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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def resize_short(src, size, interp=2):
 """Resizes shorter edge to size. .. note:: `resize_short` uses OpenCV (not the CV2 Python library). MXNet must have been built with OpenCV for `resize_short` to work. Resizes the original image by setting the shorter edge to size and setting the longer edge accordingly. Resizing function is called from OpenCV. Parameters src : NDArray The original image. size : int The length to be set for the shorter edge. interp : int, optional, default=2 Interpolation method used for resizing the image. Possible values: 0: Nearest Neighbors Interpolation. 1: Bilinear interpolation. 2: Area-based (resampling using pixel area relation). It may be a preferred method for image decimation, as it gives moire-free results. But when the image is zoomed, it is similar to the Nearest Neighbors method. (used by default). 3: Bicubic interpolation over 4x4 pixel neighborhood. 4: Lanczos interpolation over 8x8 pixel neighborhood. 9: Cubic for enlarge, area for shrink, bilinear for others 10: Random select from interpolation method metioned above. Note: When shrinking an image, it will generally look best with AREA-based interpolation, whereas, when enlarging an image, it will generally look best with Bicubic (slow) or Bilinear (faster but still looks OK). More details can be found in the documentation of OpenCV, please refer to http://docs.opencv.org/master/da/d54/group__imgproc__transform.html. Returns ------- NDArray An 'NDArray' containing the resized image. Example ------- <NDArray 2321x3482x3 @cpu(0)> <NDArray 2321x3482x3 @cpu(0)> """

    h, w, _ = src.shape
    if h > w:
        new_h, new_w = size * h // w, size
    else:
        new_h, new_w = size, size * w // h
    return imresize(src, new_w, new_h, interp=_get_interp_method(interp, (h, w, new_h, new_w)))