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 _extract_clusters(outputs, min_cluster_points):
      valid_cluster_idx = outputs.get_instances(
          min_cluster_points=min_cluster_points
      )
      clusters = [outputs.clusters[i] for i in valid_cluster_idx]
      return clusters
  
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 6 for calling the PanopticTracker obj's underlying member method code with example usage: obj._extract_clusters(outputs, min_cluster_points) and returns: clusters