def forward(self, g, feat):
        """

        Description
        -----------
        Forward computation

        Parameters
        ----------
        g : DGLGraph
            The graph.
        feat : Tensor or pair of tensors
            :math:`(N, D)` where :math:`N` is the number of nodes and
            :math:`D` is the number of feature dimensions.

            If a pair of tensors is given, the graph must be a uni-bipartite graph
            with only one edge type, and the two tensors must have the same
            dimensionality on all except the first axis.

        Returns
        -------
        torch.Tensor
            New node features.

        Raises
        ------
        DGLError
            If there are 0-in-degree nodes in the input graph, it will raise DGLError
            since no message will be passed to those nodes. This will cause invalid output.
            The error can be ignored by setting ``allow_zero_in_degree`` parameter to ``True``.
        """
        with g.local_scope():
            if not self._allow_zero_in_degree:
                if (g.in_degrees() == 0).any():
                    raise DGLError(
                        "There are 0-in-degree nodes in the graph, "
                        "output for those nodes will be invalid. "
                        "This is harmful for some applications, "
                        "causing silent performance regression. "
                        "Adding self-loop on the input graph by "
                        "calling `g = dgl.add_self_loop(g)` will resolve "
                        "the issue. Setting ``allow_zero_in_degree`` "
                        "to be `True` when constructing this module will "
                        "suppress the check and let the code run."
                    )

            h_src, h_dst = expand_as_pair(feat, g)
            g.srcdata["x"] = h_src
            g.dstdata["x"] = h_dst
            g.apply_edges(fn.v_sub_u("x", "x", "theta"))
            g.edata["theta"] = self.theta(g.edata["theta"])
            g.dstdata["phi"] = self.phi(g.dstdata["x"])
            if not self.batch_norm:
                g.update_all(fn.e_add_v("theta", "phi", "e"), fn.max("e", "x"))
            else:
                g.apply_edges(fn.e_add_v("theta", "phi", "e"))
                # Although the official implementation includes a per-edge
                # batch norm within EdgeConv, I choose to replace it with a
                # global batch norm for a number of reasons:
                #
                # (1) When the point clouds within each batch do not have the
                #     same number of points, batch norm would not work.
                #
                # (2) Even if the point clouds always have the same number of
                #     points, the points may as well be shuffled even with the
                #     same (type of) object (and the official implementation
                #     *does* shuffle the points of the same example for each
                #     epoch).
                #
                #     For example, the first point of a point cloud of an
                #     airplane does not always necessarily reside at its nose.
                #
                #     In this case, the learned statistics of each position
                #     by batch norm is not as meaningful as those learned from
                #     images.
                g.edata["e"] = self.bn(g.edata["e"])
                g.update_all(fn.copy_e("e", "e"), fn.max("e", "x"))
            return g.dstdata["x"]


        Description
        -----------
        Forward computation

        Parameters
        ----------
        g : DGLGraph
            The graph.
        feat : Tensor or pair of tensors
            :math:`(N, D)` where :math:`N` is the number of nodes and
            :math:`D` is the number of feature dimensions.

            If a pair of tensors is given, the graph must be a uni-bipartite graph
            with only one edge type, and the two tensors must have the same
            dimensionality on all except the first axis.

        Returns
        -------
        torch.Tensor
            New node features.

        Raises
        ------
        DGLError
            If there are 0-in-degree nodes in the input graph, it will raise DGLError
            since no message will be passed to those nodes. This will cause invalid output.
            The error can be ignored by setting ``allow_zero_in_degree`` parameter to ``True``.
        
python/dgl/nn/pytorch/conv/edgeconv.py