code: def __getitem__(self, idx):
      sample_info = self.annotation_db[idx]
      sample_info = self.preprocess_sample_info(sample_info)
  
      current_sample = Sample()
  
      processed_text = self.text_processor(
          {"text": sample_info["text"]}
      )
      current_sample.text = processed_text["text"]
      if "input_ids" in processed_text:
          current_sample.update(processed_text)
  
      current_sample.id = torch.tensor(
          int(sample_info["id"]), dtype=torch.int
      )
  
      # Instead of using idx directly here, use sample_info to fetch
      # the features as feature_path has been dynamically added
      features = self.features_db.get(sample_info)
      if hasattr(self, "transformer_bbox_processor"):
          features["image_info_0"] = self.transformer_bbox_processor(
              features["image_info_0"]
          )
      current_sample.update(features)
  
      fg_dataset_type = self.config.get("fg_dataset_type", None)
      if fg_dataset_type:
          current_sample = self.process_fg_labels(
              fg_dataset_type=fg_dataset_type,
              sample_info=sample_info,
              current_sample=current_sample,
          )
      else:
          if "label" in sample_info:
              current_sample.targets = torch.tensor(
                  sample_info["label"], dtype=torch.long
              )
  
      return current_sample
  
imports: import copy
  import os
  import numpy
  import omegaconf
  import torch
  from mmf.common.sample import Sample
  from mmf.datasets.mmf_dataset import MMFDataset
  from mmf.utils.general import get_mmf_root
  from mmf.utils.visualize import visualize_images
  from PIL import Image
  from torchvision import transforms
label_desc: Use image node_id 3 for calling the HatefulMemesFeaturesDataset obj's underlying member method code with example usage: obj.__getitem__(idx) and returns: current_sample