class_docstr: This provides Inference Engine for pre-trained Hovernet segmentation + Classification model.
code: def post_transforms(self, data=None) -> Sequence[Callable]:
      t = [x for x in super().post_transforms(data)]
      t.extend(
          [
              RenameKeyd(source_key="type_map", target_key="pred"),
              SqueezeDimd(keys="pred", dim=0),
              PostFilterLabeld(keys="pred"),
              FindContoursd(
                  keys="pred",
                  labels=self.labels,
                  max_poly_area=128 * 128,
              ),
          ]
      )
      return t
  
file_path: MONAILabel/sample-apps/pathology/lib/infers/hovernet_nuclei.py
imports: import logging
  from typing import Any, Callable, Dict, Sequence
  import numpy
  from lib.transforms import LoadImagePatchd, PostFilterLabeld
  from monai.transforms import FromMetaTensord, LoadImaged, SaveImaged, SqueezeDimd
  from monailabel.interfaces.tasks.infer_v2 import InferType
  from monailabel.tasks.infer.bundle import BundleInferTask
  from monailabel.transform.post import FindContoursd, RenameKeyd
  from monailabel.transform.writer import PolygonWriter
label_desc: Use image node_id 3 for calling the HovernetNuclei obj's underlying member method code with example usage: obj.post_transforms(data) and returns: t