class_docstr: Dummy classifier to test pipelining and meta-estimators.
  
  Checks some property of X and y in fit / predict.
  This allows testing whether pipelines / cross-validation or metaestimators
  changed the input.
class_docstr_tok: ["Dummy","classifier","to","test","pipelining","and","meta-estimators",".","Checks","some","property","of","X","and","y","in","fit","\/","predict",".","This","allows","testing","whether","pipelines","\/","cross-validation","or","metaestimators","changed","the","input","."]
code: def score(self, X=None, Y=None):
      if self.foo_param > 1:
          score = 1.0
      else:
          score = 0.0
      return score
  
imports: import numpy
  from ..base import BaseEstimator, ClassifierMixin
  from .testing import assert_true
  from .validation import _num_samples, check_array
label_desc: Use image node_id 4 for calling the CheckingClassifier obj's underlying member method code with example usage: obj.score(X, Y) and returns: score