code: def prior_box(
      input,
      image,
      min_sizes,
      max_sizes=None,
      aspect_ratios=[1.0],
      variance=[0.1, 0.1, 0.2, 0.2],
      flip=False,
      clip=False,
      steps=[0.0, 0.0],
      offset=0.5,
      min_max_aspect_ratios_order=False,
      name=None,
  ):
      """
  
      This op generates prior boxes for SSD(Single Shot MultiBox Detector) algorithm.
      Each position of the input produce N prior boxes, N is determined by
      the count of min_sizes, max_sizes and aspect_ratios, The size of the
      box is in range(min_size, max_size) interval, which is generated in
      sequence according to the aspect_ratios.
  
      Parameters:
         input(Tensor): 4-D tensor(NCHW), the data type should be float32 or float64.
         image(Tensor): 4-D tensor(NCHW), the input image data of PriorBoxOp,
              the data type should be float32 or float64.
         min_sizes(list|tuple|float): the min sizes of generated prior boxes.
         max_sizes(list|tuple|None): the max sizes of generated prior boxes.
              Default: None.
         aspect_ratios(list|tuple|float): the aspect ratios of generated
              prior boxes. Default: [1.].
         variance(list|tuple): the variances to be encoded in prior boxes.
              Default:[0.1, 0.1, 0.2, 0.2].
         flip(bool): Whether to flip aspect ratios. Default:False.
         clip(bool): Whether to clip out-of-boundary boxes. Default: False.
         step(list|tuple): Prior boxes step across width and height, If
              step[0] equals to 0.0 or step[1] equals to 0.0, the prior boxes step across
              height or weight of the input will be automatically calculated.
              Default: [0., 0.]
         offset(float): Prior boxes center offset. Default: 0.5
         min_max_aspect_ratios_order(bool): If set True, the output prior box is
              in order of [min, max, aspect_ratios], which is consistent with
              Caffe. Please note, this order affects the weights order of
              convolution layer followed by and does not affect the final
              detection results. Default: False.
         name(str, optional): The default value is None.  Normally there is no need for
              user to set this property. For more information, please refer to :ref:`api_guide_Name`
  
      Returns:
          Tuple: A tuple with two Variable (boxes, variances)
  
          boxes(Tensor): the output prior boxes of PriorBox.
          4-D tensor, the layout is [H, W, num_priors, 4].
          H is the height of input, W is the width of input,
          num_priors is the total box count of each position of input.
  
          variances(Tensor): the expanded variances of PriorBox.
          4-D tensor, the layput is [H, W, num_priors, 4].
          H is the height of input, W is the width of input
          num_priors is the total box count of each position of input
  
      Examples:
          .. code-block:: python
  
          import paddle
          from ppdet.modeling import ops
  
          paddle.enable_static()
          input = paddle.static.data(name="input", shape=[None,3,6,9])
          image = paddle.static.data(name="image", shape=[None,3,9,12])
          box, var = ops.prior_box(
                      input=input,
                      image=image,
                      min_sizes=[100.],
                      clip=True,
                      flip=True)
      """
      helper = LayerHelper("prior_box", **locals())
      dtype = helper.input_dtype()
      check_variable_and_dtype(
          input,
          "input",
          ["uint8", "int8", "float32", "float64"],
          "prior_box",
      )
  
      def _is_list_or_tuple_(data):
          return isinstance(data, list) or isinstance(data, tuple)
  
      if not _is_list_or_tuple_(min_sizes):
          min_sizes = [min_sizes]
      if not _is_list_or_tuple_(aspect_ratios):
          aspect_ratios = [aspect_ratios]
      if not (_is_list_or_tuple_(steps) and len(steps) == 2):
          raise ValueError(
              "steps should be a list or tuple ",
              "with length 2, (step_width, step_height).",
          )
  
      min_sizes = list(map(float, min_sizes))
      aspect_ratios = list(map(float, aspect_ratios))
      steps = list(map(float, steps))
  
      cur_max_sizes = None
      if (
          max_sizes is not None
          and len(max_sizes) > 0
          and max_sizes[0] > 0
      ):
          if not _is_list_or_tuple_(max_sizes):
              max_sizes = [max_sizes]
          cur_max_sizes = max_sizes
  
      if in_dynamic_mode():
          attrs = (
              "min_sizes",
              min_sizes,
              "aspect_ratios",
              aspect_ratios,
              "variances",
              variance,
              "flip",
              flip,
              "clip",
              clip,
              "step_w",
              steps[0],
              "step_h",
              steps[1],
              "offset",
              offset,
              "min_max_aspect_ratios_order",
              min_max_aspect_ratios_order,
          )
          if cur_max_sizes is not None:
              attrs += ("max_sizes", cur_max_sizes)
          box, var = C_ops.prior_box(input, image, *attrs)
          return box, var
      else:
          attrs = {
              "min_sizes": min_sizes,
              "aspect_ratios": aspect_ratios,
              "variances": variance,
              "flip": flip,
              "clip": clip,
              "step_w": steps[0],
              "step_h": steps[1],
              "offset": offset,
              "min_max_aspect_ratios_order": min_max_aspect_ratios_order,
          }
  
          if cur_max_sizes is not None:
              attrs["max_sizes"] = cur_max_sizes
  
          box = helper.create_variable_for_type_inference(dtype)
          var = helper.create_variable_for_type_inference(dtype)
          helper.append_op(
              type="prior_box",
              inputs={"Input": input, "Image": image},
              outputs={"Boxes": box, "Variances": var},
              attrs=attrs,
          )
          box.stop_gradient = True
          var.stop_gradient = True
          return box, var
  
imports: import paddle
  import paddle.nn.functional
  import paddle.nn
  from paddle import ParamAttr
  from paddle.regularizer import L2Decay
  from paddle import in_dynamic_mode
  from paddle.common_ops_import import Variable, LayerHelper, check_variable_and_dtype, check_type, check_dtype
label_desc: Use image node_id 9 for calling a global function with example usage: prior_box(input, image, min_sizes, max_sizes, aspect_ratios, variance, flip, clip, steps, offset, min_max_aspect_ratios_order, name) and returns: unknown, box,  var, box,  var