code: def _crop_audio_label(
      waveform: Tensor,
      label: Optional[Tensor],
      length: Tensor,
      num_frames: int,
      rand_crop: bool,
  ) -> Tuple[Tensor, Optional[Tensor], Tensor]:
      """Collate the audio and label at the same time.
      Args:
          waveform (Tensor): The waveform Tensor with dimensions `(1, time)`.
          label (Tensor, optional): The label Tensor with dimensions `(1, seq)`.
          length (Tensor): The length Tensor with dimension `(1,)`.
          num_frames (int): The final length of the waveform.
          rand_crop (bool): if ``rand_crop`` is True, the starting index of the
              waveform and label is random if the length is longer than the minimum
              length in the mini-batch.
  
      Returns:
          (Tuple(Tensor, (Tensor, optional), Tensor)): Returns the Tensors for the waveform,
              label, and the waveform length.
      """
      kernel_size = 25
      stride = 20
      sample_rate = 16  # 16 per millisecond
      frame_offset = 0
      waveform = waveform[0]
      if waveform.size(0) > num_frames and rand_crop:
          diff = waveform.size(0) - num_frames
          frame_offset = torch.randint(diff, size=(1,))
      elif waveform.size(0) < num_frames:
          num_frames = waveform.size(0)
  
      if label is not None:
          label_offset = max(
              math.floor(
                  (frame_offset - kernel_size * sample_rate)
                  / (stride * sample_rate)
              )
              + 1,
              0,
          )
          num_label = (
              math.floor(
                  (num_frames - kernel_size * sample_rate)
                  / (stride * sample_rate)
              )
              + 1
          )
          label = label[label_offset : label_offset + num_label]
      waveform = waveform[frame_offset : frame_offset + num_frames]
      length = num_frames
  
      return waveform, label, length
  
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 for calling a global function with example usage: _crop_audio_label(waveform, label, length, num_frames, rand_crop) and returns: waveform,  label,  length