code: def sync_across_processes(
      t: Union[torch.Tensor, np.ndarray],
      world_size: int,
      group: Any = None,
  ) -> Union[torch.Tensor, np.ndarray]:
      """Concatenates tensors across processes.
  
      Args:
          t: input tensor or numpy array
          world_size: world size
          group: The process group to work on
  
      Returns:
          Tensor or numpy array concatenated across all processes
      """
  
      torch.distributed.barrier()
  
      if isinstance(t, torch.Tensor):
          gather_t_tensor = [
              torch.ones_like(t) for _ in range(world_size)
          ]
  
          if t.is_cuda:
              torch.distributed.all_gather(gather_t_tensor, t)
          else:
              torch.distributed.all_gather_object(
                  gather_t_tensor, t, group=group
              )
  
          ret = torch.cat(gather_t_tensor)
      elif isinstance(t, np.ndarray):
          gather_t_array = [np.ones_like(t) for _ in range(world_size)]
          torch.distributed.all_gather_object(
              gather_t_array, t, group=group
          )
          ret = np.concatenate(gather_t_array)  # type: ignore
      else:
          raise ValueError(f"Can't synchronize {type(t)}.")
  
      return ret
  
imports: from typing import Any, Union
  import numpy
  import torch
label_desc: Use image node_id 1 for calling a global function with example usage: sync_across_processes(t, world_size, group) and returns: ret