code: def __init__(
      self,
      model,
      net_clicks_limit=None,
      with_flip=False,
      zoom_in=None,
      max_size=None,
      with_mask=True,
      **kwargs,
  ):
      self.with_flip = with_flip
      self.net_clicks_limit = net_clicks_limit
      self.original_image = None
      self.zoom_in = zoom_in
      self.prev_prediction = None
      self.model_indx = 0
      self.click_models = None
      self.net_state_dict = None
      self.with_prev_mask = with_mask
      self.net = model
      if not paddle.in_dynamic_mode():
          paddle.disable_static()
      self.normalization = BatchImageNormalize(
          [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
      )
  
      self.transforms = [zoom_in] if zoom_in is not None else []
      if max_size is not None:
          self.transforms.append(LimitLongestSide(max_size=max_size))
      self.transforms.append(SigmoidForPred())
      if with_flip:
          self.transforms.append(AddHorizontalFlip())
      self.dist_maps = DistMaps(
          norm_radius=5,
          spatial_scale=1.0,
          cpu_mode=False,
          use_disks=True,
      )
  
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 1 to create a new BasePredictor object from inherited base classes: object with example: obj = BasePredictor(model, net_clicks_limit, with_flip, zoom_in, max_size, with_mask)