code: def pad_or_trim_tensor(tensor, target_len=480000):
      curr_len = len(tensor)
      if curr_len == target_len:
          return tensor
      elif curr_len < target_len:
          return torch.cat((tensor, torch.zeros(target_len - curr_len)))
      else:
          return tensor[:target_len]
  
imports: import unittest
  import torch
  import tqdm
  import torchaudio
  import pathlib
  import jiwer
  import os
  import numpy
  from whisper.normalizers import EnglishTextNormalizer
  from examples.whisper import init_whisper, transcribe_waveform
label_desc: Use image node_id 2 for calling a global function with example usage: pad_or_trim_tensor(tensor, target_len) and returns: tensor, torch, tensor