code: def prep_bbox(
      sess,
      x,
      y,
      x_train,
      y_train,
      x_test,
      y_test,
      nb_epochs,
      batch_size,
      learning_rate,
      rng,
      nb_classes=10,
      img_rows=28,
      img_cols=28,
      nchannels=1,
  ):
      """
      Define and train a model that simulates the "remote"
      black-box oracle described in the original paper.
      :param sess: the TF session
      :param x: the input placeholder for MNIST
      :param y: the ouput placeholder for MNIST
      :param x_train: the training data for the oracle
      :param y_train: the training labels for the oracle
      :param x_test: the testing data for the oracle
      :param y_test: the testing labels for the oracle
      :param nb_epochs: number of epochs to train model
      :param batch_size: size of training batches
      :param learning_rate: learning rate for training
      :param rng: numpy.random.RandomState
      :return:
      """
  
      # Define TF model graph (for the black-box model)
      nb_filters = 64
      model = ModelBasicCNN("model1", nb_classes, nb_filters)
      loss = CrossEntropy(model, smoothing=0.1)
      predictions = model.get_logits(x)
      print("Defined TensorFlow model graph.")
  
      # Train an MNIST model
      train_params = {
          "nb_epochs": nb_epochs,
          "batch_size": batch_size,
          "learning_rate": learning_rate,
      }
      train(sess, loss, x_train, y_train, args=train_params, rng=rng)
  
      # Print out the accuracy on legitimate data
      eval_params = {"batch_size": batch_size}
      accuracy = model_eval(
          sess, x, y, predictions, x_test, y_test, args=eval_params
      )
      print(
          "Test accuracy of black-box on legitimate test "
          "examples: " + str(accuracy)
      )
  
      return model, predictions, accuracy
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  import functools
  import logging
  import numpy
  from six.moves import xrange
  import tensorflow
  from cleverhans.attacks import FastGradientMethod
  from cleverhans.utils_tf import jacobian_graph, jacobian_augmentation
  from cleverhans.compat import flags
  from cleverhans.dataset import MNIST
  from cleverhans.initializers import HeReLuNormalInitializer
  from cleverhans.loss import CrossEntropy
  from cleverhans.model import Model
  from cleverhans.train import train
  from cleverhans.utils import set_log_level
  from cleverhans.utils import TemporaryLogLevel
  from cleverhans.utils import to_categorical
  from cleverhans.utils_tf import model_eval, batch_eval
  from cleverhans.model_zoo.basic_cnn import ModelBasicCNN
label_desc: Use image node_id 2 for calling a global function with example usage: prep_bbox(sess, x, y, x_train, y_train, x_test, y_test, nb_epochs, batch_size, learning_rate, rng, nb_classes, img_rows, img_cols, nchannels) and returns: model,  predictions,  accuracy