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: Determine the number of probes / hashing rounds.
  
  :param num_elements:
      The number of elements.
  :param num_bits:
      The number of bits, i.e., the size of the Bloom filter.
  
  :return:
      The number of hashing rounds.
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code: def num_probes(num_elements: int, num_bits: int):
      """
      Determine the number of probes / hashing rounds.
  
      :param num_elements:
          The number of elements.
      :param num_bits:
          The number of bits, i.e., the size of the Bloom filter.
  
      :return:
          The number of hashing rounds.
      """
      num_bits = num_bits
      real_num_probes_k = (num_bits / num_elements) * math.log(2)
      return int(math.ceil(real_num_probes_k))
  
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 4 for calling the BloomFilterer obj's underlying member method code with example usage: obj.num_probes(num_elements, num_bits) and returns: int