code: def load_wav_to_torch(full_path, sr=None):
      data, sr = librosa.load(full_path, sr=sr)
      data = np.clip(
          data, -1, 1
      )  # potentially out of [-1, 1] due to resampling
      data = data * 32768.0  # match values loaded by scipy
      return torch.FloatTensor(data.astype(np.float32)), sr
  
imports: import collections
  import io
  import json
  import librosa
  import numpy
  import soundfile
  import time
  import torch
  from scipy.io.wavfile import read
  from .text import SOS_TOK, EOS_TOK
label_desc: Use image node_id 2 for calling a global function with example usage: load_wav_to_torch(full_path, sr) and returns: torch,  sr