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: Load state from file.
  This is required for resuming training or model fine tuning with different parameters.
  
  :param file_path: Path from where to load the state.
docstr_tok: ["Load","state","from","file",".","This","is","required","for","resuming","training","or","model","fine","tuning","with","different","parameters",".",":","param","file_path",":","Path","from","where","to","load","the","state","."]
code: def load_state(self, file_path: Union[str, BinaryIO]) -> None:
      """Load state from file.
      This is required for resuming training or model fine tuning with different parameters.
  
      :param file_path: Path from where to load the state.
      """
  
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 2 for calling the Trainer obj's underlying member method code with example usage: obj.load_state(file_path) without return types