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: Check whether a triple is contained.
  
  :param batch: shape (batch_size, 3)
      The batch of triples.
  
  :return: shape: (batch_size,)
      The result. False guarantees that the element was not contained in the indexed triples. True can be
      erroneous.
docstr_tok: ["Check","whether","a","triple","is","contained",".",":","param","batch",":","shape","(","batch_size",",","3",")","The","batch","of","triples",".",":","return",":","shape",":","(","batch_size",",",")","The","result",".","False","guarantees","that","the","element","was","not","contained","in","the","indexed","triples",".","True","can","be","erroneous","."]
code: def contains(self, batch: MappedTriples) -> torch.BoolTensor:
      """
      Check whether a triple is contained.
  
      :param batch: shape (batch_size, 3)
          The batch of triples.
  
      :return: shape: (batch_size,)
          The result. False guarantees that the element was not contained in the indexed triples. True can be
          erroneous.
      """
      result = batch.new_ones(batch.shape[:-1], dtype=torch.bool)
      for i in self.probe(batch):
          result &= self.bit_array[i]
      return result
  
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 7 for calling the BloomFilterer obj's underlying member method code with example usage: obj.contains(batch) and returns: result