code: def test_switch():
      """Test the functionality to switch the basis."""
  
      df_a = pd.DataFrame(
          {
              "a": [1.0, 2, 3, 1],
              "b": [1.0, 2, 3, 4],
              "c": [1, 2, 3, 4],
              "d": [1.7, 4.7, 6.6, 7.8],
          }
      )
  
      df_b = pd.DataFrame(
          {
              "a": [4.0, 3, 5, 1],
              "b": [11.0, 1, 2, 4],
              "c": [4, 2, 2, 4],
              "d": [4.7, 4.7, 7.6, 7.8],
          }
      )
  
      df_order = pd.concat([df_a, df_b]).reset_index(drop=True)
      df_reverse = pd.concat([df_b, df_a]).reset_index(drop=True)
  
      case = DropHighPSIFeatures(
          split_frac=0.5, bins=3, switch=False, min_pct_empty_bins=0.001
      )
      case.fit(df_order)
  
      switch_case = DropHighPSIFeatures(
          split_frac=0.5, bins=3, switch=True, min_pct_empty_bins=0.001
      )
      switch_case.fit(df_reverse)
  
      assert case.psi_values_ == switch_case.psi_values_
  
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 24 for calling a global function with example usage: test_switch() without return types