docstr: This method checks:
  
  1. strategy: whether the strategy name is valid, and sets the internal flags if it is.
  2. accelerator: if the value of the accelerator argument is a type of accelerator (instance or string),
      set self._accelerator_flag accordingly.
  3. precision: The final value of the precision flag may be determined either by the precision argument or
      by a plugin instance.
  4. plugins: The list of plugins may contain a Precision plugin, CheckpointIO, ClusterEnvironment and others.
      Additionally, other flags such as `precision` or `sync_batchnorm` can populate the list with the
      corresponding plugin instances.
code: def _check_config_and_set_final_flags(
      self,
      strategy: Union[str, Strategy],
      accelerator: Union[str, Accelerator],
      precision: Optional[_PRECISION_INPUT],
      plugins: Optional[Union[_PLUGIN_INPUT, List[_PLUGIN_INPUT]]],
      sync_batchnorm: bool,
  ) -> None:
      """This method checks:
  
      1. strategy: whether the strategy name is valid, and sets the internal flags if it is.
      2. accelerator: if the value of the accelerator argument is a type of accelerator (instance or string),
          set self._accelerator_flag accordingly.
      3. precision: The final value of the precision flag may be determined either by the precision argument or
          by a plugin instance.
      4. plugins: The list of plugins may contain a Precision plugin, CheckpointIO, ClusterEnvironment and others.
          Additionally, other flags such as `precision` or `sync_batchnorm` can populate the list with the
          corresponding plugin instances.
  
      """
      if plugins is not None:
          plugins = (
              [plugins] if not isinstance(plugins, list) else plugins
          )
  
      if isinstance(strategy, str):
          strategy = strategy.lower()
  
      self._strategy_flag = strategy
  
      if (
          strategy == "colossalai"
          and not _LIGHTNING_COLOSSALAI_AVAILABLE
      ):
          raise ModuleNotFoundError(
              str(_LIGHTNING_COLOSSALAI_AVAILABLE)
          )
  
      if strategy == "bagua" and not _LIGHTNING_BAGUA_AVAILABLE:
          raise ModuleNotFoundError(str(_LIGHTNING_BAGUA_AVAILABLE))
  
      if (
          strategy != "auto"
          and strategy not in self._registered_strategies
          and not isinstance(strategy, Strategy)
      ):
          raise ValueError(
              f"You selected an invalid strategy name: `strategy={strategy!r}`."
              " It must be either a string or an instance of `lightning.pytorch.strategies.Strategy`."
              " Example choices: auto, ddp, ddp_spawn, deepspeed, ..."
              " Find a complete list of options in our documentation at https://lightning.ai"
          )
  
      if (
          accelerator not in self._accelerator_types
          and accelerator not in ("auto", "gpu")
          and not isinstance(accelerator, Accelerator)
      ):
          raise ValueError(
              f"You selected an invalid accelerator name: `accelerator={accelerator!r}`."
              f" Available names are: auto, {', '.join(self._accelerator_types)}."
          )
  
      # MPS accelerator is incompatible with DDP family of strategies. It supports single-device operation only.
      is_ddp_str = isinstance(strategy, str) and "ddp" in strategy
      is_deepspeed_str = (
          isinstance(strategy, str) and "deepspeed" in strategy
      )
      is_parallel_strategy = (
          isinstance(strategy, ParallelStrategy)
          or is_ddp_str
          or is_deepspeed_str
      )
      is_mps_accelerator = MPSAccelerator.is_available() and (
          accelerator in ("mps", "auto", "gpu", None)
          or isinstance(accelerator, MPSAccelerator)
      )
      if is_mps_accelerator and is_parallel_strategy:
          raise ValueError(
              f"You set `strategy={strategy}` but strategies from the DDP family are not supported on the"
              f" MPS accelerator. Either explicitly set `accelerator='cpu'` or change the strategy."
          )
  
      self._accelerator_flag = accelerator
  
      precision_flag = _convert_precision_to_unified_args(precision)
  
      if plugins:
          plugins_flags_types: Dict[str, int] = Counter()
          for plugin in plugins:
              if isinstance(plugin, Precision):
                  self._precision_plugin_flag = plugin
                  plugins_flags_types[Precision.__name__] += 1
              elif isinstance(plugin, CheckpointIO):
                  self.checkpoint_io = plugin
                  plugins_flags_types[CheckpointIO.__name__] += 1
              elif isinstance(plugin, ClusterEnvironment):
                  self._cluster_environment_flag = plugin
                  plugins_flags_types[ClusterEnvironment.__name__] += 1
              elif isinstance(plugin, LayerSync):
                  if sync_batchnorm and not isinstance(
                      plugin, TorchSyncBatchNorm
                  ):
                      raise MisconfigurationException(
                          f"You set `Trainer(sync_batchnorm=True)` and provided a `{plugin.__class__.__name__}`"
                          " plugin, but this is not allowed. Choose one or the other."
                      )
                  self._layer_sync = plugin
                  plugins_flags_types[TorchSyncBatchNorm.__name__] += 1
              else:
                  raise MisconfigurationException(
                      f"Found invalid type for plugin {plugin}. Expected one of: Precision, "
                      "CheckpointIO, ClusterEnviroment, or LayerSync."
                  )
  
          duplicated_plugin_key = [
              k for k, v in plugins_flags_types.items() if v > 1
          ]
          if duplicated_plugin_key:
              raise MisconfigurationException(
                  f"Received multiple values for {', '.join(duplicated_plugin_key)} flags in `plugins`."
                  " Expected one value for each type at most."
              )
  
          if (
              plugins_flags_types.get(Precision.__name__)
              and precision_flag is not None
          ):
              raise ValueError(
                  f"Received both `precision={precision_flag}` and `plugins={self._precision_plugin_flag}`."
                  f" Choose one."
              )
  
      self._precision_flag = (
          "32-true" if precision_flag is None else precision_flag
      )
  
      # handle the case when the user passes in a strategy instance which has an accelerator, precision,
      # checkpoint io or cluster env set up
      # TODO: improve the error messages below
      if self._strategy_flag and isinstance(
          self._strategy_flag, Strategy
      ):
          if self._strategy_flag._accelerator:
              if self._accelerator_flag != "auto":
                  raise MisconfigurationException(
                      "accelerator set through both strategy class and accelerator flag, choose one"
                  )
              self._accelerator_flag = self._strategy_flag._accelerator
          if self._strategy_flag._precision_plugin:
              # [RFC] handle precision plugin set up conflict?
              if self._precision_plugin_flag:
                  raise MisconfigurationException(
                      "precision set through both strategy class and plugins, choose one"
                  )
              self._precision_plugin_flag = (
                  self._strategy_flag._precision_plugin
              )
          if self._strategy_flag._checkpoint_io:
              if self.checkpoint_io:
                  raise MisconfigurationException(
                      "checkpoint_io set through both strategy class and plugins, choose one"
                  )
              self.checkpoint_io = self._strategy_flag._checkpoint_io
          if getattr(self._strategy_flag, "cluster_environment", None):
              if self._cluster_environment_flag:
                  raise MisconfigurationException(
                      "cluster_environment set through both strategy class and plugins, choose one"
                  )
              self._cluster_environment_flag = getattr(
                  self._strategy_flag, "cluster_environment"
              )
  
          if (
              hasattr(self._strategy_flag, "parallel_devices")
              and self._strategy_flag.parallel_devices
          ):
              if self._strategy_flag.parallel_devices[0].type == "cpu":
                  if (
                      self._accelerator_flag
                      and self._accelerator_flag not in ("auto", "cpu")
                  ):
                      raise MisconfigurationException(
                          f"CPU parallel_devices set through {self._strategy_flag.__class__.__name__} class,"
                          f" but accelerator set to {self._accelerator_flag}, please choose one device type"
                      )
                  self._accelerator_flag = "cpu"
              if self._strategy_flag.parallel_devices[0].type == "cuda":
                  if (
                      self._accelerator_flag
                      and self._accelerator_flag
                      not in ("auto", "cuda", "gpu")
                  ):
                      raise MisconfigurationException(
                          f"GPU parallel_devices set through {self._strategy_flag.__class__.__name__} class,"
                          f" but accelerator set to {self._accelerator_flag}, please choose one device type"
                      )
                  self._accelerator_flag = "cuda"
              self._parallel_devices = (
                  self._strategy_flag.parallel_devices
              )
  
imports: import logging
  import os
  from collections import Counter
  from typing import Dict, List, Literal, Optional, Union
  import torch
  from lightning.fabric.connector import _PRECISION_INPUT, _PRECISION_INPUT_STR, _convert_precision_to_unified_args
  from lightning.fabric.plugins.environments import ClusterEnvironment, LightningEnvironment, LSFEnvironment, MPIEnvironment, SLURMEnvironment, TorchElasticEnvironment
  from lightning.fabric.utilities.device_parser import _determine_root_gpu_device
  from lightning.fabric.utilities.imports import _IS_INTERACTIVE
  from lightning.pytorch.accelerators import AcceleratorRegistry
  from lightning.pytorch.accelerators.accelerator import Accelerator
  from lightning.pytorch.accelerators.cuda import CUDAAccelerator
  from lightning.pytorch.accelerators.mps import MPSAccelerator
  from lightning.pytorch.accelerators.xla import XLAAccelerator
  from lightning.pytorch.plugins import _PLUGIN_INPUT, CheckpointIO, DeepSpeedPrecision, DoublePrecision, FSDPPrecision, HalfPrecision, MixedPrecision, Precision, TransformerEnginePrecision, XLAPrecision
  from lightning.pytorch.plugins.layer_sync import LayerSync, TorchSyncBatchNorm
  from lightning.pytorch.strategies import DDPStrategy, DeepSpeedStrategy, FSDPStrategy, ParallelStrategy, SingleDeviceStrategy, SingleDeviceXLAStrategy, Strategy, StrategyRegistry, XLAStrategy
  from lightning.pytorch.strategies.ddp import _DDP_FORK_ALIASES
  from lightning.pytorch.utilities.exceptions import MisconfigurationException
  from lightning.pytorch.utilities.imports import _LIGHTNING_BAGUA_AVAILABLE, _LIGHTNING_COLOSSALAI_AVAILABLE, _graphcore_available_and_importable, _habana_available_and_importable
  from lightning.pytorch.utilities.rank_zero import rank_zero_info, rank_zero_warn
label_desc: Use image node_id 2 for calling the _AcceleratorConnector obj's underlying member method code with example usage: obj._check_config_and_set_final_flags(strategy, accelerator, precision, plugins, sync_batchnorm) without return types