text: """Torch Module for DenseSAGEConv"""
  # pylint: disable= no-member, arguments-differ, invalid-name
  from torch import nn
  
  
  class DenseSAGEConv(nn.Module):
      """GraphSAGE layer where the graph structure is given by an
      adjacency matrix.
      We recommend to use this module when inducing GraphSAGE operations
      on dense graphs / k-hop graphs.
  
      Note that we only support gcn aggregator in DenseSAGEConv.
  
      Parameters
      ----------
      in_feats : int
          Input feature size.
      out_feats : int
          Output feature size.
      feat_drop : float, optional
          Dropout rate on features. Default: 0.
      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``.
  
      See also
      --------
      SAGEConv
      """
      def __init__(self,
                   in_feats,
                   out_feats,
                   feat_drop=0.,
                   bias=True,
                   norm=None,
                   activation=None):
          super(DenseSAGEConv, self).__init__()
          self._in_feats = in_feats
          self._out_feats = out_feats
          self._norm = norm
          self.feat_drop = nn.Dropout(feat_drop)
          self.activation = activation
          self.fc = nn.Linear(in_feats, out_feats, bias=bias)
          self.reset_parameters()
  
      def reset_parameters(self):
          """Reinitialize learnable parameters."""
          gain = nn.init.calculate_gain('relu')
          nn.init.xavier_uniform_(self.fc.weight, gain=gain)
  
      def forward(self, adj, feat):
          r"""Compute (Dense) Graph SAGE layer.
  
          Parameters
          ----------
          adj : torch.Tensor
              The adjacency matrix of the graph to apply Graph Convolution on,
              should be of shape :math:`(N, N)`, where a row represents the destination
              and a column represents the source.
          feat : torch.Tensor
              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.
  
          Returns
          -------
          torch.Tensor
              The output feature of shape :math:`(N, D_{out})` where :math:`D_{out}`
              is size of output feature.
          """
          adj = adj.float().to(feat.device)
          feat = self.feat_drop(feat)
          in_degrees = adj.sum(dim=1, keepdim=True)
          h_neigh = (adj @ feat + feat) / (in_degrees + 1)
          rst = self.fc(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
  