Hi @ptrblck, If in the wrapper the models, instead of instantiating the models, I would like to load them from a saved model. How can I do?  The idea is each of the model will be trained separately and saved in the memory using ‘torch.save(model.state_dict(), m_path)’ this command. Now I want to load the saved models and remove the classifier layer. Because, I want to concat the embeddings (hidden layer) for further use. A code snipped of the idea would look like following,
class wrapper(nn.Module):
    def __init__(self, input_size, hidden_size, number_of_vars):
        super().__init__()
        self.module_list = nn.ModuleList()
        for idx in range(number_of_vars):
            model = var()
            model.load_state_dict(torch.load(m_path))
            model.eval()
            # remove last layer somehow don't know how? 
            self.module_list.append(model)

The forward would be as usual!  Thanks in advance