code: def _plot_correlation_matrix(corr, cols: List[str], plt_kwargs={}):
      """
      Plot a matrix of the correlation matrix, using the columns provided in params.
      This visualisation contains all the columns in the X and Y axis, where the
      intersection of the column and row displays the correlation value.
      The closer this correlation factor is to 1, 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_matrix(df, cols=columns_to_include)
  
      :returns: Null
      :rtype: None
  
      """
      fig = plt.figure(**plt_kwargs)
      ax = fig.add_subplot(111)
      cax = ax.matshow(corr, cmap="coolwarm", vmin=-1, vmax=1)
      fig.colorbar(cax)
      ticks = np.arange(0, len(cols), 1)
      ax.set_xticks(ticks)
      plt.xticks(rotation=90)
      ax.set_yticks(ticks)
      ax.set_xticklabels(cols)
      ax.set_yticklabels(cols)
      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 7 for calling a global function with example usage: _plot_correlation_matrix(corr, cols, plt_kwargs) without return types