code: def read_binary_audio(bin_data, tar_sr=None):
      """
      read binary audio (`bytes` or `uint8` `numpy.ndarray`) to `float32`
      `numpy.ndarray`
  
      RETURNS:
          data (np.ndarray) : audio of shape (n,) or (2, n)
          tar_sr (int) : sample rate
      """
      data, ori_sr = sf.read(io.BytesIO(bin_data), dtype="float32")
      data = data.T
      if (tar_sr is not None) and (ori_sr != tar_sr):
          data = librosa.resample(data, ori_sr, tar_sr)
      else:
          tar_sr = ori_sr
      data = np.clip(data, -1, 1)
      data = data * 32768.0
      return torch.FloatTensor(data.astype(np.float32)), tar_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 3 for calling a global function with example usage: read_binary_audio(bin_data, tar_sr) and returns: torch,  tar_sr