class_docstr: A Module that converts monophonic melodies to a model specific encoding.
docstr: Constructs an EncoderPipeline.
  
  Args:
    config: A MelodyRnnConfig that specifies the encoder/decoder, pitch range,
        and what key to transpose into.
    name: A unique pipeline name.
code: def __init__(self, config, name):
      """Constructs an EncoderPipeline.
  
      Args:
        config: A MelodyRnnConfig that specifies the encoder/decoder, pitch range,
            and what key to transpose into.
        name: A unique pipeline name.
      """
      super(EncoderPipeline, self).__init__(
          input_type=note_seq.Melody,
          output_type=tf.train.SequenceExample,
          name=name,
      )
      self._melody_encoder_decoder = config.encoder_decoder
      self._min_note = config.min_note
      self._max_note = config.max_note
      self._transpose_to_key = config.transpose_to_key
  
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 to create a new EncoderPipeline object from inherited base classes: pipeline with example: obj = EncoderPipeline(config, name)