code: def check_model_compatibility(self, backend_config, save, load):
      C = self.n_channels // self.groups
      old_model = {
          "avg_mean": numpy.random.uniform(-1, 1, (C,)).astype(
              self.dtype
          ),
          "avg_projection": numpy.random.uniform(0.5, 1, (C, C)).astype(
              self.dtype
          ),
          "N": numpy.array(0),
      }
      save(self.temp_file_path, old_model)
  
      model = links.DecorrelatedBatchNormalization(
          self.n_channels, groups=self.groups, dtype=self.dtype
      )
      model.to_device(backend_config.device)
      with (
          testing.assert_warns(UserWarning)
          if self.groups != 1
          else nullcontext()
      ):
          load(self.temp_file_path, model)
      x = numpy.random.rand(5, self.n_channels, 2).astype(self.dtype)
      x = backend_config.get_array(x)
      with chainer.using_config("train", False):
          model(x)
      model(x)
  
imports: import contextlib
  import os
  import tempfile
  import unittest
  import numpy
  import chainer
  from chainer import links
  from chainer import testing
label_desc: Use image node_id 3 for calling the TestDecorrelatedBatchNormalizationCompat obj's underlying member method code with example usage: obj.check_model_compatibility(backend_config, save, load) without return types