class_docstr: Strategy for training multiple TPU devices using the :func:`torch_xla.distributed.xla_multiprocessing.spawn`
  method.
class_docstr_tok: ["Strategy","for","training","multiple","TPU","devices","using","the",":","func",":","`","torch_xla.distributed.xla_multiprocessing.spawn","`","method","."]
code: def distributed_sampler_kwargs(self) -> Dict[str, int]:
      return {"num_replicas": self.world_size, "rank": self.global_rank}
  
imports: import io
  import os
  from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union
  import torch
  from torch import Tensor
  from torch.nn import Module
  from typing_extensions import override
  import lightning.pytorch
  from lightning.fabric.accelerators.xla import _XLA_AVAILABLE, _XLA_GREATER_EQUAL_2_1
  from lightning.fabric.plugins import XLACheckpointIO
  from lightning.fabric.plugins.environments import XLAEnvironment
  from lightning.fabric.strategies import _StrategyRegistry
  from lightning.fabric.utilities.optimizer import _optimizers_to_device
  from lightning.fabric.utilities.types import _PATH, ReduceOp
  from lightning.pytorch.plugins import XLAPrecision
  from lightning.pytorch.plugins.io.wrapper import _WrappingCheckpointIO
  from lightning.pytorch.strategies.ddp import DDPStrategy
  from lightning.pytorch.strategies.launchers.xla import _XLALauncher
  from lightning.pytorch.strategies.strategy import TBroadcast
  from lightning.pytorch.trainer.states import TrainerFn
  from lightning.pytorch.utilities import find_shared_parameters, set_shared_parameters
  from lightning.pytorch.utilities.rank_zero import rank_zero_only
label_desc: Use image node_id 14 for calling the XLAStrategy obj's underlying member method code with example usage: obj.distributed_sampler_kwargs() and returns: dict