code: def run(
      seed: int = 543,
      data_path: str = "/tmp/cifar10",
      output_path: str = "/tmp/output-cifar10/",
      model: str = "resnet18",
      batch_size: int = 512,
      momentum: float = 0.9,
      weight_decay: float = 1e-4,
      num_workers: int = 12,
      num_epochs: int = 24,
      learning_rate: float = 0.4,
      num_warmup_epochs: int = 4,
      validate_every: int = 3,
      checkpoint_every: int = 1000,
      backend: Optional[str] = None,
      resume_from: Optional[str] = None,
      log_every_iters: int = 15,
      nproc_per_node: Optional[int] = None,
      stop_iteration: Optional[int] = None,
      with_clearml: bool = False,
      with_amp: bool = False,
      **spawn_kwargs: Any,
  ):
      """Main entry to train an model on CIFAR10 dataset.
  
      Args:
          seed (int): random state seed to set. Default, 543.
          data_path (str): input dataset path. Default, "/tmp/cifar10".
          output_path (str): output path. Default, "/tmp/output-cifar10".
          model (str): model name (from torchvision) to setup model to train. Default, "resnet18".
          batch_size (int): total batch size. Default, 512.
          momentum (float): optimizer's momentum. Default, 0.9.
          weight_decay (float): weight decay. Default, 1e-4.
          num_workers (int): number of workers in the data loader. Default, 12.
          num_epochs (int): number of epochs to train the model. Default, 24.
          learning_rate (float): peak of piecewise linear learning rate scheduler. Default, 0.4.
          num_warmup_epochs (int): number of warm-up epochs before learning rate decay. Default, 4.
          validate_every (int): run model's validation every ``validate_every`` epochs. Default, 3.
          checkpoint_every (int): store training checkpoint every ``checkpoint_every`` iterations. Default, 1000.
          backend (str, optional): backend to use for distributed configuration. Possible values: None, "nccl", "xla-tpu",
              "gloo" etc. Default, None.
          nproc_per_node (int, optional): optional argument to setup number of processes per node. It is useful,
              when main python process is spawning training as child processes.
          resume_from (str, optional): path to checkpoint to use to resume the training from. Default, None.
          log_every_iters (int): argument to log batch loss every ``log_every_iters`` iterations.
              It can be 0 to disable it. Default, 15.
          stop_iteration (int, optional): iteration to stop the training. Can be used to check resume from checkpoint.
          with_clearml (bool): if True, experiment ClearML logger is setup. Default, False.
          with_amp (bool): if True, enables native automatic mixed precision. Default, False.
          **spawn_kwargs: Other kwargs to spawn run in child processes: master_addr, master_port, node_rank, nnodes
  
      """
      # check to see if the num_epochs is greater than or equal to num_warmup_epochs
      if num_warmup_epochs >= num_epochs:
          raise ValueError(
              "num_epochs cannot be less than or equal to num_warmup_epochs, please increase num_epochs or decrease "
              "num_warmup_epochs"
          )
  
      # catch all local parameters
      config = locals()
      config.update(config["spawn_kwargs"])
      del config["spawn_kwargs"]
  
      spawn_kwargs["nproc_per_node"] = nproc_per_node
      if backend == "xla-tpu" and with_amp:
          raise RuntimeError(
              "The value of with_amp should be False if backend is xla"
          )
  
      with idist.Parallel(backend=backend, **spawn_kwargs) as parallel:
          parallel.run(training, config)
  
imports: from datetime import datetime
  from pathlib import Path
  from typing import Any, Optional
  import fire
  import torch
  import torch.nn
  import torch.optim
  import utils
  from torch.cuda.amp import autocast, GradScaler
  import ignite
  import ignite.distributed
  from ignite.contrib.engines import common
  from ignite.contrib.handlers import PiecewiseLinear
  from ignite.engine import Engine, Events
  from ignite.handlers import Checkpoint, DiskSaver, global_step_from_engine
  from ignite.metrics import Accuracy, Loss
  from ignite.utils import manual_seed, setup_logger
label_desc: Use image node_id 2 for calling a global function with example usage: run(seed, data_path, output_path, model, batch_size, momentum, weight_decay, num_workers, num_epochs, learning_rate, num_warmup_epochs, validate_every, checkpoint_every, backend, resume_from, log_every_iters, nproc_per_node, stop_iteration, with_clearml, with_amp) without return types