class_docstr: Multi-step learning rate scheduler with optional linear warm-up strategy
code: def add_arguments(
      cls, parser: argparse.ArgumentParser
  ) -> argparse.ArgumentParser:
      group = parser.add_argument_group(
          title="{} arguments".format(cls.__name__),
          description="{} arguments".format(cls.__name__),
      )
  
      group.add_argument(
          "--scheduler.multi-step.lr",
          type=float,
          default=0.1,
          help="LR value",
      )
      group.add_argument(
          "--scheduler.multi-step.gamma",
          type=float,
          default=None,
          help="Decay LR value by this factor",
      )
      group.add_argument(
          "--scheduler.multi-step.milestones",
          type=int,
          nargs="+",
          default=None,
          help="Decay LR value at these epoch",
      )
      return parser
  
file_path: ml-cvnets/optim/scheduler/multi_step.py
imports: import argparse
  from optim.scheduler import SCHEDULER_REGISTRY
  from optim.scheduler.base_scheduler import BaseLRScheduler
label_desc: Use image node_id 2 for calling the MultiStepLRScheduler obj's underlying member method code with example usage: obj.add_arguments(cls, parser) and returns: parser