How to use the megnet.activations.softplus2 function in megnet

else:
                model_list.sort(
                    key=lambda m_file: float(m_file.split("_")[3].replace(".hdf5", "")),
                    reverse=True,
                )

            model_file = os.path.join(
                warm_start, "kfold_{}".format(fold), "model", model_list[-1]
            )

            #  Load model from file
            if learning_rate is None:
                full_model = load_model(
                    model_file,
                    custom_objects={
                        "softplus2": softplus2,
                        "Set2Set": Set2Set,
                        "mean_squared_error_with_scale": mean_squared_error_with_scale,
                        "MEGNetLayer": MEGNetLayer,
                    },
                )

                learning_rate = K.get_value(full_model.optimizer.lr)
            # Set up model
            model = MEGNetModel(
                100,
                2,
                nblocks=args.n_blocks,
                nvocal=95,
                npass=args.n_pass,
                lr=learning_rate,
                loss=args.loss,

def make_megnet_model(nfeat_edge: int = None,
                      nfeat_global: int = None,
                      nfeat_node: int = None,
                      nblocks: int = 3,
                      n1: int = 64,
                      n2: int = 32,
                      n3: int = 16,
                      nvocal: int = 95,
                      embedding_dim: int = 16,
                      nbvocal: int = None,
                      bond_embedding_dim: int = None,
                      ngvocal: int = None,
                      global_embedding_dim: int = None,
                      npass: int = 3,
                      ntarget: int = 1,
                      act: Callable = softplus2,
                      is_classification: bool = False,
                      l2_coef: float = None,
                      dropout: float = None,
                      dropout_on_predict: bool = False
                      ) -> Model:
    """Make a MEGNet Model

    Args:
        nfeat_edge: (int) number of bond features
        nfeat_global: (int) number of state features
        nfeat_node: (int) number of atom features
        nblocks: (int) number of MEGNetLayer blocks
        n1: (int) number of hidden units in layer 1 in MEGNetLayer
        n2: (int) number of hidden units in layer 2 in MEGNetLayer
        n3: (int) number of hidden units in layer 3 in MEGNetLayer
        nvocal: (int) number of total element

nfeat_global: int = None,
                 nfeat_node: int = None,
                 nblocks: int = 3,
                 lr: float = 1e-3,
                 n1: int = 64,
                 n2: int = 32,
                 n3: int = 16,
                 nvocal: int = 95,
                 embedding_dim: int = 16,
                 nbvocal: int = None,
                 bond_embedding_dim: int = None,
                 ngvocal: int = None,
                 global_embedding_dim: int = None,
                 npass: int = 3,
                 ntarget: int = 1,
                 act: Callable = softplus2,
                 is_classification: bool = False,
                 loss: str = "mse",
                 metrics: List[str] = None,
                 l2_coef: float = None,
                 dropout: float = None,
                 graph_converter: StructureGraph = None,
                 target_scaler: Scaler = DummyScaler(),
                 optimizer_kwargs: Dict = None,
                 dropout_on_predict: bool = False
                 ):
        """
        Args:
            nfeat_edge: (int) number of bond features
            nfeat_global: (int) number of state features
            nfeat_node: (int) number of atom features
            nblocks: (int) number of MEGNetLayer blocks

nfeat_global: int = None,
                 nfeat_node: int = None,
                 nblocks: int = 3,
                 lr: float = 1e-3,
                 n1: int = 64,
                 n2: int = 32,
                 n3: int = 16,
                 nvocal: int = 95,
                 embedding_dim: int = 16,
                 nbvocal: int = None,
                 bond_embedding_dim: int = None,
                 ngvocal: int = None,
                 global_embedding_dim: int = None,
                 npass: int = 3,
                 ntarget: int = 1,
                 act: Callable = softplus2,
                 is_classification: bool = False,
                 loss: str = "mse",
                 metrics: List[str] = None,
                 l2_coef: float = None,
                 dropout: float = None,
                 graph_converter: StructureGraph = None,
                 target_scaler: Scaler = DummyScaler(),
                 optimizer_kwargs: Dict = None,
                 dropout_on_predict: bool = False
                 ):
        """
        Args:
            nfeat_edge: (int) number of bond features
            nfeat_global: (int) number of state features
            nfeat_node: (int) number of atom features
            nblocks: (int) number of MEGNetLayer blocks

def make_megnet_model(nfeat_edge: int = None,
                      nfeat_global: int = None,
                      nfeat_node: int = None,
                      nblocks: int = 3,
                      n1: int = 64,
                      n2: int = 32,
                      n3: int = 16,
                      nvocal: int = 95,
                      embedding_dim: int = 16,
                      nbvocal: int = None,
                      bond_embedding_dim: int = None,
                      ngvocal: int = None,
                      global_embedding_dim: int = None,
                      npass: int = 3,
                      ntarget: int = 1,
                      act: Callable = softplus2,
                      is_classification: bool = False,
                      l2_coef: float = None,
                      dropout: float = None,
                      dropout_on_predict: bool = False
                      ) -> Model:
    """Make a MEGNet Model

    Args:
        nfeat_edge: (int) number of bond features
        nfeat_global: (int) number of state features
        nfeat_node: (int) number of atom features
        nblocks: (int) number of MEGNetLayer blocks
        n1: (int) number of hidden units in layer 1 in MEGNetLayer
        n2: (int) number of hidden units in layer 2 in MEGNetLayer
        n3: (int) number of hidden units in layer 3 in MEGNetLayer
        nvocal: (int) number of total element