code: def bbox_transform_inv(
      self, boxes, deltas, output, variance=[0.1, 0.1, 0.2, 0.2]
  ):
      widths = boxes[:, 2] - boxes[:, 0]
      heights = boxes[:, 3] - boxes[:, 1]
      ctr_x = boxes[:, 0] + 0.5 * widths
      ctr_y = boxes[:, 1] + 0.5 * heights
  
      dx = deltas[:, 0]
      dy = deltas[:, 1]
      dw = deltas[:, 2]
      dh = deltas[:, 3]
  
      pred_ctr_x = variance[0] * dx * widths + ctr_x
      pred_ctr_y = variance[1] * dy * heights + ctr_y
      pred_w = self.be.exp(variance[2] * dw) * widths
      pred_h = self.be.exp(variance[3] * dh) * heights
  
      # pred_boxes = np.zeros(deltas.shape, dtype=deltas.dtype)
      # x1
      output[:, 0] = pred_ctr_x - 0.5 * pred_w
      # y1
      output[:, 1] = pred_ctr_y - 0.5 * pred_h
      # x2
      output[:, 2] = pred_ctr_x + 0.5 * pred_w
      # y2
      output[:, 3] = pred_ctr_y + 0.5 * pred_h
  
      return output
  
imports: from neon.layers.layer import Layer, ParameterLayer
  import numpy
  from neon.transforms import Softmax
  from neon.initializers.initializer import Constant
  import math
  from collections import OrderedDict
label_desc: Use image node_id 7 for calling the DetectionOutput obj's underlying member method code with example usage: obj.bbox_transform_inv(boxes, deltas, output, variance) and returns: output