code: def test_train_stsb(
      distilbert_base_uncased_model: SentenceTransformer,
      sts_resource: Tuple[List[InputExample], List[InputExample]],
  ) -> None:
      model = distilbert_base_uncased_model
      sts_train_samples, sts_test_samples = sts_resource
      train_dataset = SentencesDataset(sts_train_samples[:100], model)
      train_dataloader = DataLoader(
          train_dataset, shuffle=True, batch_size=16
      )
      train_loss = losses.CosineSimilarityLoss(model=model)
      model.fit(
          train_objectives=[(train_dataloader, train_loss)],
          evaluator=None,
          epochs=1,
          evaluation_steps=1000,
          warmup_steps=int(len(train_dataloader) * 0.1),
          use_amp=True,
      )
  
      evaluate_stsb_test(model, 60.0, sts_test_samples)
  
file_path: sentence-transformers/tests/test_train_stsb.py
imports: import csv
  import gzip
  import os
  from typing import Generator, List, Tuple
  import pytest
  from torch.utils.data import DataLoader
  from sentence_transformers import SentencesDataset, SentenceTransformer, losses, util
  from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator
  from sentence_transformers.readers import InputExample
label_desc: Use image node_id 5 for calling a global function with example usage: test_train_stsb(distilbert_base_uncased_model, sts_resource) without return types