class_docstr: Extracts polyphonic tracks from a quantized NoteSequence.
class_docstr_tok: ["Extracts","polyphonic","tracks","from","a","quantized","NoteSequence","."]
code: def __init__(
      self,
      min_events,
      max_events,
      num_velocity_bins,
      note_performance,
      name=None,
  ):
      super(PerformanceExtractor, self).__init__(
          input_type=music_pb2.NoteSequence,
          output_type=BasePerformance,
          name=name,
      )
      self._min_events = min_events
      self._max_events = max_events
      self._num_velocity_bins = num_velocity_bins
      self._note_performance = note_performance
  
imports: from magenta.pipelines import dag_pipeline
  from magenta.pipelines import note_sequence_pipelines
  from magenta.pipelines import pipeline
  from magenta.pipelines import pipelines_common
  from magenta.pipelines import statistics
  from note_seq import MetricPerformance
  from note_seq import Performance
  from note_seq import sequences_lib
  from note_seq.performance_lib import BasePerformance
  from note_seq.performance_lib import NotePerformance
  from note_seq.performance_lib import TooManyDurationStepsError
  from note_seq.performance_lib import TooManyTimeShiftStepsError
  from note_seq.protobuf import music_pb2
  import tensorflow.compat.v1
label_desc: Use image node_id 1 to create a new PerformanceExtractor object from inherited base classes: pipeline with example: obj = PerformanceExtractor(min_events, max_events, num_velocity_bins, note_performance, name)