```python
import argparse
from ppocr import PPStructureV2Table, load_model, set_runtime_options, preprocess_image, postprocess_results, visualize_results
import cv2

# Parse command line arguments
parser = argparse.ArgumentParser(description='PPOCR Model Inference')
parser.add_argument('--detection_model_path', type=str, required=True, help='Path to the detection model')
parser.add_argument('--recognition_model_path', type=str, required=True, help='Path to the recognition model')
parser.add_argument('--table_recognition_model_path', type=str, required=True, help='Path to the table recognition model')
parser.add_argument('--recognition_label_file', type=str, required=True, help='Path to the recognition model label file')
parser.add_argument('--table_recognition_dict_path', type=str, required=True, help='Path to the table recognition dictionary')
parser.add_argument('--recognition_batch_size', type=int, required=True, help='Recognition model inference batch size')
parser.add_argument('--test_image_path', type=str, required=True, help='Path to the test image file')
parser.add_argument('--inference_device', type=str, required=True, choices=['cpu', 'gpu'], help='Inference device type')
parser.add_argument('--device_id', type=int, required=True, help='Device ID')
parser.add_argument('--backend_type', type=str, required=True, choices=['ONNX', 'TensorRT'], help='Inference backend type')

args = parser.parse_args()

# Set runtime options based on parsed arguments
detection_options = set_runtime_options(model_path=args.detection_model_path, device=args.inference_device, device_id=args.device_id, backend=args.backend_type)
recognition_options = set_runtime_options(model_path=args.recognition_model_path, device=args.inference_device, device_id=args.device_id, backend=args.backend_type)
table_recognition_options = set_runtime_options(model_path=args.table_recognition_model_path, device=args.inference_device, device_id=args.device_id, backend=args.backend_type)

# Load models
detection_model = load_model(**detection_options)
recognition_model = load_model(**recognition_options)
table_recognition_model = load_model(**table_recognition_options)

# Set preprocessor and postprocessor for detection model
detection_model.set_preprocessor(InsightFaceRecognitionPreprocessor())
detection_model.set_postprocessor(InsightFaceRecognitionPostprocessor())

# Create PPStructureV2Table instance
ppstructure_table = PPStructureV2Table(detection_model=detection_model, recognition_model=recognition_model, table_recognition_model=table_recognition_model, recognition_batch_size=args.recognition_batch_size)

# Read input image
image = cv2.imread(args.test_image_path)

# Predict and print results
results = ppstructure_table.predict(image)
print(results)

# Visualize and save results
visualized_image = visualize_results(image, results)
cv2.imwrite('visualized_results.jpg', visualized_image)
```