code: def load_wav_to_torch(full_path):
      sampling_rate, data = read(full_path)
      if data.dtype == np.int32:
          norm_fix = 2**31
      elif data.dtype == np.int16:
          norm_fix = 2**15
      elif data.dtype == np.float16 or data.dtype == np.float32:
          norm_fix = 1.0
      else:
          raise NotImplementedError(
              f"Provided data dtype not supported: {data.dtype}"
          )
      return (
          torch.FloatTensor(data.astype(np.float32)) / norm_fix,
          sampling_rate,
      )
  
imports: import os
  from glob import glob
  from typing import Dict, List
  import librosa
  import numpy
  import torch
  import torchaudio
  from scipy.io.wavfile import read
  from TTS.utils.audio.torch_transforms import TorchSTFT
label_desc: Use image node_id 1 for calling a global function with example usage: load_wav_to_torch(full_path) and returns: unknown,  sampling_rate