code: def _decode_spectrogram(
      spectrogram,
      inflate=True,
      zero_centering=True,
      dataset_mean=0,
      dataset_stddev=1,
  ):
      """Decodes audio spectrogram.
  
      Args:
        spectrogram: input mel spectrogram
        inflate: if True, adds a channel dimension
        zero_centering: if True, zero centers the spectrogram
        dataset_mean: mean over the dataset.
        dataset_stddev: standard deviation over the dataset.
  
      Returns:
        spectrogram: decoded spectrogram.
  
      """
      if inflate:
          spectrogram = tf.expand_dims(spectrogram, -1)
          spectrogram = tf.tile(spectrogram, [1, 1, 3])
  
      # normalize spectrogram by mean and std deviation
      spectrogram = spectrogram - dataset_mean
      spectrogram = spectrogram / dataset_stddev
      if not zero_centering:
          spectrogram = spectrogram + 1.0
          spectrogram = spectrogram / 2
      return spectrogram
  
imports: from absl import logging
  from dmvr import builders
  from dmvr import processors
  from lingvo.core import spectrum_augmenter
  import tensorflow
label_desc: Use image node_id 2 for calling a global function with example usage: _decode_spectrogram(spectrogram, inflate, zero_centering, dataset_mean, dataset_stddev) and returns: spectrogram