I built up an ensemble model:
class ModelA(nn.Modue):
      def __init__(self):
           super(ModelA, self).__init__()
           self.encoder = SentenceTransformer("./paraphrase-distilroberta-base-v1")
      def forward(self, text):
          return torch.tensor(self.encoder.encode(text)) 

class ModelB(nn.Module):
      def __init__(self, option):
         self.option =option
         self.hidden2output = nn.Linear(self.option.hidden_dim, self.option.output_dim)
      def forward(self, emb, head_id, actual):
         output = self.hidden2output(emb)
         ....

class MyEnsemble(nn.Module):
       def __init__(self, modelA, modelB):
           super(MyEnsemble, self).__init__()
           self.modelA = ModelA
           self.modelB = ModelB
       def forward(self, a, b, c):
          emb = self.modelA(a)
         output = self.modelB(emb, b, c)
modelA = ModelA()
modelB = ModelB(option)
model = MyEnsemble(modelA, modelB)
for batch in all_batch:
     model(batch[0], batch[1], batch[2])

The error  I got:

TypeError:  _init_() takes 1 positional argument but 2 were given

If I just test modelA or modelB, there is no error
for batch in all_batch:
    modelA(batch[0])