# torch_geometric.nn.conv.GatedGraphConv

class GatedGraphConv(out_channels: int, num_layers: int, aggr: str = 'add', bias: bool = True, **kwargs)[source]

Bases: MessagePassing

The gated graph convolution operator from the “Gated Graph Sequence Neural Networks” paper.

\begin{align}\begin{aligned}\mathbf{h}_i^{(0)} &= \mathbf{x}_i \, \Vert \, \mathbf{0}\\\mathbf{m}_i^{(l+1)} &= \sum_{j \in \mathcal{N}(i)} e_{j,i} \cdot \mathbf{\Theta} \cdot \mathbf{h}_j^{(l)}\\\mathbf{h}_i^{(l+1)} &= \textrm{GRU} (\mathbf{m}_i^{(l+1)}, \mathbf{h}_i^{(l)})\end{aligned}\end{align}

up to representation $$\mathbf{h}_i^{(L)}$$. The number of input channels of $$\mathbf{x}_i$$ needs to be less or equal than out_channels. $$e_{j,i}$$ denotes the edge weight from source node j to target node i (default: 1)

Parameters:
Shapes:
• input: node features $$(|\mathcal{V}|, F_{in})$$, edge indices $$(2, |\mathcal{E}|)$$

• output: node features $$(|\mathcal{V}|, F_{out})$$

forward(x: Tensor, edge_index: Union[Tensor, SparseTensor], edge_weight: = None) [source]

Runs the forward pass of the module.

reset_parameters()[source]

Resets all learnable parameters of the module.