class_docstr: This provides Inference Engine for pre-trained segmentation (UNet) model over MSD Dataset.
code: def __init__(
      self,
      path,
      network=None,
      roi_size=(256, 256),
      type=InferType.SEGMENTATION,
      labels=None,
      dimension=2,
      description="A pre-trained semantic segmentation model for Pathology",
      **kwargs,
  ):
      super().__init__(
          path=path,
          network=network,
          roi_size=roi_size,
          type=type,
          labels=labels,
          dimension=dimension,
          description=description,
          load_strict=False,
          **kwargs,
      )
  
file_path: MONAILabel/sample-apps/pathology/lib/infers/segmentation_nuclei.py
imports: import logging
  from typing import Any, Callable, Dict, Sequence
  import numpy
  from lib.transforms import LoadImagePatchd, PostFilterLabeld
  from monai.transforms import Activationsd, AsDiscreted, ScaleIntensityRangeD, SqueezeDimd, Transposed
  from monailabel.interfaces.tasks.infer_v2 import InferType
  from monailabel.tasks.infer.basic_infer import BasicInferTask
  from monailabel.transform.post import FindContoursd
  from monailabel.transform.writer import PolygonWriter
label_desc: Use image node_id 1 to create a new SegmentationNuclei object from inherited base classes: BasicInferTask with example: obj = SegmentationNuclei(path, network, roi_size, type, labels, dimension, description)