code: def _load_from_state_dict(
      self,
      state_dict,
      prefix,
      local_metadata,
      strict,
      missing_keys,
      unexpected_keys,
      error_msgs,
  ):
      if "_agg_layer.wa" in state_dict:
          self.register_buffer(
              "wa",
              torch.randn(*state_dict["_agg_layer.wa"].size()).type(
                  torch.LongTensor
              ),
          )
      if "_agg_layer.wb" in state_dict:
          self.register_buffer(
              "wb",
              torch.randn(*state_dict["_agg_layer.wb"].size()).type(
                  torch.LongTensor
              ),
          )
  
      super(SSHEAggLayerGuest, self)._load_from_state_dict(
          state_dict,
          prefix,
          local_metadata,
          strict,
          missing_keys,
          unexpected_keys,
          error_msgs,
      )
      self._set_wa_wb()
  
imports: from typing import Union, Literal, Tuple
  import torch
  from fate.arch import Context
  from fate.ml.nn.model_zoo.agg_layer.agg_layer import _AggLayerBase
  from fate.arch.protocol.mpc.nn.sshe.nn_layer import SSHENeuralNetworkAggregatorLayer, SSHENeuralNetworkOptimizerSGD
label_desc: Use image node_id 4 for calling the SSHEAggLayerGuest obj's underlying member method code with example usage: obj._load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) without return types