code: def test2():
      df_train, df_test = train_test_split(df, test_size=0.15)
      X_train, y_train = (
          df_train.drop("interest_level", axis=1),
          df_train["interest_level"],
      )
      X_test, y_test = (
          df_test.drop("interest_level", axis=1),
          df_test["interest_level"],
      )
      rf = RandomForestClassifier(
          n_estimators=50,
          n_jobs=-1,
          max_features=1.0,
          min_samples_leaf=10,
          oob_score=True,
      )
      rf.fit(X_train, y_train)
      I = importances(
          rf,
          X_test,
          y_test,
          features=["bedrooms", "bathrooms", ["latitude", "longitude"]],
      )
      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 2 for calling a global function with example usage: test2() and returns: I