code: def box_coder(
      prior_box,
      prior_box_var,
      target_box,
      code_type="encode_center_size",
      box_normalized=True,
      axis=0,
      name=None,
  ):
      r"""
      **Box Coder Layer**
      Encode/Decode the target bounding box with the priorbox information.
  
      The Encoding schema described below:
      .. math::
          ox = (tx - px) / pw / pxv
          oy = (ty - py) / ph / pyv
          ow = \log(\abs(tw / pw)) / pwv
          oh = \log(\abs(th / ph)) / phv
      The Decoding schema described below:
  
      .. math::
  
          ox = (pw * pxv * tx * + px) - tw / 2
          oy = (ph * pyv * ty * + py) - th / 2
          ow = \exp(pwv * tw) * pw + tw / 2
          oh = \exp(phv * th) * ph + th / 2
      where `tx`, `ty`, `tw`, `th` denote the target box's center coordinates,
      width and height respectively. Similarly, `px`, `py`, `pw`, `ph` denote
      the priorbox's (anchor) center coordinates, width and height. `pxv`,
      `pyv`, `pwv`, `phv` denote the variance of the priorbox and `ox`, `oy`,
      `ow`, `oh` denote the encoded/decoded coordinates, width and height.
      During Box Decoding, two modes for broadcast are supported. Say target
      box has shape [N, M, 4], and the shape of prior box can be [N, 4] or
      [M, 4]. Then prior box will broadcast to target box along the
      assigned axis.
  
      Args:
          prior_box(Tensor): Box list prior_box is a 2-D Tensor with shape
              [M, 4] holds M boxes and data type is float32 or float64. Each box
              is represented as [xmin, ymin, xmax, ymax], [xmin, ymin] is the
              left top coordinate of the anchor box, if the input is image feature
              map, they are close to the origin of the coordinate system.
              [xmax, ymax] is the right bottom coordinate of the anchor box.
          prior_box_var(List|Tensor|None): prior_box_var supports three types
              of input. One is Tensor with shape [M, 4] which holds M group and
              data type is float32 or float64. The second is list consist of
              4 elements shared by all boxes and data type is float32 or float64.
              Other is None and not involved in calculation.
          target_box(Tensor): This input can be a 2-D LoDTensor with shape
              [N, 4] when code_type is 'encode_center_size'. This input also can
              be a 3-D Tensor with shape [N, M, 4] when code_type is
              'decode_center_size'. Each box is represented as
              [xmin, ymin, xmax, ymax]. The data type is float32 or float64.
          code_type(str): The code type used with the target box. It can be
              `encode_center_size` or `decode_center_size`. `encode_center_size`
              by default.
          box_normalized(bool): Whether treat the priorbox as a normalized box.
              Set true by default.
          axis(int): Which axis in PriorBox to broadcast for box decode,
              for example, if axis is 0 and TargetBox has shape [N, M, 4] and
              PriorBox has shape [M, 4], then PriorBox will broadcast to [N, M, 4]
              for decoding. It is only valid when code type is
              `decode_center_size`. Set 0 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:
          Tensor:
          output_box(Tensor): When code_type is 'encode_center_size', the
          output tensor of box_coder_op with shape [N, M, 4] representing the
          result of N target boxes encoded with M Prior boxes and variances.
          When code_type is 'decode_center_size', N represents the batch size
          and M represents the number of decoded boxes.
  
      Examples:
  
          .. code-block:: python
  
              import paddle
              from ppdet.modeling import ops
              paddle.enable_static()
              # For encode
              prior_box_encode = paddle.static.data(name='prior_box_encode',
                                    shape=[512, 4],
                                    dtype='float32')
              target_box_encode = paddle.static.data(name='target_box_encode',
                                     shape=[81, 4],
                                     dtype='float32')
              output_encode = ops.box_coder(prior_box=prior_box_encode,
                                      prior_box_var=[0.1,0.1,0.2,0.2],
                                      target_box=target_box_encode,
                                      code_type="encode_center_size")
              # For decode
              prior_box_decode = paddle.static.data(name='prior_box_decode',
                                    shape=[512, 4],
                                    dtype='float32')
              target_box_decode = paddle.static.data(name='target_box_decode',
                                     shape=[512, 81, 4],
                                     dtype='float32')
              output_decode = ops.box_coder(prior_box=prior_box_decode,
                                      prior_box_var=[0.1,0.1,0.2,0.2],
                                      target_box=target_box_decode,
                                      code_type="decode_center_size",
                                      box_normalized=False,
                                      axis=1)
      """
      check_variable_and_dtype(
          prior_box, "prior_box", ["float32", "float64"], "box_coder"
      )
      check_variable_and_dtype(
          target_box, "target_box", ["float32", "float64"], "box_coder"
      )
  
      if in_dynamic_mode():
          if isinstance(prior_box_var, Variable):
              output_box = C_ops.box_coder(
                  prior_box,
                  prior_box_var,
                  target_box,
                  "code_type",
                  code_type,
                  "box_normalized",
                  box_normalized,
                  "axis",
                  axis,
              )
  
          elif isinstance(prior_box_var, list):
              output_box = C_ops.box_coder(
                  prior_box,
                  None,
                  target_box,
                  "code_type",
                  code_type,
                  "box_normalized",
                  box_normalized,
                  "axis",
                  axis,
                  "variance",
                  prior_box_var,
              )
          else:
              raise TypeError(
                  "Input variance of box_coder must be Variable or list"
              )
          return output_box
      else:
          helper = LayerHelper("box_coder", **locals())
  
          output_box = helper.create_variable_for_type_inference(
              dtype=prior_box.dtype
          )
  
          inputs = {"PriorBox": prior_box, "TargetBox": target_box}
          attrs = {
              "code_type": code_type,
              "box_normalized": box_normalized,
              "axis": axis,
          }
          if isinstance(prior_box_var, Variable):
              inputs["PriorBoxVar"] = prior_box_var
          elif isinstance(prior_box_var, list):
              attrs["variance"] = prior_box_var
          else:
              raise TypeError(
                  "Input variance of box_coder must be Variable or list"
              )
          helper.append_op(
              type="box_coder",
              inputs=inputs,
              attrs=attrs,
              outputs={"OutputBox": output_box},
          )
          return output_box
  
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 12 for calling a global function with example usage: box_coder(prior_box, prior_box_var, target_box, code_type, box_normalized, axis, name) and returns: output_box, output_box