code: def load_item(self, idx):
      sample_info = self.annotation_db[idx]
      current_sample = Sample()
  
      processed_caption = self.text_processor(
          {"text": sample_info["captions"][0]}
      )
      current_sample.text = processed_caption["text"]
      current_sample.caption_len = torch.tensor(
          len(processed_caption["text"]), 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]
          current_sample.update(features)
  
      current_sample.answers = torch.stack([processed_caption["text"]])
  
      return current_sample
  
imports: import torch
  from mmf.common.sample import Sample
  from mmf.datasets.builders.coco import COCODataset
label_desc: Use image node_id 2 for calling the ConceptualCaptionsDataset obj's underlying member method code with example usage: obj.load_item(idx) and returns: current_sample