class_docstr: Extracts drum tracks from a quantized NoteSequence.
class_docstr_tok: ["Extracts","drum","tracks","from","a","quantized","NoteSequence","."]
code: def __init__(
      self, min_bars=7, max_steps=512, gap_bars=1.0, name=None
  ):
      super(DrumsExtractor, self).__init__(
          input_type=music_pb2.NoteSequence,
          output_type=drums_lib.DrumTrack,
          name=name,
      )
      self._min_bars = min_bars
      self._max_steps = max_steps
      self._gap_bars = gap_bars
  
file_path: magenta/magenta/pipelines/drum_pipelines.py
imports: from magenta.pipelines import pipeline
  from magenta.pipelines import statistics
  from note_seq import drums_lib
  from note_seq import DrumTrack
  from note_seq import events_lib
  from note_seq import sequences_lib
  from note_seq.protobuf import music_pb2
  import tensorflow.compat.v1
label_desc: Use image node_id 1 to create a new DrumsExtractor object from inherited base classes: pipeline with example: obj = DrumsExtractor(min_bars, max_steps, gap_bars, name)