code: def rename_Activation(self, coreml_node):
      IR_node = self.IR_graph.node.add()
  
      coreml_node_layer = coreml_node.layer
      coreml_node_activation = coreml_node_layer.activation
  
      # name, op
      for activation_name in self.activation_map.keys():
          if coreml_node_activation.HasField(activation_name):
              CoremlParser._copy_and_reop(
                  coreml_node,
                  IR_node,
                  self.activation_map[activation_name],
              )
  
      # activation type
      activation_type = coreml_node_activation.WhichOneof(
          "NonlinearityType"
      )
  
      if activation_type == "leakyReLU":
          assign_IRnode_values(
              IR_node, {"alpha": coreml_node_activation.leakyReLU.alpha}
          )
      elif activation_type == "PReLU":
          assign_IRnode_values(
              IR_node, {"gamma": coreml_node_activation.PReLU.alpha}
          )
      elif activation_type == "ELU":
          assign_IRnode_values(
              IR_node, {"alpha": coreml_node_activation.ELU.alpha}
          )
      elif activation_type == "thresholdedRelu":
          assign_IRnode_values(
              IR_node,
              {"alpha": coreml_node_activation.thresholdedReLU.alpha},
          )
      elif activation_type == "scaledTanh":
          assign_IRnode_values(
              IR_node,
              {"alpha": coreml_node_activation.scaledTanh.alpha},
          )
          assign_IRnode_values(
              IR_node, {"beta": coreml_node_activation.scaledTanh.beta}
          )
      elif activation_type == "linear":
          assign_IRnode_values(
              IR_node, {"alpha": coreml_node_activation.linear.alpha}
          )
          assign_IRnode_values(
              IR_node, {"beta": coreml_node_activation.linear.beta}
          )
      elif activation_type == "sigmoidHard":
          assign_IRnode_values(
              IR_node,
              {"alpha": coreml_node_activation.sigmoidHard.alpha},
          )
          assign_IRnode_values(
              IR_node, {"beta": coreml_node_activation.sigmoidHard.beta}
          )
      elif activation_type == "parametricSoftplus":
          assign_IRnode_values(
              IR_node,
              {
                  "alpha": coreml_node_activation.parametricSoftplus.alpha
              },
          )
          assign_IRnode_values(
              IR_node,
              {"beta": coreml_node_activation.parametricSoftplus.beta},
          )
      # else:
      # assert False
  
      # input edge
      self.convert_inedge(coreml_node, IR_node)
  
imports: import os
  from six import string_types
  import numpy
  import math
  from coremltools.models.neural_network import NeuralNetworkBuilder
  from coremltools.models import datatypes
  from coremltools.models import MLModel
  from coremltools.models.utils import save_spec
  from coremltools.models._infer_shapes_nn_mlmodel import infer_shapes
  from coremltools.proto import Model_pb2, NeuralNetwork_pb2
  from mmdnn.conversion.coreml.coreml_graph import CoremlGraph
  import mmdnn.conversion.common.IR.graph_pb2
  from mmdnn.conversion.common.IR.graph_pb2 import NodeDef, GraphDef, DataType
  from mmdnn.conversion.common.DataStructure.parser import Parser
  from mmdnn.conversion.common.utils import *
label_desc: Use image node_id 15 for calling the CoremlParser obj's underlying member method code with example usage: obj.rename_Activation(coreml_node) without return types