class_docstr: A negative sampler.
class_docstr_tok: ["A","negative","sampler","."]
docstr: Generate negative samples from the positive batch without application of any filter.
  
  :param positive_batch: shape: `(*batch_dims, 3)`
      The positive triples.
  
  :return: shape: `(*batch_dims, num_negs_per_pos, 3)`
      The negative triples. ``result[*bi, :, :]`` contains the negative examples generated from
      ``positive_batch[*bi, :]``.
docstr_tok: ["Generate","negative","samples","from","the","positive","batch","without","application","of","any","filter",".",":","param","positive_batch",":","shape",":","`","(","*","batch_dims",",","3",")","`","The","positive","triples",".",":","return",":","shape",":","`","(","*","batch_dims",",","num_negs_per_pos",",","3",")","`","The","negative","triples",".","``","result","[","*","bi",",",":",",",":","]","``","contains","the","negative","examples","generated","from","``","positive_batch","[","*","bi",",",":","]","``","."]
code: def corrupt_batch(
      self, positive_batch: torch.LongTensor
  ) -> torch.LongTensor:
      """
      Generate negative samples from the positive batch without application of any filter.
  
      :param positive_batch: shape: `(*batch_dims, 3)`
          The positive triples.
  
      :return: shape: `(*batch_dims, num_negs_per_pos, 3)`
          The negative triples. ``result[*bi, :, :]`` contains the negative examples generated from
          ``positive_batch[*bi, :]``.
      """
      raise NotImplementedError
  
file_path: pykeen/src/pykeen/sampling/negative_sampler.py
imports: from abc import abstractmethod
  from typing import Any, ClassVar, Mapping, Optional, Tuple
  import torch
  from class_resolver import HintOrType, normalize_string
  from torch import nn
  from .filtering import Filterer, filterer_resolver
  from ..typing import MappedTriples
label_desc: Use image node_id 4 for calling the NegativeSampler obj's underlying member method code with example usage: obj.corrupt_batch(positive_batch) without return types