code: def test_onnx_export_works(self, results_base_path):
      texts = [
          "I live in Berlin",
          "I live in Vienna",
          "Berlin to Germany is like Vienna to Austria",
      ]
  
      normal_sentences = [Sentence(text) for text in texts]
      onnx_sentences = [Sentence(text) for text in texts]
  
      embeddings = TransformerWordEmbeddings("distilbert-base-uncased")
      results_base_path.mkdir(exist_ok=True, parents=True)
      onnx_embeddings = embeddings.export_onnx(
          results_base_path / "onnx-export.onnx", normal_sentences
      )
  
      embeddings.embed(normal_sentences)
      onnx_embeddings.embed(onnx_sentences)
  
      for sent_a, sent_b in zip(normal_sentences, onnx_sentences):
          for token_a, token_b in zip(sent_a, sent_b):
              assert torch.isclose(
                  token_a.get_embedding(),
                  token_b.get_embedding(),
                  atol=1e-6,
              ).all()
  
imports: import importlib.util
  import warnings
  import pytest
  import torch
  from PIL import Image
  from transformers.utils import is_detectron2_available
  from flair.data import BoundingBox, Dictionary, Sentence
  from flair.embeddings import TransformerJitWordEmbeddings, TransformerWordEmbeddings
  from flair.models import SequenceTagger
  from tests.embedding_test_utils import BaseEmbeddingsTest
label_desc: Use image node_id 15 for calling the TestTransformerWordEmbeddings obj's underlying member method code with example usage: obj.test_onnx_export_works(results_base_path) without return types