text: """Tensorflow Module for GraphSAGE layer"""
  # pylint: disable= no-member, arguments-differ, invalid-name
  import tensorflow as tf
  from tensorflow.keras import layers
  
  from .... import function as fn
  from ....utils import expand_as_pair, check_eq_shape
  
  
  class SAGEConv(layers.Layer):
      r"""GraphSAGE layer from paper `Inductive Representation Learning on
      Large Graphs <https://arxiv.org/pdf/1706.02216.pdf>`__.
  
      .. math::
          h_{\mathcal{N}(i)}^{(l+1)} & = \mathrm{aggregate}
          \left(\{h_{j}^{l}, \forall j \in \mathcal{N}(i) \}\right)
  
          h_{i}^{(l+1)} & = \sigma \left(W \cdot \mathrm{concat}
          (h_{i}^{l}, h_{\mathcal{N}(i)}^{l+1} + b) \right)
  
          h_{i}^{(l+1)} & = \mathrm{norm}(h_{i}^{l})
  
      Parameters
      ----------
      in_feats : int, or pair of ints
          Input feature size.
  
          If the layer is to be applied on a unidirectional bipartite graph, ``in_feats``
          specifies the input feature size on both the source and destination nodes.  If
          a scalar is given, the source and destination node feature size would take the
          same value.
  
          If aggregator type is ``gcn``, the feature size of source and destination nodes
          are required to be the same.
      out_feats : int
          Output feature size.
      feat_drop : float
          Dropout rate on features, default: ``0``.
      aggregator_type : str
          Aggregator type to use (``mean``, ``gcn``, ``pool``, ``lstm``).
      bias : bool
          If True, adds a learnable bias to the output. Default: ``True``.
      norm : callable activation function/layer or None, optional
          If not None, applies normalization to the updated node features.
      activation : callable activation function/layer or None, optional
          If not None, applies an activation function to the updated node features.
          Default: ``None``.
      """
  
      def __init__(self,
                   in_feats,
                   out_feats,
                   aggregator_type,
                   feat_drop=0.,
                   bias=True,
                   norm=None,
                   activation=None):
          super(SAGEConv, self).__init__()
  
          self._in_src_feats, self._in_dst_feats = expand_as_pair(in_feats)
          self._out_feats = out_feats
          self._aggre_type = aggregator_type
          self.norm = norm
          self.feat_drop = layers.Dropout(feat_drop)
          self.activation = activation
          # aggregator type: mean/pool/lstm/gcn
          if aggregator_type == 'pool':
              self.fc_pool = layers.Dense(self._in_src_feats)
          if aggregator_type == 'lstm':
              self.lstm = layers.LSTM(units=self._in_src_feats)
          if aggregator_type != 'gcn':
              self.fc_self = layers.Dense(out_feats, use_bias=bias)
          self.fc_neigh = layers.Dense(out_feats, use_bias=bias)
  
      def _lstm_reducer(self, nodes):
          """LSTM reducer
          NOTE(zihao): lstm reducer with default schedule (degree bucketing)
          is slow, we could accelerate this with degree padding in the future.
          """
          m = nodes.mailbox['m']  # (B, L, D)
          rst = self.lstm(m)
          return {'neigh': rst}
  
      def call(self, graph, feat):
          r"""Compute GraphSAGE layer.
  
          Parameters
          ----------
          graph : DGLGraph
              The graph.
          feat : tf.Tensor or pair of tf.Tensor
              If a single 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 tensors are 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.
          """
          graph = graph.local_var()
  
          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)
  
          h_self = feat_dst
  
          if self._aggre_type == 'mean':
              graph.srcdata['h'] = feat_src
              graph.update_all(fn.copy_src('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_src('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_src('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_src('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
  