How to use the pyriemann.utils.covariance.covariances function in pyriemann

def test_covariances():
    """Test covariance for multiple estimator"""
    x = np.random.randn(2, 3, 100)
    cov = covariances(x)
    cov = covariances(x, estimator='oas')
    cov = covariances(x, estimator='lwf')
    cov = covariances(x, estimator='scm')
    cov = covariances(x, estimator='corr')
    cov = covariances(x, estimator='mcd')
    cov = covariances(x, estimator=np.cov)
    assert_raises(ValueError, covariances, x, estimator='truc')

def test_covariances():
    """Test covariance for multiple estimator"""
    x = np.random.randn(2, 3, 100)
    cov = covariances(x)
    cov = covariances(x, estimator='oas')
    cov = covariances(x, estimator='lwf')
    cov = covariances(x, estimator='scm')
    cov = covariances(x, estimator='corr')
    cov = covariances(x, estimator='mcd')
    cov = covariances(x, estimator=np.cov)
    assert_raises(ValueError, covariances, x, estimator='truc')

def test_covariances():
    """Test covariance for multiple estimator"""
    x = np.random.randn(2, 3, 100)
    cov = covariances(x)
    cov = covariances(x, estimator='oas')
    cov = covariances(x, estimator='lwf')
    cov = covariances(x, estimator='scm')
    cov = covariances(x, estimator='corr')
    cov = covariances(x, estimator='mcd')
    cov = covariances(x, estimator=np.cov)
    assert_raises(ValueError, covariances, x, estimator='truc')

"""

        if isinstance(self.delays, int):
            delays = range(1, self.delays)
        else:
            delays = self.delays

        X2 = []

        for x in X:
            tmp = x
            for d in delays:
                tmp = numpy.r_[tmp, numpy.roll(x, d, axis=-1)]
            X2.append(tmp)
        X2 = numpy.array(X2)
        covmats = covariances(X2, estimator=self.estimator)
        return covmats

def transform(self, X):
        """Estimate covariance matrices.

        Parameters
        ----------
        X : ndarray, shape (n_trials, n_channels, n_samples)
            ndarray of trials.

        Returns
        -------
        covmats : ndarray, shape (n_trials, n_channels, n_channels)
            ndarray of covariance matrices for each trials.
        """
        covmats = covariances(X, estimator=self.estimator)
        return covmats