code: def _configure_optimizers(
      optim_conf: Union[Dict[str, Any], List, Optimizer, Tuple]
  ) -> Tuple[List, List, Optional[str]]:
      optimizers, lr_schedulers = [], []
      monitor = None
  
      # single output, single optimizer
      if isinstance(optim_conf, Optimizable):
          optimizers = [optim_conf]
      # two lists, optimizer + lr schedulers
      elif (
          isinstance(optim_conf, (list, tuple))
          and len(optim_conf) == 2
          and isinstance(optim_conf[0], list)
          and all(isinstance(opt, Optimizable) for opt in optim_conf[0])
      ):
          opt, sch = optim_conf
          optimizers = opt
          lr_schedulers = sch if isinstance(sch, list) else [sch]
      # single dictionary
      elif isinstance(optim_conf, dict):
          _validate_optim_conf(optim_conf)
          optimizers = [optim_conf["optimizer"]]
          monitor = optim_conf.get("monitor", None)
          lr_schedulers = (
              [optim_conf["lr_scheduler"]]
              if "lr_scheduler" in optim_conf
              else []
          )
      # multiple dictionaries
      elif isinstance(optim_conf, (list, tuple)) and all(
          isinstance(d, dict) for d in optim_conf
      ):
          for opt_dict in optim_conf:
              _validate_optim_conf(opt_dict)
          optimizers = [
              opt_dict["optimizer"] for opt_dict in optim_conf
          ]
          scheduler_dict = (
              lambda scheduler: dict(scheduler)
              if isinstance(scheduler, dict)
              else {"scheduler": scheduler}
          )
          lr_schedulers = [
              scheduler_dict(opt_dict["lr_scheduler"])
              for opt_dict in optim_conf
              if "lr_scheduler" in opt_dict
          ]
      # single list or tuple, multiple optimizer
      elif isinstance(optim_conf, (list, tuple)) and all(
          isinstance(opt, Optimizable) for opt in optim_conf
      ):
          optimizers = list(optim_conf)
      # unknown configuration
      else:
          raise MisconfigurationException(
              "Unknown configuration for model optimizers."
              " Output from `model.configure_optimizers()` should be one of:\n"
              " * `Optimizer`\n"
              " * [`Optimizer`]\n"
              " * ([`Optimizer`], [`LRScheduler`])\n"
              ' * {"optimizer": `Optimizer`, (optional) "lr_scheduler": `LRScheduler`}\n'
          )
      return optimizers, lr_schedulers, monitor
  
imports: from contextlib import contextmanager
  from dataclasses import fields
  from typing import Any, Callable, Dict, Generator, List, Optional, Tuple, Union, overload
  from weakref import proxy
  import torch
  from torch import optim
  from torch.optim import Optimizer
  from typing_extensions import override
  import lightning.pytorch
  from lightning.fabric.utilities.types import Optimizable, ReduceLROnPlateau, _Stateful
  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
  from lightning.pytorch.utilities.signature_utils import is_param_in_hook_signature
  from lightning.pytorch.utilities.types import LRSchedulerConfig, LRSchedulerTypeTuple
label_desc: Use image node_id 3 for calling a global function with example usage: _configure_optimizers(optim_conf) and returns: optimizers,  lr_schedulers,  monitor