class_docstr: This class converts the output of the model to the final panoptic result, in the format expected by the
  coco panoptic API 
class_docstr_tok: ["This","class","converts","the","output","of","the","model","to","the","final","panoptic","result",",","in","the","format","expected","by","the","coco","panoptic","API"]
docstr: Parameters:
     is_thing_map: This is a whose keys are the class ids, and the values a boolean indicating whether
                    the class is  a thing (True) or a stuff (False) class
     threshold: confidence threshold: segments with confidence lower than this will be deleted
docstr_tok: ["Parameters",":","is_thing_map",":","This","is","a","whose","keys","are","the","class","ids",",","and","the","values","a","boolean","indicating","whether","the","class","is","a","thing","(","True",")","or","a","stuff","(","False",")","class","threshold",":","confidence","threshold",":","segments","with","confidence","lower","than","this","will","be","deleted"]
code: def __init__(self, is_thing_map, threshold=0.85):
      """
      Parameters:
         is_thing_map: This is a whose keys are the class ids, and the values a boolean indicating whether
                        the class is  a thing (True) or a stuff (False) class
         threshold: confidence threshold: segments with confidence lower than this will be deleted
      """
      super().__init__()
      self.threshold = threshold
      self.is_thing_map = is_thing_map
  
imports: import io
  from collections import defaultdict
  from typing import List, Optional
  import torch
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
  from PIL import Image
  import util.box_ops
  from util.misc import NestedTensor, interpolate, nested_tensor_from_tensor_list
label_desc: Use image node_id 1 to create a new PostProcessPanoptic object from inherited base classes: nn with example: obj = PostProcessPanoptic(is_thing_map, threshold)