code: def _plot_correlation_dendogram(
      corr: pd.DataFrame, cols: List[str], plt_kwargs={}
  ):
      """
      Plot dendogram of a correlation matrix, using the columns provided.
      This consists of a chart that that shows hierarchically the variables
      that are most correlated by the connecting trees. The closer to the right
      that the connection is, the more correlated the features are.
      If you would like to visualise this as a tree, please
      see the function _plot_correlation_dendogram.
  
      :Example:
  
      columns_to_include=["age", "loan", "gender"]
      xai._plot_correlation_dendogram(df, cols=columns_to_include)
  
      :returns: Null
      :rtype: None
  
      """
  
      corr = np.round(corr, 4)
      corr_condensed = hc.distance.squareform(1 - corr)
      z = hc.linkage(corr_condensed, method="average")
      fig = plt.figure(**plt_kwargs)
      dendrogram = hc.dendrogram(
          z, labels=cols, orientation="left", leaf_font_size=16
      )
      plt.show()
  
imports: import pandas
  import matplotlib.pyplot
  import numpy
  from scipy.stats import spearmanr
  from scipy.cluster import hierarchy
  from typing import List, Any, Union, Tuple, Optional, Dict
  import random, math
  from sklearn.metrics import roc_curve, precision_recall_curve, roc_auc_score, confusion_matrix
  import itertools
  import logging
label_desc: Use image node_id 6 for calling a global function with example usage: _plot_correlation_dendogram(corr, cols, plt_kwargs) without return types