parameters: (src, size, interp=2)
return_statement: return imresize(src, new_w, new_h, interp=_get_interp_method(interp, (h, w, new_h, new_w)))
docstring: 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
      -------
      >>> with open("flower.jpeg", 'rb') as fp:
      ...     str_image = fp.read()
      ...
      >>> image = mx.img.imdecode(str_image)
      >>> image
      <NDArray 2321x3482x3 @cpu(0)>
      >>> size = 640
      >>> new_image = mx.img.resize_short(image, size)
      >>> new_image
      <NDArray 2321x3482x3 @cpu(0)>
docstring_summary: Resizes shorter edge to size.
docstring_tokens: Resizes shorter edge to size .
function: 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
      -------
      >>> with open("flower.jpeg", 'rb') as fp:
      ...     str_image = fp.read()
      ...
      >>> image = mx.img.imdecode(str_image)
      >>> image
      <NDArray 2321x3482x3 @cpu(0)>
      >>> size = 640
      >>> new_image = mx.img.resize_short(image, size)
      >>> new_image
      <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)))
function_tokens: def resize_short ( src , size , interp = 2 ) : 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 ) ) )