code: def _convert_BatchNorm(node, graph, err):
      epsilon = node.attrs.get("epsilon", 1e-5)
      scale = node.input_tensors[node.inputs[1]]
      bias = node.input_tensors[node.inputs[2]]
      mean = node.input_tensors[node.inputs[3]]
      var = node.input_tensors[node.inputs[4]]
      node_name = node.name
  
      input_name = str(node.inputs[0])
      output_name = str(node.outputs[0])
  
      if input_name == output_name:
          inplace = True
      else:
          inplace = False
  
      bn_layer = myf(
          "BatchNorm",
          node_name + "_bn",
          [input_name],
          [output_name],
          eps=epsilon,
          use_global_stats=True,
          in_place=inplace,
      )
      scale_layer = myf(
          "Scale",
          node_name,
          [output_name],
          [output_name],
          in_place=True,
          bias_term=True,
      )
  
      graph.channel_dims[output_name] = graph.channel_dims[input_name]
  
      return bn_layer, scale_layer
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  from caffe import params
  import math
  import numpy
  from ._graph import Node, Graph
  from MyCaffe import Function
label_desc: Use image node_id 6 for calling a global function with example usage: _convert_BatchNorm(node, graph, err) and returns: bn_layer,  scale_layer