class_docstr: dataset that have the same format as in Point cloud registration benchmark repo.
  https://github.com/iralabdisco/point_clouds_registration_benchmark.
  it
class_docstr_tok: ["dataset","that","have","the","same","format","as","in","Point","cloud","registration","benchmark","repo",".","https",":","\/\/github.com\/iralabdisco\/point_clouds_registration_benchmark",".","it"]
docstr: a baseDataset that download a dataset,
  apply preprocessing, and compute keypoints
  
  Parameters
  ----------
  max_dist_overlap: float
      max distance between points to create a match
  num_pos_pairs: int
      number of positive pairs for the ground truth match
  min_size_block: float
      for self supervised, minimum size of the ball where points inside it will be matched
  max_size_block: float
      for self supervised, maximum size of the ball where points inside it will be matched
docstr_tok: ["a","baseDataset","that","download","a","dataset",",","apply","preprocessing",",","and","compute","keypoints","Parameters","--","--","--","--","--","max_dist_overlap",":","float","max","distance","between","points","to","create","a","match","num_pos_pairs",":","int","number","of","positive","pairs","for","the","ground","truth","match","min_size_block",":","float","for","self","supervised",",","minimum","size","of","the","ball","where","points","inside","it","will","be","matched","max_size_block",":","float","for","self","supervised",",","maximum","size","of","the","ball","where","points","inside","it","will","be","matched"]
code: def __init__(
      self,
      root,
      transform=None,
      pre_transform=None,
      pre_filter=None,
      verbose=False,
      debug=False,
      max_dist_overlap=0.01,
      num_pos_pairs=200,
      self_supervised=False,
      min_points=100,
      min_size_block=2,
      max_size_block=3,
      ss_transform=None,
      use_fps=False,
      is_name_path_int=True,
  ):
      """
      a baseDataset that download a dataset,
      apply preprocessing, and compute keypoints
  
      Parameters
      ----------
      max_dist_overlap: float
          max distance between points to create a match
      num_pos_pairs: int
          number of positive pairs for the ground truth match
      min_size_block: float
          for self supervised, minimum size of the ball where points inside it will be matched
      max_size_block: float
          for self supervised, maximum size of the ball where points inside it will be matched
      """
      self.is_name_path_int = is_name_path_int
      self.max_dist_overlap = max_dist_overlap
      self.num_pos_pairs = num_pos_pairs
      self.is_online_matching = False
      super(BasePCRBTest, self).__init__(
          root, transform, pre_transform, pre_filter
      )
      self.path_match = osp.join(self.processed_dir, "test", "matches")
      self.list_fragment = [
          f for f in os.listdir(self.path_match) if "matches" in f
      ]
      # Self supervised variables
      self.self_supervised = self_supervised
      self.ss_transform = ss_transform
      self.min_points = min_points
      self.min_size_block = min_size_block
      self.max_size_block = max_size_block
      self.use_fps = use_fps
  
imports: import json
  import logging
  import numpy
  import pandas
  import os
  import os.path
  from plyfile import PlyData
  import shutil
  import torch
  import re
  import requests
  from zipfile import ZipFile
  import random
  from torch_geometric.data import Dataset, download_url, extract_zip
  from torch_geometric.data import Data
  from torch_points3d.core.data_transform import FixedSphereDropout
  from torch_points3d.datasets.registration.base_siamese_dataset import GeneralFragment
  from torch_points3d.datasets.registration.utils import rgbd2fragment_rough
  from torch_points3d.datasets.registration.utils import rgbd2fragment_fine
  from torch_points3d.datasets.registration.utils import compute_overlap_and_matches
  from torch_points3d.datasets.registration.utils import to_list
  from torch_points3d.datasets.registration.utils import files_exist
  from torch_points3d.datasets.registration.utils import makedirs
  from torch_points3d.datasets.registration.utils import get_urls
  from torch_points3d.datasets.registration.utils import PatchExtractor
  from torch_points3d.datasets.registration.pair import Pair, MultiScalePair
  from torch_points3d.datasets.registration.utils import tracked_matches
  from torch_points_kernels.points_cpu import dense_knn
label_desc: Use image node_id 1 to create a new BasePCRBTest object from inherited base classes: Dataset, GeneralFragment with example: obj = BasePCRBTest(root, transform, pre_transform, pre_filter, verbose, debug, max_dist_overlap, num_pos_pairs, _supervised, min_points, min_size_block, max_size_block, ss_transform, use_fps, is_name_path_int)