code: def svg_decoder():
      """Basic hparams for SVG decoder."""
      hparams = common_hparams.basic_params1()
      hparams.daisy_chain_variables = False
      hparams.batch_size = 128
      hparams.hidden_size = 1024
      hparams.num_hidden_layers = 2
      hparams.initializer = "uniform_unit_scaling"
      hparams.initializer_gain = 1.0
      hparams.weight_decay = 0.0
      hparams.num_hidden_layers = 4
      hparams.force_full_predict = True
      hparams.dropout = 0.5
      hparams.learning_rate_warmup_steps = 100000
  
      # LSTM-specific hparams
      hparams.add_hparam("vocab_size", None)
  
      # VAE params
      hparams.add_hparam("bottleneck_bits", 32)
  
      # VAE loss params (don't matter but must be defined)
      hparams.add_hparam("kl_beta", 300)
      hparams.add_hparam("free_bits_div", 4)
  
      # loss params
      hparams.add_hparam("soft_k", 10)
      hparams.add_hparam("mdn_k", 1)
  
      # params required by LayerNormLSTMCell, for us to just use recurrent dropout
      hparams.add_hparam("layer_norm", False)
      hparams.add_hparam("ff_dropout", True)
      hparams.add_hparam("rec_dropout", 0.3)
  
      # Decode architecture hparams
      hparams.add_hparam("twice_decoder", False)
      hparams.add_hparam("sg_bottleneck", True)
      hparams.add_hparam("condition_on_sln", False)
      hparams.add_hparam("use_cls", True)
  
      # MDN loss hparams
      hparams.add_hparam("num_mixture", 50)
      hparams.add_hparam("mix_temperature", 0.0001)
      hparams.add_hparam("gauss_temperature", 0.0001)
      hparams.add_hparam("dont_reduce_loss", False)
  
      # VAE meta hparams (to load image encoder)
      hparams.add_hparam("vae_ckpt_dir", "")
      hparams.add_hparam("vae_hparams", "")
      hparams.add_hparam("vae_data_dir", "")
      hparams.add_hparam("vae_hparam_set", "image_vae")
      hparams.add_hparam("vae_problem", "glyph_azzn_problem")
  
      # 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", False)
      hparams.add_hparam("plus_render", False)
  
      # modality hparams
      hparams.bottom = {
          "inputs": svg_decoder_loss.real_svg_bottom,
          "targets": svg_decoder_loss.real_svg_bottom,
      }
      hparams.top = {"targets": svg_decoder_loss.real_svg_top}
      hparams.loss = {"targets": svg_decoder_loss.real_svg_loss}
  
      return hparams
  
imports: import copy
  from magenta.contrib import rnn
  from magenta.models.svg_vae import image_vae
  from magenta.models.svg_vae import svg_decoder_loss
  from tensor2tensor.layers import common_hparams
  from tensor2tensor.layers import common_layers
  from tensor2tensor.utils import registry
  from tensor2tensor.utils import t2t_model
  from tensor2tensor.utils import trainer_lib
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 1 for calling a global function with example usage: svg_decoder() and returns: hparams