code: def distribute_fpn_proposals(
      fpn_rois,
      min_level,
      max_level,
      refer_level,
      refer_scale,
      pixel_offset=False,
      rois_num=None,
      name=None,
  ):
      r"""
  
      **This op only takes LoDTensor as input.** In Feature Pyramid Networks
      (FPN) models, it is needed to distribute all proposals into different FPN
      level, with respect to scale of the proposals, the referring scale and the
      referring level. Besides, to restore the order of proposals, we return an
      array which indicates the original index of rois in current proposals.
      To compute FPN level for each roi, the formula is given as follows:
  
      .. math::
  
          roi\_scale &= \sqrt{BBoxArea(fpn\_roi)}
  
          level = floor(&\log(\\frac{roi\_scale}{refer\_scale}) + refer\_level)
  
      where BBoxArea is a function to compute the area of each roi.
  
      Args:
  
          fpn_rois(Variable): 2-D Tensor with shape [N, 4] and data type is
              float32 or float64. The input fpn_rois.
          min_level(int32): The lowest level of FPN layer where the proposals come
              from.
          max_level(int32): The highest level of FPN layer where the proposals
              come from.
          refer_level(int32): The referring level of FPN layer with specified scale.
          refer_scale(int32): The referring scale of FPN layer with specified level.
          rois_num(Tensor): 1-D Tensor contains the number of RoIs in each image.
              The shape is [B] and data type is int32. B is the number of images.
              If it is not None then return a list of 1-D Tensor. Each element
              is the output RoIs' number of each image on the corresponding level
              and the shape is [B]. None by default.
          name(str, optional): For detailed information, please refer
              to :ref:`api_guide_Name`. Usually name is no need to set and
              None by default.
  
      Returns:
          Tuple:
  
          multi_rois(List) : A list of 2-D LoDTensor with shape [M, 4]
          and data type of float32 and float64. The length is
          max_level-min_level+1. The proposals in each FPN level.
  
          restore_ind(Variable): A 2-D Tensor with shape [N, 1], N is
          the number of total rois. The data type is int32. It is
          used to restore the order of fpn_rois.
  
          rois_num_per_level(List): A list of 1-D Tensor and each Tensor is
          the RoIs' number in each image on the corresponding level. The shape
          is [B] and data type of int32. B is the number of images
  
  
      Examples:
          .. code-block:: python
  
              import paddle
              from ppdet.modeling import ops
              paddle.enable_static()
              fpn_rois = paddle.static.data(
                  name='data', shape=[None, 4], dtype='float32', lod_level=1)
              multi_rois, restore_ind = ops.distribute_fpn_proposals(
                  fpn_rois=fpn_rois,
                  min_level=2,
                  max_level=5,
                  refer_level=4,
                  refer_scale=224)
      """
      num_lvl = max_level - min_level + 1
  
      if in_dynamic_mode():
          assert (
              rois_num is not None
          ), "rois_num should not be None in dygraph mode."
          attrs = (
              "min_level",
              min_level,
              "max_level",
              max_level,
              "refer_level",
              refer_level,
              "refer_scale",
              refer_scale,
              "pixel_offset",
              pixel_offset,
          )
          (
              multi_rois,
              restore_ind,
              rois_num_per_level,
          ) = C_ops.distribute_fpn_proposals(
              fpn_rois, rois_num, num_lvl, num_lvl, *attrs
          )
  
          return multi_rois, restore_ind, rois_num_per_level
  
      else:
          check_variable_and_dtype(
              fpn_rois,
              "fpn_rois",
              ["float32", "float64"],
              "distribute_fpn_proposals",
          )
          helper = LayerHelper("distribute_fpn_proposals", **locals())
          dtype = helper.input_dtype("fpn_rois")
          multi_rois = [
              helper.create_variable_for_type_inference(dtype)
              for i in range(num_lvl)
          ]
  
          restore_ind = helper.create_variable_for_type_inference(
              dtype="int32"
          )
  
          inputs = {"FpnRois": fpn_rois}
          outputs = {
              "MultiFpnRois": multi_rois,
              "RestoreIndex": restore_ind,
          }
  
          if rois_num is not None:
              inputs["RoisNum"] = rois_num
              rois_num_per_level = [
                  helper.create_variable_for_type_inference(
                      dtype="int32"
                  )
                  for i in range(num_lvl)
              ]
              outputs["MultiLevelRoIsNum"] = rois_num_per_level
          else:
              rois_num_per_level = None
  
          helper.append_op(
              type="distribute_fpn_proposals",
              inputs=inputs,
              outputs=outputs,
              attrs={
                  "min_level": min_level,
                  "max_level": max_level,
                  "refer_level": refer_level,
                  "refer_scale": refer_scale,
                  "pixel_offset": pixel_offset,
              },
          )
          return multi_rois, restore_ind, rois_num_per_level
  
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 8 for calling a global function with example usage: distribute_fpn_proposals(fpn_rois, min_level, max_level, refer_level, refer_scale, pixel_offset, rois_num, name) and returns: multi_rois,  restore_ind,  rois_num_per_level, multi_rois,  restore_ind,  rois_num_per_level