code: def df_test_num_cat():
      np.random.seed(1)
  
      df = {
          "var_A": ["A"] * 60 + ["B"] * 100 + ["C"] * 40,
          "var_B": ["A"] * 100 + ["B"] * 60 + ["C"] * 40,
          "var_C": np.random.normal(size=200),
          "var_D": np.random.normal(size=200),
          "target": np.concatenate(
              (
                  np.random.binomial(1, 0.7, 60),
                  np.random.binomial(1, 0.2, 100),
                  np.random.binomial(1, 0.8, 40),
              ),
              axis=None,
          ),
      }
  
      df = pd.DataFrame(df)
      X = df[["var_A", "var_B", "var_C", "var_D"]]
      y = df["target"]
      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 3 for calling a global function with example usage: df_test_num_cat() and returns: X,  y