import tensorflow as tf


def _get_generator_inputs(num_images_per_class, num_classes, noise_dims):
  # Since we want a grid of numbers for the conditional generator, manually
  # construct the desired class labels.
  num_images_generated = num_images_per_class * num_classes
  noise = tf.random_normal([num_images_generated, noise_dims])
  labels = [lbl for lbl in range(num_classes) for _
            in range(num_images_per_class)]
  one_hot_labels = tf.one_hot(tf.constant(labels), num_classes)
  return noise, one_hot_labels


if __name__ == '__main__':
  app.run(main)
