def METHOD_NAME(self, graph, feat, edge_weight=None):
    r"""
    Description
    -----------
    Compute Graph Isomorphism Network layer.
    Parameters
    ----------
    graph : DGLGraph
        The graph.
    feat : torch.Tensor or pair of torch.Tensor
        If a torch.Tensor is given, 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.
        If a pair of torch.Tensor is given, the pair must contain two tensors of shape
        :math:`(N_{in}, D_{in})` and :math:`(N_{out}, D_{in})`.
        If ``apply_func`` is not None, :math:`D_{in}` should
        fit the input dimensionality requirement of ``apply_func``.
    edge_weight : torch.Tensor, optional
        Optional tensor on the edge. If given, the convolution will weight
        with regard to the message.
    Returns
    -------
    torch.Tensor
        The output feature of shape :math:`(N, D_{out})` where
        :math:`D_{out}` is the output dimensionality of ``apply_func``.
        If ``apply_func`` is None, :math:`D_{out}` should be the same
        as input dimensionality.
    """
    _reducer = getattr(fn, self._aggregator_type)
    with graph.local_scope():
        aggregate_fn = fn.copy_u("h", "m")
        if edge_weight is not None:
            assert edge_weight.shape[0] == graph.num_edges()
            graph.edata["_edge_weight"] = edge_weight
            aggregate_fn = fn.u_mul_e("h", "_edge_weight", "m")
        feat_src, feat_dst = expand_as_pair(feat, graph)
        graph.srcdata["h"] = feat_src
        graph.update_all(aggregate_fn, _reducer("m", "neigh"))
        rst = (1 + self.eps) * feat_dst + graph.dstdata["neigh"]
        if self.apply_func is not None:
            rst = self.apply_func(rst)
        # activation
        if self.activation is not None:
            rst = self.activation(rst)
        return rst