code: def __init__(
      self,
      *,
      model_name: str,
      feature_grad_mult: float,
      num_classes: int,
      dataset: str,
      dataset_path: str,
      feature_type: str,
      seconds_per_batch: float,
      learning_rate: float,
      betas: Tuple[float, float],
      eps: float,
      weight_decay: float,
      clip_norm: Optional[float],
      warmup_updates: int,
      max_updates: int,
  ):
      super().__init__()
  
      if model_name == "hubert_pretrain_base":
          self.model = torchaudio.models.hubert_pretrain_base(
              feature_grad_mult=feature_grad_mult,
              num_classes=num_classes,
          )
      elif model_name == "hubert_pretrain_large":
          self.model = torchaudio.models.hubert_pretrain_large()
      elif model_name == "hubert_pretrain_xlarge":
          self.model = torchaudio.models.hubert_pretrain_xlarge()
      else:
          raise ValueError(f"Unsupported model name: {model_name}")
      self.automatic_optimization = False
      self.scaler = torch.cuda.amp.GradScaler()
  
      self.loss = hubert_loss
      self.optimizer = torch.optim.AdamW(
          self.model.parameters(),
          lr=learning_rate,
          betas=betas,
          eps=eps,
          weight_decay=weight_decay,
      )
      self.clip_norm = clip_norm
      self.lr_scheduler = LinearDecayLRScheduler(
          self.optimizer, warmup_updates, max_updates
      )
      self.dataset = dataset
      self.dataset_path = dataset_path
      self.feature_type = feature_type
      self.seconds_per_batch = seconds_per_batch
      self.mask_stats = _reset_stats()
      self.unmask_stats = _reset_stats()
      self.nan_loss_count = 0.0
  
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 HuBERTPreTrainModule object from inherited base classes: LightningModule with example: obj = HuBERTPreTrainModule()