class_docstr: Interface for trainers.
  :param algorithm_config: Algorithm configuration including all parameter expected in .train().
  :param model: Model to train.
class_docstr_tok: ["Interface","for","trainers",".",":","param","algorithm_config",":","Algorithm","configuration","including","all","parameter","expected","in",".train","(",")",".",":","param","model",":","Model","to","train","."]
docstr: Returns the state dict composed of all encapsulated trainer components.
  
  :return: The trainer's state dict.
docstr_tok: ["Returns","the","state","dict","composed","of","all","encapsulated","trainer","components",".",":","return",":","The","trainer","'s","state","dict","."]
code: def state_dict(self) -> Dict:
      """Returns the state dict composed of all encapsulated trainer components.
  
      :return: The trainer's state dict.
      """
  
file_path: maze/maze/train/trainers/common/trainer.py
imports: from abc import ABC, abstractmethod
  from typing import Union, Optional, TypeVar, Dict
  from maze.core.agent.torch_model import TorchModel
  from maze.train.trainers.common.config_classes import AlgorithmConfig
  from typing.io import BinaryIO
label_desc: Use image node_id 3 for calling the Trainer obj's underlying member method code with example usage: obj.state_dict() without return types