code: def get_prediction_noclicker(self, clicker, prev_mask=None):
      clicks_list = clicker.get_clicks()  # one click a time todo：累计多个点
  
      input_image = self.original_image  # [1, 1, 512, 512, 12]
      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
      )  # [1, 2, 512, 512, 12]
  
      image_nd, clicks_lists, is_image_changed = self.apply_transforms(
          input_image, [clicks_list]
      )
      pred_logits = self._get_prediction(
          image_nd, clicks_lists, is_image_changed
      )
  
      pred_logits = paddle.to_tensor(
          pred_logits
      )  # [1, 1, 512, 512, 12]
      for t in reversed(self.transforms):  # conv as the final output
          pred_logits = t.inv_transform(pred_logits)
  
      self.prev_prediction = pred_logits
      return pred_logits.numpy()[0, 0]
  
imports: import os
  import sys
  import logging
  import numpy
  from copy import deepcopy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle.inference import create_predictor, Config
  from inference.ops import DistMaps3D, ScaleLayer, BatchImageNormalize3D, SigmoidForPred
label_desc: Use image node_id 5 for calling the BasePredictor obj's underlying member method code with example usage: obj.get_prediction_noclicker(clicker, prev_mask) and returns: pred_logits