class_docstr: A CIFAR-100 data provider.
docstr: Apply Inception data augmentation and preprocessing.
docstr_tok: ["Apply","Inception","data","augmentation","and","preprocessing","."]
code: def _preprocess_data(self, image, label, training, preprocess):
      """Apply Inception data augmentation and preprocessing."""
  
      # Unpack `Element` tuple.
      # image, label = element
  
      if preprocess:
          image_height, image_width = self._shape()[:2]
          if self._params.augmentation == PreprocessingType.BASIC:
              image = image_processing.resize_and_normalize(
                  image, image_height, image_width
              )
              if training:
                  image = image_processing.basic_augmentation(
                      image, image_height, image_width, self._seed
                  )
          else:
              raise ValueError(
                  "Unsupported data augmentation type: `%s`"
                  % self._params.augmentation
              )
  
          if training and self._params.cutout:
              # According to https://arxiv.org/abs/1708.04552, cutting out 16x16
              # works best.
              image = image_processing.cutout(
                  image, pad_size=8, seed=self._seed
              )
  
      # Set shapes so that they are defined.
      image.set_shape(self._shape())
      if label is not None:
          label.set_shape([1])
      return {FEATURES: image}, label
  
file_path: adanet/research/improve_nas/trainer/cifar100.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import functools
  import numpy
  import tensorflow.compat.v1
  from tensorflow.keras.datasets import cifar100
label_desc: Use image node_id 2 for calling the Provider obj's underlying member method code with example usage: obj._preprocess_data(image, label, training, preprocess) and returns: dict,  label