old_contents: import tensorflow as tf
  import ops
  
  class Discriminator:
    def __init__(self, name, use_sigmoid=False):
      self.name = name
      self.reuse = False
      self.use_sigmoid = use_sigmoid
  
    def __call__(self, input):
      """
      Args:
        input: batch_size x 128 x 128 x 3
      Returns:
        output: 1D (1 if real, 0 if fake)
      """
      with tf.variable_scope(self.name):
        C64 = ops.Ck(input, 64, reuse=self.reuse, use_batchnorm=False, name='C64') # (?, 64, 64, 64)
        C128 = ops.Ck(C64, 128, reuse=self.reuse, name='C128')                     # (?, 32, 32, 128)
        C256 = ops.Ck(C128, 256, reuse=self.reuse, name='C256')                    # (?, 16, 16, 256)
        C512 = ops.Ck(C256, 512, reuse=self.reuse, name='C512')                    # (?, 8, 8, 512)
  
        # apply a convolution to produce a 1 dimensional output (1 channel?)
        # set use_sigmoid = False if use_lsgan == True
        output = ops.last_conv(C512, reuse=self.reuse, use_sigmoid=self.use_sigmoid, name='output') # (?, 8, 8, 1)
  
      self.reuse = True
      self.variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=self.name)
  
      return output
  
new_contents: import tensorflow as tf
  import ops
  
  class Discriminator:
    def __init__(self, name, use_sigmoid=False):
      self.name = name
      self.reuse = False
      self.use_sigmoid = use_sigmoid
  
    def __call__(self, input):
      """
      Args:
        input: batch_size x 128 x 128 x 3
      Returns:
        output: 4D tensor batch_size x image_size/8 x image_size/8 x 1 filled with 1 if real, 0 if fake
      """
      with tf.variable_scope(self.name):
        C64 = ops.Ck(input, 64, reuse=self.reuse, use_batchnorm=False, name='C64') # (?, 64, 64, 64)
        C128 = ops.Ck(C64, 128, reuse=self.reuse, name='C128')                     # (?, 32, 32, 128)
        C256 = ops.Ck(C128, 256, reuse=self.reuse, name='C256')                    # (?, 16, 16, 256)
        C512 = ops.Ck(C256, 512, reuse=self.reuse, name='C512')                    # (?, 8, 8, 512)
  
        # apply a convolution to produce a 1 dimensional output (1 channel?)
        # set use_sigmoid = False if use_lsgan == True
        output = ops.last_conv(C512,
            reuse=self.reuse, use_sigmoid=self.use_sigmoid, name='output')         # (?, 8, 8, 1)
  
      self.reuse = True
      self.variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=self.name)
  
      return output
  
subject: Fix wrong comment