code: def __getitem__(self, idx: int) -> Type[Sample]:
      sample_info = self.annotation_db[idx]
      current_sample = Sample()
      processed_question = self.text_processor(
          {"text": sample_info["question"]}
      )
      current_sample.update(processed_question)
      current_sample.id = torch.tensor(
          int(sample_info["question_id"]), dtype=torch.int
      )
  
      image_path = self.get_image_path(
          sample_info["image_id"], sample_info["coco_split"]
      )
      current_sample.image = self.image_db.from_path(image_path)[
          "images"
      ][0]
  
      if "answers" in sample_info:
          answers = self.answer_processor(
              {"answers": sample_info["answers"]}
          )
          current_sample.targets = answers["answers_scores"]
  
      return current_sample
  
file_path: mmf/mmf/datasets/builders/vqacp_v2/dataset.py
imports: import os
  from typing import Type, Union
  import torch
  from mmf.common.sample import Sample
  from mmf.common.typings import MMFDatasetConfigType
  from mmf.datasets.builders.okvqa.dataset import OKVQADataset
  from mmf.datasets.builders.vqacp_v2.database import VQACPv2AnnotationDatabase
label_desc: Use image node_id 4 for calling the VQACPv2Dataset obj's underlying member method code with example usage: obj.__getitem__(idx) and returns: current_sample