class_docstr: Learning Rate Scheduler Plugin.
  
  This plugin manages learning rate scheduling inside of a strategy using the
  PyTorch scheduler passed to the constructor. The step() method of the
  scheduler is called after each training epoch or iteration.
  
  Metric-based schedulers (like ReduceLROnPlateau) are supported as well.
class_docstr_tok: ["Learning","Rate","Scheduler","Plugin",".","This","plugin","manages","learning","rate","scheduling","inside","of","a","strategy","using","the","PyTorch","scheduler","passed","to","the","constructor",".","The","step","(",")","method","of","the","scheduler","is","called","after","each","training","epoch","or","iteration",".","Metric-based","schedulers","(","like","ReduceLROnPlateau",")","are","supported","as","well","."]
code: def _check_first_epoch_or_experience(self, strategy):
      if strategy.clock.train_exp_counter > 0 and self.first_exp_only:
          return False
  
      if strategy.clock.train_exp_epochs > 0 and self.first_epoch_only:
          return False
  
      return True
  
imports: import warnings
  from typing import TYPE_CHECKING
  from typing_extensions import Literal
  from avalanche.evaluation.metrics import Mean
  from avalanche.training.plugins import SupervisedPlugin
  import inspect
label_desc: Use image node_id 12 for calling the LRSchedulerPlugin obj's underlying member method code with example usage: obj._check_first_epoch_or_experience(strategy) and returns: True, False, False