code: def next_frame_base():
    
    hparams = common_hparams.basic_params1()
    # Loss cutoff.
    hparams.add_hparam("video_modality_loss_cutoff", 0.01)
    # Additional resizing the frames before feeding them to model.
    hparams.add_hparam("preprocess_resize_frames", None)
    # How many data points to suffle. Ideally should be part of problem not model!
    hparams.add_hparam("shuffle_buffer_size", 128)
    # Tiny mode. For faster tests.
    hparams.add_hparam("tiny_mode", False)
    # In case a model supports smaller/faster version.
    hparams.add_hparam("small_mode", False)
    # In case a model has stochastic version.
    hparams.add_hparam("stochastic_model", False)
    # Internal loss for recurrent models.
    hparams.add_hparam("internal_loss", True)
    # choose from: concat, multiplicative, multi_additive
    hparams.add_hparam("action_injection", "multi_additive")
    # Scheduled sampling method. Choose between
    # ground_truth_only, prediction_only, prob, count, prob_inverse_exp.
    hparams.add_hparam("scheduled_sampling_mode", "prediction_only")
    hparams.add_hparam("scheduled_sampling_decay_steps", 10000)
    hparams.add_hparam("scheduled_sampling_max_prob", 1.0)
    hparams.add_hparam("scheduled_sampling_k", 900.0)
    return hparams
docs: Common HParams for next_frame models.