code: def __init__(self, optimizer, *args, **kwargs):
      self.use_warmup = kwargs["use_warmup"]
      self.lr_steps = kwargs["lr_steps"]
      self.lr_ratio = kwargs["lr_ratio"]
      self.warmup_iterations = (
          kwargs["warmup_iterations"] if self.use_warmup else 0
      )
      self.warmup_factor = kwargs["warmup_factor"]
      assert self.warmup_iterations < self.lr_steps[0]
      super().__init__(optimizer)
  
file_path: mmf/mmf/modules/schedulers.py
imports: from bisect import bisect_right
  from mmf.common.registry import registry
  from torch.optim.lr_scheduler import LambdaLR
label_desc: Use image node_id 1 to create a new MultiStepScheduler object from inherited base classes: PythiaScheduler with example: obj = MultiStepScheduler(optimizer)