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)
  
      metrics["{}_acc".format(self._stage)] = self._acc
      return metrics
  
imports: from typing import Dict, Any
  import torchnet
  import torch
  from .base_tracker import BaseTracker
  from .registration_metrics import compute_accuracy
  from .registration_metrics import compute_hit_ratio
  from .registration_metrics import compute_transfo_error
  from .registration_metrics import compute_scaled_registration_error
  from torch_points3d.models import model_interface
  from torch_points3d.utils.registration import estimate_transfo, fast_global_registration, get_matches
label_desc: Use image node_id 4 for calling the PatchRegistrationTracker obj's underlying member method code with example usage: obj.get_metrics(verbose) and returns: metrics