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 fit(self, X, y):
      assert_true(len(X) == len(y))
      if self.check_X is not None:
          assert_true(self.check_X(X))
      if self.check_y is not None:
          assert_true(self.check_y(y))
      self.classes_ = np.unique(
          check_array(y, ensure_2d=False, allow_nd=True)
      )
  
      return self
  
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 2 for calling the CheckingClassifier obj's underlying member method code with example usage: obj.fit(X, y) and returns: self