class_docstr: Base class for any torch model.
  
  :param device: Device the networks should be located on (cpu or cuda)
class_docstr_tok: ["Base","class","for","any","torch","model",".",":","param","device",":","Device","the","networks","should","be","located","on","(","cpu","or","cuda",")"]
docstr: Set state dict of all encapsulated networks.
  :param state_dict: The torch state dictionary.
docstr_tok: ["Set","state","dict","of","all","encapsulated","networks",".",":","param","state_dict",":","The","torch","state","dictionary","."]
code: def load_state_dict(self, state_dict: Dict) -> None:
      """Set state dict of all encapsulated networks.
      :param state_dict: The torch state dictionary.
      """
  
file_path: maze/maze/core/agent/torch_model.py
imports: from abc import abstractmethod
  from typing import List, Dict
  import torch
label_desc: Use image node_id 9 for calling the TorchModel obj's underlying member method code with example usage: obj.load_state_dict(state_dict) without return types