class_docstr: The collate class for Wav2Vec2 pre-training and fine-tuning.
  Args:
      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.
code: def __init__(
      self,
      pad: bool = False,
      rand_crop: bool = True,
  ) -> None:
      self.pad = pad
      self.rand_crop = rand_crop
  
imports: import math
  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 ..lightning_modules import Batch
label_desc: Use image node_id 1 to create a new CollateFnWav2Vec2 object  with example: obj = CollateFnWav2Vec2(pad, rand_crop)