class_docstr: Decays the learning rate of each parameter group by gamma every
  step_size epochs. Notice that such decay can happen simultaneously with
  other changes to the learning rate from outside this scheduler. 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.
      step_size (int, optional): Period of learning rate decay, defaults to 1.
      gamma (float, optional): Multiplicative factor of learning rate decay, defaults to 0.1.
      last_epoch (int, optional): The index of last epoch, defaults to -1.
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code: def __init__(
      self,
      optimizer,
      total_steps,
      step_size: int = 1,
      gamma: float = 0.1,
      last_epoch: int = -1,
  ) -> None:
      super().__init__(
          optimizer, step_size, gamma=gamma, 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 StepLR object from inherited base classes: _StepLR with example: obj = StepLR(optimizer, total_steps, step_size, gamma, last_epoch)