Thanks for your quick answer.
The reason (maybe wrong) why I’m saying it’s because of the batch size, is because when I set at 1, it works. If it’s greater, it doesn’t.
Here is the input, just before applying the first conv :
data: Batch(batch=[8552], edge_attr=[8552, 1], edge_index=[2, 8552], x=[8552, 1], y=[16])
data.x: tensor([[0.6605],
        [0.2918],
        [5.6345],
        ...,
        [0.1195],
        [0.3049],
        [0.3102]], device='cuda:0', dtype=torch.float64)
data.edge_index: tensor([[   0,    1,    2,  ..., 8549, 8550, 8551],
        [   0,    1,    2,  ..., 8549, 8550, 8551]], device='cuda:0')
data.edge_attr: tensor([[0.],
        [0.],
        [0.],
        ...,
        [0.],
        [0.],
        [0.]], device='cuda:0')
data.x.shape: torch.Size([8552, 1])
data.edge_index.shape: torch.Size([2, 8552])
data.edge_attr.shape: torch.Size([8552, 1])
message
data: Batch(batch=[8552], edge_attr=[8552, 1], edge_index=[2, 8552], x=[8552, 1], y=[16])
data.x: tensor([[0.6605],
        [0.2918],
        [5.6345],
        ...,
        [0.1195],
        [0.3049],
        [0.3102]], device='cuda:0', dtype=torch.float64)
data.edge_index: tensor([[   0,    1,    2,  ..., 8549, 8550, 8551],
        [   0,    1,    2,  ..., 8549, 8550, 8551]], device='cuda:0')
data.edge_attr: tensor([[0.],
        [0.],
        [0.],
        ...,
        [0.],
        [0.],
        [0.]], device='cuda:0')
data.x.shape: torch.Size([8552, 1])
data.edge_index.shape: torch.Size([2, 8552])
data.edge_attr.shape: torch.Size([8552, 1])

Hope you have what you are looking for.