class_docstr: Multiply the learning rate of each parameter group by the factor given
  in the specified function. When last_epoch=-1, sets initial lr as lr.
  
  Args:
      optimizer (:class:`torch.optim.Optimizer`): Wrapped optimizer.
      total_steps (int): Number of total training steps.
      lr_lambda (Union[``function``, ``list[function]``]): A function which computes a multiplicative
          factor given an integer parameter epoch, or a list of such functions,
          one for each group in optimizer.param_groups, defaults to None.
      last_epoch (int, optional): The index of last epoch, defaults to -1.
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code: def __init__(
      self, optimizer, total_steps, lr_lambda=None, last_epoch: int = -1
  ) -> None:
      super().__init__(optimizer, lr_lambda, last_epoch=last_epoch)
  
file_path: ColossalAI/colossalai/nn/lr_scheduler/torch.py
imports: from torch.optim.lr_scheduler import ExponentialLR
  from torch.optim.lr_scheduler import LambdaLR
  from torch.optim.lr_scheduler import MultiplicativeLR
  from torch.optim.lr_scheduler import StepLR
label_desc: Use image node_id 1 to create a new MultiplicativeLR object from inherited base classes: _MultiplicativeLR with example: obj = MultiplicativeLR(optimizer, total_steps, lr_lambda, last_epoch)