code: def trainer(self) -> Optional[TrainTask]:
      output_dir = os.path.join(self.model_dir, self.name)
      load_path = (
          self.path[0] if os.path.exists(self.path[0]) else self.path[1]
      )
  
      task: TrainTask = lib.trainers.SegmentationNuclei(
          model_dir=output_dir,
          network=self.network,
          load_path=load_path,
          publish_path=self.path[1],
          labels=self.labels,
          description="Train Nuclei Segmentation Model",
          config={
              "max_epochs": 10,
              "train_batch_size": 16,
              "val_batch_size": 16,
          },
      )
      return task
  
file_path: MONAILabel/sample-apps/pathology/lib/configs/segmentation_nuclei.py
imports: import json
  import logging
  import multiprocessing
  import os
  from typing import Any, Dict, Optional, Union
  import lib.infers
  import lib.trainers
  from monai.networks.nets import BasicUNet
  from monailabel.interfaces.config import TaskConfig
  from monailabel.interfaces.tasks.infer_v2 import InferTask
  from monailabel.interfaces.tasks.train import TrainTask
  from monailabel.utils.others.generic import download_file, strtobool
label_desc: Use image node_id 3 for calling the SegmentationNuclei obj's underlying member method code with example usage: obj.trainer() and returns: task