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: Return state dict composed of state dicts of all encapsulated networks.
docstr_tok: ["Return","state","dict","composed","of","state","dicts","of","all","encapsulated","networks","."]
code: def state_dict(self) -> Dict:
      """Return state dict composed of state dicts of all encapsulated networks."""
  
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 8 for calling the TorchModel obj's underlying member method code with example usage: obj.state_dict() without return types