code: def get_post_transform(orig_w, orig_h, out_w, out_h):
      """Get the post prediction affine transforms. This will be used to adjust the prediction results
      according to original coco image resolutions.
  
      Parameters
      ----------
      orig_w : int
          Original width of the image.
      orig_h : int
          Original height of the image.
      out_w : int
          Width of the output image after prediction.
      out_h : int
          Height of the output image after prediction.
  
      Returns
      -------
      numpy.ndarray
          Affine transform matrix 3x2.
  
      """
      s = max(orig_w, orig_h) * 1.0
      c = np.array([orig_w / 2.0, orig_h / 2.0], dtype=np.float32)
      trans_output = tbbox.get_affine_transform(
          c, s, 0, [out_w, out_h], inv=True
      )
      return trans_output
  
file_path: gluon-cv/gluoncv/data/transforms/presets/center_net.py
imports: from __future__ import absolute_import
  import numpy
  import mxnet
  from ..None import bbox
  from ..None import image
  from ..None import experimental
  from ....utils.filesystem import try_import_cv2
label_desc: Use image node_id 3 for calling a global function with example usage: get_post_transform(orig_w, orig_h, out_w, out_h) and returns: trans_output