code: def test_sklearn_imputer_allfeatures_with_constant(df_na):
      transformer = SklearnTransformerWrapper(
          transformer=SimpleImputer(
              fill_value="missing", strategy="constant"
          )
      )
  
      # transformed dataframe
      ref = df_na.copy()
      ref = ref.fillna("missing")
  
      dataframe_na_transformed = transformer.fit_transform(df_na)
  
      # transformed output
      assert all(dataframe_na_transformed.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 10 for calling a global function with example usage: test_sklearn_imputer_allfeatures_with_constant(df_na) without return types