code: def forward(self, x: t.Tensor) -> None:
      if self.training:
          assert isinstance(x, t.Tensor), "x should be a tensor"
          if self._model is not None:
              self._input_cache = (
                  t.from_numpy(x.cpu().detach().numpy())
                  .to(self.device)
                  .requires_grad_(True)
              )
              out_ = self._model(self._input_cache)
              self._out_cache = out_
          else:
              out_ = x
          self._send_fw_to_guest(out_.detach().cpu().numpy())
      else:
          self.predict(x)
  
imports: import torch
  from typing import List, Literal
  import torch
  from fate.arch import Context
  from torch.nn.modules.module import T
label_desc: Use image node_id 5 for calling the AggLayerHost obj's underlying member method code with example usage: obj.forward(x) without return types