code: def warmup_run():
      """This run warms up the gpu. We saw cases where the first run in the loop took longer or showed different
      time profile characteristics. This warm up run is intended to reduce this kind of fluctation.
      """
      question = [l[:-1] for l in open(questions_file)][0]
      document_size = 100_000
      input_dict = prepare_dict(sample_file, question, document_size)
      # Run once with real prediction heads
      inferencer = Inferencer.load(
          "deepset/bert-base-cased-squad2",
          batch_size=16,
          gpu=True,
          task_type=task_type,
          max_seq_len=384,
          num_processes=num_processes,
          doc_stride=128,
          dummy_ph=False,
          benchmarking=True,
      )
      inferencer.inference_from_dicts(input_dict)
  
file_path: FARM/test/benchmarks/question_answering_components.py
imports: from farm.infer import Inferencer
  from pprint import pformat
  import pandas
  from tqdm import tqdm
  import logging
  import json
  from datetime import date
label_desc: Use image node_id 2 for calling a global function with example usage: warmup_run() without return types