code: def test_train_nli(
      distilbert_base_uncased_model: SentenceTransformer,
      nli_resource: List[InputExample],
      sts_resource: Tuple[List[InputExample], List[InputExample]],
  ):
      model = distilbert_base_uncased_model
      _, sts_test_samples = sts_resource
      train_dataset = SentencesDataset(nli_resource[:100], model=model)
      train_dataloader = DataLoader(
          train_dataset, shuffle=True, batch_size=16
      )
      train_loss = losses.SoftmaxLoss(
          model=model,
          sentence_embedding_dimension=model.get_sentence_embedding_dimension(),
          num_labels=3,
      )
      model.fit(
          train_objectives=[(train_dataloader, train_loss)],
          evaluator=None,
          epochs=1,
          warmup_steps=int(len(train_dataloader) * 0.1),
          use_amp=True,
      )
  
      evaluate_stsb_test(model, 50.0, 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 7 for calling a global function with example usage: test_train_nli(distilbert_base_uncased_model, nli_resource, sts_resource) without return types