code: def prepare_model(
      model: torch.nn.Module,
      move_to_device: Union[bool, torch.device] = True,
      parallel_strategy: Optional[str] = "ddp",
      parallel_strategy_kwargs: Optional[Dict[str, Any]] = None,
  ) -> torch.nn.Module:
      """Prepares the model for distributed execution.
  
      This allows you to use the same exact code regardless of number of
      workers or the device type being used (CPU, GPU).
  
      Args:
          model (torch.nn.Module): A torch model to prepare.
          move_to_device: Either a boolean indiciating whether to move
              the model to the correct device or an actual device to
              move the model to. If set to False, the model needs
              to manually be moved to the correct device.
          parallel_strategy ("ddp", "fsdp", or None): Whether to wrap models
              in ``DistributedDataParallel``, ``FullyShardedDataParallel``,
              or neither.
          parallel_strategy_kwargs (Dict[str, Any]): Args to pass into
              ``DistributedDataParallel`` or ``FullyShardedDataParallel``
              initialization if ``parallel_strategy`` is set to "ddp"
              or "fsdp", respectively.
      """
  
      if (
          parallel_strategy == "fsdp"
          and FullyShardedDataParallel is None
      ):
          raise ImportError(
              "FullyShardedDataParallel requires torch>=1.11.0. "
              "Run `pip install 'torch>=1.11.0'` to use FullyShardedDataParallel."
          )
  
      record_extra_usage_tag(TagKey.TRAIN_TORCH_PREPARE_MODEL, "1")
      return get_accelerator(_TorchAccelerator).prepare_model(
          model,
          move_to_device=move_to_device,
          parallel_strategy=parallel_strategy,
          parallel_strategy_kwargs=parallel_strategy_kwargs,
      )
  
imports: import collections
  import logging
  import os
  import random
  import types
  from typing import Any, Callable, Dict, List, Optional, Union
  import numpy
  import torch
  from packaging.version import Version
  from torch.cuda.amp import GradScaler, autocast
  from torch.nn.parallel import DistributedDataParallel
  from torch.optim import Optimizer
  from torch.utils.data import DataLoader, DistributedSampler, IterableDataset, RandomSampler, SequentialSampler
  from ray._private.usage.usage_lib import TagKey, record_extra_usage_tag
  from ray.train._internal import session
  from ray.train._internal.accelerator import Accelerator
  from ray.train._internal.session import get_accelerator, set_accelerator
  from ray.util.annotations import Deprecated, PublicAPI
label_desc: Use image node_id 2 for calling a global function with example usage: prepare_model(model, move_to_device, parallel_strategy, parallel_strategy_kwargs) and returns: get_accelerator