def forward(self, x):
    u = x.mean((- 1), keepdim=True)
    s = (x - u).pow(2).mean((- 1), keepdim=True)
    x = ((x - u) / torch.sqrt((s + self.variance_epsilon)))
    return ((self.weight * x) + self.bias)
def forward(self, x):
    u = x.mean((- 1), keepdim=True)
    s = (x - u).pow(2).mean((- 1), keepdim=True)
    x = ((x - u) / torch.sqrt((s + self.variance_epsilon)))
    return ((self.weight * x) + self.bias)