code: def run(
      local_rank: int,
      work_run: Callable,
      main_address: str,
      main_port: int,
      num_nodes: int,
      node_rank: int,
      nprocs: int,
  ):
      fabrics = []
      strategies = []
      mps_accelerators = []
  
      for pkg_name in ("lightning.fabric", "lightning_" + "fabric"):
          try:
              pkg = importlib.import_module(pkg_name)
              fabrics.append(pkg.Fabric)
              strategies.append(pkg.strategies.DDPStrategy)
              mps_accelerators.append(pkg.accelerators.MPSAccelerator)
          except (ImportError, ModuleNotFoundError):
              continue
  
      # Used to configure PyTorch progress group
      os.environ["MASTER_ADDR"] = main_address
      os.environ["MASTER_PORT"] = str(main_port)
  
      # Used to hijack TorchElastic Cluster Environnement.
      os.environ["GROUP_RANK"] = str(node_rank)
      os.environ["RANK"] = str(local_rank + node_rank * nprocs)
      os.environ["LOCAL_RANK"] = str(local_rank)
      os.environ["WORLD_SIZE"] = str(num_nodes * nprocs)
      os.environ["LOCAL_WORLD_SIZE"] = str(nprocs)
      os.environ["TORCHELASTIC_RUN_ID"] = "1"
  
      # Used to force Fabric to setup the distributed environnement.
      os.environ["LT_CLI_USED"] = "1"
  
      # Used to pass information to Fabric directly.
      def pre_fn(fabric, *args: Any, **kwargs: Any):
          kwargs["devices"] = nprocs
          kwargs["num_nodes"] = num_nodes
  
          if any(acc.is_available() for acc in mps_accelerators):
              old_acc_value = kwargs.get("accelerator", "auto")
              kwargs["accelerator"] = "cpu"
  
              if old_acc_value != kwargs["accelerator"]:
                  warnings.warn(
                      "Forcing `accelerator=cpu` as MPS does not support distributed training."
                  )
          else:
              kwargs["accelerator"] = "auto"
          strategy = kwargs.get("strategy", None)
          if strategy:
              if isinstance(strategy, str):
                  if strategy == "ddp_spawn":
                      strategy = "ddp"
                  elif strategy == "ddp_sharded_spawn":
                      strategy = "ddp_sharded"
              elif isinstance(
                  strategy, tuple(strategies)
              ) and strategy._start_method in ("spawn", "fork"):
                  raise ValueError(
                      "DDP Spawned strategies aren't supported yet."
                  )
  
          kwargs["strategy"] = strategy
  
          return {}, args, kwargs
  
      tracer = Tracer()
      for lf in fabrics:
          tracer.add_traced(lf, "__init__", pre_fn=pre_fn)
      tracer._instrument()
      ret_val = work_run()
      tracer._restore()
      return ret_val
  
file_path: pytorch-lightning/src/lightning/app/components/multi_node/fabric.py
imports: import importlib
  import os
  import warnings
  from dataclasses import dataclass
  from typing import Any, Callable, Protocol, Type, runtime_checkable
  from lightning.app.components.multi_node.base import MultiNode
  from lightning.app.components.multi_node.pytorch_spawn import _PyTorchSpawnRunExecutor
  from lightning.app.core.work import LightningWork
  from lightning.app.utilities.packaging.cloud_compute import CloudCompute
  from lightning.app.utilities.tracer import Tracer
label_desc: Use image node_id 1 for calling the _FabricRunExecutor obj's underlying member method code with example usage: obj.run(local_rank, work_run, main_address, main_port, num_nodes, node_rank, nprocs) and returns: ret_val, dict,  args,  kwargs