class_docstr: Model for predicting autofills given context.
class_docstr_tok: ["Model","for","predicting","autofills","given","context","."]
docstr: Returns concatenates masked out pianorolls with their masks.
docstr_tok: ["Returns","concatenates","masked","out","pianorolls","with","their","masks","."]
code: def get_convnet_input(self):
      """Returns concatenates masked out pianorolls with their masks."""
      # pianorolls, masks = self.inputs['pianorolls'], self.inputs[
      #     'masks']
      pianorolls, masks = self.pianorolls, self.masks
      pianorolls *= 1.0 - masks
      if self.hparams.mask_indicates_context:
          # flip meaning of mask for convnet purposes: after flipping, mask is hot
          # where values are known. this makes more sense in light of padding done
          # by convolution operations: the padded area will have zero mask,
          # indicating no information to rely on.
          masks = 1.0 - masks
      return tf.concat([pianorolls, masks], axis=3)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  import os
  from magenta.models.coconet import lib_hparams
  from magenta.models.coconet import lib_tfutil
  import tensorflow.compat.v1
label_desc: Use image node_id 11 for calling the CoconetGraph obj's underlying member method code with example usage: obj.get_convnet_input() and returns: tf