class_docstr: Sample negative spot and asoc to construct SSI
class_docstr_tok: ["Sample","negative","spot","and","asoc","to","construct","SSI"]
code: def sample_negative(postive, candidates, k=5):
      if k < 0:
          k = len(candidates)
      negative_set = set()
      for index in torch.randperm(len(candidates))[:k].tolist():
          negative = candidates[index]
          if negative not in postive:
              negative_set.add(negative)
      return list(negative_set)
  
imports: import os
  from dataclasses import dataclass
  import torch
  import logging
  import random
  import math
  from typing import Optional, Union
  from collections import OrderedDict
  from transformers import PreTrainedTokenizerBase, PreTrainedModel
  from transformers.file_utils import PaddingStrategy
  from ...extraction.record_schema import RecordSchema
  from ...extraction.dataset_processer import spot_prompt, asoc_prompt
  from ...extraction.constants import BaseStructureMarker, text_start
  from ...extraction.utils import convert_to_record_function
  from ...extraction.noiser.spot_asoc_noiser import SpotAsocNoiser
label_desc: Use image node_id 3 for calling the DynamicSSIGenerator obj's underlying member method code with example usage: obj.sample_negative(postive, candidates, k) and returns: list