code: def __init__(self, module: nn.Module, precision: str) -> None:
      super().__init__(module)
      self.dtype = None
      if precision == "fp16":
          self.dtype = torch.float16
      elif precision == "bf16":
          self.dtype = torch.bfloat16
      if self.dtype is not None:
          module = module.to(self.dtype)
      module = module.to(get_current_device())
      self.module = module
      self.convert_fn = None
      if self.dtype is not None:
          self.convert_fn = partial(
              _convert_floating_point, dtype=self.dtype
          )
  
imports: import logging
  import os
  from functools import partial
  from pathlib import Path
  from types import MethodType
  from typing import Callable, Iterator, List, Optional, Tuple
  import torch
  import torch.nn
  from torch.optim import Optimizer
  from torch.optim.lr_scheduler import _LRScheduler
  from torch.utils._pytree import tree_map
  from torch.utils.data import DataLoader
  from colossalai.checkpoint_io import CheckpointIndexFile, CheckpointIO
  from colossalai.checkpoint_io.utils import get_optimizer_base_filenames, get_shard_filename, load_param_groups_into_optimizer, load_shard_state_dict, load_states_into_optimizer, save_param_groups, save_state_dict, sharded_optimizer_loading_epilogue
  from colossalai.interface import AMPModelMixin, ModelWrapper, OptimizerWrapper
  from colossalai.utils import get_current_device
  from colossalai.zero import LowLevelZeroOptimizer
  from .dp_plugin_base import DPPluginBase
  from .torch_ddp_plugin import TorchDDPCheckpointIO
label_desc: Use image node_id 1 to create a new LowLevelZeroModel object from inherited base classes: ModelWrapper, AMPModelMixin with example: obj = LowLevelZeroModel(module, precision)