code: def test_deepspeed_load_checkpoint_state_updated_with_client_state(
      _, tmp_path
  ):
      """Test that the DeepSpeed strategy properly updates the state variables and returns additional metadata."""
      from deepspeed import DeepSpeedEngine
  
      strategy = DeepSpeedStrategy()
      optimizer = Mock()
      model = Mock(spec=DeepSpeedEngine, optimizer=optimizer)
      model.modules.return_value = [model]
  
      # the client state contains the additional user data that was proveded when saving, plus some deepspeed metadata
      loaded_client_state = {
          "user_data": {"iteration": 5},
          "deepspeed_metadata": "data",
      }
      model.load_checkpoint.return_value = [None, loaded_client_state]
  
      state = {"model": model, "user_data": {"iteration": 0}}
      metadata = strategy.load_checkpoint(path=tmp_path, state=state)
  
      # the user's state gets updated with the loaded value
      assert state == {"model": model, "user_data": {"iteration": 5}}
      # additional metadata gets separated from client state
      assert metadata == {"deepspeed_metadata": "data"}
  
imports: import json
  from re import escape
  from unittest import mock
  from unittest.mock import ANY, Mock
  import pytest
  import torch
  from lightning.fabric.accelerators import CPUAccelerator, CUDAAccelerator
  from lightning.fabric.strategies import DeepSpeedStrategy
  from torch.optim import Optimizer
  from tests_fabric.helpers.runif import RunIf
label_desc: Use image node_id 19 for calling a global function with example usage: test_deepspeed_load_checkpoint_state_updated_with_client_state(_, tmp_path) without return types