code: def test_importance_preprocess(data, compute_backend_df):
      def pre(df, target):
          y = df[target]
          df = df.drop(target, axis=1)
          x_num = df.select_dtypes(["number"])
          x_num = x_num.fillna("-1")
          x_cat = df[
              [c for c in df.columns if c not in x_num.columns]
          ].astype(str)
          x_cat = x_cat.fillna("")
          x_cat_encoded = x_cat.apply(LabelEncoder().fit_transform)
          X = pd.concat([x_num, x_cat_encoded], axis=1)
          return X, y
  
      fig = dd.importance(compute_backend_df, "d", preprocess_func=pre)
      assert isinstance(fig, matplotlib.artist.Artist)
  
file_path: data-describe/tests/test_importance.py
imports: import pytest
  import matplotlib
  import pandas
  import numpy
  from sklearn.ensemble import RandomForestRegressor
  from sklearn.preprocessing import LabelEncoder
  import data_describe
label_desc: Use image node_id 4 for calling a global function with example usage: test_importance_preprocess(data, compute_backend_df) and returns: X,  y