code: def apply_transforms(self, image_nd, clicks_lists):
      is_image_changed = False
      for t in self.transforms:
          image_nd, clicks_lists = t.transform(image_nd, clicks_lists)
          is_image_changed |= t.image_changed
  
      return image_nd, clicks_lists, is_image_changed
  
imports: import paddle
  import paddle.nn.functional
  import numpy
  from inference.transforms import AddHorizontalFlip, SigmoidForPred, LimitLongestSide
  from .ops import DistMaps, ScaleLayer, BatchImageNormalize
label_desc: Use image node_id 10 for calling the BasePredictor obj's underlying member method code with example usage: obj.apply_transforms(image_nd, clicks_lists) and returns: image_nd,  clicks_lists,  is_image_changed