class_docstr: Convert corner boxes to center boxes.
  Corner boxes are encoded as (xmin, ymin, xmax, ymax)
  Center boxes are encoded as (center_x, center_y, width, height)
  
  Parameters
  ----------
  split : bool
      Whether split boxes to individual elements after processing.
  axis : int, default is -1
      Effective axis of the bounding box. Default is -1(the last dimension).
  
  Returns
  -------
   A BxNx4 NDArray if split is False, or 4 BxNx1 NDArray if split is True
class_docstr_tok: ["Convert","corner","boxes","to","center","boxes",".","Corner","boxes","are","encoded","as","(","xmin",",","ymin",",","xmax",",","ymax",")","Center","boxes","are","encoded","as","(","center_x",",","center_y",",","width",",","height",")","Parameters","--","--","--","--","--","split",":","bool","Whether","split","boxes","to","individual","elements","after","processing",".","axis",":","int",",","default","is","-1","Effective","axis","of","the","bounding","box",".","Default","is","-1","(","the","last","dimension",")",".","Returns","--","--","--","-","A","BxNx4","NDArray","if","split","is","False",",","or","4","BxNx1","NDArray","if","split","is","True"]
docstr: Hybrid forward
docstr_tok: ["Hybrid","forward"]
code: def hybrid_forward(self, F, x):
      """Hybrid forward"""
      xmin, ymin, xmax, ymax = F.split(
          x, axis=self._axis, num_outputs=4
      )
      # note that we do not have +1 here since our nms and box iou does not.
      # this is different that detectron.
      width = xmax - xmin
      height = ymax - ymin
      x = xmin + width * 0.5
      y = ymin + height * 0.5
      if not self._split:
          return F.concat(x, y, width, height, dim=self._axis)
      else:
          return x, y, width, height
  
imports: from __future__ import absolute_import
  import numpy
  from mxnet import gluon
label_desc: Use image node_id 2 for calling the BBoxCornerToCenter obj's underlying member method code with example usage: obj.hybrid_forward(F, x) and returns: F, x,  y,  width,  height