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 __init__(self, check_y=None, check_X=None, foo_param=0):
      self.check_y = check_y
      self.check_X = check_X
      self.foo_param = foo_param
  
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 1 to create a new CheckingClassifier object from inherited base classes: BaseEstimator, ClassifierMixin with example: obj = CheckingClassifier(check_y, check_X, foo_param)