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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def all_reduce_ring(x, parallelism, maybe_reduce=True, use_bfloat16=True):
 """Compute the sum of all Tensors and put the result everywhere. Assumes that the devices are connected in a ring. Args: x: a list of Tensors with length parallelism.n parallelism: a expert_utils.Parallelism object. maybe_reduce: a boolean - first reduce per device. use_bfloat16: a boolean - saves bandwidth but loses precision Returns: a list of Tensors with length parallelism.n """

  if parallelism.n == 1:
    return x

  if maybe_reduce:
    original_parallelism = parallelism
    parallelism, x = reduce_by_device(parallelism, x, tf.add_n)

  if parallelism.n == 1:
    y = x
  else:
    # first shard the input:
    x_flat = parallelism(tf.reshape, x, [[-1]] * parallelism.n)
    # [device, shard]
    x_split = parallelism(
        common_layers.approximate_split, x_flat, parallelism.n, 0)
    def _step(source_replica, target_replica, x_split, op="plus_eq"):
      """Helper function - one step of summing or copying.

      If op == "plus_eq", then adds source_replica into target_replica
      If op == "copy", then copies source_replica onto target_replica

      These operations happen for all shards.  The replica numbers are offset
      by the shard numbers to keep all physical links busy.

      Args:
        source_replica: an integer
        target_replica: an integer
        x_split: a list of lists of tensors
        op: a string
      """
      for shard in range(parallelism.n):
        source_device = (shard + source_replica) % parallelism.n
        target_device = (shard + target_replica) % parallelism.n
        source = x_split[source_device][shard]
        if use_bfloat16:
          with tf.device(parallelism.devices[source_device]):
            source = tf.to_bfloat16(source)
        with tf.device(parallelism.devices[target_device]):
          source = tf.to_float(source)
          if op == "plus_eq":
            x_split[target_device][shard] += source
          else:
            assert op == "copy"
            x_split[target_device][shard] = tf.identity(source)
    center = parallelism.n // 2

    # accumulate everything towards the center.
    for i in reversed(range(center, parallelism.n - 1)):
      _step(i + 1, i, x_split, op="plus_eq")
    for i in range(center):
      _step(i, i + 1, x_split, op="plus_eq")
    # copy everything away from the center.
    for i in range(center, parallelism.n - 1):
      _step(i, i + 1, x_split, op="copy")
    for i in reversed(range(center)):
      _step(i + 1, i, x_split, op="copy")
    x_concat = parallelism(tf.concat, x_split, 0)
    y = parallelism(common_layers.reshape_like_all_dims, x_concat, x)
  if maybe_reduce:
    y = expand_by_device(original_parallelism, parallelism, y)
  return y