class_docstr: Glow generative model.
  
  Reference: https://arxiv.org/abs/1807.03039
class_docstr_tok: ["Glow","generative","model",".","Reference",":","https",":","\/\/arxiv.org\/abs\/1807.03039"]
code: def body(self, features):
      exp_coupling = ["affine", "additive"]
      if self.hparams.coupling not in exp_coupling:
          raise ValueError(
              "Expected hparams.coupling to be in %s, got %s"
              % (exp_coupling, self.hparams.coupling)
          )
      if self.is_training:
          init_features = self.create_init_batch(features)
          init_op = self.objective_tower(init_features, init=True)
          init_op = tf.Print(
              init_op,
              [init_op],
              message="Triggering data-dependent init.",
              first_n=20,
          )
          tf.add_to_collection("glow_init_op", init_op)
      train_op = self.objective_tower(features, init=False)
      return tf.zeros_like(features["targets"]), {"training": train_op}
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  from tensor2tensor.layers import common_hparams
  from tensor2tensor.layers import common_layers
  from tensor2tensor.models.research import glow_init_hook
  from tensor2tensor.models.research import glow_ops
  from tensor2tensor.utils import contrib
  from tensor2tensor.utils import registry
  from tensor2tensor.utils import t2t_model
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 9 for calling the Glow obj's underlying member method code with example usage: obj.body(features) and returns: tf,  dict