I am trying to rewrite inference part so as  to also return hidden layer activations like embeddings.
is this correct implementation, any potential issues with this ?
class FeatureExtration(torch.nn.Module):
    def __init__(self, pretrained_model):
        super().__init__()
        self.__dict__ = pretrained_model.__dict__.copy()
        
    def forward(self,x):
        
        #... copy original forward ...
     
        return proba, embedings, some_other_layer


model2 = FeatureExtration(pretrained_model)