def _get_generator_inputs(num_images_per_class, num_classes, noise_dims):
    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)
def _get_generator_inputs(num_images_per_class, num_classes, noise_dims):
    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)