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 get_lr(self):
      return [
          self._get_sched_lr(base_lr, self.last_epoch)
          for base_lr in self.base_lrs
      ]
  
imports: from torch.optim import Optimizer
  from torch.optim.lr_scheduler import _LRScheduler
label_desc: Use image node_id 3 for calling the PolynomialDecayLRScheduler obj's underlying member method code with example usage: obj.get_lr() and returns: unknown