code: def tflite_compat_mel(wav_audio, hparams):
      """EXPERIMENTAL: Log mel spec with ops that can be made TFLite compatible."""
      samples, decoded_sample_rate = tf.audio.decode_wav(
          wav_audio, desired_channels=1
      )
      samples = tf.squeeze(samples, axis=1)
      # Ensure that we decoded the samples at the expected sample rate.
      with tf.control_dependencies(
          [tf.assert_equal(decoded_sample_rate, hparams.sample_rate)]
      ):
          return tflite_compat_mel_from_samples(samples, hparams)
  
imports: import collections
  import functools
  import io
  import re
  import wave
  import zlib
  import librosa
  from magenta.models.onsets_frames_transcription import audio_transform
  from magenta.models.onsets_frames_transcription import constants
  from magenta.models.onsets_frames_transcription import drum_mappings
  from magenta.models.onsets_frames_transcription import melspec_input
  from note_seq import audio_io
  from note_seq import sequences_lib
  from note_seq.protobuf import music_pb2
  import numpy
  import tensorflow.compat.v1
label_desc: Use image node_id 8 for calling a global function with example usage: tflite_compat_mel(wav_audio, hparams) and returns: tflite_compat_mel_from_samples