code: def get_default_audio(
      audio_lst: List[TorchAudioTuple],
  ) -> TorchAudioTuple:
      sampling_rates = [audio[1] for audio in audio_lst]
      tensor_list = [audio[0] for audio in audio_lst]
  
      for i, tensor in enumerate(tensor_list):
          if tensor.shape[1] > DEFAULT_AUDIO_TENSOR_LENGTH:
              tensor_list[i] = tensor[:, :DEFAULT_AUDIO_TENSOR_LENGTH]
          else:
              pad_size = DEFAULT_AUDIO_TENSOR_LENGTH - tensor.shape[1]
              tensor_list[i] = F.pad(tensor, (0, pad_size))
      default_audio_tensor = torch.mean(torch.stack(tensor_list), dim=0)
      default_sampling_rate = calculate_mean(
          sum(sampling_rates), len(sampling_rates)
      )
  
      return default_audio_tensor, default_sampling_rate
  
imports: import functools
  import logging
  from io import BytesIO
  from typing import Any, List, Optional, Union
  import torch
  import torch.nn.functional
  import torchaudio
  from packaging import version
  from ludwig.api_annotations import DeveloperAPI
  from ludwig.constants import DEFAULT_AUDIO_TENSOR_LENGTH
  from ludwig.utils.types import TorchAudioTuple
label_desc: Use image node_id 2 for calling a global function with example usage: get_default_audio(audio_lst) and returns: default_audio_tensor,  default_sampling_rate