class Conv2dQuant(nn.Module):
    def __init__(self):
        super(Conv2dQuant, self).__init__()
        self.conv_weight = 5
        self.conv_bias = 5
        self.scale = 3
    def forward(self, x):
        x = torch.round(x / self.scale) * self.scale
        x = x * self.conv_weight + self.conv_bias
        return x

If i define a module above, I know how to forward, However, When it comes to backward, I do not know the process of round function, and I wonder how to autograd this functions x = torch.round(x / self.scale) * self.scale