code: def generate_priors_from_embeddings(self):
      predictions = self.prior_embeddings.weight
      # 2 scores, 1 start_y, 1 start_x, 1 theta, 1 length, 72 coordinates, score[0] = negative prob, score[1] = positive prob
      priors = paddle.zeros(
          (self.num_priors, 2 + 2 + 2 + self.n_offsets),
          dtype=predictions.dtype,
      )
      priors[:, 2:5] = predictions.clone()
      priors[:, 6:] = (
          priors[:, 3].unsqueeze(1).clone().tile([1, self.n_offsets])
          * (self.img_w - 1)
          + (
              (
                  1
                  - self.prior_ys.tile([self.num_priors, 1])
                  - priors[:, 2]
                  .unsqueeze(1)
                  .clone()
                  .tile([1, self.n_offsets])
              )
              * self.img_h
              / paddle.tan(
                  x=priors[:, 4]
                  .unsqueeze(1)
                  .clone()
                  .tile([1, self.n_offsets])
                  * math.pi
                  + 1e-05
              )
          )
      ) / (self.img_w - 1)
      priors_on_featmap = paddle.index_select(
          priors, 6 + self.sample_x_indexs, axis=-1
      )
      return priors, priors_on_featmap
  
imports: import math
  import paddle
  import numpy
  import paddle.nn
  import paddle.nn.functional
  from ppdet.core.workspace import register
  from ppdet.modeling.initializer import normal_
  from ppdet.modeling.lane_utils import Lane
  from ppdet.modeling.losses import line_iou
  from ppdet.modeling.clrnet_utils import ROIGather, LinearModule, SegDecoder
label_desc: Use image node_id 4 for calling the CLRHead obj's underlying member method code with example usage: obj.generate_priors_from_embeddings() and returns: priors,  priors_on_featmap