class_docstr: A Pipeline that converts performances to a model specific encoding.
class_docstr_tok: ["A","Pipeline","that","converts","performances","to","a","model","specific","encoding","."]
docstr: Constructs an EncoderPipeline.
  
  Args:
    config: A PerformanceRnnConfig that specifies the encoder/decoder and
        note density conditioning behavior.
    name: A unique pipeline name.
docstr_tok: ["Constructs","an","EncoderPipeline",".","Args",":","config",":","A","PerformanceRnnConfig","that","specifies","the","encoder\/decoder","and","note","density","conditioning","behavior",".","name",":","A","unique","pipeline","name","."]
code: def __init__(self, config, name):
      """Constructs an EncoderPipeline.
  
      Args:
        config: A PerformanceRnnConfig that specifies the encoder/decoder and
            note density conditioning behavior.
        name: A unique pipeline name.
      """
      super(EncoderPipeline, self).__init__(
          input_type=BasePerformance,
          output_type=tf.train.SequenceExample,
          name=name,
      )
      self._encoder_decoder = config.encoder_decoder
      self._control_signals = config.control_signals
      self._optional_conditioning = config.optional_conditioning
  
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 to create a new EncoderPipeline object from inherited base classes: pipeline with example: obj = EncoderPipeline(config, name)