class_docstr: Class that provides tracking of semantic segmentation as well as
  instance segmentation 
class_docstr_tok: ["Class","that","provides","tracking","of","semantic","segmentation","as","well","as","instance","segmentation"]
code: def __init__(self, *args, **kwargs):
      super().__init__(*args, **kwargs)
      self._metric_func = {
          **self._metric_func,
          "pos": max,
          "neg": min,
          "map": max,
      }
  
imports: import torchnet
  from typing import NamedTuple, Dict, Any, List, Tuple
  import torch
  import numpy
  from torch_scatter import scatter_add
  from collections import OrderedDict, defaultdict
  from torch_points3d.models.model_interface import TrackerInterface
  from torch_points3d.metrics.segmentation_tracker import SegmentationTracker
  from torch_points3d.metrics.base_tracker import BaseTracker, meter_value
  from torch_points3d.models.panoptic.structures import PanopticResults, PanopticLabels
  from torch_points_kernels import instance_iou
  from .box_detection.ap import voc_ap
label_desc: Use image node_id 1 to create a new PanopticTracker object from inherited base classes: SegmentationTracker with example: obj = PanopticTracker()