code: def tflite_compat_mel_from_samples(samples, hparams):
      """EXPERIMENTAL: Log mel spec with ops that can be made TFLite compatible."""
      features = melspec_input.build_mel_calculation_graph(
          samples,
          hparams.sample_rate,
          window_length_seconds=2048 / hparams.sample_rate,
          hop_length_seconds=(
              hparams.spec_hop_length / hparams.sample_rate
          ),
          num_mel_bins=hparams.spec_n_bins,
          lower_edge_hz=hparams.spec_fmin,
          upper_edge_hz=hparams.sample_rate / 2.0,
          frame_width=1,
          frame_hop=1,
          tflite_compatible=False,
      )  # False here, but would be True on device.
      return tf.squeeze(features, 1)
  
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 9 for calling a global function with example usage: tflite_compat_mel_from_samples(samples, hparams) and returns: tf