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, **fit_params):
      assert len(X) == len(y)
      if self.check_X is not None:
          assert self.check_X(X)
      if self.check_y is not None:
          assert self.check_y(y)
      self.classes_ = np.unique(
          check_array(y, ensure_2d=False, allow_nd=True)
      )
      if self.expected_fit_params:
          missing = set(self.expected_fit_params) - set(fit_params)
          assert (
              len(missing) == 0
          ), "Expected fit parameter(s) %s not " "seen." % list(missing)
          for key, value in fit_params.items():
              assert len(value) == len(
                  X
              ), "Fit parameter %s has length" "%d; expected %d." % (
                  key,
                  len(value),
                  len(X),
              )
      return self
  
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 2 for calling the CheckingClassifier obj's underlying member method code with example usage: obj.fit(X, y) and returns: self