code: def _expand_group(
      group: RANK_TYPES, tag: str = ""
  ) -> Tuple[str, List[int], int]:
      """
      _expand_group desugars the different RANK_TYPES types into a canonical format that is traceable.
  
      By having this be part of the explicit eager codepath, we avoid having to specialize behavior inside
      torchdynamo and can still interoperate with processgroup objects or other untraceable forms.
      """
      # had to define this hack _inside_ expand_group to avoid
      # graph_break [('torch.* op returned non-Tensor int
      # caused by 'cast_*` functions being treated as 'torch.*' ops (iiuc)
      if TYPE_CHECKING:
  
          def cast_listlistint(x):
              return cast(List[List[int]], x)
  
          def cast_listint(x):
              return cast(List[int], x)
  
      else:
          # fake cast op for use at runtime since dynamo doesn't support real cast
          # also, dynamo didn't like encountering 'typing' objects ()
          # NotImplementedError: argument of type: <class 'typing._GenericAlias'>
          def cast_listlistint(x):
              return x
  
          def cast_listint(x):
              return x
  
      rankset: List[int]
      if isinstance(group, list):
          if isinstance(group[0], list):
              nested_list = cast_listlistint(group)
              rankset = []
              group_size = -1
              for rs in nested_list:
                  rankset.extend(rs)
                  if group_size != -1 and group_size != len(rs):
                      raise ValueError(
                          f"group sizes must be identical found {group_size} and {len(rs)}"
                      )
                  group_size = len(rs)
          else:
              rankset = cast_listint(group)
              group_size = len(rankset)
      elif isinstance(group, dist.ProcessGroup):
          rankset = dist.get_process_group_ranks(group)
          group_size = len(rankset)
          tag = tag or c10d._get_group_tag(group)
      elif isinstance(group, DeviceMesh):
          assert (
              group.ndim == 1
          ), "Only 1D mesh is supported, pass in (DeviceMesh, int) together if mesh > 1D"
          # TODO: it should run collective in the whole mesh instead of dim 0
          tag, rankset = group._dim_group_infos[0]
          group_size = len(rankset)
      elif isinstance(group, tuple):
          if (
              len(group) == 2
              and isinstance(group[0], DeviceMesh)
              and isinstance(group[1], int)
          ):
              dmesh = group[0]
              dim = group[1]
              tag, rankset = dmesh._dim_group_infos[dim]
              group_size = len(rankset)
          else:
              raise ValueError(
                  "Invalid tuple for group must be (DeviceMesh, int)"
              )
      else:
          raise ValueError(
              "Invalid type for group, must be one of List, Processgroup, DeviceMesh or (DeviceMesh, int)."
          )
  
      return (tag, rankset, group_size)
  
imports: import sys
  import warnings
  from typing import cast, List, Optional, Tuple, TYPE_CHECKING, Union
  import torch
  import torch.distributed
  import torch.distributed.distributed_c10d
  from torch._custom_ops import impl_abstract
  from torch.distributed.device_mesh import DeviceMesh
  from torch.fx.experimental.proxy_tensor import get_innermost_proxy_mode
  from .None import _functional_collectives_impl
  from ._functional_collectives_impl import _register_tensor_wrapper
  from torch.distributed.distributed_c10d import all_gather_into_tensor, all_reduce, reduce_scatter_tensor
label_desc: Use image node_id 12 for calling a global function with example usage: _expand_group(group, tag) and returns: tag,  rankset,  group_size, cast, cast, x, x