code: def initialize(backend: str, **kwargs: Any) -> None:
      """Initializes distributed configuration according to provided ``backend``
  
      Args:
          backend: backend: `nccl`, `gloo`, `xla-tpu`, `horovod`.
          kwargs: acceptable kwargs according to provided backend:
  
              - | "nccl" or "gloo" : ``timeout(=timedelta(minutes=30))``, ``init_method(=None)``,
                | ``rank(=None)``, ``world_size(=None)``.
                | By default, ``init_method`` will be "env://". See more info about parameters: `torch_init`_.
  
              - | "horovod" : comm(=None), more info: `hvd_init`_.
  
      Examples:
          Launch single node multi-GPU training with ``torchrun`` utility.
  
          .. code-block:: python
  
              # >>> torchrun --nproc_per_node=4 main.py
              # main.py
  
              import ignite.distributed as idist
  
              def train_fn(local_rank, a, b, c):
                  import torch.distributed as dist
                  assert dist.is_available() and dist.is_initialized()
                  assert dist.get_world_size() == 4
  
                  device = idist.device()
                  assert device == torch.device(f"cuda:{local_rank}")
  
  
              backend = "nccl"  # or "gloo" or "horovod" or "xla-tpu"
              idist.initialize(backend)
              # or for torch native distributed on Windows:
              # idist.initialize("nccl", init_method="file://tmp/shared")
              local_rank = idist.get_local_rank()
              train_fn(local_rank, a, b, c)
              idist.finalize()
  
  
      .. _torch_init: https://pytorch.org/docs/stable/distributed.html#torch.distributed.init_process_group
      .. _hvd_init: https://horovod.readthedocs.io/en/latest/api.html#module-horovod.torch
  
      .. versionchanged:: 0.4.2
          ``backend`` now accepts `horovod` distributed framework.
  
      .. versionchanged:: 0.4.5
          ``kwargs`` now accepts ``init_method``, ``rank``, ``world_size`` for PyTorch native distributed backend.
      """
      if not (
          has_xla_support or has_native_dist_support or has_hvd_support
      ):
          # nothing to do => serial model
          # maybe warn about this
          return
  
      _assert_backend(backend)
  
      for comp_model_cls in registered_computation_models:
          if backend not in comp_model_cls.available_backends:
              continue
          _set_model(comp_model_cls(backend, **kwargs))
  
imports: import socket
  from contextlib import contextmanager
  from functools import wraps
  from typing import Any, Callable, List, Mapping, Optional, Tuple, Union
  import torch
  from ignite.distributed.comp_models import _SerialModel, has_hvd_support, has_native_dist_support, has_xla_support, registered_computation_models
  from ignite.utils import setup_logger
label_desc: Use image node_id 22 for calling a global function with example usage: initialize(backend) without return types