code: def get_imdb(file_path):
      imdb = [{"dataset_name": "gqa"}]
  
      questions = json.load(open(file_path))
      print(f"Processing file {file_path}")
  
      for qid, item in tqdm.tqdm(questions.items()):
          entry = {
              "image_name": item["imageId"] + "jpg",
              "image_id": item["imageId"],
              "question_id": qid,
              "question_str": item["question"],
              "question_tokens": tokenize(item["question"]),
          }
  
          if "answer" in item:
              entry["all_answers"] = [item["answer"] for _ in range(10)]
              entry["valid_answers"] = [
                  item["answer"] for _ in range(10)
              ]
              entry["semantic_string"] = (item["semanticStr"],)
              entry["gt_object_ids"] = (
                  get_objects(item["semanticStr"]),
              )
              entry["meta_data"] = item["types"]
  
          imdb.append(entry)
  
      return np.array(imdb)
  
file_path: mmf/tools/scripts/gqa/convert_gqa_to_vqa.py
imports: import argparse
  import json
  import os
  import re
  from collections import Counter
  import h5py
  import numpy
  import tqdm
  from mmf.utils.process_answers import preprocess_answer
  from mmf.utils.text_processing import text_tokenize
label_desc: Use image node_id 3 for calling a global function with example usage: get_imdb(file_path) and returns: np