class_docstr: Extracts polyphonic tracks from a quantized NoteSequence.
class_docstr_tok: ["Extracts","polyphonic","tracks","from","a","quantized","NoteSequence","."]
code: def transform(self, input_object):
      quantized_sequence = input_object
      performances, stats = extract_performances(
          quantized_sequence,
          min_events_discard=self._min_events,
          max_events_truncate=self._max_events,
          num_velocity_bins=self._num_velocity_bins,
          note_performance=self._note_performance,
      )
      self._set_stats(stats)
      return performances
  
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 2 for calling the PerformanceExtractor obj's underlying member method code with example usage: obj.transform(input_object) and returns: performances