code: def importances_raw(rf, X_train, y_train, n_samples=5000):
      if isinstance(rf, RandomForestClassifier):
          return permutation_importances_raw(
              rf, X_train, y_train, oob_classifier_accuracy, n_samples
          )
      elif isinstance(rf, RandomForestRegressor):
          return permutation_importances_raw(
              rf, X_train, y_train, oob_regression_r2_score, n_samples
          )
      return None
  
imports: import pandas
  import numpy
  import matplotlib.pyplot
  import sklearn
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.ensemble import RandomForestRegressor
  from distutils.version import LooseVersion
  from sklearn.model_selection import cross_val_score
  from sklearn.base import clone
  from sklearn.metrics import r2_score
  from sklearn.metrics import f1_score
  from sklearn.preprocessing import LabelEncoder
  from scipy import stats
  from pandas.api.types import is_numeric_dtype
  from matplotlib.colors import ListedColormap
  from matplotlib.ticker import FormatStrFormatter
  from copy import copy
  import warnings
  import tempfile
  from os import getpid, makedirs
label_desc: Use image node_id 9 for calling a global function with example usage: importances_raw(rf, X_train, y_train, n_samples) and returns: None, permutation_importances_raw, permutation_importances_raw