code: def test_aggregation_strategy_byte_level_tokenizer(self):
      sentence = "Groenlinks praat over Schiphol."
      ner = pipeline(
          "ner",
          model="xlm-roberta-large-finetuned-conll02-dutch",
          aggregation_strategy="max",
      )
      self.assertEqual(
          nested_simplify(ner(sentence)),
          [
              {
                  "end": 10,
                  "entity_group": "ORG",
                  "score": 0.994,
                  "start": 0,
                  "word": "Groenlinks",
              },
              {
                  "entity_group": "LOC",
                  "score": 1.0,
                  "word": "Schiphol.",
                  "start": 22,
                  "end": 31,
              },
          ],
      )
  
imports: import unittest
  import numpy
  from transformers import MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, AutoModelForTokenClassification, AutoTokenizer, TokenClassificationPipeline, pipeline
  from transformers.pipelines import AggregationStrategy, TokenClassificationArgumentHandler
  from transformers.testing_utils import nested_simplify, require_tf, require_torch, require_torch_gpu, slow
  from .test_pipelines_common import ANY, PipelineTestCaseMeta
label_desc: Use image node_id 7 for calling the TokenClassificationPipelineTests obj's underlying member method code with example usage: obj.test_aggregation_strategy_byte_level_tokenizer() without return types