code: def step(self, metrics):
      if self._num_reduce_lr >= self.reduce_limit:
          # Already reduced the LR as many times as we will allow
          return
  
      return super().step(metrics)
  
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 2 for calling the ReduceLROnPLateauCappedDecreases obj's underlying member method code with example usage: obj.step(metrics) and returns: super