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

'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
    layer_map[name] = clazz

params['input1_t'] = self.get_input_variable(self.inputs[0]).type.name
        params['input2_t'] = self.get_input_variable(self.inputs[1]).type.name
        params['output_t'] = self.get_output_variable().type.name
        params['input1'] = self.get_input_variable(self.inputs[0]).name
        params['input2'] = self.get_input_variable(self.inputs[1]).name
        params['output'] = self.get_output_variable().name

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

    def config_cpp(self):
        params = self._default_config_params()
        params['n_elem'] = self.get_input_variable(self.inputs[0]).size_cpp()

        return self._config_template.format(**params)

class Concatenate(Merge):
    def initialize(self):
        assert(len(self.inputs) == 2)
        inp1 = self.get_input_variable(self.inputs[0])
        inp2 = self.get_input_variable(self.inputs[1])
        shape = [sum(x) for x in zip(inp1.shape, inp2.shape)]
        rank = len(shape)
        if rank > 1:
            dims = ['OUT_CONCAT_{}_{}'.format(i, self.index) for i in range(rank)]
        else:
            dims = ['OUT_CONCAT_{}'.format(self.index)]
        self.add_output_variable(shape, dims)

    def config_cpp(self):
        params = self._default_config_params()
        for i in range(3):
            params.setdefault('n_elem1_{}'.format(i), 0)