code: def df_test():
      X, y = make_classification(
          n_samples=1000,
          n_features=12,
          n_redundant=4,
          n_clusters_per_class=1,
          weights=[0.50],
          class_sep=2,
          random_state=1,
      )
  
      # trasform arrays into pandas df and series
      colnames = ["var_" + str(i) for i in range(12)]
      X = pd.DataFrame(X, columns=colnames)
      y = pd.Series(y)
      return X, y
  
file_path: feature_engine/tests/test_selection/conftest.py
imports: import random
  import numpy
  import pandas
  import pytest
  from sklearn.datasets import load_diabetes, make_classification
label_desc: Use image node_id 1 for calling a global function with example usage: df_test() and returns: X,  y