text: import torch
  
  from .scheduler import Scheduler
  
  
  class StepLRScheduler(Scheduler):
      """ """
  
      def __init__(
          self,
          optimizer: torch.optim.Optimizer,
          decay_t: int,
          decay_rate: float = 1.0,
          warmup_t=0,
          warmup_lr_init=0,
          t_in_epochs=True,
          initialize=True,
      ) -> None:
          super().__init__(optimizer, param_group_field="lr", initialize=initialize)
  
          self.decay_t = decay_t
          self.decay_rate = decay_rate
          self.warmup_t = warmup_t
          self.warmup_lr_init = warmup_lr_init
          self.t_in_epochs = t_in_epochs
          if self.warmup_t:
              self.warmup_steps = [
                  (v - warmup_lr_init) / self.warmup_t for v in self.base_values
              ]
              super().update_groups(self.warmup_lr_init)
          else:
              self.warmup_steps = [1 for _ in self.base_values]
  
      def _get_lr(self, t):
          if t < self.warmup_t:
              lrs = [self.warmup_lr_init + t * s for s in self.warmup_steps]
          else:
              lrs = [
                  v * (self.decay_rate ** (t // self.decay_t)) for v in self.base_values
              ]
          return lrs
  
      def get_epoch_values(self, epoch: int):
          if self.t_in_epochs:
              return self._get_lr(epoch)
          else:
              return None
  
      def get_update_values(self, num_updates: int):
          if not self.t_in_epochs:
              return self._get_lr(num_updates)
          else:
              return None
  