code: def permutation_importances(
      rf, X_train, y_train, metric, n_samples=5000
  ):
      imp = permutation_importances_raw(
          rf, X_train, y_train, metric, n_samples
      )
      I = pd.DataFrame(
          data={"Feature": X_train.columns, "Importance": imp}
      )
      I = I.set_index("Feature")
      I = I.sort_values("Importance", ascending=False)
      return I
  
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 6 for calling a global function with example usage: permutation_importances(rf, X_train, y_train, metric, n_samples) and returns: I