text: import torch
  from torch.nn import Parameter as Param
  from torch_geometric.nn.conv import MessagePassing
  
  from ..inits import uniform
  
  
  class RGCNConv(MessagePassing):
      r"""The relational graph convolutional operator from the `"Modeling
      Relational Data with Graph Convolutional Networks"
      <https://arxiv.org/abs/1703.06103>`_ paper
  
      .. math::
          \mathbf{x}^{\prime}_i = \mathbf{\Theta}_0 \cdot \mathbf{x}_i +
          \sum_{r \in \mathcal{R}} \sum_{j \in \mathcal{N}_r(i)}
          \frac{1}{|\mathcal{N}_r(i)|} \mathbf{\Theta}_r \cdot \mathbf{x}_j,
  
      where :math:`\mathcal{R}` denotes the set of relations, *i.e.* edge types.
      Edge type needs to be a one-dimensional :obj:`torch.long` tensor which
      stores a relation identifier
      :math:`\in \{ 0, \ldots, |\mathcal{R}| - 1\}` for each edge.
  
      Args:
          in_channels (int): Size of each input sample.
          out_channels (int): Size of each output sample.
          num_relations (int): Number of relations.
          num_bases (int): Number of bases used for basis-decomposition.
          bias (bool, optional): If set to :obj:`False`, the layer will not learn
              an additive bias. (default: :obj:`True`)
      """
  
      def __init__(self,
                   in_channels,
                   out_channels,
                   num_relations,
                   num_bases,
                   bias=True):
          super(RGCNConv, self).__init__()
  
          self.in_channels = in_channels
          self.out_channels = out_channels
          self.num_relations = num_relations
          self.num_bases = num_bases
  
          self.basis = Param(torch.Tensor(num_bases, in_channels, out_channels))
          self.att = Param(torch.Tensor(num_relations, num_bases))
          self.root = Param(torch.Tensor(in_channels, out_channels))
  
          if bias:
              self.bias = Param(torch.Tensor(out_channels))
          else:
              self.register_parameter('bias', None)
  
          self.reset_parameters()
  
      def reset_parameters(self):
          size = self.num_bases * self.in_channels
          uniform(size, self.basis)
          uniform(size, self.att)
          uniform(size, self.root)
          uniform(size, self.bias)
  
      def forward(self, x, edge_index, edge_type, edge_norm=None):
          """"""
          if x is None:
              x = torch.arange(
                  edge_index.max().item() + 1,
                  dtype=torch.long,
                  device=edge_index.device)
  
          return self.propagate(
              'add', edge_index, x=x, edge_type=edge_type, edge_norm=edge_norm)
  
      def message(self, x_j, edge_type, edge_norm):
          w = torch.matmul(self.att, self.basis.view(self.num_bases, -1))
  
          if x_j.dtype == torch.long:
              w = w.view(-1, self.out_channels)
              index = edge_type * self.in_channels + x_j
              out = w[index]
              return out if edge_norm is None else out * edge_norm.view(-1, 1)
          else:
              w = w.view(self.num_relations, self.in_channels, self.out_channels)
              w = w[edge_type]
              out = torch.bmm(x_j.unsqueeze(1), w).squeeze(-2)
              return out if edge_norm is None else out * edge_norm.view(-1, 1)
  
      def update(self, aggr_out, x):
          if x.dtype == torch.long:
              out = aggr_out + self.root[x]
          else:
              out = aggr_out + torch.matmul(x, self.root)
  
          if self.bias is not None:
              out = out + self.bias
          return out
  
      def __repr__(self):
          return '{}({}, {}, num_relations={})'.format(
              self.__class__.__name__, self.in_channels, self.out_channels,
              self.num_relations)
  