class_docstr: DeepLab learning rate policy
code: def __init__(self, optimizer, max_iter, power=0.9, last_step=-1):
      lambda_func = lambda s: (1 - s / (max_iter + 1)) ** power
      composite_func = (
          lambda s: lambda_func(max_iter)
          if s > max_iter
          else lambda_func(s)
      )
      super(PolyLR, self).__init__(
          optimizer, lambda s: composite_func(s), last_step
      )
  
imports: import sys
  from torch.optim import lr_scheduler
  from omegaconf import OmegaConf
  from omegaconf.dictconfig import DictConfig
  import logging
  from torch.optim.lr_scheduler import LambdaLR
  from torch_points3d.utils.enums import SchedulerUpdateOn
label_desc: Use image node_id 1 to create a new PolyLR object from inherited base classes: LambdaStepLR with example: obj = PolyLR(optimizer, max_iter, power, last_step)