How to use the hls4ml.model.hls_model.Activation function in hls4ml

def config_cpp(self):
        params = self._default_config_params()
        for i in range(3):
            params.setdefault('n_elem1_{}'.format(i), 0)
            params.setdefault('n_elem2_{}'.format(i), 0)
        inp1 = self.get_input_variable(self.inputs[0])
        inp2 = self.get_input_variable(self.inputs[1])
        for i, (s1, s2) in enumerate(zip(inp1.shape, inp2.shape)):
            params['n_elem1_{}'.format(i)] = s1
            params['n_elem2_{}'.format(i)] = s2

        return self._config_template.format(**params)

layer_map = {
    'InputLayer'         : Input,
    'Activation'         : Activation,
    'LeakyReLU'          : ParametrizedActivation,
    'ThresholdedReLU'    : ParametrizedActivation,
    'ELU'                : ParametrizedActivation,
    'PReLU'              : PReLU,
    'Dense'              : Dense,
    'BinaryDense'        : Dense,
    'TernaryDense'       : Dense,
    'Conv1D'             : Conv1D,
    'Conv2D'             : Conv2D,
    'BatchNormalization' : BatchNormalization,
    'MaxPooling1D'       : Pooling1D,
    'AveragePooling1D'   : Pooling1D,
    'MaxPooling2D'       : Pooling2D,
    'AveragePooling2D'   : Pooling2D,
    'Merge'              : Merge,
    'Concatenate'        : Concatenate,

def function_cpp(self):
        params = self._default_function_params()
        params['activation'] = self.get_attr('activation')
        params['config'] = '{}_config{}'.format(self.get_attr('activation'), self.index)

        return [self._function_template.format(**params)]

    def config_cpp(self):
        params = self._default_config_params()
        params['type'] = self.get_attr('activation')
        params['n_in'] = self.get_input_variable().size_cpp()

        return self._config_template.format(**params)

class ParametrizedActivation(Activation):
    def function_cpp(self):
        params = self._default_function_params()
        params['activation'] = self._get_act_function_name()
        params['param'] = self.get_attr('activ_param', 1.0)
        params['config'] = '{}_config{}'.format(self.get_attr('activation'), self.index)

        return [self._function_template.format(**params)]

    def _get_act_function_name(self):
        act = self.get_attr('activation').lower()
        if act == 'leakyrelu':
            return 'leaky_relu'
        elif act == 'thresholdedrelu':
            return 'thresholded_relu'
        else:
            return act # ELU activation

params['activation'] = self._get_act_function_name()
        params['param'] = self.get_attr('activ_param', 1.0)
        params['config'] = '{}_config{}'.format(self.get_attr('activation'), self.index)

        return [self._function_template.format(**params)]

    def _get_act_function_name(self):
        act = self.get_attr('activation').lower()
        if act == 'leakyrelu':
            return 'leaky_relu'
        elif act == 'thresholdedrelu':
            return 'thresholded_relu'
        else:
            return act # ELU activation

class PReLU(Activation):
    def initialize(self):
        super(PReLU, self).initialize()
        self.add_weights_variable(name='alpha', var_name='a{index}')

    def function_cpp(self):
        params = self._default_function_params()
        params['activation'] = self.get_attr('activation').lower()
        params['param'] = self.get_weights('alpha').name
        params['config'] = '{}_config{}'.format(self.get_attr('activation'), self.index)

        return [self._function_template.format(**params)]

class BatchNormalization(Layer):
    def initialize(self):
        inp = self.get_input_variable()
        shape = inp.shape