code: def byteformer_audio_collate_fn(
      batch: List[Mapping[str, Tensor]], opts: argparse.Namespace
  ) -> Mapping[str, Tensor]:
      """
      Apply augmentations specific to ByteFormer audio training, then perform
      padded collation.
  
      See `ByteFormer <https://arxiv.org/abs/2212.10553>`_ for more information
      on this modeling approach.
  
      Args:
          batch: The batch of data.
          opts: The global arguments.
  
      Returns:
          The modified batch.
      """
      batch = apply_torchaudio_save(batch, opts)
      batch = apply_padding(batch, opts, "audio")
      # Remove the metadata, which is no longer needed.
      for elem in batch:
          del elem["metadata"]
      batch = collate_functions.pytorch_default_collate_fn(batch, opts)
      return batch
  
file_path: ml-cvnets/data/collate_fns/byteformer_collate_functions.py
imports: import argparse
  from typing import Dict, List, Mapping, Optional, Union
  import torch
  from torch import Tensor
  from torch.nn import functional
  from data.collate_fns import COLLATE_FN_REGISTRY, collate_functions
  from data.transforms import audio_bytes, image_bytes
label_desc: Use image node_id 9 for calling a global function with example usage: byteformer_audio_collate_fn(batch, opts) and returns: batch