code: def __getitem__(self, idx):
      img_path = os.path.join(self.root, "images", self.images[idx])
      ann_path = os.path.join(
          self.root, "annotations", self.annotations[idx]
      )
      img = Image.open(img_path).convert("RGB")
      bboxes = []
      labels = []
      with open(ann_path, "r") as f:
          tree = ET.parse(f)
          root = tree.getroot()
          size = root.find("size")
          w = int(size.find("width").text)
          h = int(size.find("height").text)
  
          for obj in root.iter("object"):
              difficult = obj.find("difficult").text
              if int(difficult) == 1:
                  continue
              cls_id = 1
              xmlbox = obj.find("bndbox")
              b = [
                  float(xmlbox.find("xmin").text),
                  float(xmlbox.find("ymin").text),
                  float(xmlbox.find("xmax").text),
                  float(xmlbox.find("ymax").text),
              ]
              bboxes.append(b)
              labels.append(cls_id)
  
      bboxes = torch.as_tensor(np.array(bboxes), dtype=torch.float32)
      labels = torch.as_tensor(np.array(labels), dtype=torch.int64)
  
      bboxes = torch.as_tensor(np.array(bboxes), dtype=torch.float32)
      labels = torch.as_tensor(np.array(labels), dtype=torch.int64)
  
      if self.transforms is not None:
          res = self.transforms(
              image=np.array(img), bboxes=bboxes, class_labels=labels
          )
  
      target = {
          "boxes": [torch.Tensor(x) for x in res["bboxes"]],
          "labels": res["class_labels"],
      }
  
      img = res["image"]
  
      return img, target
  
imports: import os
  from functools import partial
  import numpy
  import torch
  from PIL import Image
  import xml.etree.ElementTree
  import torchvision
  from torch import nn
  from torch.utils.data import Dataset, DataLoader
  from torchvision.models.detection import _utils
  from torchvision.models.detection.ssdlite import SSDLiteClassificationHead
  import albumentations
  from albumentations.pytorch import ToTensorV2
  import time
  import math
  from deepchecks.vision.detection_data import DetectionData
  from deepchecks.vision.suites import full_suite
label_desc: Use image node_id 2 for calling the TomatoDataset obj's underlying member method code with example usage: obj.__getitem__(idx) and returns: img,  target