code: def get_pipeline(config, transposition_range=(0,), eval_ratio=0.0):
      """Returns the Pipeline instance which creates the RNN dataset.
  
      Args:
        config: A MelodyRnnConfig object.
        transposition_range: Collection of integer pitch steps to transpose.
        eval_ratio: Fraction of input to set aside for evaluation set.
  
      Returns:
        A pipeline.Pipeline instance.
      """
      partitioner = pipelines_common.RandomPartition(
          note_seq.NoteSequence,
          ["eval_melodies", "training_melodies"],
          [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
              )
          )
          transposition_pipeline = (
              note_sequence_pipelines.TranspositionPipeline(
                  transposition_range,
                  name="TranspositionPipeline_" + mode,
              )
          )
          quantizer = note_sequence_pipelines.Quantizer(
              steps_per_quarter=config.steps_per_quarter,
              name="Quantizer_" + mode,
          )
          melody_extractor = melody_pipelines.MelodyExtractor(
              min_bars=7,
              max_steps=512,
              min_unique_pitches=5,
              gap_bars=1.0,
              ignore_polyphonic_notes=False,
              name="MelodyExtractor_" + mode,
          )
          encoder_pipeline = EncoderPipeline(
              config, name="EncoderPipeline_" + mode
          )
  
          dag[time_change_splitter] = partitioner[mode + "_melodies"]
          dag[quantizer] = time_change_splitter
          dag[transposition_pipeline] = quantizer
          dag[melody_extractor] = transposition_pipeline
          dag[encoder_pipeline] = melody_extractor
          dag[
              dag_pipeline.DagOutput(mode + "_melodies")
          ] = encoder_pipeline
  
      return dag_pipeline.DAGPipeline(dag)
  
file_path: magenta/magenta/models/melody_rnn/melody_rnn_pipeline.py
imports: from magenta.pipelines import dag_pipeline
  from magenta.pipelines import melody_pipelines
  from magenta.pipelines import note_sequence_pipelines
  from magenta.pipelines import pipeline
  from magenta.pipelines import pipelines_common
  import note_seq
  import tensorflow.compat.v1
label_desc: Use image node_id 1 for calling a global function with example usage: get_pipeline(config, transposition_range, eval_ratio) and returns: dag_pipeline