class FastRGCNConv(in_channels: Union[int, Tuple[int, int]], out_channels: int, num_relations: int, num_bases: Optional[int] = None, num_blocks: Optional[int] = None, aggr: str = 'mean', root_weight: bool = True, is_sorted: bool = False, bias: bool = True, **kwargs)[source]

Bases: RGCNConv

See RGCNConv.

forward(x: Union[Tensor, None, Tuple[Optional[Tensor], Tensor]], edge_index: Union[Tensor, SparseTensor], edge_type: Optional[Tensor] = None)[source]

Runs the forward pass of the module.

  • x (torch.Tensor or tuple, optional) – The input node features. Can be either a [num_nodes, in_channels] node feature matrix, or an optional one-dimensional node index tensor (in which case input features are treated as trainable node embeddings). Furthermore, x can be of type tuple denoting source and destination node features.

  • edge_index (torch.Tensor or SparseTensor) – The edge indices.

  • edge_type (torch.Tensor, optional) – The one-dimensional relation type/index for each edge in edge_index. Should be only None in case edge_index is of type torch_sparse.SparseTensor. (default: None)


Resets all learnable parameters of the module.