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Complete this python program:
def _get_covariance_classes():
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#Here is the Python program you requested.

    """Prepare special cov estimators.
    """
    from sklearn.covariance import EmpiricalCovariance, shrunk_covariance, ShrunkCovariance
    class _RegCovariance(EmpiricalCovariance, ):
        'Aux    class.'
        def __init__(self, info, grad=0.01, mag=0.01, eeg=0.0, store_precision=False, assume_centered=False):
            self.info = info
            self.grad = grad
            self.mag = mag
            self.eeg = eeg
            self.store_precision = store_precision
            self.assume_centered = assume_centered
        def fit(self, X):
            EmpiricalCovariance.fit(self, X)
            self.covariance_ = (0.5 * (self.covariance_ + self.covariance_.T))
            cov_ = Covariance(data=self.covariance_, names=self.info['ch_names'], bads=self.info['bads'], projs=self.info['projs'], nfree=len(self.covariance_))
            cov_ = regularize(cov_, self.info, grad=self.grad, mag=self.mag, eeg=self.eeg, proj=False, exclude='bads')
            self.covariance_ = cov_.data
            return self
    class _ShrunkCovariance(ShrunkCovariance, ):
        'Aux    class.'
        def __init__(self, store_precision, assume_centered, shrinkage=0.1):
            self.store_precision = store_precision
            self.assume_centered = assume_centered
            self.shrinkage = shrinkage
        def fit(self, X):
            EmpiricalCovariance.fit(self, X)
            cov = self.covariance_
            if (not isinstance(self.shrinkage, (list, tuple))):
                shrinkage = [('all', self.shrinkage, np.arange(len(cov)))]
            else:
                shrinkage = self.shrinkage
            zero_cross_cov = np.zeros_like(cov, dtype=bool)
            for (a, b) in itt.combinations(shrinkage, 2):
                (picks_i, picks_j) = (a[2], b[2])
                ch_ = (a[0], b[0])
                if ('eeg' in ch_):
                    zero_cross_cov[np.ix_(picks_i, picks_j)] = True
                    zero_cross_cov[np.ix_(picks_j, picks_i)] = True
            self.zero_cross_cov_ = zero_cross_cov
            for (ch_type, c, picks) in...