I have a huge network (e.g. resnet)
I like to change every gradient for each function which is computed via  back-propagation.
Let say I like to add a constant C to it.
can someone help me to understand how via torch.autograd.Function and aaply i can do it?
I think my function should be like this?
class Change(torch.autograd.Function):
	
    @staticmethod
    def forward(ctx, input,C):
		
		ctx.save_for_backward(input)
        return input

    @staticmethod
    def backward(ctx, grad_output):
		
        input, = ctx.saved_tensors # why we should do input, not intpu?
        grad_input = grad_output.clone()
        
		
        return grad_input+C

I am not sure where should i put/apply it though