class_docstr: Queries low-dimensional euclidean space for approximate occupancy.
  
  
  :param float radius: scaling parameter used in hash function. Determines the size of the bin.
                       See :class:`LSH` for details.
docstr: Attempts to add ``point`` to set. Only adds there are no points in the ``point``'s bin.
  
  
  :param torch.Tensor point: Point to be queried, should be detached and on cpu.
  :return: ``True`` if point is successfully added, ``False`` if there is already a point in ``point``'s bin.
  :rtype: bool
docstr_tok: ["Attempts","to","add","``","point","``","to","set",".","Only","adds","there","are","no","points","in","the","``","point","``","'s","bin",".",":","param","torch.Tensor","point",":","Point","to","be","queried",",","should","be","detached","and","on","cpu",".",":","return",":","``","True","``","if","point","is","successfully","added",",","``","False","``","if","there","is","already","a","point","in","``","point","``","'s","bin",".",":","rtype",":","bool"]
code: def try_add(self, point):
      """
      Attempts to add ``point`` to set. Only adds there are no points in the ``point``'s bin.
  
  
      :param torch.Tensor point: Point to be queried, should be detached and on cpu.
      :return: ``True`` if point is successfully added, ``False`` if there is already a point in ``point``'s bin.
      :rtype: bool
      """
      _hash = self._hash(point)
      if _hash in self._bins:
          return False
      self._bins.add(_hash)
      return True
  
file_path: pyro/pyro/contrib/tracking/hashing.py
imports: import heapq
  import itertools
  from collections import defaultdict
  from numbers import Number
  import torch
label_desc: Use image node_id 3 for calling the ApproxSet obj's underlying member method code with example usage: obj.try_add(point) and returns: True, False