code: def _seaborn_viz_elbow_plot(
      min_topics: int, max_topics: int, coherence_values: List[float]
  ):
      """Creates an elbow plot displaying coherence values vs number of topics.
  
      Args:
          min_topics: Starting number of topics that were optimized for
          max_topics: Maximum number of topics that were optimized for
          coherence_values: A list of coherence values mapped from min_topics to
              max_topics
  
      Returns:
          Elbow plot showing coherence values vs number of topics
      """
      ax = sns.lineplot(
          x=[num for num in range(min_topics, max_topics + 1)],
          y=coherence_values,
      )
      ax.set_title("Coherence Values Across Topic Numbers")
      plt.xlabel("Number of Topics")
      plt.ylabel("Coherence Values")
      plt.figure(
          figsize=(
              get_option("display.matplotlib.fig_width"),
              get_option("display.matplotlib.fig_height"),
          )
      )
      return ax
  
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 3 for calling a global function with example usage: _seaborn_viz_elbow_plot(min_topics, max_topics, coherence_values) and returns: ax