rows and words
              as columns
      """
      tfidf = TfidfVectorizer(**kwargs)
      matrix = tfidf.fit_transform(text_docs).toarray()
      matrix_df = pd.DataFrame(
          matrix, columns=tfidf.get_feature_names()
      )
      return matrix_df
  
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 10 for calling a global function with example usage: create_tfidf_matrix(text_docs) and returns: matrix_df