code: def parse_preprocessed_example(example_proto):
      """Process an already preprocessed Example proto into input tensors."""
      features = {
          "spec": tf.VarLenFeature(dtype=tf.float32),
          "spectrogram_hash": tf.FixedLenFeature(
              shape=(), dtype=tf.int64
          ),
          "labels": tf.VarLenFeature(dtype=tf.float32),
          "label_weights": tf.VarLenFeature(dtype=tf.float32),
          "length": tf.FixedLenFeature(shape=(), dtype=tf.int64),
          "onsets": tf.VarLenFeature(dtype=tf.float32),
          "offsets": tf.VarLenFeature(dtype=tf.float32),
          "velocities": tf.VarLenFeature(dtype=tf.float32),
          "sequence_id": tf.FixedLenFeature(shape=(), dtype=tf.string),
          "note_sequence": tf.FixedLenFeature(
              shape=(), dtype=tf.string
          ),
      }
      record = tf.parse_single_example(example_proto, features)
      input_tensors = InputTensors(
          spec=tf.sparse.to_dense(record["spec"]),
          spectrogram_hash=record["spectrogram_hash"],
          labels=tf.sparse.to_dense(record["labels"]),
          label_weights=tf.sparse.to_dense(record["label_weights"]),
          length=record["length"],
          onsets=tf.sparse.to_dense(record["onsets"]),
          offsets=tf.sparse.to_dense(record["offsets"]),
          velocities=tf.sparse.to_dense(record["velocities"]),
          sequence_id=record["sequence_id"],
          note_sequence=record["note_sequence"],
      )
      return input_tensors
  
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 25 for calling a global function with example usage: parse_preprocessed_example(example_proto) and returns: input_tensors