class_docstr: optimizer (Optimizer): Wrapped optimizer.
  first_cycle_steps (int): First cycle step size.
  cycle_mult(float): Cycle steps magnification. Default: -1.
  max_lr(float): First cycle's max learning rate. Default: 0.1.
  min_lr(float): Min learning rate. Default: 0.001.
  warmup_steps(int): Linear warmup step size. Default: 0.
  gamma(float): Decrease rate of max learning rate by cycle. Default: 1.
  last_epoch (int): The index of last epoch. Default: -1.
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code: def init_lr(self):
      self.base_lrs = []
      for param_group in self.optimizer.param_groups:
          param_group["lr"] = self.min_lr
          self.base_lrs.append(self.min_lr)
  
file_path: espnet/espnet2/schedulers/cosine_anneal_warmup_restart.py
imports: import math
  import torch
  from torch.optim.lr_scheduler import _LRScheduler
  from espnet2.schedulers.abs_scheduler import AbsBatchStepScheduler
label_desc: Use image node_id 2 for calling the CosineAnnealingWarmupRestarts obj's underlying member method code with example usage: obj.init_lr() without return types