How to use the megnet.models.GraphModel function in megnet

https://github.com/materialsvirtuallab/megnet/blob/master/mvl_models/mp-2019.4.1/formation_energy.hdf5

        Args:
            url: (str) url link of the model

        Returns:
            GraphModel
        """
        import urllib.request
        fname = url.split("/")[-1]
        urllib.request.urlretrieve(url, fname)
        urllib.request.urlretrieve(url + ".json", fname + ".json")
        return cls.from_file(fname)


class MEGNetModel(GraphModel):
    """
    Construct a graph network model with or without explicit atom features
    if n_feature is specified then a general graph model is assumed,
    otherwise a crystal graph model with z number as atom feature is assumed.
    """

    def __init__(self,
                 nfeat_edge: int = None,
                 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,

https://github.com/materialsvirtuallab/megnet/blob/master/mvl_models/mp-2019.4.1/formation_energy.hdf5

        Args:
            url: (str) url link of the model

        Returns:
            GraphModel
        """
        import urllib.request
        fname = url.split("/")[-1]
        urllib.request.urlretrieve(url, fname)
        urllib.request.urlretrieve(url + ".json", fname + ".json")
        return cls.from_file(fname)


class MEGNetModel(GraphModel):
    """
    Construct a graph network model with or without explicit atom features
    if n_feature is specified then a general graph model is assumed,
    otherwise a crystal graph model with z number as atom feature is assumed.
    """

    def __init__(self,
                 nfeat_edge: int = None,
                 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,

Class method to load model from
            filename for keras model
            filename.json for additional converters

        Args:
            filename: (str) model file name

        Returns
            GraphModel
        """
        configs = loadfn(filename + '.json')
        from keras.models import load_model
        from megnet.layers import _CUSTOM_OBJECTS
        model = load_model(filename, custom_objects=_CUSTOM_OBJECTS)
        configs.update({'model': model})
        return GraphModel(**configs)

def __init__(self, model_name: str = DEFAULT_MODEL, use_cache: bool = True):
        if isinstance(model_name, str):
            model = MEGNetModel.from_file(model_name)
        elif isinstance(model_name, GraphModel):
            model = model_name
        else:
            raise ValueError('model_name only support str or GraphModel object')

        layers = model.layers
        important_prefix = ['meg', 'set', 'concatenate']
        all_names = [i.name for i in layers if any([i.name.startswith(j) for j in important_prefix])]
        valid_outputs = [i.output for i in layers if any([i.name.startswith(j) for j in important_prefix])]

        outputs = []
        valid_names = []
        for i, j in zip(all_names, valid_outputs):
            if isinstance(j, list):
                for k, l in enumerate(j):
                    valid_names.append(i + '_%d' % k)
                    outputs.append(l)

Class method to load model from
            filename for keras model
            filename.json for additional converters

        Args:
            filename: (str) model file name

        Returns
            GraphModel
        """
        configs = loadfn(filename + '.json')
        from keras.models import load_model
        from megnet.layers import _CUSTOM_OBJECTS
        model = load_model(filename, custom_objects=_CUSTOM_OBJECTS)
        configs.update({'model': model})
        return GraphModel(**configs)