How to use the ivis.nn.network.base_network function in ivis

def test_base_networks():
    network_names = get_base_networks()
    input_shape = (4,)

    for name in network_names:
        model = base_network(name, input_shape)
        assert isinstance(model, Model)

search_k=self.search_k,
                                       precompute=self.precompute,
                                       verbose=self.verbose)

        loss_monitor = 'loss'
        try:
            triplet_loss_func = triplet_loss(distance=self.distance,
                                             margin=self.margin)
        except KeyError:
            raise ValueError('Loss function `{}` not implemented.'.format(self.distance))

        if self.model_ is None:
            if type(self.model_def) is str:
                input_size = (X.shape[-1],)
                self.model_, anchor_embedding, _, _ = \
                    triplet_network(base_network(self.model_def, input_size),
                                    embedding_dims=self.embedding_dims)
            else:
                self.model_, anchor_embedding, _, _ = \
                    triplet_network(self.model_def,
                                    embedding_dims=self.embedding_dims)

            if Y is None:
                self.model_.compile(optimizer='adam', loss=triplet_loss_func)
            else:
                if is_categorical(self.supervision_metric):
                    if not is_multiclass(self.supervision_metric):
                        if not is_hinge(self.supervision_metric):
                            # Binary logistic classifier
                            if len(Y.shape) > 1:
                                self.n_classes = Y.shape[-1]
                            else: