code: def __init__(
      self,
      optimizer: Optimizer,
      monitor: str,
      *args: Any,
      **kwargs: Any,
  ) -> None:
      super().__init__(optimizer, *args, **kwargs)
      self.monitor = monitor
  
imports: import os
  import sys
  from functools import partial, update_wrapper
  from types import MethodType
  from typing import Any, Callable, Dict, Iterable, List, Optional, Set, Tuple, Type, Union
  import torch
  from lightning_utilities.core.imports import RequirementCache
  from lightning_utilities.core.rank_zero import _warn
  from torch.optim import Optimizer
  from typing_extensions import override
  import lightning.pytorch
  from lightning.fabric.utilities.cloud_io import get_filesystem
  from lightning.fabric.utilities.types import _TORCH_LRSCHEDULER
  from lightning.pytorch import Callback, LightningDataModule, LightningModule, Trainer, seed_everything
  from lightning.pytorch.utilities.exceptions import MisconfigurationException
  from lightning.pytorch.utilities.model_helpers import is_overridden
  from lightning.pytorch.utilities.rank_zero import rank_zero_warn
label_desc: Use image node_id 1 to create a new ReduceLROnPlateau object from inherited base classes: torch.optim.lr_scheduler with example: obj = ReduceLROnPlateau(optimizer, monitor)