class_docstr: Default class for the LR scheduling callback. Proposes default comportment for the scheduler
  loading and saving as well as for the epoch end handling.
class_docstr_tok: ["Default","class","for","the","LR","scheduling","callback",".","Proposes","default","comportment","for","the","scheduler","loading","and","saving","as","well","as","for","the","epoch","end","handling","."]
code: def __init__(self, torch_lr_scheduler, *args, **kwargs):
      super().__init__()
      if len(args) > 0 and isinstance(args[0], Optimizer):
          raise ValueError(
              "In the LR scheduler callbacks, the optimizer is "
              "automatically passed to the PyTorch's LR scheduler. "
              "You must remove it from the arguments."
          )
      self.args = args
      self.kwargs = kwargs
      self.scheduler = None
      self.state_to_load = None
      self.torch_lr_scheduler = torch_lr_scheduler
  
file_path: poutyne/poutyne/framework/callbacks/lr_scheduler.py
imports: import inspect
  import sys
  from typing import BinaryIO, Dict
  import torch.optim.lr_scheduler
  from torch.optim import Optimizer
  from poutyne.framework.callbacks.callbacks import Callback
label_desc: Use image node_id 1 to create a new _PyTorchLRSchedulerWrapper object from inherited base classes: Callback with example: obj = _PyTorchLRSchedulerWrapper(torch_lr_scheduler)