code: def next_frame_emily():
    
    hparams = sv2p_params.next_frame_sv2p()
    hparams.video_num_input_frames = 2
    hparams.video_num_target_frames = 10
    hparams.learning_rate_constant = 1e-4
    seq_length = hparams.video_num_input_frames + hparams.video_num_target_frames
    # The latent_loss_multiplier is divided by the number of frames because
    # the image sequence loss in t2t is averaged instead of added through
    # time as they do in the SVG-LP paper
    hparams.latent_loss_multiplier = 1e-4 / seq_length
    hparams.reward_prediction = False
    hparams.num_iterations_1st_stage = -1
    hparams.num_iterations_2nd_stage = -1
    hparams.optimizer_adam_beta1 = 0.9
    hparams.optimizer_adam_beta2 = 0.999
    hparams.optimizer_adam_epsilon = 1e-08
    hparams.anneal_end = -1
    hparams.clip_grad_norm = 5.0
    hparams.add_hparam("learned_prior", True)
    hparams.add_hparam("z_dim", 64)
    hparams.add_hparam("g_dim", 128)
    hparams.add_hparam("rnn_size", 256)
    hparams.add_hparam("prior_rnn_layers", 1)
    hparams.add_hparam("posterior_rnn_layers", 1)
    hparams.add_hparam("predictor_rnn_layers", 2)
    hparams.add_hparam("has_skips", True)
    hparams.add_hparam("has_batchnorm", True)
    return hparams
docs: Emily's model hparams.