docstr: Returns a dictionnary of all metrics and losses being tracked
          
docstr_tok: ["Returns","a","dictionnary","of","all","metrics","and","losses","being","tracked"]
code: def get_metrics(self, verbose=False) -> Dict[str, Any]:
      """Returns a dictionnary of all metrics and losses being tracked"""
      metrics = super().get_metrics(verbose)
      if self._full_acc:
          metrics["{}_full_acc".format(self._stage)] = self._full_acc
          metrics["{}_full_macc".format(self._stage)] = self._full_macc
          metrics["{}_full_miou".format(self._stage)] = self._full_miou
      return metrics
  
file_path: torch-points3d/torch_points3d/metrics/scannet_segmentation_tracker.py
imports: import os.path
  from typing import Dict, Any
  import logging
  import numpy
  import torch
  from torch_geometric.nn.unpool import knn_interpolate
  from torch_points3d.metrics.confusion_matrix import ConfusionMatrix
  from torch_points3d.metrics.segmentation_tracker import SegmentationTracker
  from torch_points3d.datasets.segmentation import IGNORE_LABEL
  from torch_points3d.core.data_transform import SaveOriginalPosId
  from torch_points3d.models import model_interface
label_desc: Use image node_id 3 for calling the ScannetSegmentationTracker obj's underlying member method code with example usage: obj.get_metrics(verbose) and returns: metrics