  Here is the Python code that parses command line arguments for the PPOCR models and sets the runtime options:
```
import argparse
import os

def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument('--detection-model', type=str, required=True, help='Path to the detection model')
    parser.add_argument('--recognition-model', type=str, required=True, help='Path to the recognition model')
    parser.add_argument('--table-recognition-model', type=str, required=True, help='Path to the table recognition model')
    parser.add_argument('--recognition-model-label-file', type=str, required=True, help='Path to the recognition model label file')
    parser.add_argument('--table-recognition-dictionary-path', type=str, required=True, help='Path to the table recognition dictionary')
    parser.add_argument('--inference-batch-size', type=int, required=True, help='Inference batch size')
    parser.add_argument('--test-image-file-path', type=str, required=True, help='Path to the test image file')
    parser.add_argument('--inference-device-type', type=str, required=True, help='Inference device type')
    parser.add_argument('--inference-device-id', type=int, required=True, help='Inference device ID')
    parser.add_argument('--inference-backend-type', type=str, required=True, help='Inference backend type')
    args = parser.parse_args()
    return args

def set_runtime_options(args):
    # Set the runtime options for the detection model
    detection_model_runtime_options = {
        'model_path': args.detection_model,
        'label_file_path': args.recognition_model_label_file,
        'batch_size': args.inference_batch_size,
        'device_type': args.inference_device_type,
        'device_id': args.inference_device_id,
        'backend_type': args.inference_backend_type
    }
    # Set the runtime options for the recognition model
    recognition_model_runtime_options = {
        'model_path': args.recognition_model,
        'label_file_path': args.recognition_model_label_file,
        'batch_size': args.inference_batch_size,
        'device_type': args.inference_device_type,
        'device_id': args.inference_device_id,
        'backend_type': args.inference_backend_type
    }
    # Set the runtime options for the table recognition model
    table_recognition_model_runtime_options = {
        'model_path': args.table_recognition_model,
        'label_file_path': args.table_recognition_dictionary_path,
        'batch_size': args.inference_batch_size,
        'device_type': args.inference_device_type,
        'device_id': args.inference_device_id,
        'backend_type': args.inference_backend_type
    }
    return detection_model_runtime_options, recognition_model_runtime_options, table_recognition_model_runtime_options

def main():
    args = parse_args()
    detection_model_runtime_options, recognition_model_runtime_options, table_recognition_model_runtime_options = set_runtime_options(args)
    # Load the models
    detection_model = the_answer.load_model(detection_model_runtime_options)
    recognition_model = the_answer.load_model(recognition_model_runtime_options)
    table_recognition_model = the_answer.load_model(table_recognition_model_runtime_options)
    # Set the preprocessor and postprocessor parameters for the detection model
    detection_model.set_preprocessor(InsightFaceRecognitionPreprocessor())
    detection_model.set_postprocessor(InsightFaceRecognitionPostprocessor())
    # Create an instance of PPStructureV2Table with the loaded models
    ppstructurev2table = PPStructureV2Table(detection_model, recognition_model, table_recognition_model)
    # Set the recognition batch size
    ppstructurev2table.set_recognition_batch_size(args.inference_batch_size)
    # Read the input image, predict and print the results, visualize the results, and save the visualized image
    input_image = cv2.imread(args.test_image_file_path)
    results = ppstructurev2table.predict(input_image)
    print(results)
    visualized_image = ppstructurev2table.visualize(input_image, results)
    cv2.imwrite('output.jpg', visualized_image)

if __name__ == '__main__':
    main()
```
This code uses the `argparse` module to parse the command line arguments and the `the_answer` module to load the models and set the runtime options. It also uses the `cv2` module to read the input image, predict and print the results, visualize the results, and save the visualized image.