code: def test_transformer_force_max_length(self):
      sentence: Sentence = Sentence(
          "I love Berlin, but Vienna is where my hearth is."
      )
      short_embeddings = TransformerWordEmbeddings(
          "distilbert-base-uncased",
          layers="-1,-2,-3,-4",
          layer_mean=False,
      )
      long_embeddings = TransformerWordEmbeddings(
          "distilbert-base-uncased",
          layers="-1,-2,-3,-4",
          layer_mean=False,
          force_max_length=True,
      )
      short_tensors = short_embeddings.prepare_tensors([sentence])
      long_tensors = long_embeddings.prepare_tensors([sentence])
      for tensor in short_tensors.values():
          if (
              tensor.dim() > 1
          ):  # all tensors that have a sequence length need to be shorter
              assert tensor.shape[1] < 512
  
      for tensor in long_tensors.values():
          if (
              tensor.dim() > 1
          ):  # all tensors that have a sequence length need to be exactly max length
              assert tensor.shape[1] == 512
      short_embeddings.embed(sentence)
      short_embedding_0 = sentence[0].get_embedding()
      sentence.clear_embeddings()
      long_embeddings.embed(sentence)
      long_embedding_0 = sentence[0].get_embedding()
      # apparently the precision is not that high on cuda, hence the absolute tolerance needs to be higher.
      assert torch.isclose(
          short_embedding_0, long_embedding_0, atol=1e-4
      ).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 11 for calling the TestTransformerWordEmbeddings obj's underlying member method code with example usage: obj.test_transformer_force_max_length() without return types