code: def convert_batchnorm(net, node, module, builder):
      
      input_name, output_name = _get_input_output_name(net, node)
      name = node['name']
      inputs = node['inputs']
  
  
      eps = 1e-3  # Default value of eps for MXNet.
      use_global_stats = False  # Default value of use_global_stats for MXNet.
      fix_gamma = True  # Default value of fix_gamma for MXNet.
      attrs = _get_attrs(node)
      if 'eps' in attrs:
          eps = literal_eval(attrs['eps'])
      if 'fix_gamma' in attrs:
          fix_gamma = literal_eval(attrs['fix_gamma'])
  
      args, aux = module.get_params()
      gamma = args[_get_node_name(net, inputs[1][0])].asnumpy()
      beta = args[_get_node_name(net, inputs[2][0])].asnumpy()
      mean = aux[_get_node_name(net, inputs[3][0])].asnumpy()
      variance = aux[_get_node_name(net, inputs[4][0])].asnumpy()
      nb_channels = gamma.shape[0]
      if fix_gamma:
          gamma.fill(1.)
      builder.add_batchnorm(
          name=name,
          channels=nb_channels,
          gamma=gamma,
          beta=beta,
          mean=mean,
          variance=variance,
          input_name=input_name,
          output_name=output_name,
          epsilon=eps)
docs: Convert a batchnorm layer from mxnet to coreml.
  
      Parameters
      ----------
      network: net
          A mxnet network object.
  
      layer: node
          Node to convert.
  
      module: module
          An module for MXNet
  
      builder: NeuralNetworkBuilder
          A neural network builder object.