What's the ideal way to convert `opencv::cv::Mat` from rust to `numpy.ndarray` in python?

I have a cv::Mat in Rust, but want to convey the data to Python, I have checked the doc and gone to PyArray3, but it requires a Vec<Vec<Vec<T>>>, which means I have to make up the Vec manually with one copy, and from PyArray3::from_vec3 it might clone another time, that's quite inefficient, what's the proper way to do that? Is there a way to prevent data clone?

    #[test]
    fn mat_2_numpy() -> PyResult<()> {
        Python::with_gil(|py| {
            println!("in gil");
            let func: Py<PyAny> = PyModule::from_code(
                py,
                c_str!(
                    "import numpy as np
def call_np(arr):
    print(\"Shape:\", arr.shape)
                "
                ),
                c_str!(""),
                c_str!(""),
            )?
            .getattr("call_np")?
            .into();

            let img = opencv::imgcodecs::imread("/some/path/sample.jpg", 0).unwrap();
            let shape = (img.rows(), img.cols(), img.channels());

            // it requires Vec<Vec<Vec<T>>>
            let array = PyArray3::from_vec3(py, v)?;

            // pass object with Rust tuple of positional arguments
            let args = (array,);
            let engine_obj = func.call1(py, args)?;

            Ok(())
        })

Begin with the PyArray3::from_vec3 call and the opencv::imgcodecs::imread example in the issue. Determine whether the requested Rust cv::Mat-to-numpy.ndarray conversion can avoid the described Vec nesting and copies; done should be a documented, concrete supported approach or a clarified limitation.