code: def Read_input(data, batch_size):
      """
      Returns:
          audios_np: a numpy array of size (batch_size, max_length) in float
          sample_rate: a numpy array
          trans: an array includes the targeted transcriptions (batch_size,)
          masks_freq: a numpy array to mask out the padding features in frequency domain
      """
      audios = []
      lengths = []
  
      for i in range(batch_size):
          name, _ = data[0, i].split(".")
          if FLAGS.adv:
              sample_rate_np, audio_temp = wav.read(
                  "./" + str(name) + "_" + FLAGS.stage + ".wav"
              )
          else:
              sample_rate_np, audio_temp = wav.read(
                  "./" + str(name) + ".wav"
              )
  
          # read the wav form range from [-32767, 32768] or [-1, 1]
          if max(audio_temp) < 1:
              audio_np = audio_temp * 32768
  
          else:
              audio_np = audio_temp
          length = len(audio_np)
  
          audios.append(audio_np)
          lengths.append(length)
  
      max_length = max(lengths)
      lengths_freq = (np.array(lengths) // 2 + 1) // 240 * 3
      max_length_freq = max(lengths_freq)
      masks_freq = np.zeros([batch_size, max_length_freq, 80])
  
      # combine the audios into one array
      audios_np = np.zeros([batch_size, max_length])
  
      for i in range(batch_size):
          audios_np[i, : lengths[i]] = audios[i]
          masks_freq[i, : lengths_freq[i], :] = 1
  
      audios_np = audios_np.astype(float)
      if FLAGS.adv:
          trans = data[2, :]
      else:
          trans = data[1, :]
  
      return audios_np, sample_rate_np, trans, masks_freq
  
file_path: cleverhans/cleverhans_v3.1.0/examples/adversarial_asr/test_imperceptible_adv.py
imports: import tensorflow
  from lingvo import model_imports
  from lingvo import model_registry
  import numpy
  import scipy.io.wavfile
  import generate_masking_threshold
  from tool import create_features, create_inputs
  import time
  from lingvo.core import cluster_factory
  from absl import flags
  from absl import app
label_desc: Use image node_id 1 for calling a global function with example usage: Read_input(data, batch_size) and returns: audios_np,  sample_rate_np,  trans,  masks_freq