code: def __init__(
      self,
      model_dir,
      network,
      tile_size=(512, 512),
      patch_size=256,
      description="Pathology Semantic Segmentation for Nuclei",
      **kwargs,
  ):
      self._network = network
      self.tile_size = tile_size
      self.patch_size = patch_size
      super().__init__(model_dir, description, **kwargs)
  
imports: import logging
  import os
  import numpy
  import torch
  from ignite.metrics import Accuracy
  from lib.handlers import TensorBoardImageHandler
  from lib.utils import split_dataset
  from monai.handlers import from_engine
  from monai.inferers import SimpleInferer
  from monai.losses import DiceCELoss
  from monai.transforms import Activationsd, AsDiscreted, EnsureChannelFirstd, EnsureTyped, LoadImaged, RandFlipd, RandRotate90d, RandTorchVisiond, ScaleIntensityRangeD, ScaleIntensityRanged
  from monailabel.interfaces.datastore import Datastore
  from monailabel.tasks.train.basic_train import BasicTrainTask, Context
label_desc: Use image node_id 1 to create a new SegmentationNuclei object from inherited base classes: BasicTrainTask with example: obj = SegmentationNuclei(model_dir, network, tile_size, patch_size, description)