class_docstr: Performs Filtering of Labels on the predicted probability map
  
  Args:
      thresh: probability threshold for classifying a pixel as a mask
      min_size: min_size objects that will be removed from the image, refer skimage remove_small_objects
      min_hole: min_hole that will be removed from the image, refer skimage remove_small_holes
      do_reconstruction: Boolean Flag, Perform a morphological reconstruction of an image, refer skimage
      allow_missing_keys: don't raise exception if key is missing.
      pred_classes: List of Predicted class for each instance
class_docstr_tok: ["Performs","Filtering","of","Labels","on","the","predicted","probability","map","Args",":","thresh",":","probability","threshold","for","classifying","a","pixel","as","a","mask","min_size",":","min_size","objects","that","will","be","removed","from","the","image",",","refer","skimage","remove_small_objects","min_hole",":","min_hole","that","will","be","removed","from","the","image",",","refer","skimage","remove_small_holes","do_reconstruction",":","Boolean","Flag",",","Perform","a","morphological","reconstruction","of","an","image",",","refer","skimage","allow_missing_keys",":","do","n't","raise","exception","if","key","is","missing",".","pred_classes",":","List","of","Predicted","class","for","each","instance"]
code: def __call__(self, data):
      d = dict(data)
  
      pred_classes = d.get(self.pred_classes)
      bounding_boxes = d[self.bounding_boxes]
      x = d[self.img_width]
      y = d[self.img_height]
  
      for key in self.keys:
          label = d[key].astype(np.uint8)
          masks = self.post_processing(
              label, self.thresh, self.min_size, self.min_hole
          )
          d[key] = self.gen_instance_map(
              masks, bounding_boxes, x, y, pred_classes=pred_classes
          ).astype(np.uint8)
      return d
  
imports: from __future__ import annotations
  import math
  from typing import Any
  import numpy
  import torch
  from monai.config import KeysCollection, NdarrayOrTensor
  from monai.networks.layers import GaussianFilter
  from monai.transforms import MapTransform, Randomizable, SpatialPad
  from monai.utils import StrEnum, convert_to_numpy, optional_import
label_desc: Use image node_id 2 for calling the PostFilterLabeld obj's underlying member method code with example usage: obj.__call__(data) and returns: d