code: def load_item(self, idx):
      sample_info = self.annotation_db[idx]
      current_sample = Sample()
  
      if "question_tokens" in sample_info:
          text_processor_argument = {
              "tokens": sample_info["question_tokens"],
              "text": sample_info["question_str"],
          }
      else:
          text_processor_argument = {"text": sample_info["question"]}
  
      processed_question = self.text_processor(text_processor_argument)
  
      current_sample.text = processed_question["text"]
      if "input_ids" in processed_question:
          current_sample.update(processed_question)
  
      current_sample.question_id = torch.tensor(
          sample_info["question_id"], dtype=torch.int
      )
  
      if isinstance(sample_info["image_id"], int):
          current_sample.image_id = torch.tensor(
              sample_info["image_id"], dtype=torch.int
          )
      else:
          current_sample.image_id = sample_info["image_id"]
  
      if "question_tokens" in sample_info:
          current_sample.text_len = torch.tensor(
              len(sample_info["question_tokens"]), dtype=torch.int
          )
  
      if self._use_features:
          features = self.features_db[idx]
          if hasattr(self, "transformer_bbox_processor"):
              features[
                  "image_info_0"
              ] = self.transformer_bbox_processor(
                  features["image_info_0"]
              )
          current_sample.update(features)
      else:
          image_path = sample_info["image_name"] + ".jpg"
          current_sample.image = self.image_db.from_path(image_path)[
              "images"
          ][0]
  
      # Add details for OCR like OCR bbox, vectors, tokens here
      current_sample = self.add_ocr_details(sample_info, current_sample)
      # Depending on whether we are using soft copy this can add
      # dynamic answer space
      current_sample = self.add_answer_info(sample_info, current_sample)
      return current_sample
  
file_path: mmf/mmf/datasets/builders/vqa2/dataset.py
imports: import logging
  import torch
  import tqdm
  from mmf.common.sample import Sample
  from mmf.datasets.mmf_dataset import MMFDataset
  from mmf.utils.distributed import is_main
label_desc: Use image node_id 5 for calling the VQA2Dataset obj's underlying member method code with example usage: obj.load_item(idx) and returns: current_sample