code: def _pyldavis_viz_visualize_topic_summary(
      model: "gensim.models.ldamodel.LdaModel",  # type: ignore
      corpus: List[List[Tuple[int, int]]],
      dictionary: "gensim.corpora.dictionary.Dictionary",  # type: ignore
  ):
      """Displays interactive pyLDAvis visual to understand topic model and documents.
  
      Args:
          model: LDA topic model
          corpus: Bag of Words (BoW) representation of documents (token_id, token_count)
          dictionary: Gensim Dictionary encapsulates the mapping between normalized words
              and their integer ids.
  
      Raises:
          EnvironmentError: Must be in a Notebook.
  
      Returns:
          A visual to understand topic model and/or documents relating to model
      """
      if not _in_notebook():
          raise EnvironmentError("Not in Jupyter Notebook")
  
      _compat["pyLDAvis"].enable_notebook()  # type: ignore
      with warnings.catch_warnings():
          warnings.filterwarnings(
              "ignore",
              category=FutureWarning,
              module="pyLDAvis",
              message="Sorting because non-concatenation axis is not aligned.",
          )
          vis = _compat["pyLDAvis"].gensim.prepare(model, corpus, dictionary)  # type: ignore
          return vis
  
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 2 for calling a global function with example usage: _pyldavis_viz_visualize_topic_summary(model, corpus, dictionary) and returns: vis