class_docstr: CIFAR-100 dataset.
class_docstr_tok: ["CIFAR-100","dataset","."]
code: def _info(self):
      return tfds.core.DatasetInfo(
          builder=self,
          description=(
              "This dataset is just like the CIFAR-10, except it has "
              "100 classes containing 600 images each. There are 500 "
              "training images and 100 testing images per class. The "
              "100 classes in the CIFAR-100 are grouped into 20 "
              'superclasses. Each image comes with a "fine" label '
              '(the class to which it belongs) and a "coarse" label '
              "(the superclass to which it belongs)."
          ),
          features=tfds.features.FeaturesDict(
              {
                  "id": tfds.features.Text(),
                  "image": tfds.features.Image(
                      shape=_CIFAR_IMAGE_SHAPE
                  ),
                  "label": tfds.features.ClassLabel(num_classes=100),
                  "coarse_label": tfds.features.ClassLabel(
                      num_classes=20
                  ),
              }
          ),
          supervised_keys=("image", "label"),
          homepage="https://www.cs.toronto.edu/~kriz/cifar.html",
          citation=_CITATION,
      )
  
file_path: datasets/tensorflow_datasets/image_classification/cifar.py
imports: import collections
  import os
  from etils import epath
  import numpy
  from tensorflow_datasets.core.utils.lazy_imports_utils import tensorflow
  import tensorflow_datasets.public_api
label_desc: Use image node_id 2 for calling the Cifar100 obj's underlying member method code with example usage: obj._info() and returns: tfds