class_docstr: Create topic model widget.
docstr: Displays most relevant terms for each topic.
  
  Args:
      num_topic_words: The number of words to be displayed for each topic. Default is 10
      topic_names: A list of pre-defined names set for each of the topics. Default is None
  
  Returns:
      display_topics_df: Pandas DataFrame displaying topics as columns and their
          relevant terms as rows. LDA/LSI models will display an extra column to
          the right of each topic column, showing each term's corresponding
          coefficient value
code: def show(
      self,
      num_topic_words: int = 10,
      topic_names: Optional[List[str]] = None,
  ):
      """Displays most relevant terms for each topic.
  
      Args:
          num_topic_words: The number of words to be displayed for each topic. Default is 10
          topic_names: A list of pre-defined names set for each of the topics. Default is None
  
      Returns:
          display_topics_df: Pandas DataFrame displaying topics as columns and their
              relevant terms as rows. LDA/LSI models will display an extra column to
              the right of each topic column, showing each term's corresponding
              coefficient value
      """
      return self.display_topic_keywords(
          num_topic_words=num_topic_words, topic_names=topic_names
      )
  
imports: import warnings
  from typing import List, Optional, Dict, Tuple, TYPE_CHECKING
  import pandas
  import numpy
  from sklearn.decomposition import TruncatedSVD, NMF
  import matplotlib.pyplot
  import seaborn
  from data_describe.config._config import get_option
  from data_describe.text.text_preprocessing import create_doc_term_matrix, create_tfidf_matrix, filter_dictionary
  from data_describe.backends import _get_viz_backend
  from data_describe.compat import _compat, _requires, _in_notebook
  from data_describe._widget import BaseWidget
label_desc: Use image node_id 12 for calling the TopicModelWidget obj's underlying member method code with example usage: obj.show(num_topic_words, topic_names) and returns: self