code: def __init__(self, img_w, img_h):
      super(CULaneTrainProcess, self).__init__()
      self.img_w = img_w
      self.img_h = img_h
      self.transforms = trainTransforms(self.img_h, self.img_w)
  
      if self.transforms is not None:
          img_transforms = []
          for aug in self.transforms:
              p = aug["p"]
              if aug["name"] != "OneOf":
                  img_transforms.append(
                      iaa.Sometimes(
                          p=p,
                          then_list=getattr(iaa, aug["name"])(
                              **aug["parameters"]
                          ),
                      )
                  )
              else:
                  img_transforms.append(
                      iaa.Sometimes(
                          p=p,
                          then_list=iaa.OneOf(
                              [
                                  getattr(iaa, aug_["name"])(
                                      **aug_["parameters"]
                                  )
                                  for aug_ in aug["transforms"]
                              ]
                          ),
                      )
                  )
      else:
          img_transforms = []
      self.iaa_transform = iaa.Sequential(img_transforms)
  
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 1 to create a new CULaneTrainProcess object from inherited base classes: BaseOperator with example: obj = CULaneTrainProcess(img_w, img_h)