class_docstr: Linear learning rate scheduler with warmup, hold, and decay.
code: def __init__(
      self,
      optimizer: Optimizer,
      warmup_updates: int,
      hold_updates: int,
      decay_updates: int,
      init_lr_scale: float = 0.01,
      final_lr_scale: float = 0.05,
      last_epoch: int = -1,
      verbose: bool = False,
  ):
      self.warmup_updates = warmup_updates
      self.hold_updates = hold_updates
      self.decay_updates = decay_updates
      self.init_lr_scale = init_lr_scale
      self.final_lr_scale = final_lr_scale
  
      super().__init__(
          optimizer, last_epoch=last_epoch, verbose=verbose
      )
  
imports: import math
  from typing import Optional, Tuple
  import torch
  import torch.nn.functional
  import torchaudio
  import torchaudio.models.wav2vec2.components
  from dataset import _get_lengths_librilightlimited, _get_lengths_librispeech, BucketizeBatchSampler, CollateFnHubert, CollateFnLibriLightLimited, DistributedBatchSampler, HuBERTDataSet
  from lightning.pytorch import LightningModule
  from loss import hubert_loss
  from torch import Tensor
  from torch.optim.optimizer import Optimizer
  from torch.utils.data import DataLoader
label_desc: Use image node_id 1 to create a new TriStageLRScheduler object from inherited base classes: torch.optim.lr_scheduler with example: obj = TriStageLRScheduler(optimizer, warmup_updates, hold_updates, decay_updates, init_lr_scale, final_lr_scale, last_epoch, verbose)