code: def __init__(
      self,
      min_sizes,
      max_sizes,
      step=None,
      aspect_ratios=[2, 3],
      img_shape=(300, 300),
      flip=True,
      clip=False,
      variance=[0.1, 0.1, 0.2, 0.2],
      offset=0.5,
      name=None,
  ):
      super(PriorBox, self).__init__(name)
      self.offset = offset
      self.variance = variance
      self.flip = flip
      self.clip = clip
      if type(step) in (dict, OrderedDict):
          assert set(step.keys()) == set(("step_w", "step_h"))
          self.step_w = step["step_w"]
          self.step_h = step["step_h"]
      else:
          assert step is not None
          self.step_w = step
          self.step_h = step
      self.prior_boxes = None
      self.img_w = img_shape[0]
      self.img_h = img_shape[1]
  
      assert isinstance(min_sizes, tuple)
      assert isinstance(max_sizes, tuple)
  
      self.min_sizes = min_sizes
      self.max_sizes = max_sizes
      assert len(self.min_sizes) == len(self.max_sizes)
  
      # compute the number of prior boxes
      # with flip, order the aspect ratios in the same was as caffe
      self.aspect_ratios = []
      for ar in aspect_ratios:
          self.aspect_ratios.extend([ar])
  
          if self.flip:
              self.aspect_ratios.extend([1.0 / float(ar)])
  
      # number of prior baxes per feature map pixel
      # there is 1 box with AR=1 for each min and max sizes
      self.num_priors_per_pixel = len(self.min_sizes) * 2
      # and one box for each aspect ratio at the min size
      self.num_priors_per_pixel += len(self.aspect_ratios) * len(
          self.min_sizes
      )
  
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 1 to create a new PriorBox object from inherited base classes: Layer with example: obj = PriorBox(min_sizes, max_sizes, step, aspect_ratios, img_shape, flip, clip, variance, offset, name)