old_contents: """Neural Networks used to represent the value functions."""
  import tensorflow as tf
  from tensorflow.contrib import layers
  
  def policy(board):
    """Builds a policy network.
  
    The policy network predicts the expected value (win chance) of actions for a
    particular state.
  
    Args:
      board: A 4D tensor of the shape [batch, width, height, features] that
        contains the features for each board position.
    Returns:
      logits: A 3D tensor of the shape [batch, width, height] that for each board
        position contains the logits of the probability that making a move there
        leads to victory.
    """
    net = layers.conv2d(board, 128, [9, 9], scope='conv9_1')
    net = layers.conv2d(net, 64, [9, 9], scope='conv9_2')
    net = layers.conv2d(net, 1, [9, 9], scope='conv9_3')
    return net
  
  def value(board):
    """Builds a value network.
  
    The policy network predicts the expected value (win chance) of a state,
    assuming we make the best move.
  
    Args:
      board: A 4D tensor of the shape [batch, width, height, features] that
        contains the features for each board position.
    Returns:
      logits: A 1D tensor of the shape [batch] that contains the logits of the
        probability that the state leads to victory.
    """
    net = layers.conv2d(board, 128, [9, 9], scope='conv9_1')
    net = layers.conv2d(net, 64, [9, 9], scope='conv9_2')
    net = layers.conv2d(net, 16, [9, 9], scope='conv9_3')
    net = layers.flatten(net)
    net = layers.fully_connected(net, 512)
    net = layers.dropout(net)
    net = layers.fully_connected(net, 1)
    net = tf.squeeze(net, squeeze_dims=[1])
    return net
  
new_contents: """Neural Networks used to represent the value functions."""
  import tensorflow as tf
  from tensorflow.contrib import layers
  
  def policy(board):
    """Builds a policy network.
  
    The policy network predicts the expected value (win chance) of actions for a
    particular state.
  
    Args:
      board: A 4D tensor of the shape [batch, width, height, features] that
        contains the features for each board position.
    Returns:
      logits: A 3D tensor of the shape [batch, width, height] that for each board
        position contains the logits of the probability that making a move there
        leads to victory.
    """
    net = layers.conv2d(board, 128, [9, 9], scope='conv9_1')
    net = layers.conv2d(net, 64, [9, 9], scope='conv9_2')
    net = layers.conv2d(net, 1, [9, 9], scope='conv9_3')
    return net
  
  def value(board):
    """Builds a value network.
  
    The policy network predicts the expected value (win chance) of a state,
    assuming we make the best move.
  
    Args:
      board: A 4D tensor of the shape [batch, width, height, features] that
        contains the features for each board position.
    Returns:
      logits: A 1D tensor of the shape [batch] that contains the logits of the
        probability that the state leads to victory.
    """
    net = layers.conv2d(board, 64, [9, 9], scope='conv9_1')
    net = layers.conv2d(net, 16, [9, 9], scope='conv9_2')
    net = layers.flatten(net)
    net = layers.fully_connected(net, 512)
    net = layers.dropout(net)
    net = layers.fully_connected(net, 1)
    net = tf.squeeze(net, squeeze_dims=[1])
    return net
  
subject: Simplify the value network