class_docstr: Callback which executes a LR scheduler. It is executed after every
  batch iteration.
class_docstr_tok: ["Callback","which","executes","a","LR","scheduler",".","It","is","executed","after","every","batch","iteration","."]
docstr: Attr:
      config(mmf_typings.DictConfig): Config for the callback
      trainer(Type[BaseTrainer]): Trainer object
docstr_tok: ["Attr",":","config","(","mmf_typings.DictConfig",")",":","Config","for","the","callback","trainer","(","Type","[","BaseTrainer","]",")",":","Trainer","object"]
code: def __init__(self, config, trainer):
      """
      Attr:
          config(mmf_typings.DictConfig): Config for the callback
          trainer(Type[BaseTrainer]): Trainer object
      """
      super().__init__(config, trainer)
  
      self._scheduler = None
      if self.training_config.lr_scheduler is True:
          self._scheduler = build_scheduler(
              trainer.optimizer, self.config
          )
  
imports: from mmf.trainers.callbacks.base import Callback
  from mmf.utils.build import build_scheduler
label_desc: Use image node_id 1 to create a new LRSchedulerCallback object from inherited base classes: Callback with example: obj = LRSchedulerCallback(config, trainer)