code: def bottleneck_layer(inputs,
                       hparams,
                       name="discrete_bottleneck"):
    
    [
        latents_dense,
        latents_discrete,
        extra_loss,
        embed_fn,
        _,
    ] = hparams.bottleneck(inputs=inputs,
                           filter_size=hparams.compress_filter_size,
                           name=name,
                           mode=hparams.mode)
    if DO_SUMMARIES:
      tf.summary.histogram("discrete_latents",
                           tf.reshape(latents_discrete, [-1]))
    return latents_dense, latents_discrete, extra_loss, embed_fn
docs: Computes latents given inputs (typically, compressed targets).