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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 imresize(src, w, h, *args, **kwargs):
 r"""Resize image with OpenCV. .. note:: `imresize` uses OpenCV (not the CV2 Python library). MXNet must have been built with USE_OPENCV=1 for `imresize` to work. Parameters src : NDArray source image w : int, required Width of resized image. h : int, required Height of resized image. interp : int, optional, default=1 Interpolation method (default=cv2.INTER_LINEAR). 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. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. Example ------- <NDArray 2321x3482x3 @cpu(0)> <NDArray 240x360x3 @cpu(0)> """

    return _internal._cvimresize(src, w, h, *args, **kwargs)