class_docstr: A filterer for negative triples based on the Bloom filter.
  
  Pure PyTorch, a proper module which can be moved to GPU, and support batch-wise computation.
  
  .. seealso ::
      * https://github.com/hiway/python-bloom-filter/ - for calculation of sizes, and rough structure of code
      * https://github.com/skeeto/hash-prospector#two-round-functions - for parts of the hash function
docstr: Add triples to the Bloom filter.
docstr_tok: ["Add","triples","to","the","Bloom","filter","."]
code: def add(self, triples: MappedTriples) -> None:
      """Add triples to the Bloom filter."""
      for i in self.probe(batch=triples):
          self.bit_array[i] = True
  
imports: import math
  from abc import abstractmethod
  from typing import Iterable
  import torch
  from class_resolver import ClassResolver
  from torch import nn
  from ..typing import MappedTriples
  from ..utils import triple_tensor_to_set
label_desc: Use image node_id 6 for calling the BloomFilterer obj's underlying member method code with example usage: obj.add(triples) without return types