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

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 register_layer(name, clazz):
    global layer_map

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