class_docstr: Defines the ImageVAE model.
class_docstr_tok: ["Defines","the","ImageVAE","model","."]
code: def bottom(self, features):
      # inputs and targets should all be images, no preprocessing needed.
      # but we do need to resize them to 64x64.
      transformed_features = collections.OrderedDict()
      transformed_features["targets"] = features["targets"]
      transformed_features["inputs"] = features["inputs"]
      transformed_features["cls"] = features["targets_cls"]
      if "bottleneck" in features:
          transformed_features["bottleneck"] = features["bottleneck"]
      return transformed_features
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  from magenta.models.image_stylization import ops
  from tensor2tensor.layers import common_hparams
  from tensor2tensor.layers import common_layers
  from tensor2tensor.utils import registry
  from tensor2tensor.utils import t2t_model
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
  import tensorflow_probability
label_desc: Use image node_id 1 for calling the ImageVAE obj's underlying member method code with example usage: obj.bottom(features) and returns: transformed_features