code: def run_pipeline_test(self, token_classifier, _):
      model = token_classifier.model
      tokenizer = token_classifier.tokenizer
      if not tokenizer.is_fast:
          return  # Slow tokenizers do not return offsets mappings, so this test will fail
  
      outputs = token_classifier("A simple string")
      self.assertIsInstance(outputs, list)
      n = len(outputs)
      self.assertEqual(
          nested_simplify(outputs),
          [
              {
                  "entity": ANY(str),
                  "score": ANY(float),
                  "start": ANY(int),
                  "end": ANY(int),
                  "index": ANY(int),
                  "word": ANY(str),
              }
              for i in range(n)
          ],
      )
      outputs = token_classifier(
          [
              "list of strings",
              "A simple string that is quite a bit longer",
          ]
      )
      self.assertIsInstance(outputs, list)
      self.assertEqual(len(outputs), 2)
      n = len(outputs[0])
      m = len(outputs[1])
  
      self.assertEqual(
          nested_simplify(outputs),
          [
              [
                  {
                      "entity": ANY(str),
                      "score": ANY(float),
                      "start": ANY(int),
                      "end": ANY(int),
                      "index": ANY(int),
                      "word": ANY(str),
                  }
                  for i in range(n)
              ],
              [
                  {
                      "entity": ANY(str),
                      "score": ANY(float),
                      "start": ANY(int),
                      "end": ANY(int),
                      "index": ANY(int),
                      "word": ANY(str),
                  }
                  for i in range(m)
              ],
          ],
      )
  
      self.run_aggregation_strategy(model, tokenizer)
  
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 is_pipeline_test, nested_simplify, require_tf, require_torch, require_torch_accelerator, slow, torch_device
  from .test_pipelines_common import ANY
label_desc: Use image node_id 2 for calling the TokenClassificationPipelineTests obj's underlying member method code with example usage: obj.run_pipeline_test(token_classifier, _) without return types