code: def parse_activation_layer(
      keras_layer, input_names, input_shapes, data_reader
  ):
      assert keras_layer["class_name"] in activation_layers
  
      layer = parse_default_keras_layer(keras_layer, input_names)
  
      if layer["class_name"] != "Activation":
          layer["activation"] = layer["class_name"]
      if layer["class_name"] == "LeakyReLU":
          layer["activ_param"] = keras_layer["config"].get("alpha", 0.3)
      elif layer["class_name"] == "ThresholdedReLU":
          layer["activ_param"] = keras_layer["config"].get("theta", 1.0)
      elif layer["class_name"] == "ELU":
          layer["activ_param"] = keras_layer["config"].get("alpha", 1.0)
      elif layer["class_name"] == "ReLU":
          layer["class_name"] = "Activation"
      elif layer["class_name"] == "PReLU":
          layer["alpha_data"] = get_weights_data(
              data_reader, layer["name"], "alpha"
          )
  
      if (
          layer["class_name"] == "Activation"
          and layer["activation"] == "softmax"
      ):
          layer["class_name"] = "Softmax"
      if (
          layer["class_name"] == "Activation"
          and layer["activation"] == "hard_sigmoid"
      ):
          layer["class_name"] = "HardActivation"
      if layer["class_name"] == "Softmax":
          layer["axis"] = keras_layer["config"].get("axis", -1)
  
      return layer, [shape for shape in input_shapes[0]]
  
imports: from hls4ml.converters.keras_to_hls import get_weights_data, keras_handler, parse_default_keras_layer
  from hls4ml.model.types import BinaryQuantizer, IntegerPrecisionType, TernaryQuantizer
label_desc: Use image node_id 3 for calling a global function with example usage: parse_activation_layer(keras_layer, input_names, input_shapes, data_reader) and returns: layer,  unknown