code: def _create_default_config(
      self,
      zero_optimization: bool,
      zero_allow_untested_optimizer: bool,
      logging_batch_size_per_gpu: Union[str, int],
      partition_activations: bool,
      cpu_checkpointing: bool,
      contiguous_memory_optimization: bool,
      synchronize_checkpoint_boundary: bool,
      offload_optimizer: bool,
      offload_parameters: bool,
      nvme_path: str,
      offload_params_device: str,
      params_buffer_count: int,
      params_buffer_size: int,
      max_in_cpu: int,
      offload_optimizer_device: str,
      optimizer_buffer_count: int,
      pin_memory: bool,
      block_size: int,
      queue_depth: int,
      single_submit: bool,
      overlap_events: bool,
      thread_count: int,
      **zero_kwargs: Any,
  ) -> Dict:
      cfg = {
          "activation_checkpointing": {
              "partition_activations": partition_activations,
              "cpu_checkpointing": cpu_checkpointing,
              "contiguous_memory_optimization": contiguous_memory_optimization,
              "synchronize_checkpoint_boundary": synchronize_checkpoint_boundary,
          },
          "aio": {
              "block_size": block_size,
              "queue_depth": queue_depth,
              "single_submit": single_submit,
              "overlap_events": overlap_events,
              "thread_count": thread_count,
          },
      }
      if zero_optimization:
          zero_config = zero_kwargs
  
          if offload_optimizer:
              zero_config["offload_optimizer"] = {
                  "device": offload_optimizer_device,
                  "nvme_path": nvme_path,
                  "buffer_count": optimizer_buffer_count,
                  "pin_memory": pin_memory,
              }
          if offload_parameters:
              zero_config["offload_param"] = {
                  "device": offload_params_device,
                  "nvme_path": nvme_path,
                  "buffer_count": params_buffer_count,
                  "buffer_size": params_buffer_size,
                  "max_in_cpu": max_in_cpu,
                  "pin_memory": pin_memory,
              }
          cfg = {
              "zero_allow_untested_optimizer": zero_allow_untested_optimizer,
              "zero_optimization": zero_config,
              **cfg,
          }
      if logging_batch_size_per_gpu != "auto":
          cfg = {
              "train_micro_batch_size_per_gpu": logging_batch_size_per_gpu,
              **cfg,
          }
      return cfg
  
imports: import argparse
  import json
  import logging
  import os
  import platform
  from collections import OrderedDict
  from contextlib import contextmanager
  from pathlib import Path
  from typing import TYPE_CHECKING, Any, Dict, Generator, List, Mapping, Optional, Tuple, Union
  import torch
  from torch.nn import Module
  from torch.optim import Optimizer
  from typing_extensions import override
  import lightning.pytorch
  from lightning.fabric.plugins import ClusterEnvironment
  from lightning.fabric.strategies import _StrategyRegistry
  from lightning.fabric.strategies.deepspeed import _DEEPSPEED_AVAILABLE, _format_precision_config, _validate_checkpoint_directory, _validate_device_index_selection
  from lightning.fabric.utilities.optimizer import _optimizers_to_device
  from lightning.fabric.utilities.seed import reset_seed
  from lightning.fabric.utilities.types import _PATH, LRScheduler, ReduceLROnPlateau
  from lightning.pytorch.accelerators.cuda import CUDAAccelerator
  from lightning.pytorch.core.optimizer import _init_optimizers_and_lr_schedulers
  from lightning.pytorch.plugins.precision import Precision
  from lightning.pytorch.strategies.ddp import DDPStrategy
  from lightning.pytorch.trainer.states import TrainerFn
  from lightning.pytorch.utilities import GradClipAlgorithmType
  from lightning.pytorch.utilities.exceptions import MisconfigurationException
  from lightning.pytorch.utilities.model_helpers import is_overridden
  from lightning.pytorch.utilities.rank_zero import WarningCache, rank_zero_info, rank_zero_warn
  from lightning.pytorch.utilities.types import LRSchedulerConfig
label_desc: Use image node_id 33 for calling the DeepSpeedStrategy obj's underlying member method code with example usage: obj._create_default_config(zero_optimization, zero_allow_untested_optimizer, logging_batch_size_per_gpu, partition_activations, cpu_checkpointing, contiguous_memory_optimization, synchronize_checkpoint_boundary, offload_optimizer, offload_parameters, nvme_path, offload_params_device, params_buffer_count, params_buffer_size, max_in_cpu, offload_optimizer_device, optimizer_buffer_count, pin_memory, block_size, queue_depth, single_submit, overlap_events, thread_count) and returns: cfg