code: def __getitem__(self, idx):
      sample_info = self.annotation_db[idx]
      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
      )
  
      # Get the first image from the set of images returned from the image_db
      current_sample.image = self.image_db[idx]["images"][0]
  
      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 HatefulMemesImageDataset obj's underlying member method code with example usage: obj.__getitem__(idx) and returns: current_sample