code: def predict(self, x):
      with torch.no_grad():
          b_out = self._bottom_model(x)
          self._agg_layer.predict(b_out)
  
imports: import torch
  import torch
  from fate.arch import Context
  from fate.ml.nn.model_zoo.agg_layer.agg_layer import AggLayerGuest, AggLayerHost
  from fate.ml.nn.model_zoo.agg_layer.fedpass.agg_layer import FedPassAggLayerGuest, FedPassAggLayerHost, get_model
  from fate.ml.nn.model_zoo.agg_layer.sshe.agg_layer import SSHEAggLayerHost, SSHEAggLayerGuest
  from typing import Any, Dict, List, Union, Callable, Literal
  from dataclasses import dataclass, fields
  from enum import Enum
  import logging
  from torch import device
label_desc: Use image node_id 9 for calling the HeteroNNModelHost obj's underlying member method code with example usage: obj.predict(x) without return types