class_docstr: Music Variational Autoencoder.
class_docstr_tok: ["Music","Variational","Autoencoder","."]
docstr: Builds encoder and decoder.
  
  Must be called within a graph.
  
  Args:
    hparams: An HParams object containing model hyperparameters. See
        `get_default_hparams` below for required values.
    output_depth: Size of final output dimension.
    is_training: Whether or not the model will be used for training.
docstr_tok: ["Builds","encoder","and","decoder",".","Must","be","called","within","a","graph",".","Args",":","hparams",":","An","HParams","object","containing","model","hyperparameters",".","See","`","get_default_hparams","`","below","for","required","values",".","output_depth",":","Size","of","final","output","dimension",".","is_training",":","Whether","or","not","the","model","will","be","used","for","training","."]
code: def build(self, hparams, output_depth, is_training):
      """Builds encoder and decoder.
  
      Must be called within a graph.
  
      Args:
        hparams: An HParams object containing model hyperparameters. See
            `get_default_hparams` below for required values.
        output_depth: Size of final output dimension.
        is_training: Whether or not the model will be used for training.
      """
      tf.logging.info(
          "Building MusicVAE model with %s, %s, and hparams:\n%s",
          self.encoder.__class__.__name__,
          self.decoder.__class__.__name__,
          hparams.values(),
      )
      self.global_step = tf.train.get_or_create_global_step()
      self._hparams = hparams
      self._encoder.build(hparams, is_training)
      self._decoder.build(hparams, output_depth, is_training)
  
imports: import abc
  from magenta.contrib import training
  import tensorflow.compat.v1
  import tensorflow_probability
  import tf_slim
label_desc: Use image node_id 2 for calling the MusicVAE obj's underlying member method code with example usage: obj.build(hparams, output_depth, is_training) without return types