code: def cat_all_gather(
      tensors: torch.Tensor,
      process_group: Optional[DistProcessGroup] = None,
  ) -> torch.Tensor:
      """
      Performs the concatenated all_reduce operation on the provided tensors.
      """
      if process_group is not None:
          gather_sz = get_process_group_size(process_group)
      else:
          gather_sz = dist.get_world_size()
      tensors_gather = [
          torch.ones_like(tensors) for _ in range(gather_sz)
      ]
      dist.all_gather(
          tensors_gather,
          tensors,
          async_op=False,
          group=process_group,
      )
      output = torch.cat(tensors_gather, dim=0)
      return output
  
imports: import functools
  import logging
  import pickle
  from typing import Any, List, Optional, Tuple, TypeVar
  import torch
  import torch.distributed
label_desc: Use image node_id 2 for calling a global function with example usage: cat_all_gather(tensors, process_group) and returns: output