class_docstr: Fragment extracted from :the Princeton 3DMatch dataset\n
  `"3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions"
  <https://arxiv.org/pdf/1603.08182.pdf>`_
  paper, containing rgbd frames of the following dataset:
  `" SUN3D: A Database of Big Spaces Reconstructed using SfM and Object Labels
  "<http://sun3d.cs.princeton.edu/>`
  `"Scene Coordinate Regression Forests for Camera Relocalization in RGB-D Images
  "<https://www.microsoft.com/en-us/research/publication/scene-coordinate-regression-forests-for-camera-relocalization-in-rgb-d-images/>`
  `"Unsupervised Feature Learning for 3D Scene Labeling
  "<http://rgbd-dataset.cs.washington.edu/dataset/rgbd-scenes-v2/>`
  `"BundleFusion: Real-time Globally Consistent 3D Reconstruction using Online
  Surface Re-integration
  "<http://graphics.stanford.edu/projects/bundlefusion/>`
  `"Learning to Navigate the Energy Landscape
  "<http://graphics.stanford.edu/projects/reloc/>`
  
  Args:
  
      root (string): Root directory where the dataset should be saved
  
      num_frame_per_fragment (int, optional): indicate the number of frames
          we use to build fragments. If it is equal to 0, then we don't
          build fragments and use the raw frames.
  
      mode (string, optional): If :obj:`True`, loads the training dataset,
      otherwise the test dataset. (default: :obj:`True`)
  
      min_overlap_ratio(float, optional): the minimum overlap we should have to match two fragments (overlap is the number of points in a fragment that matches in an other fragment divided by the number of points)
      max_overlap_ratio(float, optional): the maximum overlap we should have to match two fragments
      max_dist_overlap(float, optional): minimum distance to consider that a point match with an other.
      tsdf_voxel_size(float, optional): the size of the tsdf voxel grid to perform fine RGBD fusion to create fine fragments
      depth_thresh: threshold to remove depth pixel that are two far.
  
      is_fine: fine mode for the fragment fusion
  
  
      transform (callable, optional): A function/transform that takes in
          an :obj:`torch_geometric.data.Data` object and returns a
          transformed version. The data object will be transformed before
          every access. (default: :obj:`None`)
  
      pre_transform (callable, optional): A function/transform that takes in
          an :obj:`torch_geometric.data.Data` object and returns a
          transformed version. The data object will be transformed before
          being saved to disk. (default: :obj:`None`)
      pre_filter (callable, optional): A function that takes in an
          :obj:`torch_geometric.data.Data` object and returns a boolean
          value, indicating whether the data object should be included in the
          final dataset. (default: :obj:`None`)
      num_random_pt: number of point we select when we test
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code: def len(self):
      return len(self)
  
imports: import numpy
  import os
  import os.path
  import torch
  from torch_geometric.data import Data
  from torch_points3d.datasets.registration.base3dmatch import Base3DMatch
  from torch_points3d.datasets.registration.utils import PatchExtractor
  from torch_points3d.datasets.registration.utils import tracked_matches
  from torch_points3d.datasets.registration.pair import Pair, MultiScalePair
  from torch_points3d.metrics.registration_tracker import PatchRegistrationTracker
  from torch_points3d.metrics.registration_tracker import FragmentRegistrationTracker
  from torch_points3d.datasets.registration.base_siamese_dataset import BaseSiameseDataset
  from torch_points3d.datasets.registration.base_siamese_dataset import GeneralFragment
  from torch_points3d.datasets.registration.utils import compute_overlap_and_matches
  from torch_points3d.datasets.registration.test3dmatch import TestPair3DMatch
label_desc: Use image node_id 4 for calling the Fragment3DMatch obj's underlying member method code with example usage: obj.len() and returns: len