docstr: Take pyramid features as input to perform Cross Layer Refinement and finally output the prediction lanes.
  Each feature is a 4D tensor.
  Args:
      x: input features (list[Tensor])
  Return:
      prediction_list: each layer's prediction result
      seg: segmentation result for auxiliary loss
docstr_tok: ["Take","pyramid","features","as","input","to","perform","Cross","Layer","Refinement","and","finally","output","the","prediction","lanes",".","Each","feature","is","a","4D","tensor",".","Args",":","x",":","input","features","(","list","[","Tensor","]",")","Return",":","prediction_list",":","each","layer","'s","prediction","result","seg",":","segmentation","result","for","auxiliary","loss"]
code: def forward(self, x, inputs=None):
      """
      Take pyramid features as input to perform Cross Layer Refinement and finally output the prediction lanes.
      Each feature is a 4D tensor.
      Args:
          x: input features (list[Tensor])
      Return:
          prediction_list: each layer's prediction result
          seg: segmentation result for auxiliary loss
      """
      batch_features = list(x[len(x) - self.refine_layers :])
      batch_features.reverse()
      batch_size = batch_features[-1].shape[0]
  
      if self.training:
          (
              self.priors,
              self.priors_on_featmap,
          ) = self.generate_priors_from_embeddings()
      priors, priors_on_featmap = self.priors.tile(
          [batch_size, 1, 1]
      ), self.priors_on_featmap.tile([batch_size, 1, 1])
      predictions_lists = []
      prior_features_stages = []
  
      for stage in range(self.refine_layers):
          num_priors = priors_on_featmap.shape[1]
          prior_xs = paddle.flip(x=priors_on_featmap, axis=[2])
          batch_prior_features = self.pool_prior_features(
              batch_features[stage], num_priors, prior_xs
          )
          prior_features_stages.append(batch_prior_features)
  
          fc_features = self.roi_gather(
              prior_features_stages, batch_features[stage], stage
          )
          # return fc_features
          fc_features = fc_features.reshape(
              [num_priors, batch_size, -1]
          ).reshape([batch_size * num_priors, self.fc_hidden_dim])
          cls_features = fc_features.clone()
          reg_features = fc_features.clone()
  
          for cls_layer in self.cls_modules:
              cls_features = cls_layer(cls_features)
  
          # return cls_features
          for reg_layer in self.reg_modules:
              reg_features = reg_layer(reg_features)
          cls_logits = self.cls_layers(cls_features)
          reg = self.reg_layers(reg_features)
  
          cls_logits = cls_logits.reshape(
              [batch_size, -1, cls_logits.shape[1]]
          )
          reg = reg.reshape([batch_size, -1, reg.shape[1]])
          predictions = priors.clone()
          predictions[:, :, :2] = cls_logits
          predictions[:, :, 2:5] += reg[:, :, :3]
          predictions[:, :, 5] = reg[:, :, 3]
  
          def tran_tensor(t):
              return (
                  t.unsqueeze(axis=2)
                  .clone()
                  .tile([1, 1, self.n_offsets])
              )
  
          predictions[..., 6:] = (
              tran_tensor(predictions[..., 3]) * (self.img_w - 1)
              + (
                  (
                      1
                      - self.prior_ys.tile([batch_size, num_priors, 1])
                      - tran_tensor(predictions[..., 2])
                  )
                  * self.img_h
                  / paddle.tan(
                      tran_tensor(predictions[..., 4]) * math.pi + 1e-05
                  )
              )
          ) / (self.img_w - 1)
  
          prediction_lines = predictions.clone()
          predictions[..., 6:] += reg[..., 4:]
          predictions_lists.append(predictions)
  
          if stage != self.refine_layers - 1:
              priors = prediction_lines.detach().clone()
              priors_on_featmap = priors.index_select(
                  6 + self.sample_x_indexs, axis=-1
              )
  
      if self.training:
          seg = None
          seg_features = paddle.concat(
              [
                  F.interpolate(
                      feature,
                      size=[
                          batch_features[-1].shape[2],
                          batch_features[-1].shape[3],
                      ],
                      mode="bilinear",
                      align_corners=False,
                  )
                  for feature in batch_features
              ],
              axis=1,
          )
  
          seg = self.seg_decoder(seg_features)
  
          output = {"predictions_lists": predictions_lists, "seg": seg}
          return self.loss(output, inputs)
      return predictions_lists[-1]
  
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 6 for calling the CLRHead obj's underlying member method code with example usage: obj.forward(x, inputs) and returns: predictions_lists, self, t