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

def __init__(self, var_name, type_name, precision, data, reuse_factor, **kwargs):
        super(CompressedWeightVariable, self).__init__(var_name, type_name, precision, data, **kwargs)
        self.extra_zeros = 0
        self.data_length = np.prod(data.shape) - self.nzeros
        while self.data_length % reuse_factor != 0:
            self.extra_zeros += 1
            self.data_length += 1
        self.nonzeros = np.prod(data.shape) - self.nzeros + self.extra_zeros

        # Compress the array
        weights = []
        extra_nzero_cnt = self.extra_zeros
        it = np.nditer(data, order='C', flags=['multi_index'])
        max_idx = 0
        while not it.finished:
            val = it[0]
            if not (val == 0 and extra_nzero_cnt < 1):
                if val == 0:

data = self.model.get_weights_data(self.name, name)
        elif isinstance(data, six.string_types):
            data = self.model.get_weights_data(self.name, data)

        if quantize > 0:
            data = self.model.quantize_data(data, quantize)
            if quantize == 1:
                precision = 'ap_uint<1>'
                type_name = name + '{index}_t'
            elif quantize == 2 or quantize == 3:
                precision = 'ap_int<2>'
                type_name = name + '{index}_t'

        if compression:
            rf = self.model.config.get_reuse_factor(self)
            var = CompressedWeightVariable(var_name, type_name=type_name, precision=precision, data=data, reuse_factor=rf, index=self.index)
        else:
            var = WeightVariable(var_name, type_name=type_name, precision=precision, data=data, index=self.index)

        self.weights[name] = var
        self.precision[var.type.name] = var.type