class_docstr: A optimzied OxfordIIITPet using libjpeg_turbo to load jpg images.
class_docstr_tok: ["A","optimzied","OxfordIIITPet","using","libjpeg_turbo","to","load","jpg","images","."]
code: def __getitem__(self, idx: int):
      path = str(self._images[idx])
      target: Any = []
      for target_type in self._target_types:
          if target_type == "category":
              target.append(self._labels[idx])
          else:  # target_type == "segmentation"
              target.append(Image.open(self._segs[idx]))
  
      if not target:
          target = None
      elif len(target) == 1:
          target = target[0]
      else:
          target = tuple(target)
  
      if path in self.special_images:
          img = Image.open(path).convert("RGB")
      else:
          if path.endswith(".jpg") or path.endswith(".jpeg"):
              img_str = self._read_image_to_bytes(path)
              img = self._decode_img_libjpeg_turbo(img_str)
          else:
              img = cv2.imread(path)
          img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
      if self.transform:
          img, target = self.transforms(img, target)
  
      img = img.numpy()
      return img.astype("float32"), target
  
file_path: BigDL/python/nano/src/bigdl/nano/pytorch/vision/datasets/oxfordpet_datasets.py
imports: from typing import Union, Callable, Optional
  from turbojpeg import TurboJPEG, TJPF_GRAY
  import torchvision
  import cv2
  import os
  import numpy
  import torch
  from logging import warning
  from os.path import split, join, realpath
  from PIL import Image
  from typing import Any
label_desc: Use image node_id 4 for calling the OxfordIIITPet obj's underlying member method code with example usage: obj.__getitem__(idx) and returns: img,  target