code: def prepare_data_loader(
      dataloader: DataLoader,
      device: Optional[torch.device] = None,
      num_processes: Optional[int] = None,
      process_index: Optional[int] = None,
      split_batches: bool = False,
      put_on_device: bool = False,
      rng_types: Optional[List[Union[str, RNGType]]] = None,
      dispatch_batches: Optional[bool] = None,
      even_batches: bool = True,
      slice_fn_for_dispatch: Optional[Callable] = None,
      use_seedable_sampler: bool = False,
  ) -> DataLoader:
      """
      Wraps a PyTorch `DataLoader` to generate batches for one of the processes only.
  
      Depending on the value of the `drop_last` attribute of the `dataloader` passed, it will either stop the iteration
      at the first batch that would be too small / not present on all processes or loop with indices from the beginning.
  
      Args:
          dataloader (`torch.utils.data.dataloader.DataLoader`):
              The data loader to split across several devices.
          device (`torch.device`):
              The target device for the returned `DataLoader`.
          num_processes (`int`, *optional*):
              The number of processes running concurrently. Will default to the value given by
              [`~state.AcceleratorState`].
          process_index (`int`, *optional*):
              The index of the current process. Will default to the value given by [`~state.AcceleratorState`].
          split_batches (`bool`, *optional*, defaults to `False`):
              Whether the resulting `DataLoader` should split the batches of the original data loader across devices or
              yield full batches (in which case it will yield batches starting at the `process_index`-th and advancing of
              `num_processes` batches at each iteration).
  
              Another way to see this is that the observed batch size will be the same as the initial `dataloader` if
              this option is set to `True`, the batch size of the initial `dataloader` multiplied by `num_processes`
              otherwise.
  
              Setting this option to `True` requires that the batch size of the `dataloader` is a round multiple of
              `batch_size`.
          put_on_device (`bool`, *optional*, defaults to `False`):
              Whether or not to put the batches on `device` (only works if the batches are nested list, tuples or
              dictionaries of tensors).
          rng_types (list of `str` or [`~utils.RNGType`]):
              The list of random number generators to synchronize at the beginning of each iteration. Should be one or
              several of:
  
              - `"torch"`: the base torch random number generator
              - `"cuda"`: the CUDA random number generator (GPU only)
              - `"xla"`: the XLA random number generator (TPU only)
              - `"generator"`: the `torch.Generator` of the sampler (or batch sampler if there is no sampler in your
                dataloader) or of the iterable dataset (if it exists) if the underlying dataset is of that type.
  
          dispatch_batches (`bool`, *optional*):
              If set to `True`, the datalaoder prepared is only iterated through on the main process and then the batches
              are split and broadcast to each process. Will default to `True` when the underlying dataset is an
              `IterableDataset`, `False` otherwise.
          even_batches (`bool`, *optional*, defaults to `True`):
              If set to `True`, in cases where the total batch size across all processes does not exactly divide the
              dataset, samples at the start of the dataset will be duplicated so the batch can be divided equally among
              all workers.
          slice_fn_for_dispatch (`Callable`, *optional*`):
              If passed, this function will be used to slice tensors across `num_processes`. Will default to
              [`~utils.slice_tensors`]. This argument is used only when `dispatch_batches` is set to `True` and will be
              ignored otherwise.
          use_seedable_sampler (`bool`, *optional*, defaults to `False`):
              Whether to use the [`~data_loader.SeedableRandomSampler`] instead of a `RandomSampler` for better
              reproducability. Comes at a cost of potentially different performances due to different shuffling
              algorithms but ensures results will be the *exact* same.
  
      Returns:
          `torch.utils.data.dataloader.DataLoader`: A new data loader that will yield the portion of the batches
  
      <Tip warning={true}>
  
      `BatchSampler`s with varying batch sizes are not enabled by default. To enable this behaviour, set `even_batches`
      equal to `False`
  
      </Tip>
      """
      if dispatch_batches is None:
          if not put_on_device:
              dispatch_batches = False
          else:
              dispatch_batches = isinstance(
                  dataloader.dataset, IterableDataset
              )
  
      if dispatch_batches and not put_on_device:
          raise ValueError(
              "Using `dispatch_batches=True` requires `put_on_device=True`."
          )
      # Grab defaults from AcceleratorState
      state = AcceleratorState()
      if num_processes is None:
          num_processes = state.num_processes
      if process_index is None:
          process_index = state.process_index
  
      # Sanity check
      batch_size = (
          dataloader.batch_size
          if dataloader.batch_size is not None
          else dataloader.batch_sampler.batch_size
      )
      if (
          split_batches
          and batch_size > 1
          and batch_size % num_processes != 0
      ):
          raise ValueError(
              f"To use a `DataLoader` in `split_batches` mode, the batch size ({dataloader.batch_size}) "
              f"needs to be a round multiple of the number of processes ({num_processes})."
          )
  
      new_dataset = dataloader.dataset
      # Iterable dataset doesn't like batch_sampler, but data_loader creates a default one for it
      new_batch_sampler = (
          dataloader.batch_sampler
          if not isinstance(new_dataset, IterableDataset)
          else None
      )
      sampler_is_batch_sampler = False
      synchronized_generator = None
      sampler_is_batch_sampler = isinstance(
          dataloader.sampler, BatchSampler
      )
      if sampler_is_batch_sampler:
          sampler = getattr(dataloader.sampler, "sampler", None)
      else:
          sampler = getattr(dataloader.batch_sampler, "sampler", None)
      if isinstance(sampler, RandomSampler) and use_seedable_sampler:
          # When iterating through the dataloader during distributed processes
          # we want to ensure that on each process we are iterating through the same
          # samples in the same order if a seed is set. This requires a tweak
          # to the `torch.utils.data.RandomSampler` class (if used).
          sampler = SeedableRandomSampler(
              data_source=sampler.data_source,
              replacement=sampler.replacement,
              num_samples=sampler._num_samples,
              generator=getattr(
                  sampler, "generator", torch.Generator()
              ),
          )
  
      # No change if no multiprocess
      if (
          num_processes != 1
          or state.distributed_type == DistributedType.MEGATRON_LM
      ) and not dispatch_batches:
          if isinstance(new_dataset, IterableDataset):
              if (
                  getattr(dataloader.dataset, "generator", None)
                  is not None
              ):
                  synchronized_generator = dataloader.dataset.generator
              new_dataset = IterableDatasetShard(
                  new_dataset,
                  batch_size=dataloader.batch_size,
                  drop_last=dataloader.drop_last,
                  num_processes=num_processes,
                  process_index=process_index,
                  split_batches=split_batches,
              )
          else:
              batch_sampler = (
                  dataloader.sampler
                  if sampler_is_batch_sampler
                  else dataloader.batch_sampler
              )
              new_batch_sampler = BatchSamplerShard(
                  batch_sampler,
                  num_processes=num_processes,
                  process_index=process_index,
                  split_batches=split_batches,
                  even_batches=even_batches,
              )
  
      # We ignore all of those since they are all dealt with by our new_batch_sampler
      ignore_kwargs = [
          "batch_size",
          "shuffle",
          "sampler",
          "batch_sampler",
          "drop_last",
      ]
  
      if (
          rng_types is not None
          and synchronized_generator is None
          and "generator" in rng_types
      ):
          rng_types.remove("generator")
  
      kwargs = {
          k: getattr(dataloader, k, _PYTORCH_DATALOADER_KWARGS[k])
          for k in _PYTORCH_DATALOADER_KWARGS
          if k not in ignore_kwargs
      }
  
      # Need to provide batch_size as batch_sampler is None for Iterable dataset
      if new_batch_sampler is None:
          kwargs["drop_last"] = dataloader.drop_last
          kwargs["batch_size"] = (
              dataloader.batch_size // num_processes
              if split_batches and not dispatch_batches
              else dataloader.batch_size
          )
      if (
          isinstance(sampler, SeedableRandomSampler)
          and use_seedable_sampler
      ):
          if sampler_is_batch_sampler:
              dataloader.sampler.sampler = sampler
          else:
              dataloader.batch_sampler.sampler = sampler
      if dispatch_batches:
          kwargs.pop("generator")
          dataloader = DataLoaderDispatcher(
              new_dataset,
              split_batches=split_batches,
              batch_sampler=new_batch_sampler,
              _drop_last=dataloader.drop_last,
              slice_fn=slice_fn_for_dispatch,
              **kwargs,
          )
      elif sampler_is_batch_sampler:
          dataloader = DataLoaderShard(
              new_dataset,
              device=device
              if put_on_device
              and state.distributed_type != DistributedType.TPU
              else None,
              sampler=new_batch_sampler,
              batch_size=dataloader.batch_size,
              rng_types=rng_types,
              _drop_last=dataloader.drop_last,
              synchronized_generator=synchronized_generator,
              **kwargs,
          )
      else:
          dataloader = DataLoaderShard(
              new_dataset,
              device=device
              if put_on_device
              and state.distributed_type != DistributedType.TPU
              else None,
              batch_sampler=new_batch_sampler,
              rng_types=rng_types,
              synchronized_generator=synchronized_generator,
              _drop_last=dataloader.drop_last,
              **kwargs,
          )
  
      if state.distributed_type == DistributedType.TPU:
          return MpDeviceLoaderWrapper(dataloader, device)
      return dataloader
  
imports: import math
  from contextlib import suppress
  from typing import Callable, List, Optional, Union
  import torch
  from torch.utils.data import BatchSampler, DataLoader, IterableDataset, RandomSampler
  from .logging import get_logger
  from .state import AcceleratorState, DistributedType, GradientState, is_tpu_available
  from .utils import RNGType, broadcast, broadcast_object_list, concatenate, find_batch_size, get_data_structure, initialize_tensors, is_torch_version, send_to_device, slice_tensors, synchronize_rng_states
label_desc: Use image node_id 1 for calling a global function with example usage: prepare_data_loader(dataloader, device, num_processes, process_index, split_batches, put_on_device, rng_types, dispatch_batches, even_batches, slice_fn_for_dispatch, use_seedable_sampler) and returns: dataloader, MpDeviceLoaderWrapper