(self, x: torch.Tensor, t: torch.Tensor)
| 200 | self.conv = nn.Conv2d(n_channels, n_channels, (3, 3), (2, 2), (1, 1)) |
| 201 | |
| 202 | def forward(self, x: torch.Tensor, t: torch.Tensor): |
| 203 | # `t` is not used, but it's kept in the arguments because for the attention layer function signature |
| 204 | # to match with `ResidualBlock`. |
| 205 | _ = t |
| 206 | return self.conv(x) |
| 207 | |
| 208 | |
| 209 | class UNet(nn.Module): |
nothing calls this directly
no outgoing calls
no test coverage detected