code: def apply_lr(self):
      if self._num_reduce_lr == 0:
          return
  
      for i, param_group in enumerate(self.optimizer.param_groups):
          old_lr = float(param_group["lr"])
          new_lr = max(
              old_lr * math.pow(self.factor, self._num_reduce_lr),
              self.min_lrs[i],
          )
          if old_lr - new_lr > self.eps:
              param_group["lr"] = new_lr
              if self.verbose:
                  logger.info(
                      f"From ReduceLROnPLateauCappedDecreases, reducing learning rate to {new_lr}"
                  )
  
imports: import logging
  import math
  from typing import Any, Callable, Dict
  from torch.optim import Optimizer
  from torch.optim.lr_scheduler import CosineAnnealingWarmRestarts, LambdaLR, ReduceLROnPlateau, SequentialLR
  from ludwig.constants import MINIMIZE, TRAINING, VALIDATION
  from ludwig.modules.metric_registry import get_metric_objective
  from ludwig.schema.lr_scheduler import LRSchedulerConfig
  from ludwig.utils.metric_utils import TrainerMetric
  from ludwig.utils.trainer_utils import ProgressTracker
label_desc: Use image node_id 5 for calling the ReduceLROnPLateauCappedDecreases obj's underlying member method code with example usage: obj.apply_lr() without return types