class_docstr: Best model selection strategy.
  
  :param dump_file: Specifies the file path to dump the policy state for the best reward.
  :param model: The model to be dumped.
  :param dump_interval: Update count interval between regularly dumping the model parameters.
  :param verbose: If true status messages get printed to the command line.
class_docstr_tok: ["Best","model","selection","strategy",".",":","param","dump_file",":","Specifies","the","file","path","to","dump","the","policy","state","for","the","best","reward",".",":","param","model",":","The","model","to","be","dumped",".",":","param","dump_interval",":","Update","count","interval","between","regularly","dumping","the","model","parameters",".",":","param","verbose",":","If","true","status","messages","get","printed","to","the","command","line","."]
docstr: Reset model to overall best state dict.
          
docstr_tok: ["Reset","model","to","overall","best","state","dict","."]
code: def reset_to_best(self) -> None:
      """Reset model to overall best state dict."""
      self.model.load_state_dict(self.best_state_dict)
  
file_path: maze/maze/train/trainers/common/model_selection/best_model_selection.py
imports: import os
  from typing import Optional
  import numpy
  import torch
  from maze.core.agent.torch_model import TorchModel
  from maze.core.annotations import override
  from maze.train.trainers.common.model_selection.model_selection_base import ModelSelectionBase
  from maze.utils.bcolors import BColors
label_desc: Use image node_id 3 for calling the BestModelSelection obj's underlying member method code with example usage: obj.reset_to_best() without return types