def forward(self, graph, Y, X, alp, lam):
        r"""

        Description
        -----------
        Propagation forward.

        Parameters
        ----------
        graph : DGLGraph
            The graph.
        Y : torch.Tensor
            The feature under propagation. Corresponds to :math:`Z^{(k)}` in eq.28 in the paper.
        X : torch.Tensor
            The original feature. Corresponds to :math:`Z^{(0)}` in eq.28 in the paper.
        alp : float
            The step size. Corresponds to :math:`\alpha` in the paper.
        lam : torch.Tensor
            The coefficient of smoothing term. Corresponds to :math:`\lambda` in the paper.
        Returns
        -------
        torch.Tensor
            Propagated feature. :math:`Z^{(k+1)}` in eq.28 in the paper.
        """

        return (
            (1 - alp) * Y
            + alp * lam * self._prop(graph, Y, lam)
            + alp * D_power_bias_X(graph, X, -1, lam, 1 - lam)
        )


        Description
        -----------
        Propagation forward.

        Parameters
        ----------
        graph : DGLGraph
            The graph.
        Y : torch.Tensor
            The feature under propagation. Corresponds to :math:`Z^{(k)}` in eq.28 in the paper.
        X : torch.Tensor
            The original feature. Corresponds to :math:`Z^{(0)}` in eq.28 in the paper.
        alp : float
            The step size. Corresponds to :math:`\alpha` in the paper.
        lam : torch.Tensor
            The coefficient of smoothing term. Corresponds to :math:`\lambda` in the paper.
        Returns
        -------
        torch.Tensor
            Propagated feature. :math:`Z^{(k+1)}` in eq.28 in the paper.
        
python/dgl/nn/pytorch/conv/twirlsconv.py