class_docstr: Wrapper for most torch scheduler functions.
  
  Parameters
  ---------
  scheduler_fn : torch.optim.lr_scheduler
      Torch scheduling class
  scheduler_params : dict
      Dictionnary containing all parameters for the scheduler_fn
  is_batch_level : bool (default = False)
      If set to False : lr updates will happen at every epoch
      If set to True : lr updates happen at every batch
      Set this to True for OneCycleLR for example
class_docstr_tok: ["Wrapper","for","most","torch","scheduler","functions",".","Parameters","--","--","--","--","-","scheduler_fn",":","torch.optim.lr_scheduler","Torch","scheduling","class","scheduler_params",":","dict","Dictionnary","containing","all","parameters","for","the","scheduler_fn","is_batch_level",":","bool","(","default","=","False",")","If","set","to","False",":","lr","updates","will","happen","at","every","epoch","If","set","to","True",":","lr","updates","happen","at","every","batch","Set","this","to","True","for","OneCycleLR","for","example"]
code: def __post_init__(
      self,
  ):
      self.is_metric_related = hasattr(self.scheduler_fn, "is_better")
      self.scheduler = self.scheduler_fn(
          self.optimizer, **self.scheduler_params
      )
      super().__init__()
  
imports: import time
  import datetime
  import copy
  import numpy
  from dataclasses import dataclass, field
  from typing import List, Any
  import warnings
label_desc: Use image node_id 1 for calling the LRSchedulerCallback obj's underlying member method code with example usage: obj.__post_init__() without return types