docstr: Load the state dict into model.
  
  This copies the classifier specific params to init_model and then calls the
  load_state_dict method of TorchAgent.
docstr_tok: ["Load","the","state","dict","into","model",".","This","copies","the","classifier","specific","params","to","init_model","and","then","calls","the","load_state_dict","method","of","TorchAgent","."]
code: def load_state_dict(self, state_dict):
      """
      Load the state dict into model.
  
      This copies the classifier specific params to init_model and then calls the
      load_state_dict method of TorchAgent.
      """
  
      # the random_model_states: model states to be overriden by init model states
      random_model_states = self.model_api.state_dict()
      if "copy_decoder" in self.init_classifier_layers:
          new_states_to_init = [
              k for k in random_model_states if k not in state_dict
          ]
          n_decoder_layers = self.model_api.seq2seq_decoder.n_layers
          key_prefix_mapping = {
              "classifier_ffn.": f"seq2seq_decoder.layers.{n_decoder_layers-1}.ffn.",
              "classifier_norm3.": f"seq2seq_decoder.layers.{n_decoder_layers-1}.norm3.",
              "classifier_layer.": f"seq2seq_decoder.layers.{n_decoder_layers-1}.",
              "classifier_layer1.": f"seq2seq_decoder.layers.{n_decoder_layers-2}.",
              "classifier_layer2.": f"seq2seq_decoder.layers.{n_decoder_layers-1}.",
          }
          for (
              model_prefix,
              decoder_prefix,
          ) in key_prefix_mapping.items():
              states_to_update = [
                  k
                  for k in new_states_to_init
                  if k.startswith(model_prefix)
              ]
              for new_key in states_to_update:
                  decoder_key = (
                      decoder_prefix + new_key[len(model_prefix) :]
                  )
                  state_dict[new_key] = (
                      random_model_states[new_key].cpu()
                      * self.init_epsilon
                      + state_dict[decoder_key]
                  )
  
      # classifier head states or all classifier_layers states if not copying from decoder
      for k, v in random_model_states.items():
          if k not in state_dict:
              state_dict[k] = v
      super().load_state_dict(state_dict)
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from typing import Optional, Any, Dict, Tuple, Union, List
  from abc import abstractmethod
  from parlai.agents.transformer.modules import TransformerDecoderLayer
  from parlai.agents.fid.fid import FidAgent, FidModel
  from parlai.core.message import Message
  from parlai.core.metrics import AverageMetric
  from parlai.core.opt import Opt
  from parlai.core.params import ParlaiParser
  from parlai.core.torch_generator_agent import PPLMetric
  from parlai.core.torch_agent import Batch, DictionaryAgent
  import parlai.utils.logging
  from projects.blenderbot2.agents.blenderbot2 import BlenderBot2FidAgent, BlenderBot2FidModel, T5BlenderBot2FidModel
  from parlai.agents.fid.fid import WizIntGoldDocRetrieverFiDAgent
  import parlai.agents.transformer.modules.ffn
  from parlai.agents.transformer.modules import LAYER_NORM_EPS
label_desc: Use image node_id 3 for calling the DirectorFidAgent obj's underlying member method code with example usage: obj.load_state_dict(state_dict) without return types