code: def all_to_all(
      x,
      concat_dimension,
      split_dimension,
      split_count,
      group_assignment=None,
      name=None,
  ):
      """Exchange data across TPU replicas.
  
      Args:
        x: The local tensor.
        concat_dimension: The dimension number to concatenate.
        split_dimension: The dimension number to split.
        split_count: The number of splits, this number must equal to the sub-group
          size(group_assignment.get_shape()[1])
        group_assignment: Optional 2d int32 lists with shape [num_groups,
          num_replicas_per_group]. `group_assignment[i]` represents the replica ids
          in the ith subgroup.
        name: Optional op name.
  
      Returns:
        A `Tensor` which is concatenated by data from different replicas.
      """
      if group_assignment is None:
          group_assignment = _create_default_group_assignment()
      return gen_tpu_ops.all_to_all(
          x,
          group_assignment,
          concat_dimension=concat_dimension,
          split_dimension=split_dimension,
          split_count=split_count,
          name=name,
      )
  
imports: from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gen_tpu_ops
  from tensorflow.python.ops.gen_tpu_ops import *
  from tensorflow.python.platform import tf_logging
  from tensorflow.python.tpu import tpu_function
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 2 for calling a global function with example usage: all_to_all(x, concat_dimension, split_dimension, split_count, group_assignment, name) and returns: gen_tpu_ops