code: def test_layoutlmv3_embeddings_with_long_context(
      self, tasks_base_path
  ):
      with Image.open(
          tasks_base_path / "example_images" / "i_love_berlin.png"
      ) as img:
          img.load()
          img = img.convert("RGB")
  
      sentence = Sentence(["I", "love", "Berlin"] * 512)
      sentence.add_metadata("image", img)
      for i in range(512):
          sentence[i * 3].add_metadata(
              "bbox", BoundingBox(0, 0, 10, 10)
          )
          sentence[i * 3 + 1].add_metadata("bbox", (12, 0, 22, 10))
          sentence[i * 3 + 2].add_metadata("bbox", (0, 12, 0, 10))
      emb = TransformerWordEmbeddings(
          "microsoft/layoutlmv3-base",
          layers="-1,-2,-3,-4",
          layer_mean=True,
      )
      emb.eval()
      emb.embed(sentence)
  
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 6 for calling the TestTransformerWordEmbeddings obj's underlying member method code with example usage: obj.test_layoutlmv3_embeddings_with_long_context(tasks_base_path) without return types