code: def df():
      # create array with 4 correlated features and 2 independent ones
      X, y = make_classification(
          n_samples=1000,
          n_features=6,
          n_redundant=2,
          n_clusters_per_class=1,
          weights=[0.50],
          class_sep=2,
          random_state=1,
      )
  
      # transform array into pandas df
      colnames = ["var_" + str(i) for i in range(6)]
      X = pd.DataFrame(X, columns=colnames)
  
      # Add drifted features that will be dropped during transformation.
      X["drift_1"] = [number for number in range(X.shape[0])]
      X["drift_2"] = [number / 2 for number in range(X.shape[0])]
  
      return X
  
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 1 for calling a global function with example usage: df() and returns: X