code: def test_fit_attributes_auto(df):
      """Check the value of the fit attributes.
      The expected PSI values used in the assertion were determined using
      the Probatus package.
      ```
      from probatus.stat_tests import AutoDist
      psi_calculator = AutoDist(statistical_tests=["PSI"],
                      binning_strategies="QuantileBucketer",
                      bin_count=10)
      train_df = data.iloc[0:500,:]
      test_df = data.iloc[500:, :]
      psi = psi_calculator.compute(train_df, test_df)
      ```
      """
      transformer = DropHighPSIFeatures(threshold="auto", bins=10)
      transformer.fit_transform(df)
  
      expected_psi = {
          "var_0": 0.043828484052281,
          "var_1": 0.040929870747665395,
          "var_2": 0.04330418495156895,
          "var_3": 0.03773286532548153,
          "var_4": 0.05047388515663041,
          "var_5": 0.014717735595712466,
          "drift_1": 8.283089355027482,
          "drift_2": 8.283089355027482,
      }
      assert transformer.variables_ == [
          "var_0",
          "var_1",
          "var_2",
          "var_3",
          "var_4",
          "var_5",
          "drift_1",
          "drift_2",
      ]
      assert transformer.psi_values_ == pytest.approx(expected_psi, 12)
      assert transformer.features_to_drop_ == ["drift_1", "drift_2"]
  
imports: from datetime import date
  import math
  import numpy
  import pandas
  import pytest
  from sklearn.datasets import make_classification
  from feature_engine.selection import DropHighPSIFeatures
label_desc: Use image node_id 5 for calling a global function with example usage: test_fit_attributes_auto(df) without return types