class_docstr: Learning rate scheduler that implements `new_evaluation_result` and accordingly adaptively adjust the  learning
  rate.
class_docstr_tok: ["Learning","rate","scheduler","that","implements","`","new_evaluation_result","`","and","accordingly","adaptively","adjust","the","learning","rate","."]
docstr: Returns true if the parameters should be reset to the ones with the best validation score.
  
  :param has_improved: Whether the model improved on held-out validation data.
  :return: True if parameters should be reset to the ones with best validation score.
docstr_tok: ["Returns","true","if","the","parameters","should","be","reset","to","the","ones","with","the","best","validation","score",".",":","param","has_improved",":","Whether","the","model","improved","on","held-out","validation","data",".",":","return",":","True","if","parameters","should","be","reset","to","the","ones","with","best","validation","score","."]
code: def new_evaluation_result(self, has_improved: bool) -> bool:
      """
      Returns true if the parameters should be reset to the ones with the best validation score.
  
      :param has_improved: Whether the model improved on held-out validation data.
      :return: True if parameters should be reset to the ones with best validation score.
      """
      return False
  
imports: import logging
  from math import sqrt
  from typing import Any, Dict, List, Optional, Tuple, Type
  import torch
  import sockeye.constants
  from sockeye.utils import check_condition
label_desc: Use image node_id 1 for calling the AdaptiveLearningRateScheduler obj's underlying member method code with example usage: obj.new_evaluation_result(has_improved) and returns: False