<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def shake_shake_skip_connection(x, output_filters, stride, is_training):
 """Adds a residual connection to the filter x for the shake-shake model."""

  curr_filters = common_layers.shape_list(x)[-1]
  if curr_filters == output_filters:
    return x
  stride_spec = [1, stride, stride, 1]
  # Skip path 1.
  path1 = tf.nn.avg_pool(x, [1, 1, 1, 1], stride_spec, "VALID")
  path1 = tf.layers.conv2d(
      path1, int(output_filters / 2), (1, 1), padding="SAME", name="path1_conv")

  # Skip path 2.
  pad_arr = [[0, 0], [0, 1], [0, 1], [0, 0]]  # First pad with 0's then crop.
  path2 = tf.pad(x, pad_arr)[:, 1:, 1:, :]
  path2 = tf.nn.avg_pool(path2, [1, 1, 1, 1], stride_spec, "VALID")
  path2 = tf.layers.conv2d(
      path2, int(output_filters / 2), (1, 1), padding="SAME", name="path2_conv")

  # Concat and apply BN.
  final_path = tf.concat(values=[path1, path2], axis=-1)
  final_path = tf.layers.batch_normalization(
      final_path, training=is_training, name="final_path_bn")
  return final_path