class_docstr: The collate class for HuBERT pre-training and fine-tuning.
  Args:
      feature_type (str): The type of features for KMeans clustering.
          Options: [``mfcc``, ``hubert``].
      pad (bool): If ``True``, the waveforms and labels will be padded to the
          max length in the mini-batch. If ``pad`` is False, the waveforms
          and labels will be cropped to the minimum length in the mini-batch.
          (Default: False)
      rand_crop (bool): if ``True``, the starting index of the waveform
          and label is random if the length is longer than the minimum
          length in the mini-batch.
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code: def __init__(
      self,
      feature_type: str,
      pad: bool = False,
      rand_crop: bool = True,
  ) -> None:
      self.feature_type = feature_type
      self.pad = pad
      self.rand_crop = rand_crop
  
imports: import math
  import os
  import sys
  from pathlib import Path
  from typing import Dict, Iterator, List, Optional, Tuple, Union
  import numpy
  import torch
  import torch.distributed
  import torchaudio
  from torch import Tensor
  from torch.utils.data import BatchSampler, Dataset, DistributedSampler
  from utils import _get_label2id
label_desc: Use image node_id 1 to create a new CollateFnHubert object  with example: obj = CollateFnHubert(feature_type, pad, rand_crop)