class_docstr: Radius search with support for multiscale for sparse graphs
  
  Arguments:
      radius {Union[float, List[float]]}
  
  Keyword Arguments:
      max_num_neighbors {Union[int, List[int]]}  (default: {64})
  
  Raises:
      ValueError: [description]
docstr: Sparse interface of the neighboorhood finder
          
docstr_tok: ["Sparse","interface","of","the","neighboorhood","finder"]
code: def __call__(self, x, y, batch_x=None, batch_y=None, scale_idx=0):
      """Sparse interface of the neighboorhood finder"""
      return self.find_neighbours(x, y, batch_x, batch_y, scale_idx)
  
imports: from abc import ABC, abstractmethod
  from typing import List, Union, cast
  import torch
  from torch_geometric.nn import knn, radius
  import torch_points_kernels
  from torch_points3d.utils.config import is_list
  from torch_points3d.utils.enums import ConvolutionFormat
  from torch_points3d.utils.debugging_vars import DEBUGGING_VARS, DistributionNeighbour
label_desc: Use image node_id 4 for calling the MultiscaleRadiusNeighbourFinder obj's underlying member method code with example usage: obj.__call__(x, y, batch_x, batch_y, scale_idx) and returns: self