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 get_lr(self) -> List[float]:
      lr = (
          self._warmup(self._t)
          if self.warmup > 0 and self._t <= self.warmup
          else self.lr
      )
      if self._t == self.warmup:
          self.warmed_up = True
      return [lr for _ in self.optimizer.param_groups]
  
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 4 for calling the LearningRateSchedulerPlateauReduce obj's underlying member method code with example usage: obj.get_lr() and returns: unknown