class_docstr: Extracts a chord progression from a quantized NoteSequence.
class_docstr_tok: ["Extracts","a","chord","progression","from","a","quantized","NoteSequence","."]
code: def __init__(
      self, max_steps=512, all_transpositions=False, name=None
  ):
      super(ChordsExtractor, self).__init__(
          input_type=music_pb2.NoteSequence,
          output_type=chords_lib.ChordProgression,
          name=name,
      )
      self._max_steps = max_steps
      self._all_transpositions = all_transpositions
  
file_path: magenta/magenta/pipelines/chord_pipelines.py
imports: import copy
  from magenta.pipelines import pipeline
  from magenta.pipelines import statistics
  from note_seq import chord_symbols_lib
  from note_seq import ChordProgression
  from note_seq import chords_lib
  from note_seq import events_lib
  from note_seq import sequences_lib
  from note_seq.chords_lib import CoincidentChordsError
  from note_seq.protobuf import music_pb2
  import tensorflow.compat.v1
label_desc: Use image node_id 1 to create a new ChordsExtractor object from inherited base classes: pipeline with example: obj = ChordsExtractor(max_steps, all_transpositions, name)