code: def to_array(self, sample_y_range, img_w, img_h):
      self.sample_y = range(
          sample_y_range[0], sample_y_range[1], sample_y_range[2]
      )
      sample_y = self.sample_y
      img_w, img_h = img_w, img_h
      ys = np.array(sample_y) / float(img_h)
      xs = self(ys)
      valid_mask = (xs >= 0) & (xs < 1)
      lane_xs = xs[valid_mask] * img_w
      lane_ys = ys[valid_mask] * img_h
      lane = np.concatenate(
          (lane_xs.reshape(-1, 1), lane_ys.reshape(-1, 1)), axis=1
      )
      return lane
  
file_path: PaddleDetection/ppdet/modeling/lane_utils.py
imports: import os
  import cv2
  import numpy
  from scipy.interpolate import InterpolatedUnivariateSpline
label_desc: Use image node_id 4 for calling the Lane obj's underlying member method code with example usage: obj.to_array(sample_y_range, img_w, img_h) and returns: lane