code: def check_layer(rank, world_size, port):
      launch(
          config={},
          rank=rank,
          world_size=world_size,
          host="localhost",
          port=port,
          backend="nccl",
      )
  
      physical_mesh_id = torch.arange(0, 4)
      assert rank == dist.get_rank()
  
      tensor_to_check = torch.tensor([2, 2, 2, 2]).cuda()
      mesh_shape = (2, 2)
      # [[0, 1,
      #  [2, 3]]
      device_mesh = DeviceMesh(
          physical_mesh_id, mesh_shape, init_process_group=True
      )
  
      for axis in range(len(mesh_shape)):
          tensor = torch.ones(4).cuda()
          pg = device_mesh.get_process_group(axis=axis)
          dist.all_reduce(tensor, op=ReduceOp.SUM, group=pg)
          assert tensor.equal(tensor_to_check)
  
file_path: ColossalAI/tests/test_device/test_init_logical_pg.py
imports: import pytest
  import torch
  import torch.distributed
  from torch.distributed import ReduceOp
  from colossalai.device.device_mesh import DeviceMesh
  from colossalai.initialize import launch
  from colossalai.testing import rerun_if_address_is_in_use, spawn
label_desc: Use image node_id 1 for calling a global function with example usage: check_layer(rank, world_size, port) without return types