code: def test_ema_buffer(handle_buffers):
      """Test if the tensors in buffer are also correctly updated"""
      model = nn.BatchNorm2d(2)
      model.running_mean.data.fill_(1.5)
      model.running_var.data.fill_(1.5)
  
      # manually register a buffer to test if it will be correctly updated
      model.register_buffer(
          "dummy_buffer", tensor=torch.tensor(1.0, dtype=torch.float32)
      )
  
      if handle_buffers == "invalid":
          with pytest.raises(
              ValueError, match="handle_buffers can only"
          ):
              _ = EMAHandler(
                  model, momentum=0.5, handle_buffers=handle_buffers
              )
      else:
          ema_handler = EMAHandler(
              model, momentum=0.5, handle_buffers=handle_buffers
          )
  
          def _bn_step_fn(engine, batch):
              x = torch.rand(4, 2, 32, 32)
              _ = model(x)
              # manually increment the dummy_buffer at every step
              model.dummy_buffer += 1.0
              return 1
  
          engine = Engine(_bn_step_fn)
          ema_handler.attach(engine)
  
          ema_model = ema_handler.ema_model
          if handle_buffers == "ema_train":
              assert ema_model.training
          else:
              assert not ema_model.training
  
          @engine.on(Events.ITERATION_COMPLETED)
          def check_buffers():
              if handle_buffers == "update":
                  # the buffers with torch.int64 data type should be directly copied
                  assert ema_model.num_batches_tracked.allclose(
                      model.num_batches_tracked
                  )
  
                  # buffers with floating type will be updated rather than copied
                  assert not ema_model.dummy_buffer.allclose(
                      model.dummy_buffer
                  )
                  assert not ema_model.running_mean.allclose(
                      model.running_mean
                  )
                  assert not ema_model.running_var.allclose(
                      model.running_var
                  )
              elif handle_buffers == "copy":
                  # the buffers with torch.int64 data type should be directly copied
                  assert ema_model.num_batches_tracked.allclose(
                      model.num_batches_tracked
                  )
  
                  assert ema_model.dummy_buffer.allclose(
                      model.dummy_buffer
                  )
                  assert ema_model.running_mean.allclose(
                      model.running_mean
                  )
                  assert ema_model.running_var.allclose(
                      model.running_var
                  )
              else:
                  # buffers will not be copied or EMA updated
                  assert ema_model.num_batches_tracked.allclose(
                      torch.tensor(0, dtype=torch.int64)
                  )
                  assert ema_model.dummy_buffer.allclose(
                      torch.tensor(1.0, dtype=torch.float32)
                  )
  
          # engine will run 4 iterations
          engine.run([0, 1], max_epochs=2)
  
          if handle_buffers == "update":
              assert ema_model.num_batches_tracked.allclose(
                  model.num_batches_tracked
              )
              assert ema_model.dummy_buffer.allclose(
                  torch.tensor(4.0625, dtype=torch.float32)
              )
              assert not ema_model.dummy_buffer.allclose(
                  model.dummy_buffer
              )
              assert not ema_model.running_mean.allclose(
                  model.running_mean
              )
              assert not ema_model.running_var.allclose(
                  model.running_var
              )
          elif handle_buffers == "copy":
              assert ema_model.num_batches_tracked.allclose(
                  model.num_batches_tracked
              )
              assert ema_model.dummy_buffer.allclose(model.dummy_buffer)
              assert ema_model.running_mean.allclose(model.running_mean)
              assert ema_model.running_var.allclose(model.running_var)
          else:
              # buffers will not be copied or EMA updated
              assert ema_model.num_batches_tracked.allclose(
                  torch.tensor(0, dtype=torch.int64)
              )
              assert ema_model.dummy_buffer.allclose(
                  torch.tensor(1.0, dtype=torch.float32)
              )
  
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 12 for calling a global function with example usage: test_ema_buffer(handle_buffers) and returns: int