code: def 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
docs: Glow Hparams.