code: def get_lr(self):
      if self.last_epoch == -1:
          return [
              self.warmup_lr_init for _ in self.optimizer.param_groups
          ]
      if self.last_epoch < self.warmup_steps:
          return self.get_warmup_lr()
      else:
          alpha = pow(
              1
              - float(self.last_epoch - self.warmup_steps)
              / float(self.max_steps - self.warmup_steps),
              self.power,
          )
          return [
              self.base_lr * alpha for _ in self.optimizer.param_groups
          ]
  
file_path: insightface/reconstruction/jmlr/lr_scheduler.py
imports: import torch
  from torch.optim.lr_scheduler import _LRScheduler
label_desc: Use image node_id 3 for calling the PolyScheduler obj's underlying member method code with example usage: obj.get_lr() and returns: unknown, self, unknown