code: def get_lr(self):
      if (
          self.last_epoch <= self.warmup_iterations
          and self.use_warmup is True
      ):
          alpha = float(self.last_epoch) / float(self.warmup_iterations)
          lr_ratio = self.warmup_factor * (1.0 - alpha) + alpha
  
          return [base_lr * lr_ratio for base_lr in self.base_lrs]
      else:
          return [
              base_lr
              * self.lr_ratio
              ** bisect_right(self.lr_steps, self.last_epoch)
              for base_lr in self.base_lrs
          ]
  
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 2 for calling the MultiStepScheduler obj's underlying member method code with example usage: obj.get_lr() and returns: unknown, unknown