class_docstr: A pipeline that converts an EventSequence to a model encoding.
class_docstr_tok: ["A","pipeline","that","converts","an","EventSequence","to","a","model","encoding","."]
docstr: Constructs an EncoderPipeline.
  
  Args:
    input_type: The type this pipeline expects as input.
    encoder_decoder: An EventSequenceEncoderDecoder.
    name: A unique pipeline name.
docstr_tok: ["Constructs","an","EncoderPipeline",".","Args",":","input_type",":","The","type","this","pipeline","expects","as","input",".","encoder_decoder",":","An","EventSequenceEncoderDecoder",".","name",":","A","unique","pipeline","name","."]
code: def __init__(self, input_type, encoder_decoder, name=None):
      """Constructs an EncoderPipeline.
  
      Args:
        input_type: The type this pipeline expects as input.
        encoder_decoder: An EventSequenceEncoderDecoder.
        name: A unique pipeline name.
      """
      super(EncoderPipeline, self).__init__(
          input_type=input_type,
          output_type=tf.train.SequenceExample,
          name=name,
      )
      self._encoder_decoder = encoder_decoder
  
imports: from magenta.pipelines import pipeline
  from magenta.pipelines import pipelines_common
  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(input_type, encoder_decoder, name)