code: def step(self):
      x_train, y_train = self.train_data
      x_train, y_train = x_train[:NUM_SAMPLES], y_train[:NUM_SAMPLES]
      x_test, y_test = self.test_data
      x_test, y_test = x_test[:NUM_SAMPLES], y_test[:NUM_SAMPLES]
  
      aug_gen = ImageDataGenerator(
          # set input mean to 0 over the dataset
          featurewise_center=False,
          # set each sample mean to 0
          samplewise_center=False,
          # divide inputs by dataset std
          featurewise_std_normalization=False,
          # divide each input by its std
          samplewise_std_normalization=False,
          # apply ZCA whitening
          zca_whitening=False,
          # randomly rotate images in the range (degrees, 0 to 180)
          rotation_range=0,
          # randomly shift images horizontally (fraction of total width)
          width_shift_range=0.1,
          # randomly shift images vertically (fraction of total height)
          height_shift_range=0.1,
          # randomly flip images
          horizontal_flip=True,
          # randomly flip images
          vertical_flip=False,
      )
  
      aug_gen.fit(x_train)
      batch_size = self.config.get("batch_size", 64)
      gen = aug_gen.flow(x_train, y_train, batch_size=batch_size)
      self.model.fit_generator(
          generator=gen,
          epochs=self.config.get("epochs", 1),
          validation_data=None,
      )
  
      # loss, accuracy
      _, accuracy = self.model.evaluate(x_test, y_test, verbose=0)
      return {"mean_accuracy": accuracy}
  
imports: from __future__ import print_function
  import argparse
  import numpy
  import tensorflow
  from tensorflow.keras.datasets import cifar10
  from tensorflow.keras.layers import Input, Dense, Dropout, Flatten
  from tensorflow.keras.layers import Convolution2D, MaxPooling2D
  from tensorflow.keras.models import Model, load_model
  from tensorflow.keras.preprocessing.image import ImageDataGenerator
  from ray import train, tune
  from ray.tune import Trainable
  from ray.tune.schedulers import PopulationBasedTraining
label_desc: Use image node_id 4 for calling the Cifar10Model obj's underlying member method code with example usage: obj.step() and returns: dict