code: def trim_for_encoding(wav_data, sample_length, hop_length=512):
      """Make sure audio is a even multiple of hop_size.
  
      Args:
        wav_data: 1-D or 2-D array of floats.
        sample_length: Max length of audio data.
        hop_length: Pooling size of WaveNet autoencoder.
  
      Returns:
        wav_data: Trimmed array.
        sample_length: Length of trimmed array.
      """
      if wav_data.ndim == 1:
          # Max sample length is the data length
          if sample_length > wav_data.size:
              sample_length = wav_data.size
          # Multiple of hop_length
          sample_length = (sample_length // hop_length) * hop_length
          # Trim
          wav_data = wav_data[:sample_length]
      # Assume all examples are the same length
      elif wav_data.ndim == 2:
          # Max sample length is the data length
          if sample_length > wav_data[0].size:
              sample_length = wav_data[0].size
          # Multiple of hop_length
          sample_length = (sample_length // hop_length) * hop_length
          # Trim
          wav_data = wav_data[:, :sample_length]
  
      return wav_data, sample_length
  
imports: import importlib
  import os
  import librosa
  import numpy
  import tensorflow.compat.v1
  import tf_slim
label_desc: Use image node_id 7 for calling a global function with example usage: trim_for_encoding(wav_data, sample_length, hop_length) and returns: wav_data,  sample_length