code: def __init__(
      self,
      optimizer: Optimizer,
      strategy: Strategy,
      auto_lr: bool,
      num_processes: Optional[int],
  ) -> None:
      super().__init__(optimizer, strategy)
      self.cur_lr_ratio = 1.0
      self.max_lr_ratio = num_processes
      self.cur_step = 0
      self.max_step = 1000
      self.auto_lr = auto_lr
  
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 1 to create a new _TorchNanoOptimizer object from inherited base classes: _LiteOptimizer with example: obj = _TorchNanoOptimizer(optimizer, strategy, auto_lr, num_processes)