class_docstr: This provides Inference Engine for pre-trained NuClick segmentation (UNet) model.
code: def __init__(self, path: str, conf: Dict[str, str], **kwargs):
      super().__init__(
          path,
          conf,
          type=InferType.ANNOTATION,
          add_post_restore=False,
          load_strict=False,
          **kwargs,
          pre_filter=[LoadImaged, SqueezeDimd],
          post_filter=[KeepLargestConnectedComponentd, SaveImaged],
      )
  
      # Override Labels
      self.labels = {"Nuclei": 1}
      self.label_colors = {"Nuclei": (0, 255, 255)}
      self._config["label_colors"] = self.label_colors
      self.task_classification: Optional[BasicInferTask] = None
  
imports: import copy
  import logging
  from typing import Any, Callable, Dict, Optional, Sequence
  import numpy
  import torch
  from lib.transforms import ConvertInteractiveClickSignals, LoadImagePatchd
  from monai.apps.nuclick.transforms import AddLabelAsGuidanced, NuclickKeys, PostFilterLabeld
  from monai.transforms import KeepLargestConnectedComponentd, LoadImaged, SaveImaged, SqueezeDimd
  from monailabel.interfaces.tasks.infer_v2 import InferType
  from monailabel.tasks.infer.basic_infer import BasicInferTask
  from monailabel.tasks.infer.bundle import BundleInferTask
  from monailabel.transform.post import FindContoursd
  from monailabel.transform.writer import PolygonWriter
label_desc: Use image node_id 1 to create a new NuClick object from inherited base classes: BundleInferTask with example: obj = NuClick(path, conf)