code: def test_rouge_n_alpha(ngram, candidate, reference, expected):
      for alpha in [0, 1, 0.3, 0.5, 0.8]:
          rouge = RougeN(ngram=ngram, alpha=alpha)
          rouge.update(([candidate], [[reference]]))
          results = rouge.compute()
          assert results[f"Rouge-{ngram}-P"] == expected[0]
          assert results[f"Rouge-{ngram}-R"] == expected[1]
          try:
              F = (
                  expected[0]
                  * expected[1]
                  / ((1 - alpha) * expected[0] + alpha * expected[1])
              )
          except ZeroDivisionError:
              F = 0
          assert results[f"Rouge-{ngram}-F"] == F
  
imports: import os
  import nltk
  import pytest
  import rouge
  import torch
  import ignite.distributed
  from ignite.exceptions import NotComputableError
  from ignite.metrics.nlp import Rouge
  from ignite.metrics.nlp.rouge import compute_ngram_scores, RougeL, RougeN
  from .None import CorpusForTest
label_desc: Use image node_id 3 for calling a global function with example usage: test_rouge_n_alpha(ngram, candidate, reference, expected) without return types