code: def rename_BatchNorm(self, source_node):
      IR_node = self._convert_identity_operation(
          source_node, new_op="BatchNorm"
      )
      kwargs = {}
      IR_node.attr["use_global_stats"].b = source_node.get_attr(
          "use_global_stats"
      )
      IR_node.attr["bias"].b = source_node.get_attr("use_global_stats")
      IR_node.attr["scale"].b = source_node.get_attr("use_global_stats")
      IR_node.attr["epsilon"].f = 1e-5
  
      assign_IRnode_values(IR_node, kwargs)
  
      innode = self.dk_graph.get_node(source_node.in_edges[0])
      input_shape = innode.get_attr("_output_shape")
      kernel = innode.get_attr("kernel")
      kernel = np.zeros([kernel[-1], kernel[-2], kernel[0], kernel[1]])
  
      # buf, start, scale_layer['name'], bn_layer['name'], conv_layer['name']
      # print("==============",self.start)
      bias = np.zeros(input_shape[-1])
      scale = np.zeros(input_shape[-1])
      mean = np.zeros(input_shape[-1])
      var = np.zeros(input_shape[-1])
      # print(bias.shape)
      # print(scale.shape)
      # print(mean.shape)
      # print(var.shape)
      # print(kernel.shape)
  
      bias_content = np.reshape(
          self.buf[self.start : self.start + bias.size], bias.shape
      )
      self.start = self.start + bias.size
      self.set_weight(source_node.name, "bias", bias_content)
  
      scale_content = np.reshape(
          self.buf[self.start : self.start + scale.size], scale.shape
      )
      self.start = self.start + scale.size
      self.set_weight(source_node.name, "scale", scale_content)
  
      mean_content = np.reshape(
          self.buf[self.start : self.start + mean.size], mean.shape
      )
      self.start = self.start + mean.size
      self.set_weight(source_node.name, "mean", mean_content)
  
      var_content = np.reshape(
          self.buf[self.start : self.start + var.size], var.shape
      )
      self.start = self.start + var.size
      self.set_weight(source_node.name, "var", var_content)
  
      W = np.reshape(
          self.buf[self.start : self.start + kernel.size], kernel.shape
      )
      self.start = self.start + kernel.size
      W = np.transpose(W, (2, 3, 1, 0))
      # print(W)
      # assert False
      self.set_weight(innode.name, "weights", W)
  
imports: import os
  import numpy
  from mmdnn.conversion.common.utils import *
  from mmdnn.conversion.darknet.prototxt import *
  from mmdnn.conversion.darknet.darknet_utils import *
  from mmdnn.conversion.darknet.darknet_graph import DarknetGraph
  import mmdnn.conversion.common.IR.graph_pb2
  from mmdnn.conversion.common.DataStructure.parser import Parser
  from mmdnn.conversion.common.IR.graph_pb2 import NodeDef, GraphDef, DataType
label_desc: Use image node_id 10 for calling the DarknetParser obj's underlying member method code with example usage: obj.rename_BatchNorm(source_node) without return types