class_docstr: An interface for filtering methods for negative triples.
docstr: Check whether a triple is contained.
  
  Supports batching.
  
  :param batch: shape (batch_size, 3)
      The batch of triples.
  
  :return: shape: (batch_size,)
      Whether the triples are contained in the training triples.
docstr_tok: ["Check","whether","a","triple","is","contained",".","Supports","batching",".",":","param","batch",":","shape","(","batch_size",",","3",")","The","batch","of","triples",".",":","return",":","shape",":","(","batch_size",",",")","Whether","the","triples","are","contained","in","the","training","triples","."]
code: def contains(self, batch: MappedTriples) -> torch.BoolTensor:
      """
      Check whether a triple is contained.
  
      Supports batching.
  
      :param batch: shape (batch_size, 3)
          The batch of triples.
  
      :return: shape: (batch_size,)
          Whether the triples are contained in the training triples.
      """
      raise NotImplementedError
  
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 2 for calling the Filterer obj's underlying member method code with example usage: obj.contains(batch) without return types