code: def apply(self, sample, context=None):
      data = {}
      line_strings = sample["lanes"]
      line_strings.clip_out_of_image_()
      new_anno = {"lanes": linestrings_to_lanes(line_strings)}
  
      for i in range(30):
          try:
              annos = transform_annotation(
                  self.img_w,
                  self.img_h,
                  self.max_lanes,
                  self.n_offsets,
                  self.offsets_ys,
                  self.n_strips,
                  self.strip_size,
                  new_anno,
              )
              label = annos["label"]
              lane_endpoints = annos["lane_endpoints"]
              break
          except:
              if (i + 1) == 30:
                  logger.critical(
                      "Transform annotation failed 30 times :("
                  )
                  exit()
  
      sample["image"] = sample["image"].astype(np.float32) / 255.0
      data["image"] = sample["image"].transpose(2, 0, 1)
      data["lane_line"] = label
      data["seg"] = sample["seg"]
      data["full_img_path"] = sample["full_img_path"]
      data["img_name"] = sample["img_name"]
      data["im_id"] = sample["im_id"]
  
      if "mask" in sample.keys():
          data["seg"] = sample["mask"].get_arr()
  
      data["im_shape"] = np.array(
          [self.img_w, self.img_h], dtype=np.float32
      )
      data["scale_factor"] = np.array([1.0, 1.0], dtype=np.float32)
  
      return data
  
imports: import numpy
  import imgaug.augmenters
  from .operators import BaseOperator, register_op
  from ppdet.utils.logger import setup_logger
  from ppdet.data.culane_utils import linestrings_to_lanes, transform_annotation
label_desc: Use image node_id 2 for calling the CULaneDataProcess obj's underlying member method code with example usage: obj.apply(sample, context) and returns: data