class_docstr: Linear learning rate scheduler with warmup, hold, and decay.
code: def get_lr(self):
      if self._step_count <= self.warmup_updates:
          return [
              base_lr
              * (
                  self.init_lr_scale
                  + self._step_count
                  / self.warmup_updates
                  * (1 - self.init_lr_scale)
              )
              for base_lr in self.base_lrs
          ]
      elif (
          self.warmup_updates
          < self._step_count
          <= (self.warmup_updates + self.hold_updates)
      ):
          return list(self.base_lrs)
      elif self._step_count <= (
          self.warmup_updates + self.hold_updates + self.decay_updates
      ):
          return [
              base_lr
              * math.exp(
                  math.log(self.final_lr_scale)
                  * (
                      self._step_count
                      - self.warmup_updates
                      - self.hold_updates
                  )
                  / self.decay_updates
              )
              for base_lr in self.base_lrs
          ]
      else:
          return [
              base_lr * self.final_lr_scale for base_lr in self.base_lrs
          ]
  
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 2 for calling the TriStageLRScheduler obj's underlying member method code with example usage: obj.get_lr() and returns: unknown, list, unknown, unknown