d parameters given when calling `fit`.

Attributes
----------
classes_ : int
    The classes seen during `fit`.

n_features_in_ : int
    The number of features seen during `fit`.

Examples
--------
>>> from sklearn.utils._mocking import CheckingClassifier

This helper allow to assert to specificities regarding `X` or `y`. In this
case we expect `check_X` or `check_y` to return a boolean.

>>> from sklearn.datasets import load_iris
>>> X, y = load_iris(return_X_y=True)
>>> clf = CheckingClassifier(check_X=lambda x: x.shape == (150, 4))
>>> clf.fit(X, y)
CheckingClassifier(...)

We can also provide a check which might raise an error. In this case, we
expect `check_X` to return `X` and `check_y` to return `y`.

>>> from sklearn.utils import check_array
>>> clf = CheckingClassifier(check_X=check_array)
>>> clf.fit(X, y)
CheckingClassifier(...)
N