old_contents: import tensorflow as tf
  import ops
  
  class Discriminator:
    def __init__(self, name, is_training, patch_size=70, use_sigmoid=False):
      self.name = name
      self.is_training = is_training
      self.reuse = False
      self.patch_size = 70
      self.use_sigmoid = use_sigmoid
  
    def __call__(self, input):
      """
      Args:
        input: batch_size x image_size x image_size x 3
      Returns:
        output: 4D tensor batch_size x out_size x out_size x 1 (default 1x5x5x1)
                filled with 0.9 if real, 0.0 if fake
      """
      with tf.variable_scope(self.name):
        batch_size = input.get_shape().as_list()[0]
        patch = tf.random_crop(input,
            [batch_size, self.patch_size, self.patch_size, 3])   # (?, 70, 70, 70)
        C64 = ops.Ck(patch, 64, reuse=self.reuse,
            is_training=self.is_training,
            use_batchnorm=False, name='C64')                     # (?, 35, 35, 64)
        print(C64.get_shape())
        C128 = ops.Ck(C64, 128, reuse=self.reuse,
            is_training=self.is_training, name='C128')           # (?, 18, 18, 128)
        print(C128.get_shape())
        C256 = ops.Ck(C128, 256, reuse=self.reuse,
            is_training=self.is_training, name='C256')           # (?, 9, 9, 256)
        print(C256.get_shape())
        C512 = ops.Ck(C256, 512,reuse=self.reuse,
            is_training=self.is_training, name='C512')           # (?, 5, 5, 512)
        print(C512.get_shape())
  
        # 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')         # (?, 5, 5, 1)
        print(output.get_shape())
  
      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, is_training, patch_size=70, use_sigmoid=False):
      self.name = name
      self.is_training = is_training
      self.reuse = False
      self.patch_size = 70
      self.use_sigmoid = use_sigmoid
  
    def __call__(self, input):
      """
      Args:
        input: batch_size x image_size x image_size x 3
      Returns:
        output: 4D tensor batch_size x out_size x out_size x 1 (default 1x5x5x1)
                filled with 0.9 if real, 0.0 if fake
      """
      with tf.variable_scope(self.name):
        batch_size = input.get_shape().as_list()[0]
        patch = tf.random_crop(input,
            [batch_size, self.patch_size, self.patch_size, 3])   # (?, 70, 70, 70)
        C64 = ops.Ck(patch, 64, reuse=self.reuse,
            is_training=self.is_training,
            use_batchnorm=False, name='C64')                     # (?, 35, 35, 64)
        C128 = ops.Ck(C64, 128, reuse=self.reuse,
            is_training=self.is_training, name='C128')           # (?, 18, 18, 128)
        C256 = ops.Ck(C128, 256, reuse=self.reuse,
            is_training=self.is_training, name='C256')           # (?, 9, 9, 256)
        C512 = ops.Ck(C256, 512,reuse=self.reuse,
            is_training=self.is_training, name='C512')           # (?, 5, 5, 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')         # (?, 5, 5, 1)
  
      self.reuse = True
      self.variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=self.name)
  
      return output
  
subject: Remove unneeded comments