使用python语言，帮我写一个完整的将.onnx权重文件转换为.trt文件的python代码

当然，下面是一个完整的Python代码示例，使用TensorRT将.onnx权重文件转换为.trt文件：

import tensorrt as trt
import pycuda.driver as cuda
import pycuda.autoinit

def convert_onnx_to_trt(onnx_file_path, trt_file_path):
    TRT_LOGGER = trt.Logger(trt.Logger.WARNING)
    builder = trt.Builder(TRT_LOGGER)
    network = builder.create_network()

    parser = trt.OnnxParser(network, TRT_LOGGER)
    with open(onnx_file_path, 'rb') as f:
        if not parser.parse(f.read()):
            for error in range(parser.num_errors):
                print(parser.get_error(error))
    
    builder.max_workspace_size = 1 << 30
    builder.max_batch_size = 1
    builder.fp16_mode = True  # Enable FP16 mode if supported

    engine = builder.build_cuda_engine(network)
    trt.save_engine_to_file(engine, trt_file_path)

    print("TensorRT engine is successfully created and saved to", trt_file_path)

if __name__ == '__main__':
    onnx_file_path = 'path/to/your_model.onnx'
    trt_file_path = 'path/to/save/your_model.trt'
    convert_onnx_to_trt(onnx_file_path, trt_file_path)

请确保你已经正确安装了TensorRT和pycuda库，并将`onnx_file_path`和`trt_file_path`替换为你的实际文件路径。此代码将加载.onnx权重文件并将其转换为.trt文件，使用FP16模式进行优化，并保存到指定的文件路径中。