code: def test3():
      cancer = load_breast_cancer()
  
      X, y = cancer.data, cancer.target
      # show first 5 columns only
      # df = pd.DataFrame(X[:, 0:10], columns=cancer.feature_names[0:10])
      df = pd.DataFrame(X, columns=cancer.feature_names)
      # df['diagnosis'] = cancer.target
      X_train, X_test, y_train, y_test = train_test_split(
          df, y, test_size=0.3
      )
  
      cl = RandomForestClassifier(n_estimators=20)
      cl.fit(X_train, y_train)
  
      I = importances(cl, X_test, y_test)
      return I
  
file_path: random-forest-importances/src/play_plot.py
imports: import pandas
  import matplotlib.pyplot
  import numpy
  import seaborn
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.metrics import mean_absolute_error, mean_squared_error, mean_absolute_error
  from sklearn.model_selection import cross_val_score
  from sklearn.model_selection import train_test_split
  from sklearn.base import clone
  from rfpimp import *
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.datasets import load_breast_cancer
  from sklearn.model_selection import train_test_split
  from sklearn.metrics import accuracy_score
label_desc: Use image node_id 3 for calling a global function with example usage: test3() and returns: I