class_docstr: Polynomial decay LR scheduler.
  
  Args:
      optimizer (Optimizer): Torch optimizer.
      warmup_steps (int): Number of warmup steps.
      total_steps (int): Total number of steps.
      end_lr (float): Final learning rate to achieve over total number of steps.
      zero_lr_warmup_steps (int): Number of steps with a learning rate of value 0.
      power (float): Decay exponent.
class_docstr_tok: ["Polynomial","decay","LR","scheduler",".","Args",":","optimizer","(","Optimizer",")",":","Torch","optimizer",".","warmup_steps","(","int",")",":","Number","of","warmup","steps",".","total_steps","(","int",")",":","Total","number","of","steps",".","end_lr","(","float",")",":","Final","learning","rate","to","achieve","over","total","number","of","steps",".","zero_lr_warmup_steps","(","int",")",":","Number","of","steps","with","a","learning","rate","of","value","0.","power","(","float",")",":","Decay","exponent","."]
code: def __init__(
      self,
      optimizer: Optimizer,
      warmup_steps: int,
      total_steps: int,
      end_lr: float = 0.0,
      zero_lr_warmup_steps: int = 0,
      power: float = 1.0,
  ):
      self.warmup_steps = warmup_steps
      self.total_steps = total_steps
      self.end_lr = end_lr
      self.zero_lr_warmup_steps = zero_lr_warmup_steps
      self.power = power
      super().__init__(optimizer)
  
imports: from torch.optim import Optimizer
  from torch.optim.lr_scheduler import _LRScheduler
label_desc: Use image node_id 1 to create a new PolynomialDecayLRScheduler object from inherited base classes: _LRScheduler with example: obj = PolynomialDecayLRScheduler(optimizer, warmup_steps, total_steps, end_lr, zero_lr_warmup_steps, power)