code: def extract_performances(
      quantized_sequence,
      start_step=0,
      min_events_discard=None,
      max_events_truncate=None,
      max_steps_truncate=None,
      num_velocity_bins=0,
      split_instruments=False,
      note_performance=False,
  ):
      """Extracts one or more performances from the given quantized NoteSequence.
  
      Args:
        quantized_sequence: A quantized NoteSequence.
        start_step: Start extracting a sequence at this time step.
        min_events_discard: Minimum length of tracks in events. Shorter tracks are
            discarded.
        max_events_truncate: Maximum length of tracks in events. Longer tracks are
            truncated.
        max_steps_truncate: Maximum length of tracks in quantized time steps. Longer
            tracks are truncated.
        num_velocity_bins: Number of velocity bins to use. If 0, velocity events
            will not be included at all.
        split_instruments: If True, will extract a performance for each instrument.
            Otherwise, will extract a single performance.
        note_performance: If True, will create a NotePerformance object. If
            False, will create either a MetricPerformance or Performance based on
            how the sequence was quantized.
  
      Returns:
        performances: A python list of Performance or MetricPerformance (if
            `quantized_sequence` is quantized relative to meter) instances.
        stats: A dictionary mapping string names to `statistics.Statistic` objects.
      """
      sequences_lib.assert_is_quantized_sequence(quantized_sequence)
  
      # pylint: disable=g-complex-comprehension
      stats = dict(
          (stat_name, statistics.Counter(stat_name))
          for stat_name in [
              "performances_discarded_too_short",
              "performances_truncated",
              "performances_truncated_timewise",
              "performances_discarded_more_than_1_program",
              "performance_discarded_too_many_time_shift_steps",
              "performance_discarded_too_many_duration_steps",
          ]
      )
      # pylint: enable=g-complex-comprehension
  
      if sequences_lib.is_absolute_quantized_sequence(
          quantized_sequence
      ):
          steps_per_second = (
              quantized_sequence.quantization_info.steps_per_second
          )
          # Create a histogram measuring lengths in seconds.
          stats[
              "performance_lengths_in_seconds"
          ] = statistics.Histogram(
              "performance_lengths_in_seconds",
              [5, 10, 20, 30, 40, 60, 120],
          )
      else:
          steps_per_bar = (
              sequences_lib.steps_per_bar_in_quantized_sequence(
                  quantized_sequence
              )
          )
          # Create a histogram measuring lengths in bars.
          stats["performance_lengths_in_bars"] = statistics.Histogram(
              "performance_lengths_in_bars",
              [1, 10, 20, 30, 40, 50, 100, 200, 500],
          )
  
      if split_instruments:
          instruments = set(
              note.instrument for note in quantized_sequence.notes
          )
      else:
          instruments = set([None])
          # Allow only 1 program.
          programs = set()
          for note in quantized_sequence.notes:
              programs.add(note.program)
          if len(programs) > 1:
              stats[
                  "performances_discarded_more_than_1_program"
              ].increment()
              return [], list(stats.values())
  
      performances = []
  
      for instrument in instruments:
          # Translate the quantized sequence into a Performance.
          if note_performance:
              try:
                  performance = NotePerformance(
                      quantized_sequence,
                      start_step=start_step,
                      num_velocity_bins=num_velocity_bins,
                      instrument=instrument,
                  )
              except TooManyTimeShiftStepsError:
                  stats[
                      "performance_discarded_too_many_time_shift_steps"
                  ].increment()
                  continue
              except TooManyDurationStepsError:
                  stats[
                      "performance_discarded_too_many_duration_steps"
                  ].increment()
                  continue
          elif sequences_lib.is_absolute_quantized_sequence(
              quantized_sequence
          ):
              performance = Performance(
                  quantized_sequence,
                  start_step=start_step,
                  num_velocity_bins=num_velocity_bins,
                  instrument=instrument,
              )
          else:
              performance = MetricPerformance(
                  quantized_sequence,
                  start_step=start_step,
                  num_velocity_bins=num_velocity_bins,
                  instrument=instrument,
              )
  
          if (
              max_steps_truncate is not None
              and performance.num_steps > max_steps_truncate
          ):
              performance.set_length(max_steps_truncate)
              stats["performances_truncated_timewise"].increment()
  
          if (
              max_events_truncate is not None
              and len(performance) > max_events_truncate
          ):
              performance.truncate(max_events_truncate)
              stats["performances_truncated"].increment()
  
          if (
              min_events_discard is not None
              and len(performance) < min_events_discard
          ):
              stats["performances_discarded_too_short"].increment()
          else:
              performances.append(performance)
              if sequences_lib.is_absolute_quantized_sequence(
                  quantized_sequence
              ):
                  stats["performance_lengths_in_seconds"].increment(
                      performance.num_steps // steps_per_second
                  )
              else:
                  stats["performance_lengths_in_bars"].increment(
                      performance.num_steps // steps_per_bar
                  )
  
      return performances, list(stats.values())
  
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 a global function with example usage: extract_performances(quantized_sequence, start_step, min_events_discard, max_events_truncate, max_steps_truncate, num_velocity_bins, split_instruments, note_performance) and returns: performances,  list, list,  list