class_docstr: :param model: 训练使用的模型。
  
      * 如果不想自己初始化分布式环境，类型应为 :class:`paddle.nn.Layer`；
      * 如果已经在外面初始化了分布式环境，类型应为 :class:`paddle.DataParallel`；
  
  :param parallel_device: 多卡训练时使用的设备，必须是一个列表。
      当使用 ``python -m paddle.distributed.launch`` 启动时，该参数无效。
  :param is_pull_by_paddle_run: 标记当前进程是否为通过 ``python -m paddle.distributed.launch`` 启动的。
      这个参数仅在 :class:`~fastNLP.core.Trainer` 中初始化 driver 时使用
  :param fp16: 是否开启混合精度训练
  :param paddle_kwargs:
      * *fleet_kwargs* -- 用于在使用 ``PaddleFleetDriver`` 时指定 ``DataParallel`` 和 ``fleet`` 初始化时的参数，包括：
          
          * *is_collective* -- 是否使用 paddle 集群式的分布式训练方法，目前仅支持为 ``True`` 的情况。
          * *role_maker* -- 初始化 ``fleet`` 分布式训练 API 时使用的 ``RoleMaker``。
          * 其它用于初始化 ``DataParallel`` 的参数。
      * *gradscaler_kwargs* -- 用于 ``fp16=True`` 时，提供给 :class:`paddle.amp.GradScaler` 的参数
  
  :kwargs:
      * *wo_auto_param_call* (``bool``) -- 是否关闭在训练时调用我们的 ``auto_param_call`` 函数来自动匹配 batch 和前向函数的参数的行为
  
      .. note::
  
          关于该参数的详细说明，请参见 :class:`~fastNLP.core.controllers.Trainer` 中的描述；函数 ``auto_param_call`` 详见 :func:`fastNLP.core.utils.auto_param_call`。
code: def set_dist_repro_dataloader(
      self,
      dataloader,
      dist: Optional[
          Union[str, ReproducibleSampler, ReproduceBatchSampler]
      ],
      reproducible: bool = False,
  ):
      # 暂时不支持iterableDataset
      assert (
          dataloader.dataset_kind != _DatasetKind.ITER
      ), "FastNLP does not support `IteratorDataset` now."
      # 如果 dist 为 ReproducibleBatchSampler, ReproducibleSampler 说明是在断点重训时 driver.load_checkpoint 函数调用；
      if isinstance(dist, ReproducibleBatchSampler):
          dist.set_distributed(
              num_replicas=self.world_size,
              rank=self.global_rank,
              pad=True,
          )
          return replace_batch_sampler(dataloader, dist)
      if isinstance(dist, ReproducibleSampler):
          dist.set_distributed(
              num_replicas=self.world_size,
              rank=self.global_rank,
              pad=True,
          )
          return replace_sampler(dataloader, dist)
  
      # 如果 dist 为 str 或者 None，说明是在 trainer 初试化时调用；
      # trainer, evaluator
      if dist is None:
          if reproducible:
              raise RuntimeError(
                  "It is not allowed to save checkpoint if the sampler is not allowed to be replaced."
              )
          else:
              args = self.get_dataloader_args(dataloader)
              if isinstance(
                  args.batch_sampler, ReproducibleBatchSampler
              ):
                  batch_sampler = re_instantiate_sampler(
                      args.batch_sampler
                  )
                  return replace_batch_sampler(
                      dataloader, batch_sampler
                  )
              if isinstance(args.sampler, ReproducibleSampler):
                  sampler = re_instantiate_sampler(args.sampler)
                  return replace_sampler(dataloader, sampler)
              return dataloader
      # trainer
      elif dist == "dist":
          args = self.get_dataloader_args(dataloader)
          # 如果用户的 trainer.use_dist_sampler 为 True，那么此时其是否进行断点重训，不影响这里的行为；
          if isinstance(args.batch_sampler, ReproducibleBatchSampler):
              batch_sampler = re_instantiate_sampler(args.batch_sampler)
              batch_sampler.set_distributed(
                  num_replicas=self.world_size,
                  rank=self.global_rank,
                  pad=True,
              )
              return replace_batch_sampler(dataloader, batch_sampler)
          elif isinstance(args.sampler, ReproducibleSampler):
              sampler = re_instantiate_sampler(args.sampler)
              sampler.set_distributed(
                  num_replicas=self.world_size,
                  rank=self.global_rank,
                  pad=True,
              )
              return replace_sampler(dataloader, sampler)
          else:
              _check_dataloader_args_for_distributed(
                  args, controller="Trainer"
              )
              sampler = RandomSampler(
                  dataset=args.dataset,
                  shuffle=args.shuffle,
                  seed=int(os.environ.get(FASTNLP_GLOBAL_SEED, 0)),
              )
              sampler.set_distributed(
                  num_replicas=self.world_size,
                  rank=self.global_rank,
                  pad=True,
              )
              return replace_sampler(dataloader, sampler)
      # evaluator
      elif dist == "unrepeatdist":
          args = self.get_dataloader_args(dataloader)
          if type(args.batch_sampler) != BatchSampler:
              # TODO 这里的目的是判断用户的 batch_sampler 是定制的，可能需要完善
              logger.warning(
                  "Note that you are using customized ``batch_sampler`` in evaluate dataloader or"
                  "train dataloader while testing ``overfit_batches``, which may cause that"
                  "the data for distributed evaluation is not unrepeated."
              )
          if isinstance(args.sampler, ReproducibleSampler):
              sampler = conversion_between_reproducible_and_unrepeated_sampler(
                  args.sampler
              )
          elif not isinstance(args.sampler, UnrepeatedSampler):
              _check_dataloader_args_for_distributed(
                  args, controller="Evaluator"
              )
              sampler = UnrepeatedSequentialSampler(
                  dataset=args.dataset
              )
          else:
              sampler = re_instantiate_sampler(args.sampler)
          sampler.set_distributed(
              num_replicas=self.world_size, rank=self.global_rank
          )
          # TODO 这里暂时统一替换为 BatchSampler
          batch_sampler = BatchSampler(
              dataset=args.dataset,
              batch_size=args.batch_size,
              drop_last=False,
          )
          batch_sampler.sampler = sampler
          return replace_batch_sampler(dataloader, batch_sampler)
      else:
          raise ValueError(
              "Parameter `dist_sampler` can only be one of three values: ('dist', 'unrepeatdist', None)."
          )
  
imports: import os
  from typing import List, Union, Optional, Dict, Tuple, Callable
  from .paddle_driver import PaddleDriver
  from .fleet_launcher import FleetLauncher
  from .utils import _FleetWrappingModel, replace_sampler, replace_batch_sampler, _check_dataloader_args_for_distributed
  from .dist_utils import fastnlp_paddle_all_gather, fastnlp_paddle_broadcast_object
  from fastNLP.envs.imports import _NEED_IMPORT_PADDLE
  from fastNLP.core.utils import auto_param_call, check_user_specific_params, is_in_paddle_dist, get_paddle_device_id
  from fastNLP.core.utils.paddle_utils import _convert_data_device
  from fastNLP.envs.distributed import rank_zero_rm
  from fastNLP.core.samplers import ReproduceBatchSampler, ReproducibleSampler, ReproducibleBatchSampler, RandomSampler, UnrepeatedSampler, UnrepeatedSequentialSampler, re_instantiate_sampler, conversion_between_reproducible_and_unrepeated_sampler
  from fastNLP.envs.env import FASTNLP_DISTRIBUTED_CHECK, FASTNLP_GLOBAL_SEED, FASTNLP_NO_SYNC, USER_CUDA_VISIBLE_DEVICES
  from fastNLP.core.log import logger
label_desc: Use image node_id 16 for calling the PaddleFleetDriver obj's underlying member method code with example usage: obj.set_dist_repro_dataloader(dataloader, dist, reproducible) and returns: replace_batch_sampler, replace_sampler, dataloader, replace_batch_sampler, replace_sampler, replace_batch_sampler, replace_batch_sampler, replace_sampler, replace_sampler