code: def generator(self, z, is_training, out_shape):
      
      hparams = self.hparams
      height, width, c_dim = out_shape
      batch_size = hparams.batch_size
      with tf.variable_scope(
          "generator",
          initializer=tf.random_normal_initializer(stddev=0.02)):
        net = tf.layers.dense(z, 1024, name="g_fc1")
        net = tf.layers.batch_normalization(net, training=is_training,
                                            momentum=0.999, name="g_bn1")
        net = lrelu(net)
        net = tf.layers.dense(net, 128 * (height // 4) * (width // 4),
                              name="g_fc2")
        net = tf.layers.batch_normalization(net, training=is_training,
                                            momentum=0.999, name="g_bn2")
        net = lrelu(net)
        net = tf.reshape(net, [batch_size, height // 4, width // 4, 128])
        net = deconv2d(net, [batch_size, height // 2, width // 2, 64],
                       4, 4, 2, 2, name="g_dc3")
        net = tf.layers.batch_normalization(net, training=is_training,
                                            momentum=0.999, name="g_bn3")
        net = lrelu(net)
        net = deconv2d(net, [batch_size, height, width, c_dim],
                       4, 4, 2, 2, name="g_dc4")
        out = tf.nn.sigmoid(net)
        return common_layers.convert_real_to_rgb(out)
docs: Generator outputting image in [0, 1].