Glow Hparams.

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