class_docstr: Tests the BestOfNSampler class
class_docstr_tok: ["Tests","the","BestOfNSampler","class"]
docstr: Tests different sample sizes and n_candidates values
docstr_tok: ["Tests","different","sample","sizes","and","n_candidates","values"]
code: def test_different_sample_sizes_and_n_candidates_values(self):
      r"""
      Tests different sample sizes and n_candidates values
      """
      generation_config = GenerationConfig(
          min_length=-1,
          top_k=0.0,
          top_p=1.0,
          do_sample=True,
          pad_token_id=self.tokenizer.eos_token_id,
      )
  
      output_length_sampler = LengthSampler(6, 10)
  
      for sample_value, n_candidates_values, expected in [
          (4, 2, 2),
          (10, 3, 3),
          (6, 4, 4),
      ]:
          best_of_n = BestOfNSampler(
              self.model,
              self.tokenizer,
              queries_to_scores,
              length_sampler=output_length_sampler,
              generation_config=generation_config,
              sample_size=sample_value,
              n_candidates=n_candidates_values,
          )
  
          queries = ["hello world", "troll the world"]
          tokenized_queries = [
              self.tokenizer.encode(query) for query in queries
          ]
          results = best_of_n.generate(tokenized_queries)
          for result in results:
              assert len(result) == expected
  
file_path: trl/tests/test_best_of_n_sampler.py
imports: import unittest
  import torch
  from transformers import AutoTokenizer, GenerationConfig
  from trl import AutoModelForCausalLMWithValueHead
  from trl.core import LengthSampler
  from trl.extras import BestOfNSampler
label_desc: Use image node_id 2 for calling the BestOfNSamplerTester obj's underlying member method code with example usage: obj.test_different_sample_sizes_and_n_candidates_values() without return types