code: def test_error_when_inverse_transform_not_implemented(transformer):
      X = fetch_california_housing(as_frame=True).frame
      y = X["MedHouseVal"]
      X = X.drop(["MedHouseVal"], axis=1)
  
      tr_wrap = SklearnTransformerWrapper(transformer=transformer)
      tr_wrap.fit(X, y)
      X_tr = tr_wrap.transform(X)
  
      with pytest.raises(NotImplementedError):
          tr_wrap.inverse_transform(X_tr)
  
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 18 for calling a global function with example usage: test_error_when_inverse_transform_not_implemented(transformer) without return types