code: def get_pipeline(config, min_events, max_events, eval_ratio):
      """Returns the Pipeline instance which creates the RNN dataset.
  
      Args:
        config: A PerformanceRnnConfig.
        min_events: Minimum number of events for an extracted sequence.
        max_events: Maximum number of events for an extracted sequence.
        eval_ratio: Fraction of input to set aside for evaluation set.
  
      Returns:
        A pipeline.Pipeline instance.
      """
      # Stretch by -5%, -2.5%, 0%, 2.5%, and 5%.
      stretch_factors = [0.95, 0.975, 1.0, 1.025, 1.05]
  
      # Transpose no more than a major third.
      transposition_range = list(range(-3, 4))
  
      partitioner = pipelines_common.RandomPartition(
          music_pb2.NoteSequence,
          ["eval_performances", "training_performances"],
          [eval_ratio],
      )
      dag = {partitioner: dag_pipeline.DagInput(music_pb2.NoteSequence)}
  
      for mode in ["eval", "training"]:
          sustain_pipeline = note_sequence_pipelines.SustainPipeline(
              name="SustainPipeline_" + mode
          )
          stretch_pipeline = note_sequence_pipelines.StretchPipeline(
              stretch_factors if mode == "training" else [1.0],
              name="StretchPipeline_" + mode,
          )
          splitter = note_sequence_pipelines.Splitter(
              hop_size_seconds=30.0, name="Splitter_" + mode
          )
          quantizer = note_sequence_pipelines.Quantizer(
              steps_per_second=config.steps_per_second,
              name="Quantizer_" + mode,
          )
          transposition_pipeline = (
              note_sequence_pipelines.TranspositionPipeline(
                  transposition_range if mode == "training" else [0],
                  name="TranspositionPipeline_" + mode,
              )
          )
          perf_extractor = PerformanceExtractor(
              min_events=min_events,
              max_events=max_events,
              num_velocity_bins=config.num_velocity_bins,
              note_performance=config.note_performance,
              name="PerformanceExtractor_" + mode,
          )
          encoder_pipeline = EncoderPipeline(
              config, name="EncoderPipeline_" + mode
          )
  
          dag[sustain_pipeline] = partitioner[mode + "_performances"]
          dag[stretch_pipeline] = sustain_pipeline
          dag[splitter] = stretch_pipeline
          dag[quantizer] = splitter
          dag[transposition_pipeline] = quantizer
          dag[perf_extractor] = transposition_pipeline
          dag[encoder_pipeline] = perf_extractor
          dag[
              dag_pipeline.DagOutput(mode + "_performances")
          ] = encoder_pipeline
  
      return dag_pipeline.DAGPipeline(dag)
  
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 for calling a global function with example usage: get_pipeline(config, min_events, max_events, eval_ratio) and returns: dag_pipeline