code: def rename_BatchNormalization(self, source_node):
      # TODO
      # output_shape
  
      IR_node = self._convert_identity_operation(
          source_node, new_op="BatchNorm"
      )
  
      attr = source_node.attrs
      # epsilon
      IR_node.attr["epsilon"].f = attr["epsilon"]
      weights_scope = self.get_weight_name(source_node)
  
      bias_name = "{0}.bias".format(weights_scope)
      weights_name = "{0}.weight".format(weights_scope)
      mean_name = "{0}.running_mean".format(weights_scope)
      var_name = "{0}.running_var".format(weights_scope)
  
      if bias_name in self.state_dict:
          beta = self.state_dict[bias_name].numpy()
          IR_node.attr["bias"].b = True
      else:
          IR_node.attr["bias"].b = False
  
      if weights_name in self.state_dict:
          gamma = self.state_dict[weights_name].numpy()
          IR_node.attr["scale"].b = True
      else:
          IR_node.attr["scale"].b = False
  
      mean = self.state_dict[mean_name].numpy()
      variance = self.state_dict[var_name].numpy()
  
      if IR_node.attr["scale"].b:
          self.set_weight(source_node.name, "scale", gamma)
  
      if IR_node.attr["bias"].b:
          self.set_weight(source_node.name, "bias", beta)
  
      # mean
      self.set_weight(source_node.name, "mean", mean)
  
      # var
      self.set_weight(source_node.name, "var", variance)
  
imports: import os
  import numpy
  import mmdnn.conversion.common.IR.graph_pb2
  from mmdnn.conversion.common.IR.graph_pb2 import NodeDef, GraphDef, DataType
  from mmdnn.conversion.common.utils import *
  from mmdnn.conversion.common.DataStructure.parser import Parser
  from mmdnn.conversion.pytorch.pytorch_graph import PytorchGraph040
  from mmdnn.conversion.pytorch.pytorch_graph import PytorchGraph151
  import torch
  import torchvision
label_desc: Use image node_id 10 for calling the PytorchParser obj's underlying member method code with example usage: obj.rename_BatchNormalization(source_node) without return types