docstr: base dataset for siamese inputs
docstr_tok: ["base","dataset","for","siamese","inputs"]
code: def __init__(self, dataset_opt):
      """
      base dataset for siamese inputs
      """
      super().__init__(dataset_opt)
      self.num_points = dataset_opt.num_points
      self.tau_1 = dataset_opt.tau_1
      self.tau_2 = dataset_opt.tau_2
      self.trans_thresh = dataset_opt.trans_thresh
      self.rot_thresh = dataset_opt.rot_thresh
      self.is_patch = False
      self.is_end2end = False
  
imports: import numpy
  import os.path
  import random
  import torch
  import logging
  from torch_geometric.data import Data
  from torch_points_kernels.points_cpu import ball_query
  from functools import partial
  from torch_points3d.core.data_transform import MultiScaleTransform
  from torch_points3d.core.data_transform import PairTransform
  from torch_points3d.datasets.registration.pair import DensePairBatch
  from torch_points3d.utils.enums import ConvolutionFormat
  from torch_points3d.utils.config import ConvolutionFormatFactory
  from torch_points3d.datasets.registration.pair import PairMultiScaleBatch, PairBatch
  from torch_points3d.datasets.registration.pair import Pair, MultiScalePair
  from torch_points3d.datasets.registration.utils import tracked_matches
  from torch_points3d.datasets.registration.utils import compute_overlap_and_matches
  from torch_points3d.datasets.registration.utils import fps_sampling
  from torch_points3d.datasets.base_dataset import BaseDataset
  from torch_points3d.metrics.registration_tracker import PatchRegistrationTracker
  from torch_points3d.metrics.registration_tracker import FragmentRegistrationTracker
label_desc: Use image node_id 1 to create a new BaseSiameseDataset object from inherited base classes: BaseDataset with example: obj = BaseSiameseDataset(dataset_opt)