docstr: Predict full resolution results based on votes 
docstr_tok: ["Predict","full","resolution","results","based","on","votes"]
code: def _predict_full_res(self):
      """Predict full resolution results based on votes"""
      for id_scan in self._votes:
          has_prediction = self._vote_counts[id_scan] > 0
          self._votes[id_scan][has_prediction] /= self._vote_counts[
              id_scan
          ][has_prediction].unsqueeze(-1)
  
          # Upsample and predict
          full_pred = knn_interpolate(
              self._votes[id_scan][has_prediction],
              self._raw_datas[id_scan].pos[has_prediction],
              self._raw_datas[id_scan].pos,
              k=1,
          )
          self._full_preds[id_scan] = full_pred.argmax(-1)
  
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 7 for calling the ScannetSegmentationTracker obj's underlying member method code with example usage: obj._predict_full_res() without return types