code: def __init__(
      self,
      optimizer: Optimizer,
      mode: str,
      reduce_limit: int,
      factor: float,
      patience: int,
  ):
      super().__init__(
          optimizer, mode=mode, factor=factor, patience=patience
      )
      self.reduce_limit = reduce_limit
      self._num_reduce_lr = 0
  
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 1 to create a new ReduceLROnPLateauCappedDecreases object from inherited base classes: ReduceLROnPlateau with example: obj = ReduceLROnPLateauCappedDecreases(optimizer, mode, reduce_limit, factor, patience)