code: def glow_hparams():
      """Glow Hparams."""
      hparams = common_hparams.basic_params1()
      hparams.clip_grad_norm = None
      hparams.weight_decay = 0.0
      hparams.learning_rate_constant = 3e-4
      hparams.batch_size = 32
      # can be prev_level, prev_step or normal.
      # see: glow_ops.merge_level_and_latent_dist
      hparams.add_hparam("level_scale", "prev_level")
      hparams.add_hparam("n_levels", 3)
      hparams.add_hparam("n_bits_x", 8)
      hparams.add_hparam("depth", 32)
      # Activation - Relu or Gatu
      hparams.add_hparam("activation", "relu")
      # Coupling layer, additive or affine.
      hparams.add_hparam("coupling", "affine")
      hparams.add_hparam("coupling_width", 512)
      hparams.add_hparam("coupling_dropout", 0.0)
      hparams.add_hparam("top_prior", "single_conv")
      # init_batch_size denotes the number of examples used for data-dependent
      # initialization. A higher init_batch_size is required for training
      # stability especially when hparams.batch_size is low.
      hparams.add_hparam("init_batch_size", 256)
      hparams.add_hparam("temperature", 1.0)
  
      return hparams
  
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 1 for calling a global function with example usage: glow_hparams() and returns: hparams