code: def test_forward_conv_batch_norm():
      """test batch normalization layer"""
      net = _lib().make_network(1)
      layer = _lib().make_convolutional_layer(
          1, 224, 224, 3, 32, 1, 3, 2, 0, 1, 1, 0, 0, 0
      )
      for i in range(32):
          layer.rolling_mean[i] = np.random.rand(1)
          layer.rolling_variance[i] = np.random.rand(1) + 0.5
      net.layers[0] = layer
      net.w = net.h = 224
      _lib().resize_network(net, 224, 224)
      verify_darknet_frontend(net)
      _lib().free_network(net)
  
imports: from cffi import FFI
  import numpy
  import tvm
  from tvm.contrib import graph_executor
  from tvm.contrib.download import download_testdata
  from tvm.relay.testing.darknet import LAYERTYPE
  from tvm.relay.testing.darknet import __darknetffi__
  from tvm.relay.frontend.darknet import ACTIVATION
  from tvm import relay
label_desc: Use image node_id 20 for calling a global function with example usage: test_forward_conv_batch_norm() without return types