code: def scoring_method(
      self,
  ) -> Union[None, ScoringMethod, Dict[str, ScoringMethod]]:
      methods: Dict[str, ScoringMethod] = {}
  
      if self.epistemic_enabled:
          methods[f"{self.name}_epistemic"] = CVATEpistemicScoring(
              top_k=int(self.conf.get("epistemic_top_k", "10")),
              infer_task=lib.infers.DeepEdit(
                  path=self.path,
                  network=self.network_with_dropout,
                  labels=self.labels,
                  roi_size=self.roi_size,
                  train_mode=True,
                  skip_writer=True,
              ),
              function="monailabel.endoscopy.tooltracking",
              max_samples=self.epistemic_max_samples,
              simulation_size=self.epistemic_simulation_size,
              use_variance=True,
          )
      return methods
  
imports: import logging
  import os
  from typing import Any, Dict, Optional, Union
  import lib.infers
  import lib.trainers
  from lib.scoring import CVATEpistemicScoring
  from monai.networks.nets import BasicUNet
  from monailabel.interfaces.config import TaskConfig
  from monailabel.interfaces.tasks.infer_v2 import InferTask
  from monailabel.interfaces.tasks.scoring import ScoringMethod
  from monailabel.interfaces.tasks.strategy import Strategy
  from monailabel.interfaces.tasks.train import TrainTask
  from monailabel.tasks.activelearning.epistemic import Epistemic
  from monailabel.utils.others.generic import download_file, strtobool
label_desc: Use image node_id 5 for calling the DeepEdit obj's underlying member method code with example usage: obj.scoring_method() and returns: methods