code: def read_gt_log(path):
      """
      read the gt.log of evaluation set of 3DMatch or ETH Dataset and parse it.
      """
      list_pair = []
      list_mat = []
      with open(path, "r") as f:
          all_mat = f.readlines()
      mat = np.zeros((4, 4))
      for i in range(len(all_mat)):
          if i % 5 == 0:
              if i != 0:
                  list_mat.append(mat)
              mat = np.zeros((4, 4))
              list_pair.append(
                  list(map(int, all_mat[i].split("\t")[:-1]))
              )
          else:
              line = all_mat[i].split("\t")
  
              mat[i % 5 - 1] = np.asarray(line[:4], dtype=np.float)
      list_mat.append(mat)
      return list_pair, list_mat
  
file_path: torch-points3d/scripts/test_registration_scripts/descriptor_matcher.py
imports: import pandas
  import hydra
  import numpy
  from sklearn.neighbors import KDTree
  import os
  import os.path
  from omegaconf import OmegaConf
  import sys
  import matplotlib.pyplot
label_desc: Use image node_id 1 for calling a global function with example usage: read_gt_log(path) and returns: list_pair,  list_mat