code: def __getitem__(self, index):
      img_id = self.img_ids[index]
      annotation_ids = self.lvis_api.get_ann_ids(img_ids=[img_id])
      annotation_dicts: List[
          LVISAnnotationEntry
      ] = self.lvis_api.load_anns(annotation_ids)
  
      n_annotations = len(annotation_dicts)
  
      category_tensor = torch.empty((n_annotations,), dtype=torch.long)
      target_dict: LVISImgTargets = {
          "bbox": torch.empty((n_annotations, 4), dtype=torch.float32),
          "category_id": category_tensor,
          "id": torch.empty((n_annotations,), dtype=torch.long),
          "area": torch.empty((n_annotations,), dtype=torch.float32),
          "image_id": torch.full((1,), img_id, dtype=torch.long),
          "segmentation": [],
          "labels": category_tensor,  # Alias of category_id
      }
  
      for ann_idx, annotation in enumerate(annotation_dicts):
          target_dict["bbox"][ann_idx] = torch.as_tensor(
              annotation["bbox"]
          )
          target_dict["category_id"][ann_idx] = annotation[
              "category_id"
          ]
          target_dict["id"][ann_idx] = annotation["id"]
          target_dict["area"][ann_idx] = annotation["area"]
          target_dict["segmentation"].append(annotation["segmentation"])
  
      return target_dict
  
imports: from pathlib import Path
  from typing import Optional, Union, List, Sequence
  import torch
  from PIL import Image
  from torchvision.datasets.folder import default_loader
  from torchvision.transforms import ToTensor
  from typing_extensions import TypedDict
  from avalanche.benchmarks.datasets import DownloadableDataset, default_dataset_location
  from avalanche.benchmarks.datasets.lvis_dataset.lvis_data import lvis_archives
label_desc: Use image node_id 3 for calling the LVISDetectionTargets obj's underlying member method code with example usage: obj.__getitem__(index) and returns: target_dict