code: def test_impute_numerical_variables(df_na):
      # set up transformer
      imputer = CategoricalImputer(
          imputation_method="missing",
          fill_value=0,
          variables=["Name", "City", "Studies", "Age", "Marks"],
          ignore_format=True,
      )
      X_transformed = imputer.fit_transform(df_na)
  
      # set up expected output
      X_reference = df_na.copy()
      X_reference = X_reference.fillna(0)
  
      # test init params
      assert imputer.imputation_method == "missing"
      assert imputer.variables == [
          "Name",
          "City",
          "Studies",
          "Age",
          "Marks",
      ]
  
      # test fit attributes
      assert imputer.variables_ == [
          "Name",
          "City",
          "Studies",
          "Age",
          "Marks",
      ]
      assert imputer.n_features_in_ == 6
  
      # test transform params
      pd.testing.assert_frame_equal(X_transformed, X_reference)
  
file_path: feature_engine/tests/test_imputation/test_categorical_imputer.py
imports: import pandas
  import pytest
  from feature_engine.imputation import CategoricalImputer
label_desc: Use image node_id 9 for calling a global function with example usage: test_impute_numerical_variables(df_na) without return types