code: def forward(self, x: t.Tensor = None) -> t.Tensor:
      if self.training:
          if self._has_ctx:
              self._host_input_caches = []
              host_x = self._get_fw_from_host()
              for h in range(len(host_x)):
                  host_input_cache = t.from_numpy(
                      host_x[h]
                  ).requires_grad_(True)
                  self._host_input_caches.append(host_input_cache)
          else:
              self._host_input_caches = None
  
          final_out = self._forward(x, self._host_input_caches)
          return final_out
  
      else:
          return 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 6 for calling the AggLayerGuest obj's underlying member method code with example usage: obj.forward(x) and returns: final_out, self