text: """Torch Module for Topology Adaptive Graph Convolutional layer"""
  # pylint: disable= no-member, arguments-differ, invalid-name
  import torch as th
  from torch import nn
  
  from .... import function as fn
  
  
  class TAGConv(nn.Module):
      r"""Topology Adaptive Graph Convolutional layer from paper `Topology
      Adaptive Graph Convolutional Networks <https://arxiv.org/pdf/1710.10370.pdf>`__.
  
      .. math::
          \mathbf{X}^{\prime} = \sum_{k=0}^K \mathbf{D}^{-1/2} \mathbf{A}
          \mathbf{D}^{-1/2}\mathbf{X} \mathbf{\Theta}_{k},
  
      where :math:`\mathbf{A}` denotes the adjacency matrix and
      :math:`D_{ii} = \sum_{j=0} A_{ij}` its diagonal degree matrix.
  
      Parameters
      ----------
      in_feats : int
          Input feature size.
      out_feats : int
          Output feature size.
      k: int, optional
          Number of hops :math: `k`. (default: 2)
      bias: bool, optional
          If True, adds a learnable bias to the output. Default: ``True``.
      activation: callable activation function/layer or None, optional
          If not None, applies an activation function to the updated node features.
          Default: ``None``.
  
      Attributes
      ----------
      lin : torch.Module
          The learnable linear module.
      """
      def __init__(self,
                   in_feats,
                   out_feats,
                   k=2,
                   bias=True,
                   activation=None):
          super(TAGConv, self).__init__()
          self._in_feats = in_feats
          self._out_feats = out_feats
          self._k = k
          self._activation = activation
          self.lin = nn.Linear(in_feats * (self._k + 1), out_feats, bias=bias)
  
          self.reset_parameters()
  
      def reset_parameters(self):
          """Reinitialize learnable parameters."""
          gain = nn.init.calculate_gain('relu')
          nn.init.xavier_normal_(self.lin.weight, gain=gain)
  
      def forward(self, graph, feat):
          r"""Compute topology adaptive graph convolution.
  
          Parameters
          ----------
          graph : DGLGraph
              The graph.
          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.
          """
          graph = graph.local_var()
  
          norm = th.pow(graph.in_degrees().float().clamp(min=1), -0.5)
          shp = norm.shape + (1,) * (feat.dim() - 1)
          norm = th.reshape(norm, shp).to(feat.device)
  
          #D-1/2 A D -1/2 X
          fstack = [feat]
          for _ in range(self._k):
  
              rst = fstack[-1] * norm
              graph.ndata['h'] = rst
  
              graph.update_all(fn.copy_src(src='h', out='m'),
                               fn.sum(msg='m', out='h'))
              rst = graph.ndata['h']
              rst = rst * norm
              fstack.append(rst)
  
          rst = self.lin(th.cat(fstack, dim=-1))
  
          if self._activation is not None:
              rst = self._activation(rst)
  
          return rst
  