import tensorflow as tf


    # Tensorboard visualization
    tf.summary.scalar(name='Autoencoder Loss', tensor=autoencoder_loss)
    tf.summary.scalar(name='Discriminator gauss Loss', tensor=dc_g_loss)
    tf.summary.scalar(name='Discriminator categorical Loss', tensor=dc_c_loss)
    tf.summary.scalar(name='Generator Loss', tensor=generator_loss)
    tf.summary.scalar(name='Supervised Encoder Loss', tensor=supervised_encoder_loss)
    tf.summary.histogram(name='Encoder Gauss Distribution', values=encoder_output_latent)
    tf.summary.histogram(name='Real Gauss Distribution', values=real_distribution)
    tf.summary.histogram(name='Encoder Categorical Distribution', values=encoder_output_label)
    tf.summary.histogram(name='Real Categorical Distribution', values=categorial_distribution)
    tf.summary.image(name='Input Images', tensor=input_images, max_outputs=10)
    tf.summary.image(name='Generated Images', tensor=generated_images, max_outputs=10)
    summary_op = tf.summary.merge_all()

    # Saving the model
