code: def test_sklearn_imputer_object_with_constant(df_na):
      variables_to_impute = ["Name", "City"]
      na_variables_left_after_imputation = [
          col
          for col in df_na.loc[:, df_na.isna().any()].columns
          if col not in variables_to_impute
      ]
  
      transformer = SklearnTransformerWrapper(
          transformer=SimpleImputer(
              fill_value="missing", strategy="constant"
          ),
          variables=variables_to_impute,
      )
  
      # transformed dataframe
      ref = df_na.copy()
      ref[variables_to_impute] = ref[variables_to_impute].fillna(
          "missing"
      )
  
      dataframe_na_transformed = transformer.fit_transform(df_na)
  
      # transformed output
      assert all(
          dataframe_na_transformed[na_variables_left_after_imputation]
          .isna()
          .sum()
          != 0
      )
      assert all(
          dataframe_na_transformed[variables_to_impute].isna().sum()
          == 0
      )
      pd.testing.assert_frame_equal(ref, dataframe_na_transformed)
  
imports: import numpy
  import pandas
  import pytest
  from sklearn.base import clone
  from sklearn.datasets import fetch_california_housing
  from sklearn.decomposition import PCA
  from sklearn.ensemble import RandomForestClassifier, VotingClassifier
  from sklearn.feature_selection import RFE, SelectFromModel, SelectKBest, VarianceThreshold, f_regression
  from sklearn.impute import MissingIndicator, SimpleImputer
  from sklearn.linear_model import Lasso
  from sklearn.model_selection import cross_val_score
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import Binarizer, FunctionTransformer, KBinsDiscretizer, MinMaxScaler, Normalizer, OneHotEncoder, OrdinalEncoder, PolynomialFeatures, PowerTransformer, StandardScaler
  from feature_engine.wrappers import SklearnTransformerWrapper
label_desc: Use image node_id 9 for calling a global function with example usage: test_sklearn_imputer_object_with_constant(df_na) without return types