class_docstr: Iterates over all batches in the dataloader (one epoch) that the user returns in their
  :meth:`~lightning.pytorch.core.LightningModule.train_dataloader` method.
  
  Its main responsibilities are calling the ``*_epoch_{start,end}`` hooks, accumulating outputs if the user request
  them in one of these hooks, and running validation at the requested interval.
  
  The validation is carried out by yet another loop,
  :class:`~lightning.pytorch.loops._EvaluationLoop`.
  
  In the ``run()`` method, the training epoch loop could in theory simply call the
  ``LightningModule.training_step`` already and perform the optimization.
  However, Lightning has built-in support for automatic optimization with multiple optimizers.
  For this reason there are actually two more loops nested under
  :class:`~lightning.pytorch.loops._TrainingEpochLoop`.
  
  Args:
      min_steps: The minimum number of steps (batches) to process
      max_steps: The maximum number of steps (batches) to process
class_docstr_tok: ["Iterates","over","all","batches","in","the","dataloader","(","one","epoch",")","that","the","user","returns","in","their",":","meth",":","`","~lightning.pytorch.core.LightningModule.train_dataloader","`","method",".","Its","main","responsibilities","are","calling","the","``","*","_epoch_","{","start",",","end","}","``","hooks",",","accumulating","outputs","if","the","user","request","them","in","one","of","these","hooks",",","and","running","validation","at","the","requested","interval",".","The","validation","is","carried","out","by","yet","another","loop",",",":","class",":","`","~lightning.pytorch.loops._EvaluationLoop","`",".","In","the","``","run","(",")","``","method",",","the","training","epoch","loop","could","in","theory","simply","call","the","``","LightningModule.training_step","``","already","and","perform","the","optimization",".","However",",","Lightning","has","built-in","support","for","automatic","optimization","with","multiple","optimizers",".","For","this","reason","there","are","actually","two","more","loops","nested","under",":","class",":","`","~lightning.pytorch.loops._TrainingEpochLoop","`",".","Args",":","min_steps",":","The","minimum","number","of","steps","(","batches",")","to","process","max_steps",":","The","maximum","number","of","steps","(","batches",")","to","process"]
docstr: Updates the lr schedulers based on the given interval.
docstr_tok: ["Updates","the","lr","schedulers","based","on","the","given","interval","."]
code: def update_lr_schedulers(
      self, interval: str, update_plateau_schedulers: bool
  ) -> None:
      """Updates the lr schedulers based on the given interval."""
      if interval == "step" and self._should_accumulate():
          return
      self._update_learning_rates(
          interval=interval,
          update_plateau_schedulers=update_plateau_schedulers,
      )
  
imports: import math
  from collections import OrderedDict
  from typing import Any, Dict, Optional, Union
  from typing_extensions import override
  import lightning.pytorch
  from lightning.pytorch import loops
  from lightning.pytorch.loops.fetchers import _DataFetcher, _DataLoaderIterDataFetcher
  from lightning.pytorch.loops.optimization import _AutomaticOptimization, _ManualOptimization
  from lightning.pytorch.loops.optimization.automatic import _OUTPUTS_TYPE
  from lightning.pytorch.loops.optimization.manual import _OUTPUTS_TYPE
  from lightning.pytorch.loops.progress import _BatchProgress, _SchedulerProgress
  from lightning.pytorch.loops.utilities import _is_max_limit_reached
  from lightning.pytorch.trainer import call
  from lightning.pytorch.trainer.connectors.logger_connector.result import _ResultCollection
  from lightning.pytorch.trainer.states import RunningStage, TrainerFn
  from lightning.pytorch.utilities.exceptions import MisconfigurationException, SIGTERMException
  from lightning.pytorch.utilities.rank_zero import WarningCache, rank_zero_warn
  from lightning.pytorch.utilities.signature_utils import is_param_in_hook_signature
label_desc: Use image node_id 21 for calling the _TrainingEpochLoop obj's underlying member method code with example usage: obj.update_lr_schedulers(interval, update_plateau_schedulers) without return types