from torch.autograd import Function


class GradientReversal(Function):
    @staticmethod
    def forward(ctx, x, alpha):
        ctx.save_for_backward(x, alpha)
        return x

    @staticmethod
    def backward(ctx, grad_output):
        grad_input = None
        _, alpha = ctx.saved_tensors
        if ctx.needs_input_grad[0]:
            grad_input = -alpha * grad_output
        return grad_input, None


revgrad = GradientReversal.apply
