code: def get_pipeline(config, eval_ratio):
      """Returns the Pipeline instance which creates the RNN dataset.
  
      Args:
        config: A DrumsRnnConfig object.
        eval_ratio: Fraction of input to set aside for evaluation set.
  
      Returns:
        A pipeline.Pipeline instance.
      """
      partitioner = pipelines_common.RandomPartition(
          note_seq.NoteSequence,
          ["eval_drum_tracks", "training_drum_tracks"],
          [eval_ratio],
      )
      dag = {partitioner: dag_pipeline.DagInput(note_seq.NoteSequence)}
  
      for mode in ["eval", "training"]:
          time_change_splitter = (
              note_sequence_pipelines.TimeChangeSplitter(
                  name="TimeChangeSplitter_" + mode
              )
          )
          quantizer = note_sequence_pipelines.Quantizer(
              steps_per_quarter=config.steps_per_quarter,
              name="Quantizer_" + mode,
          )
          drums_extractor = drum_pipelines.DrumsExtractor(
              min_bars=7,
              max_steps=512,
              gap_bars=1.0,
              name="DrumsExtractor_" + mode,
          )
          encoder_pipeline = event_sequence_pipeline.EncoderPipeline(
              note_seq.DrumTrack,
              config.encoder_decoder,
              name="EncoderPipeline_" + mode,
          )
  
          dag[time_change_splitter] = partitioner[mode + "_drum_tracks"]
          dag[quantizer] = time_change_splitter
          dag[drums_extractor] = quantizer
          dag[encoder_pipeline] = drums_extractor
          dag[
              dag_pipeline.DagOutput(mode + "_drum_tracks")
          ] = encoder_pipeline
  
      return dag_pipeline.DAGPipeline(dag)
  
imports: from magenta.pipelines import dag_pipeline
  from magenta.pipelines import drum_pipelines
  from magenta.pipelines import event_sequence_pipeline
  from magenta.pipelines import note_sequence_pipelines
  from magenta.pipelines import pipelines_common
  import note_seq
label_desc: Use image node_id 1 for calling a global function with example usage: get_pipeline(config, eval_ratio) and returns: dag_pipeline