new_contents: import tensorflow as tf
  
  # USEFUL LAYERS
  fc = tf.contrib.layers.fully_connected
  conv = tf.contrib.layers.conv2d
  # convsep = tf.contrib.layers.separable_conv2d
  deconv = tf.contrib.layers.conv2d_transpose
  relu = tf.nn.relu
  maxpool = tf.contrib.layers.max_pool2d
  dropout_layer = tf.layers.dropout
  # bn = tf.contrib.layers.batch_norm
  # bn_params = {"is_training": self.is_training}
  winit = tf.contrib.layers.xavier_initializer()
  repeat = tf.contrib.layers.repeat
  arg_scope = tf.contrib.framework.arg_scope
  l2_regularizer = tf.contrib.layers.l2_regularizer
  
  
  # ==============================================================================
  #                                                                  GET_VGG_SCOPE
  # ==============================================================================
  def get_vgg_argscope(weight_decay=0.0005):
      """ Gets the arg scope needed for VGG.
      Args:
          weight_decay: The l2 regularization coefficient.
      Returns:
          An arg_scope with the default arguments for layers in VGG .
      """
      with tf.contrib.framework.arg_scope(
          [conv],
          activation_fn=tf.nn.relu,
          weights_regularizer=l2_regularizer(weight_decay),
          biases_initializer=tf.zeros_initializer()):
          with tf.contrib.framework.arg_scope([conv], padding='SAME') as scope:
                  return scope
  
  
  
subject: Add vgg arg scope