old_contents: import tensorflow as tf
  import ops
  
  class Generator:
    def __init__(self, name):
      self.name = name
      self.reuse = False
  
    def __call__(self, input):
      """
      Args:
        input: batch_size x 128 x 128 x 3
      Returns:
        output: same size as input
      """
      with tf.variable_scope(self.name):
        # convolution
        c7s1_32 = ops.c7s1_k(input, 32, reuse=self.reuse, name='c7s1_32') # (?, 128, 128, 32)
        d64 = ops.dk(c7s1_32, 64, reuse=self.reuse, name='d64')           # (?, 64, 64, 64)
        d128 = ops.dk(d64, 128, reuse=self.reuse, name='d128')            # (?, 32, 32, 128)
  
        # 6 residual blocks
        R128_1 = ops.Rk(d128, 128, reuse=self.reuse, name='R128_1')       # (?, 32, 32, 128)
        R128_2 = ops.Rk(R128_1, 128, reuse=self.reuse, name='R128_2')     # (?, 32, 32, 128)
        R128_3 = ops.Rk(R128_2, 128, reuse=self.reuse, name='R128_3')     # (?, 32, 32, 128)
        R128_4 = ops.Rk(R128_3, 128, reuse=self.reuse, name='R128_4')     # (?, 32, 32, 128)
        R128_5 = ops.Rk(R128_4, 128, reuse=self.reuse, name='R128_5')     # (?, 32, 32, 128)
        R128_6 = ops.Rk(R128_5, 128, reuse=self.reuse, name='R128_6')     # (?, 32, 32, 128)
  
        # fractional-strided convolution
        u64 = ops.uk(R128_6, 64, reuse=self.reuse, name='u64')            # (?, 64, 64, 64)
        u32 = ops.uk(u64, 32, reuse=self.reuse, name='u32')               # (?, 128, 128, 32)
  
        # convolution
        output = ops.c7s1_k(u32, 3, reuse=self.reuse, name='output')      # (?, 128, 128, 3)
  
      # set reuse=True for next call
      self.reuse = True
      self.variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=self.name)
      # print(self.variables)
      return output
new_contents: import tensorflow as tf
  import ops
  
  class Generator:
    def __init__(self, name):
      self.name = name
      self.reuse = False
  
    def __call__(self, input):
      """
      Args:
        input: batch_size x 128 x 128 x 3
      Returns:
        output: same size as input
      """
      with tf.variable_scope(self.name):
        # convolution
        c7s1_32 = ops.c7s1_k(input, 32, reuse=self.reuse, name='c7s1_32') # (?, 128, 128, 32)
        d64 = ops.dk(c7s1_32, 64, reuse=self.reuse, name='d64')           # (?, 64, 64, 64)
        d128 = ops.dk(d64, 128, reuse=self.reuse, name='d128')            # (?, 32, 32, 128)
  
        # 6 residual blocks
        R128_1 = ops.Rk(d128, 128, reuse=self.reuse, name='R128_1')       # (?, 32, 32, 128)
        R128_2 = ops.Rk(R128_1, 128, reuse=self.reuse, name='R128_2')     # (?, 32, 32, 128)
        R128_3 = ops.Rk(R128_2, 128, reuse=self.reuse, name='R128_3')     # (?, 32, 32, 128)
        R128_4 = ops.Rk(R128_3, 128, reuse=self.reuse, name='R128_4')     # (?, 32, 32, 128)
        R128_5 = ops.Rk(R128_4, 128, reuse=self.reuse, name='R128_5')     # (?, 32, 32, 128)
        R128_6 = ops.Rk(R128_5, 128, reuse=self.reuse, name='R128_6')     # (?, 32, 32, 128)
  
        # fractional-strided convolution
        u64 = ops.uk(R128_6, 64, reuse=self.reuse, name='u64')            # (?, 64, 64, 64)
        u32 = ops.uk(u64, 32, reuse=self.reuse, name='u32')               # (?, 128, 128, 32)
  
        # convolution
        c7s1_3 = ops.c7s1_k(u32, 3, reuse=self.reuse, name='c7s1_3')      # (?, 128, 128, 3)
        output = tf.tanh(c7s1_3, name='output')
  
      # set reuse=True for next call
      self.reuse = True
      self.variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=self.name)
      # print(self.variables)
      return output
subject: Add tanh to generator