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