code: def test_large_model_course(self):
      question_answerer = pipeline("question-answering")
      long_context = """
  🤗 Transformers: State of the Art NLP
  
  🤗 Transformers provides thousands of pretrained models to perform tasks on texts such as classification, information extraction,
  question answering, summarization, translation, text generation and more in over 100 languages.
  Its aim is to make cutting-edge NLP easier to use for everyone.
  
  🤗 Transformers provides APIs to quickly download and use those pretrained models on a given text, fine-tune them on your own datasets and
  then share them with the community on our model hub. At the same time, each python module defining an architecture is fully standalone and
  can be modified to enable quick research experiments.
  
  Why should I use transformers?
  
  1. Easy-to-use state-of-the-art models:
    - High performance on NLU and NLG tasks.
    - Low barrier to entry for educators and practitioners.
    - Few user-facing abstractions with just three classes to learn.
    - A unified API for using all our pretrained models.
    - Lower compute costs, smaller carbon footprint:
  
  2. Researchers can share trained models instead of always retraining.
    - Practitioners can reduce compute time and production costs.
    - Dozens of architectures with over 10,000 pretrained models, some in more than 100 languages.
  
  3. Choose the right framework for every part of a model's lifetime:
    - Train state-of-the-art models in 3 lines of code.
    - Move a single model between TF2.0/PyTorch frameworks at will.
    - Seamlessly pick the right framework for training, evaluation and production.
  
  4. Easily customize a model or an example to your needs:
    - We provide examples for each architecture to reproduce the results published by its original authors.
    - Model internals are exposed as consistently as possible.
    - Model files can be used independently of the library for quick experiments.
  
  🤗 Transformers is backed by the three most popular deep learning libraries — Jax, PyTorch and TensorFlow — with a seamless integration
  between them. It's straightforward to train your models with one before loading them for inference with the other.
  """
      question = "Which deep learning libraries back 🤗 Transformers?"
      outputs = question_answerer(
          question=question, context=long_context
      )
  
      self.assertEqual(
          nested_simplify(outputs),
          {
              "answer": "Jax, PyTorch and TensorFlow",
              "end": 1919,
              "score": 0.971,
              "start": 1892,
          },
      )
  
imports: import unittest
  from transformers import MODEL_FOR_QUESTION_ANSWERING_MAPPING, TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING, LxmertConfig, QuestionAnsweringPipeline
  from transformers.data.processors.squad import SquadExample
  from transformers.pipelines import QuestionAnsweringArgumentHandler, pipeline
  from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, require_torch_or_tf, slow
  from .test_pipelines_common import ANY
label_desc: Use image node_id 11 for calling the QAPipelineTests obj's underlying member method code with example usage: obj.test_large_model_course() without return types