class_docstr: `FER2013
  <https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge>`_ Dataset.
  
  Args:
      root (string): Root directory of dataset where directory
          ``root/fer2013`` exists.
      split (string, optional): The dataset split, supports ``"train"`` (default), or ``"test"``.
      transform (callable, optional): A function/transform that takes in a PIL image and returns a transformed
          version. E.g, ``transforms.RandomCrop``
      target_transform (callable, optional): A function/transform that takes in the target and transforms it.
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code: def __getitem__(self, idx: int) -> Tuple[Any, Any]:
      image_tensor, target = self._samples[idx]
      image = Image.fromarray(image_tensor.numpy())
  
      if self.transform is not None:
          image = self.transform(image)
  
      if self.target_transform is not None:
          target = self.target_transform(target)
  
      return image, target
  
file_path: vision/torchvision/datasets/fer2013.py
imports: import csv
  import pathlib
  from typing import Any, Callable, Optional, Tuple
  import torch
  from PIL import Image
  from .utils import check_integrity, verify_str_arg
  from .vision import VisionDataset
label_desc: Use image node_id 3 for calling the FER2013 obj's underlying member method code with example usage: obj.__getitem__(idx) and returns: image,  target