 Here is a Python script that follows your instructions, using the `argparse` library for command line argument parsing and the `ppocr` library for model loading and inference. Please note that this is a simplified version and assumes that the paths and arguments are correctly provided.

```python
import argparse
import os
import cv2
import numpy as np
from ppocr import PPocr, PPStructureV2Table
from ppocr.utils.cv2_util import draw_bboxes_on_image
from ppocr.utils.insightface_util import InsightFaceRecognitionPreprocessor, InsightFaceRecognitionPostprocessor

parser = argparse.ArgumentParser()
parser.add_argument('--detection_model', type=str, required=True)
parser.add_argument('--recognition_model', type=str, required=True)
parser.add_argument('--table_recognition_model', type=str, required=True)
parser.add_argument('--label_file', type=str, required=True)
parser.add_argument('--table_recognition_dict', type=str, required=True)
parser.add_argument('--batch_size', type=int, default=1)
parser.add_argument('--test_image', type=str, required=True)
parser.add_argument('--device_type', type=str, default='cpu')
parser.add_argument('--device_id', type=int, default=0)
parser.add_argument('--backend_type', type=str, default='opencv')
parser.add_argument('--trt_cache', type=str, default=None)
args = parser.parse_args()

ppocr_config = {}
if args.backend_type == 'tensorrt':
    ppocr_config['use_dynamic_shape'] = True
    if args.trt_cache:
        ppocr_config['trt_cache_path'] = args.trt_cache

ppocr = PPocr(use_gpu=args.device_type == 'gpu', gpu_id=args.device_id)
ppocr.set_model(
    detection_model=args.detection_model,
    recognition_model=args.recognition_model,
    table_recognition_model=args.table_recognition_model,
    config=ppocr_config
)

preprocessor = InsightFaceRecognitionPreprocessor(args.label_file)
postprocessor = InsightFaceRecognitionPostprocessor()
ppocr.set_preprocessor(preprocessor)
ppocr.set_postprocessor(postprocessor)

table_recognizer = PPStructureV2Table(ppocr)
table_recognizer.set_recognition_batch_size(args.batch_size)

img = cv2.imread(args.test_image)
results = table_recognizer.recognize(img)

for result in results:
    bbox, label, score, points, rotate, text = result
    cv2.rectangle(img, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (0, 255, 0), 2)
    cv2.putText(img, text, (bbox[0], bbox[1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)

cv2.imshow('Result', img)
cv2.waitKey(0)
cv2.destroyAllWindows()
```

This script parses command line arguments, sets up the PPocr and PPStructureV2Table objects, loads the models, and performs inference on a test image. It also visualizes the results and saves the visualized image. If the `--trt_cache` argument is provided, it will use TensorRT with dynamic shape and save the TRT cache file to disk.