code: def get_prediction(self, clicker, prev_mask=None):
      clicks_list = clicker.get_clicks()
  
      input_image = self.original_image
      if prev_mask is None:
          if not self.with_prev_mask:
              prev_mask = self.prev_edge
          else:
              prev_mask = self.prev_prediction
  
      input_image = paddle.concat([input_image, prev_mask], axis=1)
  
      image_nd, clicks_lists, is_image_changed = self.apply_transforms(
          input_image, [clicks_list]
      )
      pred_logits, pred_edges = self._get_prediction(
          image_nd, clicks_lists, is_image_changed
      )
  
      pred_logits = paddle.to_tensor(pred_logits)
  
      prediction = F.interpolate(
          pred_logits,
          mode="bilinear",
          align_corners=True,
          size=image_nd.shape[2:],
      )
      if pred_edges is not None:
          pred_edge = paddle.to_tensor(pred_edges)
          edge_prediction = F.interpolate(
              pred_edge,
              mode="bilinear",
              align_corners=True,
              size=image_nd.shape[2:],
          )
  
      for t in reversed(self.transforms):
          if pred_edges is not None:
              edge_prediction = t.inv_transform(edge_prediction)
              self.prev_edge = edge_prediction
          prediction = t.inv_transform(prediction)
  
      if (
          self.zoom_in is not None
          and self.zoom_in.check_possible_recalculation()
      ):
          return self.get_prediction(clicker)
  
      self.prev_prediction = prediction
      return prediction.numpy()[0, 0]
  
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 4 for calling the BasePredictor obj's underlying member method code with example usage: obj.get_prediction(clicker, prev_mask) and returns: prediction, self