class_docstr: Args:
      input_shape (int): network input image size
      ori_shape (int): ori image shape of before padding
      scale_factor (float): scale factor of ori image
      enable_mkldnn (bool): whether to open MKLDNN
class_docstr_tok: ["Args",":","input_shape","(","int",")",":","network","input","image","size","ori_shape","(","int",")",":","ori","image","shape","of","before","padding","scale_factor","(","float",")",":","scale","factor","of","ori","image","enable_mkldnn","(","bool",")",":","whether","to","open","MKLDNN"]
code: def __init__(
      self,
      img_w,
      ori_img_h,
      cut_height,
      conf_threshold,
      nms_thres,
      max_lanes,
      num_points,
  ):
      self.img_w = img_w
      self.conf_threshold = conf_threshold
      self.nms_thres = nms_thres
      self.max_lanes = max_lanes
      self.num_points = num_points
      self.n_strips = num_points - 1
      self.n_offsets = num_points
      self.ori_img_h = ori_img_h
      self.cut_height = cut_height
  
      self.prior_ys = paddle.linspace(
          start=1, stop=0, num=self.n_offsets
      ).astype("float64")
  
imports: import numpy
  import paddle
  import paddle.nn
  from scipy.special import softmax
  from scipy.interpolate import InterpolatedUnivariateSpline
label_desc: Use image node_id 1 to create a new CLRNetPostProcess object from inherited base classes: object with example: obj = CLRNetPostProcess(img_w, ori_img_h, cut_height, conf_threshold, nms_thres, max_lanes, num_points)