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"]
docstr: Returns a batch of size "hparams.init_batch_size" for initialization.
  
  Args:
    features: input features.
  Returns:
    init_features: initialization features.
docstr_tok: ["Returns","a","batch","of","size","``","hparams.init_batch_size","''","for","initialization",".","Args",":","features",":","input","features",".","Returns",":","init_features",":","initialization","features","."]
code: def create_init_batch(self, features):
      """Returns a batch of size "hparams.init_batch_size" for initialization.
  
      Args:
        features: input features.
      Returns:
        init_features: initialization features.
      """
      train_dataset = self.hparams.problem.dataset(
          tf_estimator.ModeKeys.TRAIN, hparams=self.hparams
      )
      train_dataset = train_dataset.batch(self.hparams.init_batch_size)
      train_dataset = self.init_preprocess(train_dataset)
      return train_dataset.make_one_shot_iterator().get_next()
  
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 6 for calling the Glow obj's underlying member method code with example usage: obj.create_init_batch(features) and returns: train_dataset