code: def next_frame_savp():
    
    hparams = sv2p_params.next_frame_sv2p()
    hparams.add_hparam("z_dim", 8)
    hparams.add_hparam("num_discriminator_filters", 32)
    hparams.add_hparam("use_vae", True)
    hparams.add_hparam("use_gan", False)
    hparams.add_hparam("use_spectral_norm", True)
    hparams.add_hparam("gan_loss", "cross_entropy")
    hparams.add_hparam("gan_loss_multiplier", 0.01)
    hparams.add_hparam("gan_vae_loss_multiplier", 0.01)
    hparams.add_hparam("gan_optimization", "joint")
    hparams.bottom = {
        "inputs": modalities.video_raw_bottom,
        "targets": modalities.video_raw_targets_bottom,
    }
    hparams.loss = {
        "targets": modalities.video_l1_raw_loss,
    }
    hparams.top = {
        "targets": modalities.video_raw_top,
    }
    hparams.latent_loss_multiplier_schedule = "linear"
    hparams.upsample_method = "bilinear_upsample_conv"
    hparams.internal_loss = False
    hparams.reward_prediction = False
    hparams.anneal_end = 100000
    hparams.num_iterations_1st_stage = 0
    hparams.num_iterations_2nd_stage = 50000
    return hparams
docs: SAVP model hparams.