class_docstr: Music Variational Autoencoder.
class_docstr_tok: ["Music","Variational","Autoencoder","."]
docstr: Evaluate on the given sequences, returning metric update ops.
  
  Args:
    input_sequence: The sequence to be fed to the encoder.
    output_sequence: The sequence expected from the decoder.
    sequence_length: The length of the given sequences (which must be
      identical).
    control_sequence: (Optional) sequence on which to condition the decoder.
  
  Returns:
    metric_update_ops: tf.metrics update ops.
docstr_tok: ["Evaluate","on","the","given","sequences",",","returning","metric","update","ops",".","Args",":","input_sequence",":","The","sequence","to","be","fed","to","the","encoder",".","output_sequence",":","The","sequence","expected","from","the","decoder",".","sequence_length",":","The","length","of","the","given","sequences","(","which","must","be","identical",")",".","control_sequence",":","(","Optional",")","sequence","on","which","to","condition","the","decoder",".","Returns",":","metric_update_ops",":","tf.metrics","update","ops","."]
code: def eval(
      self,
      input_sequence,
      output_sequence,
      sequence_length,
      control_sequence=None,
  ):
      """Evaluate on the given sequences, returning metric update ops.
  
      Args:
        input_sequence: The sequence to be fed to the encoder.
        output_sequence: The sequence expected from the decoder.
        sequence_length: The length of the given sequences (which must be
          identical).
        control_sequence: (Optional) sequence on which to condition the decoder.
  
      Returns:
        metric_update_ops: tf.metrics update ops.
      """
      metric_map, scalars_to_summarize = self._compute_model_loss(
          input_sequence,
          output_sequence,
          sequence_length,
          control_sequence,
      )
  
      for n, t in scalars_to_summarize.items():
          metric_map[n] = tf.metrics.mean(t)
  
      (
          metrics_to_values,
          metrics_to_updates,
      ) = tf_slim.metrics.aggregate_metric_map(metric_map)
  
      for metric_name, metric_value in metrics_to_values.items():
          tf.summary.scalar(metric_name, metric_value)
  
      return list(metrics_to_updates.values())
  
imports: import abc
  from magenta.contrib import training
  import tensorflow.compat.v1
  import tensorflow_probability
  import tf_slim
label_desc: Use image node_id 9 for calling the MusicVAE obj's underlying member method code with example usage: obj.eval(input_sequence, output_sequence, sequence_length, control_sequence) and returns: list