code: def fprop(self, inputs, inference=True):
      # the priors will be of shape layer_width * layer_height * num_priors_per_pixel * 4
      # with 2 chans per element (one for mean fnd one or vairance)
  
      # we only need to calculate these once if the image size does not change
      # right now we don't support changing image sizes anyways
      if self.prior_boxes is not None:
          return self.outputs
  
      img_shape = [self.img_w, self.img_h]
  
      self.prior_boxes = []
  
      def gen_box(center, box_size, image_size, variance, clip):
          box_ = [None] * 4
          box_[0] = (center[0] - box_size[0] * 0.5) / image_size[
              0
          ]  # xmin
          box_[1] = (center[1] - box_size[1] * 0.5) / image_size[
              1
          ]  # ymin
          box_[2] = (center[0] + box_size[0] * 0.5) / image_size[
              0
          ]  # xmax
          box_[3] = (center[1] + box_size[1] * 0.5) / image_size[
              1
          ]  # ymax
  
          if clip:
              for ind in range(4):
                  box_[ind] = min([max([box_[ind], 0.0]), 1.0])
          return box_
  
      offset = self.offset
  
      # the output is 2 chans (the 4 prior coordinates, the 4 prior variances) for
      # each output feature map pixel  so the output array is
      # 2 x layer_height x layer_width x num_priors x 4
      center = [0, 0]
      for h in range(self.layer_height):
          center[1] = (h + offset) * self.step_h
          for w in range(self.layer_width):
              center[0] = (w + offset) * self.step_w
              # do the min and max boxes with aspect ratio 1
              for min_size, max_size in zip(
                  self.min_sizes, self.max_sizes
              ):
                  # do the min box
                  box_shape = [min_size, min_size]
                  self.prior_boxes += gen_box(
                      center,
                      box_shape,
                      img_shape,
                      self.variance,
                      self.clip,
                  )
  
                  # do the max size box
                  sz_ = math.sqrt(min_size * max_size)
                  box_shape = [sz_, sz_]
                  self.prior_boxes += gen_box(
                      center,
                      box_shape,
                      img_shape,
                      self.variance,
                      self.clip,
                  )
  
                  # now do the different aspect ratio boxes
                  for ar in self.aspect_ratios:
                      assert np.abs(ar - 1.0) > 1.0e-6
                      box_width = min_size * math.sqrt(ar)
                      box_height = min_size / math.sqrt(ar)
                      box_shape = [box_width, box_height]
                      self.prior_boxes += gen_box(
                          center,
                          box_shape,
                          img_shape,
                          self.variance,
                          self.clip,
                      )
  
      self.outputs.set(np.array(self.prior_boxes).reshape(-1, 4))
      return self.outputs
  
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 4 for calling the PriorBox obj's underlying member method code with example usage: obj.fprop(inputs, inference) and returns: self, self, box_