code: def prepare_data_loader(
      data_loader: torch.utils.data.DataLoader,
      add_dist_sampler: bool = True,
      move_to_device: bool = True,
      auto_transfer: bool = True,
  ) -> torch.utils.data.DataLoader:
      """Prepares :class:`~torch.utils.data.DataLoader` 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).
  
      .. note::
  
          This method adds a `DistributedSampler` to the `DataLoader` if the
          number of training workers is greater than 1. If shuffling is
          enabled on the original `DataLoader`, then `shuffle=True` will also
          be passed into the `DistributedSampler` constructor. `shuffle=False`
          on the original `DataLoader` also means that shuffling is disabled
          on the sampler.
  
          With more than 1 worker, calling the `DistributedSampler.set_epoch` method
          at the beginning of each epoch before creating the DataLoader iterator
          is necessary to make shuffling work properly across multiple epochs.
          Otherwise, the same ordering will be always used.
          See: https://pytorch.org/docs/stable/data.html#torch.utils.data.distributed.DistributedSampler  # noqa: E501
  
      Example:
  
      .. testcode:
          :skipif: True
  
          import torch
  
          import ray.train.torch
  
          train_dataloader = torch.utils.data.DataLoader(
              ..., batch_size=..., shuffle=True
          )
          train_dataloader = ray.train.torch.prepare_data_loader(train_loader)
  
          for epoch in range(10):
              if ray.train.get_context().get_world_size() > 1:
                  # Required for the distributed sampler to shuffle properly across epochs
                  train_dataloader.sampler.set_epoch(epoch)
  
              for X, y in train_loader:
                  # No need to move data to GPU, this is done by `prepare_data_loader`!
                  # X, y = X.to("cuda"), y.to("cuda")
                  ...
  
      Args:
          data_loader (torch.utils.data.DataLoader): The DataLoader to
              prepare.
          add_dist_sampler: Whether to add a DistributedSampler to
              the provided DataLoader.
          move_to_device: If set, automatically move the data
              returned by the data loader to the correct device.
          auto_transfer: If set and device is GPU, another CUDA stream
              is created to automatically copy data from host (CPU) memory
              to device (GPU) memory (the default CUDA stream still runs the
              training procedure). If device is CPU, it will be disabled
              regardless of the setting. This configuration will be ignored
              if ``move_to_device`` is False.
      """
      record_extra_usage_tag(TagKey.TRAIN_TORCH_PREPARE_DATALOADER, "1")
      return get_accelerator(_TorchAccelerator).prepare_data_loader(
          data_loader,
          add_dist_sampler=add_dist_sampler,
          move_to_device=move_to_device,
          auto_transfer=auto_transfer,
      )
  
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 3 for calling a global function with example usage: prepare_data_loader(data_loader, add_dist_sampler, move_to_device, auto_transfer) and returns: get_accelerator