code: def _test_ema_final_weight(model, device=None, ddp=False, interval=1):
      """Test if final smoothed weights are correct"""
      if device is None:
          # let horovod decide the device
          device = idist.device()
      if isinstance(device, str):
          device = torch.device(device)
      model = model.to(device)
      if ddp:
          model = idist.auto_model(model)
      step_fn = _get_dummy_step_fn(model)
      engine = Engine(step_fn)
  
      ema_handler = EMAHandler(model, momentum=0.5)
      ema_handler.attach(
          engine,
          "model",
          event=Events.ITERATION_COMPLETED(every=interval),
      )
  
      # engine will run 4 iterations
      engine.run(range(2), max_epochs=2)
  
      # ema_model and model can be DP or DDP
      # explicitly cast to float32 to avoid test failure on XLA devices
      ema_weight = _unwrap_model(ema_handler.ema_model).weight.data.to(
          torch.float32
      )
      model_weight = _unwrap_model(model).weight.data.to(torch.float32)
      assert ema_weight.device == device
      assert model_weight.device == device
      if interval == 1:
          assert ema_weight.allclose(
              ema_weight.new_full((1, 2), 4.0625)
          )
      elif interval == 2:
          assert ema_weight.allclose(ema_weight.new_full((1, 2), 3.5))
      else:
          pass
      assert model_weight.allclose(model_weight.new_full((1, 2), 5.0))
  
imports: import os
  from typing import Any, Callable, Union
  import pytest
  import torch
  import torch.nn
  from torch.nn.parallel import DataParallel, DistributedDataParallel
  import ignite.distributed
  from ignite.engine import Engine, Events
  from ignite.handlers import EMAHandler
label_desc: Use image node_id 14 for calling a global function with example usage: _test_ema_final_weight(model, device, ddp, interval) without return types