code: def _generate_anchors(
      self, spatial_shapes=None, grid_size=0.05, dtype="float32"
  ):
      if spatial_shapes is None:
          spatial_shapes = [
              [int(self.eval_size[0] / s), int(self.eval_size[1] / s)]
              for s in self.feat_strides
          ]
      anchors = []
      for lvl, (h, w) in enumerate(spatial_shapes):
          grid_y, grid_x = paddle.meshgrid(
              paddle.arange(end=h, dtype=dtype),
              paddle.arange(end=w, dtype=dtype),
          )
          grid_xy = paddle.stack([grid_x, grid_y], -1)
  
          valid_WH = paddle.to_tensor([h, w]).astype(dtype)
          grid_xy = (grid_xy.unsqueeze(0) + 0.5) / valid_WH
          wh = paddle.ones_like(grid_xy) * grid_size * (2.0**lvl)
          anchors.append(
              paddle.concat([grid_xy, wh], -1).reshape([-1, h * w, 4])
          )
  
      anchors = paddle.concat(anchors, 1)
      valid_mask = (
          (anchors > self.eps) * (anchors < 1 - self.eps)
      ).all(-1, keepdim=True)
      anchors = paddle.log(anchors / (1 - anchors))
      anchors = paddle.where(
          valid_mask, anchors, paddle.to_tensor(float("inf"))
      )
      return anchors, valid_mask
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle import ParamAttr
  from paddle.regularizer import L2Decay
  from ppdet.core.workspace import register
  from ..layers import MultiHeadAttention
  from ..heads.detr_head import MLP
  from .deformable_transformer import MSDeformableAttention
  from ..initializer import linear_init_, constant_, xavier_uniform_, normal_, bias_init_with_prob
  from .utils import _get_clones, get_sine_pos_embed, get_contrastive_denoising_training_group, inverse_sigmoid
label_desc: Use image node_id 7 for calling the RTDETRTransformer obj's underlying member method code with example usage: obj._generate_anchors(spatial_shapes, grid_size, dtype) and returns: anchors,  valid_mask