code: def load_and_transform_audio_data(
      audio_paths,
      device,
      num_mel_bins=128,
      target_length=204,
      sample_rate=16000,
      clip_duration=2,
      clips_per_video=3,
      mean=-4.268,
      std=9.138,
  ):
      if audio_paths is None:
          return None
  
      audio_outputs = []
      clip_sampler = ConstantClipsPerVideoSampler(
          clip_duration=clip_duration, clips_per_video=clips_per_video
      )
  
      for audio_path in audio_paths:
          waveform, sr = torchaudio.load(audio_path)
          if sample_rate != sr:
              waveform = torchaudio.functional.resample(
                  waveform, orig_freq=sr, new_freq=sample_rate
              )
          all_clips_timepoints = get_clip_timepoints(
              clip_sampler, waveform.size(1) / sample_rate
          )
          all_clips = []
          for clip_timepoints in all_clips_timepoints:
              waveform_clip = waveform[
                  :,
                  int(clip_timepoints[0] * sample_rate) : int(
                      clip_timepoints[1] * sample_rate
                  ),
              ]
              waveform_melspec = waveform2melspec(
                  waveform_clip,
                  sample_rate,
                  num_mel_bins,
                  target_length,
              )
              all_clips.append(waveform_melspec)
  
          normalize = transforms.Normalize(mean=mean, std=std)
          all_clips = [normalize(ac).to(device) for ac in all_clips]
  
          all_clips = torch.stack(all_clips, dim=0)
          audio_outputs.append(all_clips)
  
      return torch.stack(audio_outputs, dim=0)
  
imports: import logging
  import math
  import torch
  import torch.nn
  import torchaudio
  from PIL import Image
  from pytorchvideo import transforms
  from pytorchvideo.data.clip_sampling import ConstantClipsPerVideoSampler
  from pytorchvideo.data.encoded_video import EncodedVideo
  from torchvision import transforms
  from torchvision.transforms._transforms_video import NormalizeVideo
  from imagebind.models.multimodal_preprocessors import SimpleTokenizer
label_desc: Use image node_id 5 for calling a global function with example usage: load_and_transform_audio_data(audio_paths, device, num_mel_bins, target_length, sample_rate, clip_duration, clips_per_video, mean, std) and returns: torch, None