class_docstr: This provides Inference Engine for pre-trained segmentation (UNet) model over MSD Dataset.
code: def pre_transforms(self, data=None) -> Sequence[Callable]:
      return [
          LoadImagePatchd(
              keys="image", mode="RGB", dtype=np.uint8, padding=False
          ),
          Transposed(keys="image", indices=[2, 0, 1]),
          ScaleIntensityRangeD(
              keys="image",
              a_min=0.0,
              a_max=255.0,
              b_min=-1.0,
              b_max=1.0,
          ),
      ]
  
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 3 for calling the SegmentationNuclei obj's underlying member method code with example usage: obj.pre_transforms(data) and returns: list