def METHOD_NAME(self, graph, feat, edge_weight=None):
    r"""
    Description
    -----------
    Compute topology adaptive graph convolution.
    Parameters
    ----------
    graph : DGLGraph
        The graph.
    feat : torch.Tensor
        The input feature of shape :math:`(N, D_{in})` where :math:`D_{in}`
        is size of input feature, :math:`N` is the number of nodes.
    edge_weight: torch.Tensor, optional
        edge_weight to use in the message passing process. This is equivalent to
        using weighted adjacency matrix in the equation above, and
        :math:`\tilde{D}^{-1/2}\tilde{A} \tilde{D}^{-1/2}`
        is based on :class:`dgl.nn.pytorch.conv.graphconv.EdgeWeightNorm`.
    Returns
    -------
    torch.Tensor
        The output feature of shape :math:`(N, D_{out})` where :math:`D_{out}`
        is size of output feature.
    """
    with graph.local_scope():
        assert graph.is_homogeneous, "Graph is not homogeneous"
        if edge_weight is None:
            norm = th.pow(graph.in_degrees().to(feat).clamp(min=1), -0.5)
            shp = norm.shape + (1,) * (feat.dim() - 1)
            norm = th.reshape(norm, shp).to(feat.device)
        msg_func = fn.copy_u("h", "m")
        if edge_weight is not None:
            graph.edata["_edge_weight"] = EdgeWeightNorm("both")(
                graph, edge_weight
            )
            msg_func = fn.u_mul_e("h", "_edge_weight", "m")
        # D-1/2 A D -1/2 X
        fstack = [feat]
        for _ in range(self._k):
            if edge_weight is None:
                rst = fstack[-1] * norm
            else:
                rst = fstack[-1]
            graph.ndata["h"] = rst
            graph.update_all(msg_func, fn.sum(msg="m", out="h"))
            rst = graph.ndata["h"]
            if edge_weight is None:
                rst = rst * norm
            fstack.append(rst)
        rst = self.lin(th.cat(fstack, dim=-1))
        if self._activation is not None:
            rst = self._activation(rst)
        return rst