text: import os
  from typing import Optional
  from unittest import mock
  
  import pytest
  import torch
  
  from pytorch_lightning import Trainer
  from pytorch_lightning.callbacks import Callback
  from pytorch_lightning.plugins.training_type.rpc_sequential import RPCPlugin
  from pytorch_lightning.utilities import _RPC_AVAILABLE
  from tests.helpers.boring_model import BoringModel
  
  
  @mock.patch.dict(
      os.environ,
      {
          "CUDA_VISIBLE_DEVICES": "0,1",
          "SLURM_NTASKS": "2",
          "SLURM_JOB_NAME": "SOME_NAME",
          "SLURM_NODEID": "0",
          "LOCAL_RANK": "0",
          "SLURM_LOCALID": "0",
      },
  )
  @mock.patch("torch.cuda.device_count", return_value=2)
  @pytest.mark.parametrize(
      ["ddp_backend", "gpus", "num_processes"],
      [("ddp_cpu", None, 2), ("ddp", 2, 0), ("ddp_spawn", 2, 0)],
  )
  @pytest.mark.skipif(not _RPC_AVAILABLE, reason="RPC is not available")
  def test_rpc_choice(tmpdir, ddp_backend, gpus, num_processes):
  
      class CB(Callback):
  
          def on_fit_start(self, trainer, pl_module):
              assert isinstance(trainer.training_type_plugin, RPCPlugin)
              raise RuntimeError('finished plugin check')
  
      model = BoringModel()
      trainer = Trainer(
          fast_dev_run=True,
          gpus=gpus,
          num_processes=num_processes,
          distributed_backend=ddp_backend,
          callbacks=[CB()],
          plugins=[RPCPlugin()]
      )
  
      with pytest.raises(RuntimeError, match='finished plugin check'):
          trainer.fit(model)
  
  
  class CustomRPCPlugin(RPCPlugin):
  
      def __init__(self, **kwargs):
          super().__init__(**kwargs)
          self.rpc_save_model_count = 0
          self.worker_optimizer_step_count = 0
  
      def rpc_save_model(self, save_model_fn, last_filepath, trainer, pl_module) -> None:
          self.rpc_save_model_count += 1
  
      def barrier(self, name: Optional[str] = None) -> None:
          return
  
  
  @pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires GPU machine")
  @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
  @pytest.mark.skipif(not _RPC_AVAILABLE, reason="RPC is not available")
  @pytest.mark.skipif(
      not os.getenv("PL_RUNNING_SPECIAL_TESTS", '0') == '1', reason="test should be run outside of pytest"
  )
  def test_rpc_function_calls_ddp(tmpdir):
      model = BoringModel()
      plugin = CustomRPCPlugin()
      max_epochs = 2
      limit_train_batches = 2
      trainer = Trainer(
          limit_train_batches=limit_train_batches,
          limit_val_batches=2,
          max_epochs=max_epochs,
          gpus=2,
          distributed_backend='ddp',
          plugins=[plugin]
      )
  
      trainer.fit(model)
      if trainer.global_rank == 0:  # Main process
          assert plugin.rpc_save_model_count == max_epochs
      else:  # Worker process
          assert plugin.rpc_save_model_count == max_epochs
  