s = 8
out_channels = unet.conv_in.out_channels
unet.register_to_config(in_channels=in_channels)

with torch.no_grad():
    new_conv_in = nn.Conv2d(
        in_channels, out_channels, unet.conv_in.kernel_size, unet.conv_in.stride, unet.conv_in.padding
    )
    new_conv_in.weight.zero_()
    new_conv_in.weight[:, :4, :, :].copy_(unet.conv_in.weight)
    unet.conv_in = new_conv_in These UNet parameters are updated by the optimizer:   Copied optim