code: def prepare_trainer(trainer: pl.Trainer) -> pl.Trainer:
      """Prepare the PyTorch Lightning Trainer for distributed execution."""
  
      # Check strategy class
      valid_strategy_class = [
          RayDDPStrategy,
          RayFSDPStrategy,
          RayDeepSpeedStrategy,
      ]
  
      if not any(
          isinstance(trainer.strategy, cls)
          for cls in valid_strategy_class
      ):
          raise RuntimeError(
              f"Invalid strategy class: {type(trainer.strategy)}. To use "
              "PyTorch Lightning with Ray, the strategy object should be one of "
              f"{[cls.__name__ for cls in valid_strategy_class]} class "
              "or its subclass."
          )
  
      # Check cluster environment
      cluster_environment = getattr(
          trainer.strategy, "cluster_environment", None
      )
      if cluster_environment and not isinstance(
          cluster_environment, RayLightningEnvironment
      ):
          raise RuntimeError(
              "Invalid cluster environment plugin. The expected class is"
              "`ray.train.lightning.RayLightningEnvironment` "
              f"but got {type(cluster_environment)}!"
          )
  
      record_extra_usage_tag(
          TagKey.TRAIN_LIGHTNING_PREPARE_TRAINER, "1"
      )
      return trainer
  
imports: import logging
  import os
  import shutil
  import tempfile
  from typing import Any, Dict
  import torch
  from packaging.version import Version
  import ray
  from ray import train
  from ray._private.usage.usage_lib import TagKey, record_extra_usage_tag
  from ray.train import Checkpoint
  from ray.util import PublicAPI
label_desc: Use image node_id 3 for calling a global function with example usage: prepare_trainer(trainer) and returns: trainer