code: def test_with_pipeline_and_df(df_vartypes, input_features):
      pipe = Pipeline([("transformer", MockTransformer())])
      pipe.fit(df_vartypes)
      assert (
          pipe.get_feature_names_out(input_features=input_features)
          == pipe.named_steps["transformer"].feature_names_in_
      )
      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 5 for calling a global function with example usage: test_with_pipeline_and_df(df_vartypes, input_features) without return types