docstr: Args:
      idx (int): Index
  
  Returns:
      tuple: (image, target) where target is the image segmentation.
docstr_tok: ["Args",":","idx","(","int",")",":","Index","Returns",":","tuple",":","(","image",",","target",")","where","target","is","the","image","segmentation","."]
code: def __getitem__(self, idx):
      """
      Args:
          idx (int): Index
  
      Returns:
          tuple: (image, target) where target is the image segmentation.
      """
      img = Image.open(self.images_path[idx]).convert("RGB")
      target = self.objects_bboxes[idx]
      masks = self.parse_mask(idx)
      target["masks"] = masks
  
      if self.transforms is not None:
          img, target = self.transforms(img, target)
  
      return img, target
  
file_path: deep-learning-for-image-processing/pytorch_object_detection/mask_rcnn/my_dataset_voc.py
imports: import os
  import json
  from lxml import etree
  import numpy
  from PIL import Image
  import torch
  from torch.utils.data import Dataset
  from train_utils import convert_to_coco_api
label_desc: Use image node_id 3 for calling the VOCInstances obj's underlying member method code with example usage: obj.__getitem__(idx) and returns: img,  target