class_docstr: Sliced GAN for demonstration.
class_docstr_tok: ["Sliced","GAN","for","demonstration","."]
code: def infer(self, *args, **kwargs):  # pylint: disable=arguments-differ
      del args, kwargs
  
      try:
          num_channels = self.hparams.problem.num_channels
      except AttributeError:
          num_channels = 1
  
      with tf.variable_scope("body/vanilla_gan", reuse=tf.AUTO_REUSE):
          hparams = self.hparams
          z = tf.random_uniform(
              [hparams.batch_size, hparams.bottleneck_bits],
              minval=-1,
              maxval=1,
              name="z",
          )
          out_shape = (
              hparams.sample_height,
              hparams.sample_width,
              num_channels,
          )
          g_sample = self.generator(z, False, out_shape)
          return g_sample
  
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 2 for calling the SlicedGan obj's underlying member method code with example usage: obj.infer() and returns: g_sample