def METHOD_NAME(self):
    """Fuse Pad + Conv2D/DepthwiseConv2dNative/Conv3D --> Conv2D/DepthwiseConv2dNative/Conv3D."""
    cur_graph = GraphAnalyzer()
    cur_graph.graph = self.model
    graph_info = cur_graph.parse_graph()
    target_nodes = cur_graph.query_fusion_pattern_nodes(
        [["Pad"], ["Conv2D", "Conv3D", "DepthwiseConv2dNative"], ("BiasAdd", "Add", "AddV2")]
    )
    padding_tensor_dict = {}
    for node_combination in target_nodes:
        conv_name = node_combination[1]
        pattern = node_combination[-1]
        if conv_name not in self.cfg:
            continue
        is_perchannel = self.cfg[conv_name][0]
        # Line 55 to line 65 should be removed once the TFDO enabling the single quantized
        # conv2D supporting.
        if len(pattern) == 2:
            # TODO we need to enable single quantizedconv2d with s8 input.
            if not is_perchannel and not cur_graph.has_positive_input(conv_name):
                continue
            # TFDO has the limitation that the single QuantizedConv2DPerchannel doesn't
            # support padding_list filed.
            if is_perchannel:
                continue
        if conv_name in self.excluded_conv:
            continue
        padding_tensor = None
        pad_node = None
        if node_combination[0] not in padding_tensor_dict:
            pad_node = graph_info[node_combination[0]].node
            if graph_info[pad_node.input[1]].node.op != "Const":
                input_node = graph_info[pad_node.input[1]].node
                if input_node.op == "DataFormatVecPermute":
                    parent_input_node = graph_info[input_node.input[0]].node
                    if parent_input_node.op == "Const":
                        padding_tensor = tensor_util.MakeNdarray(parent_input_node.attr["value"].tensor).flatten()
                    else:
                        continue
                else:
                    continue
            else:
                padding_tensor = tensor_util.MakeNdarray(
                    graph_info[pad_node.input[1]].node.attr["value"].tensor
                ).flatten()
            padding_tensor_dict[node_combination[0]] = padding_tensor
        else:
            padding_tensor = padding_tensor_dict[node_combination[0]]
        if self.itex_qdq_mode:
            enabled_pad_conv2d = bool(
                tf.version.VERSION == "1.15.0-up3" or version1_gt_version2(tf.version.VERSION, "2.7")
            )
        else:
            enabled_pad_conv2d = bool(tf.version.VERSION == "1.15.0-up3" or self.new_api)
        if any(padding_tensor) and not enabled_pad_conv2d:  # pragma: no cover
            continue
        if pad_node:
            if graph_info[pad_node.input[1]].node.op != "Const":
                cur_graph.node_name_details[pad_node.name].node.input.remove(pad_node.input[1])
                cur_graph.remove_node_with_single_input_output(pad_node.name)
            else:
                cur_graph.remove_node_with_single_input_output(pad_node.name)
                cur_graph.remove_node(pad_node.input[1])
        conv_node = graph_info[node_combination[1]].node
        if self.itex_qdq_mode:
            if any(padding_tensor) and enabled_pad_conv2d:  # pragma: no cover
                Helper.set_attr_string(conv_node, "padding", b"EXPLICIT")
                Helper.set_attr_int_list(conv_node, "explicit_paddings", padding_tensor)
        else:
            Helper.set_attr_int_list(conv_node, "padding_list", padding_tensor)
            if any(padding_tensor) and enabled_pad_conv2d:  # pragma: no cover
                Helper.set_attr_string(conv_node, "padding", b"EXPLICIT")
    return cur_graph.dump_graph()