class_docstr: Learning rate scheduler that performs linear warmup and exponential annealing.
  
  Args:
      optimizer (torch.optim.Optimizer): optimizer to use.
      warmup_steps (int): number of scheduler steps for which to warm up learning rate.
      force_anneal_step (int): scheduler step at which annealing of learning rate begins.
      anneal_factor (float): factor to scale base learning rate by at each annealing step.
      last_epoch (int, optional): The index of last epoch. (Default: -1)
      verbose (bool, optional): If ``True``, prints a message to stdout for
          each update. (Default: ``False``)
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code: def __init__(
      self,
      optimizer: torch.optim.Optimizer,
      warmup_steps: int,
      force_anneal_step: int,
      anneal_factor: float,
      last_epoch=-1,
      verbose=False,
  ):
      self.warmup_steps = warmup_steps
      self.force_anneal_step = force_anneal_step
      self.anneal_factor = anneal_factor
      super().__init__(
          optimizer, last_epoch=last_epoch, verbose=verbose
      )
  
imports: import logging
  import math
  from collections import namedtuple
  from typing import List, Tuple
  import sentencepiece
  import torch
  import torchaudio
  from pytorch_lightning import LightningModule
  from torchaudio.models import Hypothesis, RNNTBeamSearch
  from torchaudio.prototype.models import conformer_rnnt_base
label_desc: Use image node_id 1 to create a new WarmupLR object from inherited base classes: torch.optim.lr_scheduler with example: obj = WarmupLR(optimizer, warmup_steps, force_anneal_step, anneal_factor, last_epoch, verbose)