code: def test_numerical_variables(df_classification):
      X, y = df_classification
  
      tr = TargetMeanClassifier(variables="num_var_A", bins=2)
      tr.fit(X, y)
  
      pred = tr.predict(X)
      prob = tr.predict_proba(X)
  
      exp_pred = np.array(
          [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
      )
      exp_prob = np.array(
          [
              [1.0, 0.0],
              [1.0, 0.0],
              [1.0, 0.0],
              [1.0, 0.0],
              [1.0, 0.0],
              [1.0, 0.0],
              [1.0, 0.0],
              [1.0, 0.0],
              [1.0, 0.0],
              [1.0, 0.0],
              [0.0, 1.0],
              [0.0, 1.0],
              [0.0, 1.0],
              [0.0, 1.0],
              [0.0, 1.0],
              [0.0, 1.0],
              [0.0, 1.0],
              [0.0, 1.0],
              [0.0, 1.0],
              [0.0, 1.0],
          ]
      )
  
      assert np.array_equal(pred, exp_pred)
      assert np.array_equal(prob, exp_prob)
  
      tr = TargetMeanClassifier(variables="num_var_B", bins=2)
      tr.fit(X, y)
  
      pred = tr.predict(X)
      prob = tr.predict_proba(X)
  
      exp_pred = np.array(
          [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]
      )
      exp_prob = np.array(
          [
              [0.8, 0.2],
              [0.8, 0.2],
              [0.8, 0.2],
              [0.8, 0.2],
              [0.8, 0.2],
              [0.8, 0.2],
              [0.8, 0.2],
              [0.8, 0.2],
              [0.2, 0.8],
              [0.2, 0.8],
              [0.8, 0.2],
              [0.8, 0.2],
              [0.2, 0.8],
              [0.2, 0.8],
              [0.2, 0.8],
              [0.2, 0.8],
              [0.2, 0.8],
              [0.2, 0.8],
              [0.2, 0.8],
              [0.2, 0.8],
          ]
      )
      assert np.array_equal(pred, exp_pred)
      assert np.allclose(prob, exp_prob)
  
      tr = TargetMeanClassifier(
          variables=["num_var_A", "num_var_B"], bins=2
      )
      tr.fit(X, y)
  
      pred = tr.predict(X)
      prob = tr.predict_proba(X)
  
      exp_pred = np.array(
          [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
      )
      exp_prob = np.array(
          [
              [0.9, 0.1],
              [0.9, 0.1],
              [0.9, 0.1],
              [0.9, 0.1],
              [0.9, 0.1],
              [0.9, 0.1],
              [0.9, 0.1],
              [0.9, 0.1],
              [0.6, 0.4],
              [0.6, 0.4],
              [0.4, 0.6],
              [0.4, 0.6],
              [0.1, 0.9],
              [0.1, 0.9],
              [0.1, 0.9],
              [0.1, 0.9],
              [0.1, 0.9],
              [0.1, 0.9],
              [0.1, 0.9],
              [0.1, 0.9],
          ]
      )
  
      assert np.array_equal(pred, exp_pred)
      assert np.allclose(prob, exp_prob)
  
imports: import numpy
  import pandas
  from feature_engine._prediction.target_mean_classifier import TargetMeanClassifier
label_desc: Use image node_id 3 for calling a global function with example usage: test_numerical_variables(df_classification) without return types