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.
docstr: Args:
      batch (List[Tuple(Tensor, Tensor, int)]):
          The list of tuples that contains the waveforms, labels, and audio lengths.
  
  Returns:
      Dictionary
          "input": Tuple of waveforms and lengths.
              waveforms Tensor with dimensions `(batch, time)`.
              lengths Tensor with dimension `(batch,)`.
          "label": None
docstr_tok: ["Args",":","batch","(","List","[","Tuple","(","Tensor",",","Tensor",",","int",")","]",")",":","The","list","of","tuples","that","contains","the","waveforms",",","labels",",","and","audio","lengths",".","Returns",":","Dictionary","``","input","''",":","Tuple","of","waveforms","and","lengths",".","waveforms","Tensor","with","dimensions","`","(","batch",",","time",")","`",".","lengths","Tensor","with","dimension","`","(","batch",",",")","`",".","``","label","''",":","None"]
code: def __call__(self, batch: List[Tuple[Tensor, Tensor, int]]) -> Dict:
      """
      Args:
          batch (List[Tuple(Tensor, Tensor, int)]):
              The list of tuples that contains the waveforms, labels, and audio lengths.
  
      Returns:
          Dictionary
              "input": Tuple of waveforms and lengths.
                  waveforms Tensor with dimensions `(batch, time)`.
                  lengths Tensor with dimension `(batch,)`.
              "label": None
      """
      if self.pad:
          num_frames = max([sample[0].shape[1] for sample in batch])
      else:
          num_frames = min([sample[0].shape[1] for sample in batch])
      waveforms, lengths = [], []
      for sample in batch:
          waveform, length = sample
          waveform, _, length = _crop_audio_label(
              waveform, None, length, num_frames, self.rand_crop
          )
          waveforms.append(waveform)
          lengths.append(length)
      # make sure the shapes are the same if not apply zero-padding
      if not self.pad:
          assert all(
              [
                  waveform.shape[0] == waveforms[0].shape[0]
                  for waveform in waveforms
              ]
          ), "The dimensions of the waveforms should be identical in the same batch."
      waveforms = torch.nn.utils.rnn.pad_sequence(
          waveforms, batch_first=True
      )
      lengths = torch.tensor(lengths)
      batch = Batch((waveforms, lengths), (None,))
      return batch
  
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 2 for calling the CollateFnWav2Vec2 obj's underlying member method code with example usage: obj.__call__(batch) and returns: batch