Pytorch: a pit about the implementation of gradreverselayer

I've been working on one recently domain adaptation Project , Yes gradreverselayer：`class GradReverse(Function):

def forward(ctx, x, alpha, **kwargs:None):
    ctx.alpha = alpha

    return x.view_as(x)

def backward(ctx, grad_output):
    output = grad_output * -ctx.alpha
  
    return output, None

There seems to be no problem , But keep reporting the wrong ：

I haven't done it for a long time , So I made a stupid decision , I added one ：

src_domain_loss=torch.tensor(src_domain_loss, requires_grad=True)

As a result, the error disappeared , I happily used this silly * The code took two months , Results the experimental results have been unsatisfactory , Until today, check the training gradient , It is found that the gradient is always zero ！！！

Speechless , It must be the pot of this code , But without it, an error will be reported , I searched every corner of the Internet , No solution has been found , And paid people to consult （ cry ）.

result , A flash of light , I found something missing in the code of gradient flip layer ：

class GradReverse(Function):
    @staticmethod
    def forward(ctx, x, alpha, **kwargs:None):
        ctx.alpha = alpha 
        
        return x.view_as(x)
    @staticmethod
    def backward(ctx, grad_output):
        output = grad_output * -ctx.alpha
  
    return output, None`

Have you found it? ？@staticmethod！！！ I always thought this was a comment , Delete... Directly , Great speechless event , cause sb. to die of anger ！！！
In fact, this is declaring a static method ！
I hope you will take a warning .（ cry ）