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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 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