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 predict(self, T):
      if self.check_X is not None:
          assert self.check_X(T)
      return self.classes_[np.zeros(_num_samples(T), dtype=np.int32)]
  
imports: import os
  from ast import literal_eval
  import numpy
  from distributed import Lock, Variable, get_worker
  from sklearn.base import BaseEstimator, ClassifierMixin
  from sklearn.utils.validation import _num_samples, check_array
label_desc: Use image node_id 3 for calling the CheckingClassifier obj's underlying member method code with example usage: obj.predict(T) and returns: self