code: def __init__(
      self,
      zero_optimization: Optional[Dict[str, Any]] = None,
      fp16: Optional[Dict[str, Any]] = None,
      bf16: Optional[Dict[str, Any]] = None,
      compression_training: Optional[Dict[str, Any]] = None,
      **kwargs,
  ):
      # If we're initializing from a `deepspeed` CLI command, deepspeed will have already been initialized, as
      # indicated by the presence of the LOCAL_RANK var. Otherwise, we're initializing from Ray / torchrun, and will
      # need to set this var ourselves, then init DeepSpeed here.
      local_rank, local_size = os.environ.get(
          "LOCAL_RANK"
      ), os.environ.get("LOCAL_SIZE")
      init_deepspeed = local_rank is None or local_size is None
  
      super().__init__(**kwargs)
      self.zero_optimization = (
          zero_optimization or DEFAULT_ZERO_OPTIMIZATION
      )
      self.fp16 = fp16
      self.bf16 = bf16
      self.compression_training = compression_training
  
      if init_deepspeed:
          os.environ["LOCAL_RANK"] = str(self.local_rank())
          os.environ["LOCAL_SIZE"] = str(self.local_size())
          os.environ["RANK"] = str(self.rank())
          os.environ["WORLD_SIZE"] = str(self.size())
          deepspeed.init_distributed()
  
imports: import logging
  import os
  import warnings
  from typing import Any, Dict, List, Mapping, Optional, Tuple, TYPE_CHECKING, Union
  import deepspeed
  import deepspeed.comm
  import torch
  from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
  from packaging import version
  from torch import nn
  from torch.optim.optimizer import Optimizer
  from ludwig.constants import MIN_POSSIBLE_BATCH_SIZE
  from ludwig.distributed.ddp import DDPStrategy
  from ludwig.modules.optimization_modules import get_optimizer_class_and_kwargs
  from ludwig.utils.checkpoint_utils import Checkpoint
  from ludwig.utils.model_utils import extract_tensors, replace_tensors
label_desc: Use image node_id 1 to create a new DeepSpeedStrategy object from inherited base classes: DDPStrategy with example: obj = DeepSpeedStrategy(zero_optimization, fp16, bf16, compression_training)