code: def parse_activation_layer(
      reader, node, inputs_map, input_shapes, graph, config
  ):
      layer = {}
  
      layer["name"] = node.name
      layer["class_name"] = activation_map[node.op_type]
      layer["activation"] = node.op_type.lower()
      layer["inputs"] = get_onnx_input_name(node, graph)
  
      if layer["class_name"] != "Activation":
          if layer["class_name"] == "Softmax":
              layer["activation"] = "softmax"
  
          elif layer["class_name"] in [
              "ELU",
              "LeakyReLU",
              "ThresholdedReLU",
          ]:
              layer["activation"] = layer["class_name"]
              layer["activ_param"] = get_onnx_attribute(
                  node, "alpha", 0.01
              )
  
          elif layer["class_name"] == "Clip":
              clip_min_node = [
                  x for x in graph.initializer if x.name in node.input
              ]
              clip_min = clip_min_node[0].float_data[0]
  
              # Check if it's relu or not
              if clip_min == 0.0:
                  layer["class_name"] = "Activation"
                  layer["activation"] = "ReLU"
              else:
                  raise Exception(
                      "Clip with min != 0 is not supported yet!"
                  )
  
          else:
              layer["activation"] = layer["class_name"]
              layer["class_name"] = "Activation"
  
      return layer, [shape for shape in input_shapes[0]]
  
label_desc: Use image node_id 2 for calling a global function with example usage: parse_activation_layer(reader, node, inputs_map, input_shapes, graph, config) and returns: layer,  unknown