code: def test_repetition_penalty_dist_process(self):
      input_ids = torch.tensor(
          [[0, 1], [5, 0]], device=torch_device, dtype=torch.long
      )
      vocab_size = 10
  
      scores = self._get_uniform_logits(batch_size=2, length=vocab_size)
  
      # give values special values
      scores[0, 0] = -(1 / vocab_size)
      scores[1, 5] = 4 / vocab_size
  
      rep_penalty_proc = RepetitionPenaltyLogitsProcessor(penalty=2.0)
  
      scores = rep_penalty_proc(input_ids, scores.clone())
  
      # check that values were correctly changed
      self.assertAlmostEqual(scores[0, 0].item(), -(1 / vocab_size) * 2)
      self.assertAlmostEqual(scores[0, 1].item(), (1 / vocab_size) / 2)
  
      self.assertAlmostEqual(scores[1, 0].item(), (1 / vocab_size) / 2)
      self.assertAlmostEqual(scores[1, 5].item(), (4 / vocab_size) / 2)
  
imports: import unittest
  from typing import List, Union
  from parameterized import parameterized
  from transformers import is_torch_available
  from transformers.testing_utils import require_torch, torch_device
  from ..test_modeling_common import ids_tensor
label_desc: Use image node_id 5 for calling the LogitsProcessorTest obj's underlying member method code with example usage: obj.test_repetition_penalty_dist_process() without return types