class_docstr: Polynomial learning rate scheduler.
  
  Args:
      optimizer (:class:`torch.optim.Optimizer`): Wrapped optimizer.
      total_steps (int): Number of total training steps.
      end_lr (float, optional): Minimum learning rate, defaults to 0.0001.
      power (float, optional): The power of polynomial, defaults to 1.0.
      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 get_lr(self):
      return self._get_closed_form_lr()
  
file_path: colossalai/colossalai/nn/lr_scheduler/poly.py
imports: from torch.optim.lr_scheduler import _LRScheduler
  from .delayed import WarmupScheduler
label_desc: Use image node_id 2 for calling the PolynomialLR obj's underlying member method code with example usage: obj.get_lr() and returns: self