Implement dgl check for graphs with different numbers of nodes

[VHolstein]

Is your feature request related to a problem? Please describe.
PHOTONAI-Graph only supports dgl graphs with the same number of node features. If you use dgl graphs with varying numbers of nodes/features (eg, dgl mini dataset), the pipeline does not work.

Describe the solution you'd like
Implement a new architecture that uses an MLP to sample down the number of node features to a fixed size. Also build a check that allows for using node degree from within the different architectures.

Additional context
This will be important for version 2.0

Unit Tests
Reimplement these unit tests that use the dgl mini dataset

    def test_gcn_classifier_dgl(self):
        gat_clf = GCNClassifierModel(nn_epochs=20)
        gat_clf.fit(self.X_dgl, self.y)
        output = gat_clf.predict(self.X_dgl)
        self.assertTrue(np.array_equal(np.array(output.shape), self.y.shape))

    def test_gat_classifier_dgl(self):
        gat_clf = GATClassifierModel(nn_epochs=20)
        gat_clf.fit(self.X_dgl, self.y)
        output = gat_clf.predict(self.X_dgl)
        self.assertEqual(output.shape, self.y.shape)