code: def block35(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None):
    
    with tf.variable_scope(scope, 'Block35', [net], reuse=reuse):
      with tf.variable_scope('Branch_0'):
        tower_conv = slim.conv2d(net, 32, 1, scope='Conv2d_1x1')
      with tf.variable_scope('Branch_1'):
        tower_conv1_0 = slim.conv2d(net, 32, 1, scope='Conv2d_0a_1x1')
        tower_conv1_1 = slim.conv2d(tower_conv1_0, 32, 3, scope='Conv2d_0b_3x3')
      with tf.variable_scope('Branch_2'):
        tower_conv2_0 = slim.conv2d(net, 32, 1, scope='Conv2d_0a_1x1')
        tower_conv2_1 = slim.conv2d(tower_conv2_0, 48, 3, scope='Conv2d_0b_3x3')
        tower_conv2_2 = slim.conv2d(tower_conv2_1, 64, 3, scope='Conv2d_0c_3x3')
      mixed = tf.concat(
          axis=3, values=[tower_conv, tower_conv1_1, tower_conv2_2])
      up = slim.conv2d(mixed, net.get_shape()[3], 1, normalizer_fn=None,
                       activation_fn=None, scope='Conv2d_1x1')
      net += scale * up
      if activation_fn:
        net = activation_fn(net)
    return net
docs: Builds the 35x35 resnet block.