code: def train_evaluation_single(seed=42):
      ##########################
      ########## Settings
      ##########################
      set_all_seeds(seed=seed)
      device, n_gpu = initialize_device_settings(use_cuda=True)
      # GPU utilization on 4x V100
      # 40*4, 14.3/16GB on master, 12.6/16 on others
      batch_size = 40 * n_gpu_factor
      n_epochs = 2
      evaluate_every = 2000000  # disabling dev eval
      lang_model = "roberta-base"
      do_lower_case = False  # roberta is a cased model
      test_assertions = False
      train_filename = "train-v2.0.json"
      dev_filename = "dev-v2.0.json"
  
      # Load model and train
      tokenizer = Tokenizer.load(
          pretrained_model_name_or_path=lang_model,
          do_lower_case=do_lower_case,
      )
      processor = SquadProcessor(
          tokenizer=tokenizer,
          max_seq_len=256,
          label_list=["start_token", "end_token"],
          metric="squad",
          train_filename=train_filename,
          dev_filename=dev_filename,
          test_filename=None,
          data_dir=Path("testsave/data/squad20"),
      )
      data_silo = DataSilo(processor=processor, batch_size=batch_size)
      language_model = LanguageModel.load(lang_model)
      prediction_head = QuestionAnsweringHead(
          n_best=5, n_best_per_sample=1
      )
      model = AdaptiveModel(
          language_model=language_model,
          prediction_heads=[prediction_head],
          embeds_dropout_prob=0.1,
          lm_output_types=["per_token"],
          device=device,
      )
      model, optimizer, lr_schedule = initialize_optimizer(
          model=model,
          learning_rate=3e-5,
          schedule_opts={
              "name": "LinearWarmup",
              "warmup_proportion": 0.2,
          },
          n_batches=len(data_silo.loaders["train"]),
          n_epochs=n_epochs,
          device=device,
      )
      trainer = Trainer(
          model=model,
          optimizer=optimizer,
          data_silo=data_silo,
          epochs=n_epochs,
          n_gpu=n_gpu,
          lr_schedule=lr_schedule,
          evaluate_every=evaluate_every,
          device=device,
      )
      starttime = time()
      trainer.train()
      elapsed = time() - starttime
  
      save_dir = Path("testsave/roberta-qa-dev")
      model.save(save_dir)
      processor.save(save_dir)
  
      # Create Evaluator
      evaluator = Evaluator(
          data_loader=data_silo.get_data_loader("dev"),
          tasks=data_silo.processor.tasks,
          device=device,
      )
  
      results = evaluator.eval(model)
      f1_score = results[0]["f1"] * 100
      em_score = results[0]["EM"] * 100
      tnacc = results[0]["top_n_accuracy"] * 100
  
      print(results)
      print(elapsed)
  
      gold_f1 = 82.155
      gold_EM = 78.4385
      gold_tnrecall = 97.3721
      gold_elapsed = 1135
      if test_assertions:
          np.testing.assert_allclose(
              f1_score,
              gold_f1,
              rtol=0.01,
              err_msg=f"FARM Training changed for f1 score by: {f1_score - gold_f1}",
          )
          np.testing.assert_allclose(
              em_score,
              gold_EM,
              rtol=0.01,
              err_msg=f"FARM Training changed for EM by: {em_score - gold_EM}",
          )
          np.testing.assert_allclose(
              tnacc,
              gold_tnrecall,
              rtol=0.01,
              err_msg=f"FARM Training changed for top 5 accuracy by: {tnacc - gold_tnrecall}",
          )
          np.testing.assert_allclose(
              elapsed,
              gold_elapsed,
              rtol=0.1,
              err_msg=f"FARM Training speed changed significantly by: {elapsed - gold_elapsed} seconds",
          )
      if not np.allclose(f1_score, gold_f1, rtol=0.01):
          error_messages.append(
              f"FARM Training changed for f1 score by: {round(f1_score - gold_f1, 4)}"
          )
      if not np.allclose(em_score, gold_EM, rtol=0.01):
          error_messages.append(
              f"FARM Training changed for EM by: {round(em_score - gold_EM, 4)}"
          )
      if not np.allclose(tnacc, gold_tnrecall, rtol=0.01):
          error_messages.append(
              f"FARM Training changed for top 5 accuracy by: {round(tnacc - gold_tnrecall, 4)}"
          )
      if not np.allclose(elapsed, gold_elapsed, rtol=0.1):
          error_messages.append(
              f"FARM Training speed changed significantly by: {round(elapsed - gold_elapsed, 4)} seconds"
          )
  
      benchmark_result = [
          {
              "run": "train evaluation",
              "f1_change": round(f1_score - gold_f1, 4),
              "em_change": round(em_score - gold_EM, 4),
              "tnacc_change": round(tnacc - gold_tnrecall, 4),
              "elapsed_change": round(elapsed - gold_elapsed, 4),
              "f1": f1_score,
              "em": em_score,
              "tnacc": round(tnacc, 4),
              "elapsed": elapsed,
              "f1_gold": gold_f1,
              "em_gold": gold_EM,
              "tnacc_gold": gold_tnrecall,
              "elapsed_gold": gold_elapsed,
          }
      ]
      logger.info("\n\n" + pformat(benchmark_result) + "\n")
      return benchmark_result
  
imports: import logging
  import os
  from pathlib import Path
  from time import time
  import numpy
  from pprint import pformat
  import pandas
  from dotmap import DotMap
  from farm.data_handler.data_silo import DataSilo
  from farm.data_handler.processor import SquadProcessor
  from farm.data_handler.utils import write_squad_predictions
  from farm.eval import Evaluator
  from farm.evaluation import squad_evaluation
  from farm.infer import Inferencer
  from farm.modeling.adaptive_model import AdaptiveModel
  from farm.modeling.language_model import LanguageModel
  from farm.modeling.optimization import initialize_optimizer, optimize_model
  from farm.modeling.prediction_head import QuestionAnsweringHead
  from farm.modeling.tokenization import Tokenizer
  from farm.train import Trainer
  from farm.utils import set_all_seeds, initialize_device_settings
label_desc: Use image node_id 2 for calling a global function with example usage: train_evaluation_single(seed) and returns: benchmark_result