class_docstr: This provides Inference Engine for pre-trained segmentation (UNet) model over MSD Dataset.
code: def post_transforms(self, data=None) -> Sequence[Callable]:
      return [
          Activationsd(keys="pred", softmax=True),
          AsDiscreted(keys="pred", argmax=True),
          SqueezeDimd(keys="pred", dim=0),
          PostFilterLabeld(keys="pred"),
          FindContoursd(
              keys="pred", labels=self.labels, max_poly_area=128 * 128
          ),
      ]
  
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 4 for calling the SegmentationNuclei obj's underlying member method code with example usage: obj.post_transforms(data) and returns: list