def apply(self, dataset, can_fit=True):
        X = dataset.get_design_matrix()
        if can_fit:
            self._mean = X.mean(axis=self._axis)
        else:
            if self._mean is None:
                raise ValueError("can_fit is False, but RemoveMean object "
                                 "has no stored mean or standard deviation")
        X -= self._mean
        dataset.set_design_matrix(X)
.. todo::
dataset/ETHPy150Open lisa-lab/pylearn2/pylearn2/datasets/preprocessing.py/RemoveMean.apply