code: def __getitem__(self, idx):
      sample_info = self.annotation_db[idx]
      current_sample = Sample()
  
      text_processor_argument = {"text": sample_info["question_str"]}
      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 self._use_features is True:
          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)
  
      # 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/gqa/dataset.py
imports: import torch
  from mmf.common.sample import Sample
  from mmf.datasets.mmf_dataset import MMFDataset
label_desc: Use image node_id 2 for calling the GQADataset obj's underlying member method code with example usage: obj.__getitem__(idx) and returns: current_sample