code: def anchor_generator(
      input,
      anchor_sizes=None,
      aspect_ratios=None,
      variance=[0.1, 0.1, 0.2, 0.2],
      stride=None,
      offset=0.5,
  ):
      """
      **Anchor generator operator**
      Generate anchors for Faster RCNN algorithm.
      Each position of the input produce N anchors, N =
      size(anchor_sizes) * size(aspect_ratios). The order of generated anchors
      is firstly aspect_ratios loop then anchor_sizes loop.
      Args:
         input(Variable): 4-D Tensor with shape [N,C,H,W]. The input feature map.
         anchor_sizes(float32|list|tuple, optional): The anchor sizes of generated
            anchors, given in absolute pixels e.g. [64., 128., 256., 512.].
            For instance, the anchor size of 64 means the area of this anchor
            equals to 64**2. None by default.
         aspect_ratios(float32|list|tuple, optional): The height / width ratios
             of generated anchors, e.g. [0.5, 1.0, 2.0]. None by default.
         variance(list|tuple, optional): The variances to be used in box
             regression deltas. The data type is float32, [0.1, 0.1, 0.2, 0.2] by
             default.
         stride(list|tuple, optional): The anchors stride across width and height.
             The data type is float32. e.g. [16.0, 16.0]. None by default.
         offset(float32, optional): Prior boxes center offset. 0.5 by default.
      Returns:
          Tuple:
          Anchors(Variable): The output anchors with a layout of [H, W, num_anchors, 4].
          H is the height of input, W is the width of input,
          num_anchors is the box count of each position.
          Each anchor is in (xmin, ymin, xmax, ymax) format an unnormalized.
  
          Variances(Variable): The expanded variances of anchors
          with a layout of [H, W, num_priors, 4].
          H is the height of input, W is the width of input
          num_anchors is the box count of each position.
          Each variance is in (xcenter, ycenter, w, h) format.
      Examples:
          .. code-block:: python
              import paddle.fluid as fluid
              conv1 = fluid.data(name='conv1', shape=[None, 48, 16, 16], dtype='float32')
              anchor, var = fluid.layers.anchor_generator(
                  input=conv1,
                  anchor_sizes=[64, 128, 256, 512],
                  aspect_ratios=[0.5, 1.0, 2.0],
                  variance=[0.1, 0.1, 0.2, 0.2],
                  stride=[16.0, 16.0],
                  offset=0.5)
      """
  
      def _is_list_or_tuple_(data):
          return isinstance(data, list) or isinstance(data, tuple)
  
      if not _is_list_or_tuple_(anchor_sizes):
          anchor_sizes = [anchor_sizes]
      if not _is_list_or_tuple_(aspect_ratios):
          aspect_ratios = [aspect_ratios]
      if not (_is_list_or_tuple_(stride) and len(stride) == 2):
          raise ValueError(
              "stride should be a list or tuple ",
              "with length 2, (stride_width, stride_height).",
          )
  
      anchor_sizes = list(map(float, anchor_sizes))
      aspect_ratios = list(map(float, aspect_ratios))
      stride = list(map(float, stride))
  
      if in_dynamic_mode():
          attrs = (
              "anchor_sizes",
              anchor_sizes,
              "aspect_ratios",
              aspect_ratios,
              "variances",
              variance,
              "stride",
              stride,
              "offset",
              offset,
          )
          anchor, var = C_ops.anchor_generator(input, *attrs)
          return anchor, var
  
      helper = LayerHelper("anchor_generator", **locals())
      dtype = helper.input_dtype()
      attrs = {
          "anchor_sizes": anchor_sizes,
          "aspect_ratios": aspect_ratios,
          "variances": variance,
          "stride": stride,
          "offset": offset,
      }
  
      anchor = helper.create_variable_for_type_inference(dtype)
      var = helper.create_variable_for_type_inference(dtype)
      helper.append_op(
          type="anchor_generator",
          inputs={"Input": input},
          outputs={"Anchors": anchor, "Variances": var},
          attrs=attrs,
      )
      anchor.stop_gradient = True
      var.stop_gradient = True
      return anchor, 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 7 for calling a global function with example usage: anchor_generator(input, anchor_sizes, aspect_ratios, variance, stride, offset) and returns: anchor,  var, unknown, anchor,  var