def call(self, graph, feat):
        r"""Compute GraphSAGE layer.

        Parameters
        ----------
        graph : DGLGraph
            The graph.
        feat : tf.Tensor or pair of tf.Tensor
            If a tf.Tensor is given, it represents 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 tf.Tensor is given, the pair must contain two tensors of shape
            :math:`(N_{in}, D_{in_{src}})` and :math:`(N_{out}, D_{in_{dst}})`.

        Returns
        -------
        tf.Tensor
            The output feature of shape :math:`(N, D_{out})` where :math:`D_{out}`
            is size of output feature.
        """
        with graph.local_scope():
            if isinstance(feat, tuple):
                feat_src = self.feat_drop(feat[0])
                feat_dst = self.feat_drop(feat[1])
            else:
                feat_src = feat_dst = self.feat_drop(feat)
                if graph.is_block:
                    feat_dst = feat_src[: graph.number_of_dst_nodes()]

            h_self = feat_dst

            # Handle the case of graphs without edges
            if graph.num_edges() == 0:
                graph.dstdata["neigh"] = tf.cast(
                    tf.zeros((graph.number_of_dst_nodes(), self._in_src_feats)),
                    tf.float32,
                )

            if self._aggre_type == "mean":
                graph.srcdata["h"] = feat_src
                graph.update_all(fn.copy_u("h", "m"), fn.mean("m", "neigh"))
                h_neigh = graph.dstdata["neigh"]
            elif self._aggre_type == "gcn":
                check_eq_shape(feat)
                graph.srcdata["h"] = feat_src
                graph.dstdata["h"] = feat_dst  # same as above if homogeneous
                graph.update_all(fn.copy_u("h", "m"), fn.sum("m", "neigh"))
                # divide in_degrees
                degs = tf.cast(graph.in_degrees(), tf.float32)
                h_neigh = (graph.dstdata["neigh"] + graph.dstdata["h"]) / (
                    tf.expand_dims(degs, -1) + 1
                )
            elif self._aggre_type == "pool":
                graph.srcdata["h"] = tf.nn.relu(self.fc_pool(feat_src))
                graph.update_all(fn.copy_u("h", "m"), fn.max("m", "neigh"))
                h_neigh = graph.dstdata["neigh"]
            elif self._aggre_type == "lstm":
                graph.srcdata["h"] = feat_src
                graph.update_all(fn.copy_u("h", "m"), self._lstm_reducer)
                h_neigh = graph.dstdata["neigh"]
            else:
                raise KeyError(
                    "Aggregator type {} not recognized.".format(
                        self._aggre_type
                    )
                )
            # GraphSAGE GCN does not require fc_self.
            if self._aggre_type == "gcn":
                rst = self.fc_neigh(h_neigh)
            else:
                rst = self.fc_self(h_self) + self.fc_neigh(h_neigh)
            # activation
            if self.activation is not None:
                rst = self.activation(rst)
            # normalization
            if self.norm is not None:
                rst = self.norm(rst)
            return rst
Compute GraphSAGE layer.

        Parameters
        ----------
        graph : DGLGraph
            The graph.
        feat : tf.Tensor or pair of tf.Tensor
            If a tf.Tensor is given, it represents 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 tf.Tensor is given, the pair must contain two tensors of shape
            :math:`(N_{in}, D_{in_{src}})` and :math:`(N_{out}, D_{in_{dst}})`.

        Returns
        -------
        tf.Tensor
            The output feature of shape :math:`(N, D_{out})` where :math:`D_{out}`
            is size of output feature.
        
python/dgl/nn/tensorflow/conv/sageconv.py