class_docstr: Linearly warmup learning rate and then linearly decay.
  
  Args:
      optimizer (:class:`torch.optim.Optimizer`): Wrapped optimizer.
      total_steps (int): Number of total training steps.
      warmup_steps (int, optional): Number of warmup steps, defaults to 0
      last_epoch (int, optional): The index of last epoch, defaults to -1. When last_epoch=-1,
          the schedule is started from the beginning or When last_epoch=-1, sets initial lr as lr.
class_docstr_tok: ["Linearly","warmup","learning","rate","and","then","linearly","decay",".","Args",":","optimizer","(",":","class",":","`","torch.optim.Optimizer","`",")",":","Wrapped","optimizer",".","total_steps","(","int",")",":","Number","of","total","training","steps",".","warmup_steps","(","int",",","optional",")",":","Number","of","warmup","steps",",","defaults","to","0","last_epoch","(","int",",","optional",")",":","The","index","of","last","epoch",",","defaults","to","-1",".","When","last_epoch=-1",",","the","schedule","is","started","from","the","beginning","or","When","last_epoch=-1",",","sets","initial","lr","as","lr","."]
code: def __init__(
      self,
      optimizer,
      total_steps: int,
      warmup_steps: int = 0,
      last_epoch: int = -1,
      **kwargs,
  ):
      self.warmup_steps = warmup_steps
      self.total_steps = total_steps
      super().__init__(optimizer, last_epoch=last_epoch)
  
imports: from torch.optim.lr_scheduler import _LRScheduler
label_desc: Use image node_id 1 to create a new LinearWarmupLR object from inherited base classes: _LRScheduler with example: obj = LinearWarmupLR(optimizer, total_steps, warmup_steps, last_epoch)