def METHOD_NAME(zs=None, zs_n=None, batch=None, sigma=None, **kwargs):
    '''The InfoNCE (NT-XENT) loss in contrastive learning.
    Args:
        zs (list, optipnal): List of tensors of shape [batch_size, z_dim].
        zs_n (list, optional): List of tensors of shape [nodes, z_dim].
        batch (Tensor, optional): Required when both :obj:`zs` and :obj:`zs_n` are given.
        sigma (ndarray, optional): A 2D-array of shape [:obj:`n_views`, :obj:`n_views`] with boolean 
            values, indicating contrast between which two views are computed. Only required 
            when number of views is greater than 2. If :obj:`sigma[i][j]` = :obj:`True`, 
            infoNCE between :math:`view_i` and :math:`view_j` will be computed.
        tau (int, optional): The temperature used in NT-XENT.
    :rtype: :class:`Tensor`
    '''
    assert zs is not None or zs_n is not None
    if 'tau' in kwargs:
        tau = kwargs['tau']
    else:
        tau = 0.5
    if 'norm' in kwargs:
        norm = kwargs['norm']
    else:
        norm = True
    mean = kwargs['mean'] if 'mean' in kwargs else True
    if zs_n is not None:
        if zs is None:
            # InfoNCE in GRACE
            assert len(zs_n)==2
            return (infoNCE_local_intra_node(zs_n[0], zs_n[1], tau, norm, batch)+
                    infoNCE_local_intra_node(zs_n[1], zs_n[0], tau, norm, batch))*0.5
        else:
            assert len(zs_n)==len(zs)
            assert batch is not None
            if len(zs)==1:
                return infoNCE_local_global(zs[0], zs_n[0], batch, tau, norm)
            elif len(zs)==2:
                return (infoNCE_local_global(zs[0], zs_n[1], batch, tau, norm)+
                        infoNCE_local_global(zs[1], zs_n[0], batch, tau, norm))
            else:
                assert len(zs)==len(sigma)
                loss = 0
                for (i, j) in itertools.combinations(range(len(zs)), 2):
                    if sigma[i][j]:
                        loss += (infoNCE_local_global(zs[i], zs_n[j], batch, tau, norm)+
                                 infoNCE_local_global(zs[j], zs_n[i], batch, tau, norm))
                return loss
    if len(zs)==2:
        return NT_Xent(zs[0], zs[1], tau, norm)
    elif len(zs)>2:
        assert len(zs)==len(sigma)
        loss = 0
        for (i, j) in itertools.combinations(range(len(zs)), 2):
            if sigma[i][j]:
                loss += NT_Xent(zs[i], zs[j], tau, norm)
        return loss