code: def test_split_distinct_with_numerical_values(
      split_distinct, a_values, b_values
  ):
      """Test the split_distinct functionality with numerical variables."""
      # Define the testing dataframe
      df = pd.DataFrame(
          {
              "ID": [1, 1, 2, 3, 1, 4, 5, 1, 1, 6],
              "numerical": [1, 1, 1, 4, 1, 4, 3, 7, 1, 3],
          }
      )
      a_expected = df[df.ID.isin(a_values)]
      b_expected = df[df.ID.isin(b_values)]
      # Run the split_dataframe method to extract the input of the PSI calculation.
      transformer = DropHighPSIFeatures(
          split_col="ID", split_distinct=split_distinct
      )
      a, b = transformer._split_dataframe(df)
      # Test if the functionality provides the expected results.
      pd.testing.assert_frame_equal(a, a_expected)
      pd.testing.assert_frame_equal(b, b_expected)
  
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 16 for calling a global function with example usage: test_split_distinct_with_numerical_values(split_distinct, a_values, b_values) without return types