code: def run_fragmented_model(
      model, config_dict, hidden_dim, dtype, validate_func
  ):
      model, _, _, _ = deepspeed.initialize(
          model=model,
          model_parameters=model.parameters(),
          config=config_dict,
      )
      data_loader = random_dataloader(
          model=model,
          total_samples=10,
          hidden_dim=hidden_dim,
          device=model.device,
          dtype=dtype,
      )
      dist.barrier()
      for n, batch in enumerate(data_loader):
          loss = model(batch[0], batch[1])
          loss = loss[1]
          model.backward(loss)
          validate_func(model)
          model.step()
  
      # Needed in ZeRO 3. Not doing so can give memory leak
      model.destroy()
  
imports: import pytest
  import deepspeed.comm
  import torch
  from unit.common import DistributedTest
  from unit.simple_model import random_dataloader, SimpleModel
  from unit.util import bf16_required_version_check
  import deepspeed
  from deepspeed.utils import safe_get_full_fp32_param, safe_get_full_grad, safe_get_full_optimizer_state
  from deepspeed.utils import safe_set_full_fp32_param, safe_set_full_optimizer_state
  from deepspeed.utils import safe_get_local_fp32_param, safe_get_local_grad, safe_get_local_optimizer_state
  from deepspeed.utils import safe_set_local_fp32_param, safe_set_local_optimizer_state
  from deepspeed.runtime.zero.offload_config import OffloadDeviceEnum
  from deepspeed.ops.aio import AsyncIOBuilder
label_desc: Use image node_id 3 for calling a global function with example usage: run_fragmented_model(model, config_dict, hidden_dim, dtype, validate_func) without return types