code: def init_and_run(fn, args, rank, world_size, filename, filename_rpc):
      """Initialize and run the unit test for testing replicated MoE groups."""
      dist_init(rank, world_size, filename, filename_rpc)
      torch.cuda.set_device(rank)
      group = torch.distributed.new_group()
      # Specify the moe_group used to initialize the MoE layers with.
      fn(rank, group, *args, expert_group=get_moe_group())
  
imports: import copy
  import functools
  from time import time
  import unittest
  from parameterized import parameterized
  import torch
  from torch import nn
  from torch.optim import SGD, Adadelta, Adam
  from fairscale.fair_dev.testing.testing import dist_init, objects_are_equal, spawn_for_all_world_sizes
  from fairscale.internal.params import recursive_copy_to_device
  from fairscale.nn.data_parallel import FullyShardedDataParallel, get_fsdp_instances
  from fairscale.nn.data_parallel.fsdp_optim_utils import is_singleton_tensor
  from .test_fsdp import DistributedTest, DummyProcessGroup, MixtureOfExperts, TransformerWithSharedParams, rename_test, spawn_and_init
label_desc: Use image node_id 6 for calling a global function with example usage: init_and_run(fn, args, rank, world_size, filename, filename_rpc) without return types