code: def sliced_gan():
      """Basic parameters for a vanilla_gan."""
      hparams = common_hparams.basic_params1()
      hparams.optimizer = "adam"
      hparams.learning_rate_constant = 0.0002
      hparams.learning_rate_warmup_steps = 500
      hparams.learning_rate_schedule = "constant * linear_warmup"
      hparams.label_smoothing = 0.0
      hparams.batch_size = 128
      hparams.hidden_size = 128
      hparams.initializer = "uniform_unit_scaling"
      hparams.initializer_gain = 1.0
      hparams.weight_decay = 1e-6
      hparams.kernel_height = 4
      hparams.kernel_width = 4
      hparams.bottleneck_bits = 128
      hparams.add_hparam("discriminator_batchnorm", True)
      hparams.add_hparam("num_sliced_vecs", 4096)
      return hparams
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  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
label_desc: Use image node_id 4 for calling a global function with example usage: sliced_gan() and returns: hparams