code: def plot_permutation_importance(clf, X, y, ax):
      result = permutation_importance(
          clf, X, y, n_repeats=10, random_state=42, n_jobs=2
      )
      perm_sorted_idx = result.importances_mean.argsort()
  
      ax.boxplot(
          result.importances[perm_sorted_idx].T,
          vert=False,
          labels=X.columns[perm_sorted_idx],
      )
      ax.axvline(x=0, color="k", linestyle="--")
      return ax
  
file_path: scikit-learn/examples/inspection/plot_permutation_importance_multicollinear.py
imports: from sklearn.inspection import permutation_importance
  from sklearn.datasets import load_breast_cancer
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.model_selection import train_test_split
  import matplotlib.pyplot
  import numpy
  import pandas
  from scipy.cluster import hierarchy
  from scipy.spatial.distance import squareform
  from scipy.stats import spearmanr
  from collections import defaultdict
label_desc: Use image node_id 1 for calling a global function with example usage: plot_permutation_importance(clf, X, y, ax) and returns: ax