code: # coding=utf-8
  # Copyright 2022 The TensorFlow GAN Authors.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  """Evaluates a conditional TF-GAN trained MNIST model."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from absl import app
  from absl import flags
  
  import tensorflow.compat.v1 as tf
  from tensorflow_gan.examples.mnist import conditional_eval_lib
  
  flags.DEFINE_string('checkpoint_dir', '/tmp/mnist/',
                      'Directory where the model was written to.')
  
  flags.DEFINE_string('eval_dir', '/tmp/mnist/',
                      'Directory where the results are saved to.')
  
  flags.DEFINE_integer('num_images_per_class', 10,
                       'Number of images to generate per class.')
  
  flags.DEFINE_integer('noise_dims', 64,
                       'Dimensions of the generator noise vector')
  
  flags.DEFINE_integer(
      'max_number_of_evaluations', None,
      'Number of times to run evaluation. If `None`, run '
      'forever.')
  
  flags.DEFINE_boolean('write_to_disk', True, 'If `True`, run images to disk.')
  
  FLAGS = flags.FLAGS
  
  
  def main(_):
    hparams = conditional_eval_lib.HParams(FLAGS.checkpoint_dir, FLAGS.eval_dir,
                                           FLAGS.num_images_per_class,
                                           FLAGS.noise_dims,
                                           FLAGS.max_number_of_evaluations,
                                           FLAGS.write_to_disk)
    conditional_eval_lib.evaluate(hparams, run_eval_loop=True)
  
  
  if __name__ == '__main__':
    tf.disable_v2_behavior()
    app.run(main)
  
apis: tensorflow.compat.v1.disable_v2_behavior