code: def all_to_all_single(
      output,
      input,
      output_split_sizes=None,
      input_split_sizes=None,
      group=group.WORLD,
  ):
      """
      Each process splits input tensor and then scatters the split list
      to all processes in a group. Then concatenate the received tensors from all
      the processes in the group and return single output tensor.
  
      Arguments:
          output (Tensor): Gathered cancatenated output tensor.
          input (Tensor): Input tensor to scatter.
          output_split_sizes: (list[Int], optional): Output split sizes for dim 0
              if specified None or empty, dim 0 of ``output`` tensor must divide
              equally by ``world_size``.
          input_split_sizes: (list[Int], optional): Input split sizes for dim 0
              if specified None or empty, dim 0 of ``input`` tensor must divide
              equally by ``world_size``.
  
      Returns:
          Tensor: Output of the collective.
  
      """
      return _AlltoAllSingle.apply(
          group, output, output_split_sizes, input_split_sizes, input
      )
  
imports: import torch
  import torch.distributed
  from torch.autograd import Function
label_desc: Use image node_id 9 for calling a global function with example usage: all_to_all_single(output, input, output_split_sizes, input_split_sizes, group) and returns: _AlltoAllSingle