code: def __call__(self, midi: Union[bytes, os.PathLike, str]):
      if not isinstance(midi, bytes):
          with open(midi, "rb") as f:
              midi = f.read()
  
      ns = note_seq.midi_to_note_sequence(midi)
      ns_sus = note_seq.apply_sustain_control_changes(ns)
  
      for note in ns_sus.notes:
          if not note.is_drum:
              note.program = program_to_slakh_program(note.program)
  
      samples = np.zeros(int(ns_sus.total_time * SAMPLE_RATE))
  
      _, frame_times = audio_to_frames(samples, HOP_SIZE, FRAME_RATE)
      times, values = note_sequence_to_onsets_and_offsets_and_programs(
          ns_sus
      )
  
      events = encode_and_index_events(
          state=NoteEncodingState(),
          event_times=times,
          event_values=values,
          frame_times=frame_times,
          codec=self.codec,
          encode_event_fn=note_event_data_to_events,
          encoding_state_to_events_fn=note_encoding_state_to_events,
      )
  
      events = [
          note_representation_processor_chain(
              event, self.codec, self.note_representation_config
          )
          for event in events
      ]
      input_tokens = [
          self.tokenizer.encode(event["inputs"]) for event in events
      ]
  
      return input_tokens
  
imports: import dataclasses
  import math
  import os
  from typing import Any, Callable, List, Mapping, MutableMapping, Optional, Sequence, Tuple, Union
  import numpy
  import torch
  import torch.nn.functional
  from ...utils import is_note_seq_available
  from .pipeline_spectrogram_diffusion import TARGET_FEATURE_LENGTH
label_desc: Use image node_id 2 for calling the MidiProcessor obj's underlying member method code with example usage: obj.__call__(midi) and returns: input_tokens