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你好请问能提供下python脚本运行吗？

# -*- coding: utf-8 -*-

import sys
import os
import cv2
import numpy as np
import paddle
from paddle.inference import Config, DataType
from paddle.inference import create_predictor

if __name__ == "__main__":
    if len(sys.argv) < 4:
        print("Usage: {model_dir} {pic} {threshold}")

    model_dir = sys.argv[1]
    pic = sys.argv[2]
    threshold = sys.argv[3]

    # 载入模型
    config = Config(
        os.path.join(model_dir, "model.pdmodel"),
        os.path.join(model_dir, "model.pdiparams"),
    )
    config.switch_ir_optim(True)
    config.switch_specify_input_names(True)
    config.switch_ir_debug(False)
    config.disable_glog_info()
    config.disable_gpu()

    predictor = create_predictor(config)
    feed_target_names = predictor.get_input_names()

    # 处理输入图片
    img = cv2.imread(pic)
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    # 对图片做Resize处理，当输入图片尺寸为(640x426)时, 等价于Keep_ratio resize
    img_scale_x = float(800) / img.shape[1]
    img_scale_y = float(532) / img.shape[0]
    img_info = [532, 800, img_scale_x] # 因为keep_ratio，所以img_scale_x == img_scale_y
    img = cv2.resize(np.asarray(img), None, None, fx=img_scale_x, fy=img_scale_y, interpolation=2)
    img = np.array(img).astype(np.float32)
    # 对图片做归一化
    mean = [123.675, 116.28, 103.53]
    scale = [0.017124753831663668, 0.01750700280112045, 0.017429193899782137]
    img_mean = np.array(mean).astype("float32")[np.newaxis, np.newaxis, :]
    img_scale = np.array(scale).astype("float32")[np.newaxis, np.newaxis, :]
    img -= img_mean
    img = img * img_scale
    # 对图片做补pad对齐到32操作,补齐到(800, 544)
    img_h, img_w = img.shape[:2]
    padded_img = np.zeros((544, 800, 3), dtype=np.float32)
    padded_img[:img_h, :img_w, :] = img

    # 图片喂给模型
    print("Switch to CHW")
    data = np.swapaxes(padded_img, 1, 2)
    data = np.swapaxes(data, 1, 0)
    _, data_h, data_w = data.shape
    for index, name in enumerate(feed_target_names):
        if name == "image":
            tensor = predictor.get_input_handle("image")
            tensor.reshape([1, 3, data_h, data_w])
            tensor.copy_from_cpu(np.array([data.copy()]).astype("float32"))
        elif name == "im_shape":
            tensor = predictor.get_input_handle("im_shape")
            tensor.reshape([1, 2])
            tensor.copy_from_cpu(
                np.array([img_info[:2]]).astype("float32")
            )  # 图像经过resize后的大小
        elif name == "scale_factor":
            # 输入图像大小比真实图像大小
            np_data = np.array([[1.25, 1.25]]).astype("float32")
            tensor = predictor.get_input_handle("scale_factor")
            tensor.reshape([1, 2])
            tensor.copy_from_cpu(np_data)

    predictor.run()

    # 获取推理结果
    output_names = predictor.get_output_names()
    print("output_names:", output_names)
    output_tensors = [predictor.get_output_handle(name_i) for name_i in output_names]

    # 整理输出
    output_datas = [tensor_j.copy_to_cpu() for tensor_j in output_tensors]
    ret = np.array(output_datas[0])
    idx = ret[:, 1].argsort()[::-1]
    ma = ret[idx]
    for item in ma:
        if float(item[1]) < float(threshold):
            continue
        print("index: ", int(item[0]), ", prob: ", float(item[1]), ", [x1, y1, x2, y2]:", [float(item[2]), float(item[3]), float(item[4]), float(item[5])])
    print("done")

可以参考上面这个脚本
python ./paddle_inference.py ./ ./cascade.jpeg 0.3 正常推理
python ./paddle_inference.py ./ ./cascade2.jpeg 0.3 在paddle2.3.2版本正常推理，在paddle2.4.0, 2.4.1版本会报错上面的错