code: def test_custom_tokenizer(self):
      tokenizer = PreTrainedTokenizer()
  
      # Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
      image_classifier = pipeline(
          "image-classification",
          model="hf-internal-testing/tiny-random-vit",
          tokenizer=tokenizer,
      )
  
      self.assertIs(image_classifier.tokenizer, tokenizer)
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/tests/pipelines/test_pipelines_image_classification.py
imports: import unittest
  from transformers import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING, TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING, PreTrainedTokenizer, is_vision_available
  from transformers.pipelines import ImageClassificationPipeline, pipeline
  from transformers.testing_utils import nested_simplify, require_tf, require_torch, require_torch_or_tf, require_vision, slow
  from .test_pipelines_common import ANY, PipelineTestCaseMeta
label_desc: Use image node_id 5 for calling the ImageClassificationPipelineTests obj's underlying member method code with example usage: obj.test_custom_tokenizer() without return types