class_docstr: This provides Inference Engine for pre-trained Hovernet segmentation + Classification model.
code: def pre_transforms(self, data=None) -> Sequence[Callable]:
      t = [
          LoadImagePatchd(
              keys="image", mode="RGB", dtype=np.uint8, padding=False
          )
      ]
      t.extend([x for x in super().pre_transforms(data)])
      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 2 for calling the HovernetNuclei obj's underlying member method code with example usage: obj.pre_transforms(data) and returns: t