code: def discriminator(self, imgs, y):
          
          yv = y
          y = tf.reshape(y, [-1, 1, 1, 10])
          with argscope(Conv2D, kernel_size=5, strides=1):
              l = (LinearWrap(imgs)
                   .ConcatWith(tf.tile(y, [1, 28, 28, 1]), 3)
                   .Conv2D('conv0', 11)
                   .tf.nn.leaky_relu()
  
                   .ConcatWith(tf.tile(y, [1, 14, 14, 1]), 3)
                   .Conv2D('conv1', 74)
                   .BatchNorm('bn1')
                   .tf.nn.leaky_relu()
  
                   .apply(batch_flatten)
                   .ConcatWith(yv, 1)
                   .FullyConnected('fc1', 1024, activation=tf.identity)
                   .BatchNorm('bn2')
                   .tf.nn.leaky_relu()
  
                   .ConcatWith(yv, 1)
                   .FullyConnected('fct', 1, activation=tf.identity)())
          return l
docs: return a (b, 1) logits