def METHOD_NAME(self):
    """Scale propagation pattern match and create new const nodes."""
    target_nodes = self.cur_graph.query_fusion_pattern_nodes(self.cac_pattern)
    quantize_v2_min_index = 1
    requntize_min_index = 3
    quantize_v2_max_index = 2
    requntize_max_index = 4
    for match in target_nodes:
        pre_node_name = match[0]
        pre_node = self.graph_info[pre_node_name].node
        output_nodes_count = len(set(self.graph_info[pre_node_name].outputs))
        if output_nodes_count > 1:
            continue
        # Skip transformation if avgpool has multi output nodes.
        pooling_nodes_count = len(set(self.graph_info[match[1]].outputs))
        if pooling_nodes_count > 1:
            continue
        if pre_node.op == "QuantizeV2":
            pre_min_index, pre_max_index = quantize_v2_min_index, quantize_v2_max_index
        else:
            pre_min_index, pre_max_index = requntize_min_index, requntize_max_index
        requantize_node_name = match[3]
        requantize_node = self.graph_info[requantize_node_name].node
        requantize_min = self.graph_info[
            Helper.node_name_from_input(requantize_node.input[requntize_min_index])
        ].node
        requantize_max = self.graph_info[
            Helper.node_name_from_input(requantize_node.input[requntize_max_index])
        ].node
        requantize_min_value = (requantize_min.attr["value"].tensor.float_val)[0]
        requantize_max_value = (requantize_max.attr["value"].tensor.float_val)[0]
        self._create_new_const_node(
            pre_node_name + "_cac_requantize_min_value", requantize_min_value, pre_node.input[pre_min_index]
        )
        self._create_new_const_node(
            pre_node_name + "_cac_requantize_max_value", requantize_max_value, pre_node.input[pre_max_index]
        )