code: def next_frame_basic_stochastic_discrete():
    
    hparams = basic_deterministic_params.next_frame_sampling()
    hparams.batch_size = 4
    hparams.video_num_target_frames = 6
    hparams.scheduled_sampling_mode = "prob_inverse_lin"
    hparams.scheduled_sampling_decay_steps = 40000
    hparams.scheduled_sampling_max_prob = 1.0
    hparams.dropout = 0.15
    hparams.filter_double_steps = 3
    hparams.hidden_size = 96
    hparams.learning_rate_constant = 0.002
    hparams.learning_rate_warmup_steps = 2000
    hparams.learning_rate_schedule = "linear_warmup * constant"
    hparams.concat_internal_states = True
    hparams.video_modality_loss_cutoff = 0.03
    hparams.add_hparam("bottleneck_bits", 128)
    hparams.add_hparam("bottleneck_noise", 0.1)
    hparams.add_hparam("discretize_warmup_steps", 40000)
    hparams.add_hparam("latent_rnn_warmup_steps", 40000)
    hparams.add_hparam("latent_rnn_max_sampling", 0.5)
    hparams.add_hparam("latent_use_max_probability", 0.8)
    hparams.add_hparam("full_latent_tower", False)
    hparams.add_hparam("latent_predictor_state_size", 128)
    hparams.add_hparam("latent_predictor_temperature", 1.0)
    hparams.add_hparam("complex_addn", True)
    hparams.add_hparam("recurrent_state_size", 64)
    return hparams
docs: Basic 2-frame conv model with stochastic discrete latent.