code: def preprocess(
      self, f: str, second_text: str | None = None
  ) -> BatchEncoding:
      assert self.tokenizer is not None
      return self.tokenizer(
          f, text_pair=second_text, return_tensors=self.framework
      )
  
imports: from __future__ import annotations
  import importlib
  import typing
  import numpy
  import requests
  import tensorflow
  import transformers
  from datasets import load_dataset
  from PIL import Image
  from transformers import Pipeline
  from transformers.pipelines import get_supported_tasks
  from transformers.pipelines import get_task
  from transformers.testing_utils import nested_simplify
  from transformers.trainer_utils import set_seed
  from transformers.utils import is_tf_available
  from transformers.utils import is_torch_available
  import bentoml
  from bentoml._internal.frameworks.transformers import SimpleDefaultMapping
  from bentoml._internal.frameworks.transformers import TaskDefinition
  from bentoml._internal.frameworks.transformers import convert_to_autoclass
  from bentoml._internal.frameworks.transformers import delete_pipeline
  from bentoml._internal.frameworks.transformers import register_pipeline
  from .None import FrameworkTestModel
  from .None import FrameworkTestModelConfiguration
  from .None import FrameworkTestModelInput
label_desc: Use image node_id 2 for calling the PairClassificationPipeline obj's underlying member method code with example usage: obj.preprocess(f, second_text) and returns: self