code: def load_checkpoint(self, checkpoint_path: _PATH) -> Dict[str, Any]:
      if self.load_full_weights and self.zero_stage_3:
          # Broadcast to ensure we load from the rank 0 checkpoint
          # This doesn't have to be the case when using deepspeed sharded checkpointing
          checkpoint_path = self.broadcast(checkpoint_path)
          return super().load_checkpoint(checkpoint_path)
  
      _validate_checkpoint_directory(checkpoint_path)
  
      # Rely on deepspeed to load the checkpoint and necessary information
      assert self.lightning_module is not None
  
      from lightning.pytorch.trainer.states import TrainerFn
  
      is_fitting = (
          self.lightning_module.trainer.state.fn == TrainerFn.FITTING
      )
  
      _, client_state = self.deepspeed_engine.load_checkpoint(
          checkpoint_path,
          load_optimizer_states=is_fitting,
          load_lr_scheduler_states=False,
      )
      if client_state is None:
          raise MisconfigurationException(
              "DeepSpeed was unable to load the checkpoint. Ensure you passed in a DeepSpeed compatible checkpoint "
              "or a single checkpoint file with `Trainer(strategy=DeepSpeedStrategy(load_full_weights=True))`."
          )
      return client_state
  
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 24 for calling the DeepSpeedStrategy obj's underlying member method code with example usage: obj.load_checkpoint(checkpoint_path) and returns: client_state, super