def forward(self, graph, feat, get_attention=False):
        r"""

        Description
        -----------
        Apply dot product version of self attention in GCN.

        Parameters
        ----------
        graph: DGLGraph or bi_partities graph
            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_{src}})` and :math:`(N_{out}, D_{in_{dst}})`.
        get_attention : bool, optional
            Whether to return the attention values. Default to False.

        Returns
        -------
        torch.Tensor
            The output feature of shape :math:`(N, D_{out})` where :math:`D_{out}` is size
            of output feature.
        torch.Tensor, optional
            The attention values of shape :math:`(E, 1)`, where :math:`E` is the number of
            edges. This is returned only when :attr:`get_attention` is ``True``.

        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``.
        """

        graph = graph.local_var()

        if not self._allow_zero_in_degree:
            if (graph.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."
                )

        # check if feat is a tuple
        if isinstance(feat, tuple):
            h_src = feat[0]
            h_dst = feat[1]
            feat_src = self.fc_src(h_src).view(
                -1, self._num_heads, self._out_feats
            )
            feat_dst = self.fc_dst(h_dst).view(
                -1, self._num_heads, self._out_feats
            )
        else:
            h_src = feat
            feat_src = feat_dst = self.fc(h_src).view(
                -1, self._num_heads, self._out_feats
            )
            if graph.is_block:
                feat_dst = feat_src[: graph.number_of_dst_nodes()]

        # Assign features to nodes
        graph.srcdata.update({"ft": feat_src})
        graph.dstdata.update({"ft": feat_dst})

        # Step 1. dot product
        graph.apply_edges(fn.u_dot_v("ft", "ft", "a"))

        # Step 2. edge softmax to compute attention scores
        graph.edata["sa"] = edge_softmax(
            graph, graph.edata["a"] / self._out_feats**0.5
        )

        # Step 3. Broadcast softmax value to each edge, and aggregate dst node
        graph.update_all(
            fn.u_mul_e("ft", "sa", "attn"), fn.sum("attn", "agg_u")
        )

        # output results to the destination nodes
        rst = graph.dstdata["agg_u"]

        if get_attention:
            return rst, graph.edata["sa"]
        else:
            return rst


        Description
        -----------
        Apply dot product version of self attention in GCN.

        Parameters
        ----------
        graph: DGLGraph or bi_partities graph
            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_{src}})` and :math:`(N_{out}, D_{in_{dst}})`.
        get_attention : bool, optional
            Whether to return the attention values. Default to False.

        Returns
        -------
        torch.Tensor
            The output feature of shape :math:`(N, D_{out})` where :math:`D_{out}` is size
            of output feature.
        torch.Tensor, optional
            The attention values of shape :math:`(E, 1)`, where :math:`E` is the number of
            edges. This is returned only when :attr:`get_attention` is ``True``.

        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/dotgatconv.py