class_docstr: Lower the learning rate as soon as the validation score plateaus.
  
  :param optimizer: Optimizer.
  :param base_lr: Base learning rate.
  :param reduce_factor: Factor to reduce learning rate with.
  :param reduce_num_not_improved: Number of checkpoints with no improvement
                                  after which learning rate is reduced.
  :param warmup: Number of initial updates during which the learning rate
                 linearly increases.
class_docstr_tok: ["Lower","the","learning","rate","as","soon","as","the","validation","score","plateaus",".",":","param","optimizer",":","Optimizer",".",":","param","base_lr",":","Base","learning","rate",".",":","param","reduce_factor",":","Factor","to","reduce","learning","rate","with",".",":","param","reduce_num_not_improved",":","Number","of","checkpoints","with","no","improvement","after","which","learning","rate","is","reduced",".",":","param","warmup",":","Number","of","initial","updates","during","which","the","learning","rate","linearly","increases","."]
code: def __repr__(self) -> str:
      return (
          "LearningRateSchedulerPlateauReduce(reduce_factor=%.2f, reduce_num_not_improved=%d, num_not_improved=%d,"
          " base_lr=%s, lr=%s, warmup=%d, warmed_up=%s)"
          % (
              self.reduce_factor,
              self.reduce_num_not_improved,
              self.num_not_improved,
              self.base_lr,
              self.lr,
              self.warmup,
              self.warmed_up,
          )
      )
  
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 2 for calling the LearningRateSchedulerPlateauReduce obj's underlying member method code with example usage: obj.__repr__() and returns: unknown