code: def post_processing_function(
      examples, features, predictions, stage="eval"
  ):
      # Post-processing: we match the start logits and end logits to answers in the original context.
      predictions = postprocess_qa_predictions(
          examples=examples,
          features=features,
          predictions=predictions,
          version_2_with_negative=args.version_2_with_negative,
          n_best_size=args.n_best_size,
          max_answer_length=args.max_answer_length,
          null_score_diff_threshold=args.null_score_diff_threshold,
          output_dir=args.output_dir,
          prefix=stage,
      )
      # Format the result to the format the metric expects.
      if args.version_2_with_negative:
          formatted_predictions = [
              {
                  "id": k,
                  "prediction_text": v,
                  "no_answer_probability": 0.0,
              }
              for k, v in predictions.items()
          ]
      else:
          formatted_predictions = [
              {"id": k, "prediction_text": v}
              for k, v in predictions.items()
          ]
  
      references = [
          {"id": ex["id"], "answers": ex[answer_column_name]}
          for ex in examples
      ]
      return EvalPrediction(
          predictions=formatted_predictions, label_ids=references
      )
  
imports: import argparse
  import logging
  import os
  import time
  import timeit
  import datasets
  import numpy
  import torch
  from absl import logging
  from datasets import load_dataset, load_metric
  from torch.utils.data import DataLoader
  import pycuda.autoinit
  import pycuda.driver
  import tensorrt
  import transformers
  from accelerate import Accelerator
  from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
  from transformers.trainer_pt_utils import nested_concat, nested_truncate
  from utils_qa import postprocess_qa_predictions
label_desc: Use image node_id 3 for calling a global function with example usage: post_processing_function(examples, features, predictions, stage) and returns: EvalPrediction