code: def nano(
      num_processes: Optional[int] = None,
      use_ipex: bool = False,
      distributed_backend: str = "subprocess",
      precision: Union[str, int] = 32,
      cpu_for_each_process: Optional[List[List[int]]] = None,
      channels_last: bool = False,
      auto_lr: bool = True,
      *args,
      **kwargs,
  ):
      """
      Run ``TorchNano.train`` through a convenient decorator function.
  
      :param num_processes: number of processes in distributed training, defaults to ``1``
      :param use_ipex: whether use ipex acceleration, defaults to ``False``
      :param distributed_backend: use which backend in distributed mode, defaults to
          ``'subprocess'``, now avaiable backends are ``'subprocess'`` and ``'ray'``.
          ``bigdl.nano.pytorch.nano`` decorator does not support ``'spawn'``.
      :param precision: Double precision (``64``), full precision (``32``), half precision (``16``)
          or bfloat16 precision (``'bf16'``), defaults to ``32``.
          Enable ipex bfloat16 weight prepack when ``use_ipex=True`` and ``precision='bf16'``
      :param cpu_for_each_process: specify the cpu cores which will be used by each process,
          if ``None``, cpu cores will be distributed evenly by all processes,
          only take effect when ``num_processes`` > 1
      :param channels_last: whether convert input to channels last memory formats,
          defaults to ``False``.
      :param auto_lr: whether to scale the learning rate linearly by ``num_processes`` times.
          Defaults to ``True``.
          If ``num_processes=1`` or other ``lr_scheduler`` is set, ``auto_lr`` will be ignored.
      """
      if "strategy" in kwargs:
          strategy = kwargs["strategy"]
          if strategy == "deepspeed" or isinstance(
              strategy, DeepSpeedStrategy
          ):
              invalidInputError(
                  False,
                  "bigdl.nano.pytorch.nano do not support deepspeed strategy",
              )
  
      # spawn has a wierd pickle error
      invalidInputError(
          distributed_backend != "spawn",
          "bigdl.nano.pytorch.nano do not support spawn",
      )
  
      def decorator(func):
          # todo check the func signature
          @wraps(func)
          def wrapper(*inner_args, **inner_kwargs):
              return _DecoratedTorchNano(
                  num_processes=num_processes,
                  use_ipex=use_ipex,
                  distributed_backend=distributed_backend,
                  precision=precision,
                  cpu_for_each_process=cpu_for_each_process,
                  channels_last=channels_last,
                  auto_lr=auto_lr,
                  *args,
                  **kwargs,
              ).train(func, *inner_args, **inner_kwargs)
  
          return wrapper
  
      return decorator
  
imports: from typing import Any, Union, List, Optional, Mapping
  from logging import warning
  from functools import partial, wraps
  from abc import abstractmethod
  import torch
  from torch import nn
  from torch.optim import Optimizer
  from torch.utils.data import DataLoader
  from torch.nn.parallel.distributed import DistributedDataParallel
  from pytorch_lightning.lite import LightningLite
  from pytorch_lightning.lite.wrappers import _LiteModule, _LiteOptimizer
  from pytorch_lightning.utilities.apply_func import apply_to_collection
  from pytorch_lightning.strategies import Strategy
  from pytorch_lightning.strategies import DeepSpeedStrategy
  from bigdl.nano.utils.common import _avx512_checker
  from bigdl.nano.utils.common import invalidInputError
  from bigdl.nano.utils.pytorch import TORCH_VERSION_LESS_1_11, TORCH_VERSION_LESS_1_13, check_ccl
  from bigdl.nano.deps.ipex.ipex_api import ipex_optimize
  from bigdl.nano.pytorch.strategies import IPEXStrategy, DDPSpawnStrategy, DDPSubprocessStrategy, create_ray_strategy, DDPK8sStrategy
label_desc: Use image node_id 3 for calling a global function with example usage: nano(num_processes, use_ipex, distributed_backend, precision, cpu_for_each_process, channels_last, auto_lr) and returns: decorator, wrapper, _DecoratedTorchNano