code: def init(
      self,
      name: str,
      model_dir: str,
      conf: Dict[str, str],
      planner: Any,
      **kwargs,
  ):
      super().init(name, model_dir, conf, planner, **kwargs)
  
      # Labels
      self.labels = {"Tool": 1}
  
      # Model Files
      self.path = [
          os.path.join(
              self.model_dir, f"pretrained_{self.name}.pt"
          ),  # pretrained
          os.path.join(self.model_dir, f"{self.name}.pt"),  # published
      ]
  
      # Download PreTrained Model
      if strtobool(self.conf.get("use_pretrained_model", "true")):
          url = f"{self.conf.get('pretrained_path', self.PRE_TRAINED_PATH)}"
          url = f"{url}/endoscopy_deepedit_bunet_tooltracking.pt"
          try:
              download_file(url, self.path[0])
          except:
              logger.warning(
                  f"Failed to download pre-trained model from {url}; Ignoring the same..."
              )
  
      # Network
      f = (32, 64, 128, 256, 512, 32)
      self.network = BasicUNet(
          spatial_dims=2, in_channels=5, out_channels=1, features=f
      )
      self.network_with_dropout = BasicUNet(
          spatial_dims=2,
          in_channels=5,
          out_channels=1,
          features=f,
          dropout=0.2,
      )
  
      # others
      self.roi_size = (736, 480)
      self.epistemic_enabled = bool(
          strtobool(conf.get("epistemic_enabled", "false"))
      )
      self.epistemic_enabled = (
          self.epistemic_enabled
          if self.conf.get("models") == "deepedit"
          else False
      )
      self.epistemic_max_samples = int(
          conf.get("epistemic_max_samples", "0")
      )
      self.epistemic_simulation_size = int(
          conf.get("epistemic_simulation_size", "5")
      )
  
      logger.info(
          f"EPISTEMIC Enabled: {self.epistemic_enabled}; Samples: {self.epistemic_max_samples}"
      )
  
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 1 for calling the DeepEdit obj's underlying member method code with example usage: obj.init(name, model_dir, conf, planner) without return types