code: def get_hparams(self):
      hparams = transformer.transformer_small()
      hparams.add_hparam("prior_type", "affine")
      hparams.add_hparam("factor", 2)  # squeezing factor
      hparams.add_hparam("n_layers_transform_params", 1)
      hparams.add_hparam("n_1x1_heads", N_1X1_HEADS)
      hparams.add_hparam("flow_num_1x1_heads", 4)
      hparams.add_hparam("flow_num_heads", 4)
      hparams.add_hparam("flow_hidden_size", 64)
      hparams.add_hparam("flow_filter_size", 128)
      hparams.add_hparam("flow_layer_prepostprocess_dropout", 0.0)
      hparams.add_hparam("flow_attention_dropout", 0.0)
      hparams.add_hparam("flow_relu_dropout", 0.0)
      hparams.add_hparam("latent_size", N_CHANNELS)
      hparams.add_hparam("use_weightnorm", True)
      hparams.add_hparam("kl_startup_steps", 2000)
      hparams.add_hparam("affine_scale", "glow")
      hparams.add_hparam("scale_width", 0.999)
      hparams.add_hparam("step_fn", "glow")  # glow / chunting
      hparams.add_hparam("conv_fn", "np")  # np / tf
      hparams.add_hparam("posterior_type", "diagonal_normal")
      hparams.causal_decoder_self_attention = False
      hparams.hidden_size = HIDDEN_SIZE
      hparams.weight_dtype = "float32"
      hparams.add_hparam("pos_attn", False)
      return hparams
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import tempfile
  from absl.testing import parameterized
  import numpy
  from tensor2tensor.layers import common_attention
  from tensor2tensor.layers import transformer_glow_layers
  from tensor2tensor.layers import transformer_glow_layers_ops
  from tensor2tensor.models import transformer
  import tensorflow.compat.v1
label_desc: Use image node_id 1 for calling the TransformerGlowLayersTest obj's underlying member method code with example usage: obj.get_hparams() and returns: hparams