code: def test_question_answering_pytorch(
      adaptive_model_qa,
      benchmark,
      max_seq_len,
      batch_size,
      use_gpu,
      document_size,
  ):
      if use_gpu and not torch.cuda.is_available():
          pytest.skip(
              "Skipping benchmarking on GPU as it not available."
          )
  
      if not use_gpu and document_size > 10_000:
          pytest.skip("Document size is large for CPU")
  
      with open("benchmarks/sample_file.txt") as f:
          context = f.read()[:document_size]
      QA_input = [
          {
              "qas": [
                  "When were the first traces of Human life found in France?"
              ],
              "context": context,
          }
      ]
  
      adaptive_model_qa.batch_size = batch_size
      adaptive_model_qa.max_seq_len = max_seq_len
      benchmark.pedantic(
          target=adaptive_model_qa.inference_from_dicts,
          args=(QA_input,),
          warmup_rounds=1,
          iterations=3,
      )
  
imports: import logging
  import pytest
  import torch
label_desc: Use image node_id 1 for calling a global function with example usage: test_question_answering_pytorch(adaptive_model_qa, benchmark, max_seq_len, batch_size, use_gpu, document_size) without return types