code: def score(self, X, y=None, **score_params):
      Xt = X
      for name, transform in self.steps[:-1]:
          if transform is not None:
              Xt = transform.transform(Xt)
      return self.steps[-1][-1].score(Xt, y, **score_params)
  
imports: from __future__ import absolute_import
  from typing import List, Any
  import numpy
  from scipy.stats import entropy
  from sklearn.pipeline import Pipeline
  from sklearn.utils import check_random_state, issparse
  from sklearn.utils.metaestimators import if_delegate_has_method
  from sklearn.utils import shuffle
  from eli5.utils import vstack
  from eli5.sklearn.utils import sklearn_version
label_desc: Use image node_id 1 for calling the _PipelinePatched obj's underlying member method code with example usage: obj.score(X, y) and returns: self