code: def all_to_all(
      output_tensor_list, input_tensor_list, group=group.WORLD
  ):
      """
      Each process scatters list of input tensors to all processes in a group and
      return gathered list of tensors in output list.
  
      Arguments:
          out_tensor_list (list[Tensor]): list of tensors to gather one per rank.
          input_tensor_list (list[Tensor]): List of tensors to scatter one per rank.
          group (ProcessGroup, optional): The process group to work on.
  
      Returns:
          tuple([Tensor]): Output of the collective.
  
      """
      return _AlltoAll.apply(
          group, output_tensor_list, *input_tensor_list
      )
  
imports: import torch
  import torch.distributed
  from torch.autograd import Function
label_desc: Use image node_id 8 for calling a global function with example usage: all_to_all(output_tensor_list, input_tensor_list, group) and returns: _AlltoAll