class_docstr: A light-weight wrapper class for torch.distributed API.
  Only a subset of functions are wrapped. Once the init_process_group
  is initialized, standard torch.distributed.* can be used directly
  so no need to wrap all the functions. We can keep adding wrappers as
  needed.
class_docstr_tok: ["A","light-weight","wrapper","class","for","torch.distributed","API",".","Only","a","subset","of","functions","are","wrapped",".","Once","the","init_process_group","is","initialized",",","standard","torch.distributed",".","*","can","be","used","directly","so","no","need","to","wrap","all","the","functions",".","We","can","keep","adding","wrappers","as","needed","."]
code: def all_to_all_single(
      self,
      output,
      input,
      output_split_sizes=None,
      input_split_sizes=None,
      group=None,
      async_op=False,
  ):
      return torch.distributed.all_to_all_single(
          output=output,
          input=input,
          output_split_sizes=output_split_sizes,
          input_split_sizes=input_split_sizes,
          group=group,
          async_op=async_op,
      )
  
imports: from deepspeed import utils
  from .utils import *
  from .backend import *
  from .comm import *
  import os
label_desc: Use image node_id 18 for calling the TorchBackend obj's underlying member method code with example usage: obj.all_to_all_single(output, input, output_split_sizes, input_split_sizes, group, async_op) and returns: torch