code: def get_pipeline(config, eval_ratio):
      """Returns the Pipeline instance which creates the RNN dataset.
  
      Args:
        config: An ImprovRnnConfig object.
        eval_ratio: Fraction of input to set aside for evaluation set.
  
      Returns:
        A pipeline.Pipeline instance.
      """
      all_transpositions = config.transpose_to_key is None
      partitioner = pipelines_common.RandomPartition(
          note_seq.NoteSequence,
          ["eval_lead_sheets", "training_lead_sheets"],
          [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,
          )
          lead_sheet_extractor = (
              lead_sheet_pipelines.LeadSheetExtractor(
                  min_bars=7,
                  max_steps=512,
                  min_unique_pitches=3,
                  gap_bars=1.0,
                  ignore_polyphonic_notes=False,
                  all_transpositions=all_transpositions,
                  name="LeadSheetExtractor_" + mode,
              )
          )
          encoder_pipeline = EncoderPipeline(
              config, name="EncoderPipeline_" + mode
          )
  
          dag[time_change_splitter] = partitioner[mode + "_lead_sheets"]
          dag[quantizer] = time_change_splitter
          dag[lead_sheet_extractor] = quantizer
          dag[encoder_pipeline] = lead_sheet_extractor
          dag[
              dag_pipeline.DagOutput(mode + "_lead_sheets")
          ] = encoder_pipeline
  
      return dag_pipeline.DAGPipeline(dag)
  
file_path: magenta/magenta/models/improv_rnn/improv_rnn_pipeline.py
imports: from magenta.pipelines import dag_pipeline
  from magenta.pipelines import lead_sheet_pipelines
  from magenta.pipelines import note_sequence_pipelines
  from magenta.pipelines import pipeline
  from magenta.pipelines import pipelines_common
  from magenta.pipelines import statistics
  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, eval_ratio) and returns: dag_pipeline