class_docstr: `MS Coco Detection <https://cocodataset.org/#detection-2016>`_ Dataset.
  
  It requires the `COCO API to be installed <https://github.com/pdollar/coco/tree/master/PythonAPI>`_.
  
  Args:
      root (string): Root directory where images are downloaded to.
      annFile (string): Path to json annotation file.
      transform (callable, optional): A function/transform that takes in a PIL image
          and returns a transformed version. E.g, ``transforms.PILToTensor``
      target_transform (callable, optional): A function/transform that takes in the
          target and transforms it.
      transforms (callable, optional): A function/transform that takes input sample and its target as entry
          and returns a transformed version.
code: def __init__(
      self,
      root: str,
      annFile: str,
      transform: Optional[Callable] = None,
      target_transform: Optional[Callable] = None,
      transforms: Optional[Callable] = None,
  ) -> None:
      super().__init__(root, transforms, transform, target_transform)
      from pycocotools.coco import COCO
  
      self.coco = COCO(annFile)
      self.ids = list(sorted(self.coco.imgs.keys()))
  
file_path: vision/torchvision/datasets/coco.py
imports: import os.path
  from typing import Any, Callable, List, Optional, Tuple
  from PIL import Image
  from .vision import VisionDataset
label_desc: Use image node_id 1 to create a new CocoDetection object from inherited base classes: VisionDataset with example: obj = CocoDetection(root, annFile, transform, target_transform, transforms)