class_docstr: Graph for Gibbs sampling from Coconet.
docstr: Evalutes the model once and returns predictions.
docstr_tok: ["Evalutes","the","model","once","and","returns","predictions","."]
code: def predict(self, pianorolls, masks):
      """Evalutes the model once and returns predictions."""
      direct_inputs = dict(
          pianorolls=pianorolls,
          masks=masks,
          lengths=tf.to_float([tf.shape(pianorolls)[1]]),
      )
  
      model = lib_graph.build_graph(
          is_training=False,
          hparams=self.hparams,
          direct_inputs=direct_inputs,
          use_placeholders=False,
      )
      self.logits = model.logits
      return self.logits
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import time
  from magenta.models.coconet import lib_graph
  from magenta.models.coconet import lib_hparams
  import numpy
  import tensorflow.compat.v1
label_desc: Use image node_id 6 for calling the CoconetSampleGraph obj's underlying member method code with example usage: obj.predict(pianorolls, masks) and returns: self