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","."]
docstr: Create a OxfordIIITPet.
  
  :param root: A string represting the root directory path.
  :param transform: A function/transform that takes in an ndarray image
      and returns a transformed version. E.g, ``transforms.RandomCrop``
  :param target_transform: A function/transform that takes in the
      target and transforms it.
  :param download: If True, downloads the dataset from the internet
docstr_tok: ["Create","a","OxfordIIITPet",".",":","param","root",":","A","string","represting","the","root","directory","path",".",":","param","transform",":","A","function\/transform","that","takes","in","an","ndarray","image","and","returns","a","transformed","version",".","E.g",",","``","transforms.RandomCrop","``",":","param","target_transform",":","A","function\/transform","that","takes","in","the","target","and","transforms","it",".",":","param","download",":","If","True",",","downloads","the","dataset","from","the","internet"]
code: def __init__(
      self,
      root: str,
      transform: Optional[Callable] = None,
      target_transform: Optional[Callable] = None,
      download: bool = False,
  ):
      """
      Create a OxfordIIITPet.
  
      :param root: A string represting the root directory path.
      :param transform: A function/transform that takes in an ndarray image
          and returns a transformed version. E.g, ``transforms.RandomCrop``
      :param target_transform: A function/transform that takes in the
          target and transforms it.
      :param download: If True, downloads the dataset from the internet
      """
      super(OxfordIIITPet, self).__init__(
          root,
          transform=transform,
          target_transform=target_transform,
          download=download,
      )
      self.jpeg: Optional[TurboJPEG] = None
      self.special_images = []
      for image in SPECIAL_IMAGES:
          self.special_images.append(os.path.join(root, image))
  
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 1 to create a new OxfordIIITPet object from inherited base classes: torchvision.datasets with example: obj = OxfordIIITPet(root, transform, target_transform, download)