class_docstr: Classifier based on Transformer.
class_docstr_tok: ["Classifier","based","on","Transformer","."]
docstr: Load the state dict into model.
  
  This is easily overridable to facilitate transfer of state dicts.
docstr_tok: ["Load","the","state","dict","into","model",".","This","is","easily","overridable","to","facilitate","transfer","of","state","dicts","."]
code: def load_state_dict(self, state_dict):
      """
      Load the state dict into model.
  
      This is easily overridable to facilitate transfer of state dicts.
      """
      if self.is_finetune and self.opt["load_from_pretrained_ranker"]:
          self.base_model.load_state_dict(state_dict, strict=False)
      else:
          self.model.load_state_dict(state_dict)
  
imports: from typing import Optional
  from parlai.core.params import ParlaiParser
  from parlai.core.opt import Opt
  from parlai.core.agents import Agent
  from parlai.utils.torch import padded_3d
  from parlai.core.torch_classifier_agent import TorchClassifierAgent
  from parlai.core.torch_ranker_agent import TorchRankerAgent
  from parlai.core.torch_generator_agent import TorchGeneratorAgent
  from parlai.utils.misc import recursive_getattr, warn_once
  from parlai.utils.logging import logging
  from parlai.utils.fsdp import should_use_fsdp
  from .modules import TransformerMemNetModel, TransformerGeneratorModel, TransformerLinearWrapper
  import torch
label_desc: Use image node_id 6 for calling the TransformerClassifierAgent obj's underlying member method code with example usage: obj.load_state_dict(state_dict) without return types