code: def get_hparams(self):
      hparams = transformer.transformer_small()
      hparams.add_hparam("prior_type", "affine")
      hparams.add_hparam("depths", "12")  # infer n_levels from depths
      hparams.add_hparam("split_plans", "tca")
      hparams.add_hparam("factor", 2)  # squeezing factor
      hparams.add_hparam("n_layers_transform_params", 1)
      hparams.add_hparam("n_layers_multiscale_prior", 3)
      hparams.add_hparam("flow_num_heads", 4)
      hparams.add_hparam("flow_num_1x1_heads", N_1X1_HEADS)
      hparams.add_hparam("flow_hidden_size", 64)
      hparams.add_hparam("flow_filter_size", 128)
      hparams.add_hparam("cond_prior_on_src", True)
      hparams.add_hparam("bottom_prior_std", False)
      hparams.add_hparam("latent_size", N_CHANNELS)
      hparams.add_hparam("scale_width", 0.999)
      hparams.add_hparam("coupling_transform_ratio", 0.5)
      hparams.add_hparam("actnorm_type", "actnorm")
      hparams.add_hparam("actnorm_weightnorm", True)
      hparams.add_hparam("perm_type", "1x1")
      hparams.add_hparam("init_permutation", True)
      hparams.causal_decoder_self_attention = False
      hparams.hidden_size = HIDDEN_SIZE
      return hparams
  
file_path: tensor2tensor/tensor2tensor/layers/transformer_glow_layers_ops_test.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  import numpy
  from tensor2tensor.layers import transformer_glow_layers_ops
  from tensor2tensor.models import transformer
  import tensorflow.compat.v1
label_desc: Use image node_id 2 for calling the TransformerFlowOpsTest obj's underlying member method code with example usage: obj.get_hparams() and returns: hparams