code: def resize_short_within(src, short, max_size, mult_base=1, interp=2):
      """Resizes shorter edge to size but make sure it's capped at maximum size.
      Note: `resize_short_within` uses OpenCV (not the CV2 Python library).
      MXNet must have been built with OpenCV for `resize_short_within` to work.
      Resizes the original image by setting the shorter edge to size
      and setting the longer edge accordingly. Also this function will ensure
      the new image will not exceed ``max_size`` even at the longer side.
      Resizing function is called from OpenCV.
  
      Parameters
      ----------
      src : NDArray
          The original image.
      short : int
          Resize shorter side to ``short``.
      max_size : int
          Make sure the longer side of new image is smaller than ``max_size``.
      mult_base : int, default is 1
          Width and height are rounded to multiples of `mult_base`.
      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 mentioned 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
      -------
      >>> with open("flower.jpeg", 'rb') as fp:
      ...     str_image = fp.read()
      ...
      >>> image = mx.img.imdecode(str_image)
      >>> image
      <NDArray 2321x3482x3 @cpu(0)>
      >>> new_image = resize_short_within(image, short=800, max_size=1000)
      >>> new_image
      <NDArray 667x1000x3 @cpu(0)>
      >>> new_image = resize_short_within(image, short=800, max_size=1200)
      >>> new_image
      <NDArray 800x1200x3 @cpu(0)>
      >>> new_image = resize_short_within(image, short=800, max_size=1200, mult_base=32)
      >>> new_image
      <NDArray 800x1184x3 @cpu(0)>
      """
      from mxnet.image.image import _get_interp_method as get_interp
  
      h, w, _ = src.shape
      im_size_min, im_size_max = (h, w) if w > h else (w, h)
      scale = float(short) / float(im_size_min)
      if (
          np.round(scale * im_size_max / mult_base) * mult_base
          > max_size
      ):
          # fit in max_size
          scale = float(
              np.floor(max_size / mult_base) * mult_base
          ) / float(im_size_max)
      new_w, new_h = (
          int(np.round(w * scale / mult_base) * mult_base),
          int(np.round(h * scale / mult_base) * mult_base),
      )
      return imresize(
          src,
          new_w,
          new_h,
          interp=get_interp(interp, (h, w, new_h, new_w)),
      )
  
file_path: gluon-cv/gluoncv/data/transforms/image.py
imports: from __future__ import division
  import random
  import numpy
  import mxnet
  from mxnet import nd
  from mxnet.base import numeric_types
label_desc: Use image node_id 3 for calling a global function with example usage: resize_short_within(src, short, max_size, mult_base, interp) and returns: imresize