code: def test_LabelAccuracyEvaluator(
      paraphrase_distilroberta_base_v1_model: SentenceTransformer,
  ) -> None:
      """Tests that the LabelAccuracyEvaluator can be loaded correctly"""
      model = paraphrase_distilroberta_base_v1_model
      nli_dataset_path = "datasets/AllNLI.tsv.gz"
      if not os.path.exists(nli_dataset_path):
          util.http_get(
              "https://sbert.net/datasets/AllNLI.tsv.gz",
              nli_dataset_path,
          )
  
      label2int = {"contradiction": 0, "entailment": 1, "neutral": 2}
      dev_samples = []
      with gzip.open(nli_dataset_path, "rt", encoding="utf8") as fIn:
          reader = csv.DictReader(
              fIn, delimiter="\t", quoting=csv.QUOTE_NONE
          )
          for row in reader:
              if row["split"] == "train":
                  label_id = label2int[row["label"]]
                  dev_samples.append(
                      InputExample(
                          texts=[row["sentence1"], row["sentence2"]],
                          label=label_id,
                      )
                  )
                  if len(dev_samples) >= 100:
                      break
  
      train_loss = losses.SoftmaxLoss(
          model=model,
          sentence_embedding_dimension=model.get_sentence_embedding_dimension(),
          num_labels=len(label2int),
      )
  
      dev_dataloader = DataLoader(
          dev_samples, shuffle=False, batch_size=16
      )
      evaluator = evaluation.LabelAccuracyEvaluator(
          dev_dataloader, softmax_model=train_loss
      )
      acc = evaluator(model)
      assert acc > 0.2
  
file_path: sentence-transformers/tests/test_evaluator.py
imports: import csv
  import gzip
  import os
  import numpy
  from sklearn.metrics import accuracy_score, f1_score
  from torch.utils.data import DataLoader
  from sentence_transformers import InputExample, SentenceTransformer, evaluation, losses, util
label_desc: Use image node_id 3 for calling a global function with example usage: test_LabelAccuracyEvaluator(paraphrase_distilroberta_base_v1_model) without return types