code: def _test_bn_global_stats(model_list, **kwargs):
      class _BatchNorm(mx.gluon.nn.BatchNorm):
          def __init__(
              self,
              axis=1,
              momentum=0.9,
              epsilon=1e-5,
              center=True,
              scale=True,
              use_global_stats=False,
              beta_initializer="zeros",
              gamma_initializer="ones",
              running_mean_initializer="zeros",
              running_variance_initializer="ones",
              in_channels=0,
              **kwargs,
          ):
              assert use_global_stats
              super(_BatchNorm, self).__init__(
                  axis,
                  momentum,
                  epsilon,
                  center,
                  scale,
                  use_global_stats,
                  beta_initializer,
                  gamma_initializer,
                  running_mean_initializer,
                  running_variance_initializer,
                  in_channels,
                  **kwargs,
              )
  
      for model in model_list:
          gcv.model_zoo.get_model(
              model,
              norm_layer=_BatchNorm,
              use_global_stats=True,
              **kwargs,
          )
  
imports: from __future__ import print_function
  import warnings
  import logging
  import mxnet
  from mxnet.contrib.quantization import *
  from ..unittests.common import try_gpu, with_cpu
  import gluoncv
label_desc: Use image node_id 4 for calling a global function with example usage: _test_bn_global_stats(model_list) without return types