class_docstr: The BiSeNetLane use BiseNet V2 to process lane segmentation .
  
  Args:
      num_classes (int): The unique number of target classes.
      lambd (float, optional): A factor for controlling the size of semantic branch channels. Default: 0.25.
      pretrained (str, optional): The path or url of pretrained model. Default: None.
class_docstr_tok: ["The","BiSeNetLane","use","BiseNet","V2","to","process","lane","segmentation",".","Args",":","num_classes","(","int",")",":","The","unique","number","of","target","classes",".","lambd","(","float",",","optional",")",":","A","factor","for","controlling","the","size","of","semantic","branch","channels",".","Default",":","0.25.","pretrained","(","str",",","optional",")",":","The","path","or","url","of","pretrained","model",".","Default",":","None","."]
code: def forward(self, x: paddle.Tensor) -> List[paddle.Tensor]:
      logit_list = self.net(x)
      return logit_list
  
imports: import time
  import argparse
  import os
  from typing import Union, List, Tuple
  import cv2
  import paddle
  from paddle import nn
  import paddle.nn.functional
  import numpy
  from paddlehub.module.module import moduleinfo, runnable, serving
  import paddleseg.transforms
  from paddleseg.utils import logger, progbar, visualize
  from paddlehub.module.cv_module import ImageSegmentationModule
  import paddleseg.utils
  from paddleseg.models import layers
  from paddleseg.models import BiSeNetV2
  from bisenet_lane_segmentation.processor import Crop, reverse_transform, cv2_to_base64, base64_to_cv2
  from bisenet_lane_segmentation.lane_processor.tusimple_processor import TusimpleProcessor
label_desc: Use image node_id 2 for calling the BiSeNetLane obj's underlying member method code with example usage: obj.forward(x) and returns: logit_list