code: def test_with_pipe_and_skl_transformer_input_df(
      df_vartypes, input_features
  ):
      pipe = Pipeline(
          [
              ("imputer", SimpleImputer(strategy="constant")),
              ("transformer", MockTransformer()),
          ]
      )
      pipe.fit(df_vartypes)
      assert (
          pipe.get_feature_names_out(input_features=input_features)
          == variables_str
      )
  
imports: import numpy
  import pytest
  from sklearn.exceptions import NotFittedError
  from sklearn.impute import SimpleImputer
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import PolynomialFeatures
  from feature_engine._base_transformers.mixins import GetFeatureNamesOutMixin
  from feature_engine.dataframe_checks import check_X
label_desc: Use image node_id 7 for calling a global function with example usage: test_with_pipe_and_skl_transformer_input_df(df_vartypes, input_features) without return types