code: def train_dataloader(self):
      dataset = HuBERTDataSet(self.dataset_path, self.dataset, "train")
      sampler = BucketizeBatchSampler(
          dataset.len_list,
          num_buckets=1000,
          max_token_count=self.seconds_per_batch * 16000,
          min_len=32000,
          max_len=250000,
          shuffle=True,
          seed=self.trainer.current_epoch,
      )
      sampler = DistributedBatchSampler(sampler, shuffle=True)
      sampler.set_epoch(self.current_epoch)
      dataloader = DataLoader(
          dataset,
          batch_sampler=sampler,
          collate_fn=CollateFnHubert(
              feature_type=self.feature_type, pad=False, rand_crop=True
          ),
          num_workers=10,
      )
      return dataloader
  
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 7 for calling the HuBERTPreTrainModule obj's underlying member method code with example usage: obj.train_dataloader() and returns: dataloader