code: def image_vae():
      """Basic Image VAE model hparams."""
      hparams = common_hparams.basic_params1()
      hparams.daisy_chain_variables = False
      hparams.batch_size = 64
      hparams.hidden_size = 32
      hparams.initializer = "uniform_unit_scaling"
      hparams.initializer_gain = 1.0
      hparams.weight_decay = 0.0
  
      # VAE hparams
      hparams.add_hparam("base_depth", 32)
      hparams.add_hparam("bottleneck_bits", 32)
  
      # loss hparams
      hparams.add_hparam("kl_beta", 300)
      hparams.add_hparam("free_bits_div", 4)
      hparams.add_hparam("free_bits", 0.15)
  
      # data format hparams
      hparams.add_hparam("num_categories", 62)
  
      # problem hparams (required, don't modify)
      hparams.add_hparam("absolute", False)
      hparams.add_hparam("just_render", True)
      hparams.add_hparam("plus_render", False)
  
      return hparams
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  from magenta.models.image_stylization import ops
  from tensor2tensor.layers import common_hparams
  from tensor2tensor.layers import common_layers
  from tensor2tensor.utils import registry
  from tensor2tensor.utils import t2t_model
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
  import tensorflow_probability
label_desc: Use image node_id 2 for calling a global function with example usage: image_vae() and returns: hparams