code: def _convert_BatchNorm(net, node, graph, err):
      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
      np.copyto(
          net.params[node_name + "_bn"][0].data,
          mean,
          casting="same_kind",
      )
      np.copyto(
          net.params[node_name + "_bn"][1].data,
          var,
          casting="same_kind",
      )
      net.params[node_name + "_bn"][2].data[...] = 1.0
      np.copyto(
          net.params[node_name][0].data, scale, casting="same_kind"
      )
      np.copyto(
          net.params[node_name][1].data, bias, casting="same_kind"
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  import numpy
  from ._graph import Node, Graph
label_desc: Use image node_id 4 for calling a global function with example usage: _convert_BatchNorm(net, node, graph, err) without return types