ready to write the train function :D

GraphTransformer.py

             graphs.append(G_sub)
             if G_sub.number_of_nodes() > max_nodes:
                 max_nodes = G_sub.number_of_nodes()
-
     print('Loaded')
+
     return graphs
 
 
     piazche = np.zeros((len(esm), 3))
     for i, (k, v) in enumerate(esm.items()):
         piazche[i][v-1] = 1
+
     return piazche
 
 
         # print(y.shape)
         # print(self.weight.shape)
         y = torch.matmul(y, self.weight.double())
+
         return y
 
 
         relu = self.relu(hidden)
         output = self.fully_connected2(relu)
         output = self.sigmoid(output)
+
         return output
 
 
   def forward(self, x, adj):
     gcn_outputs = self.GCN(x, adj)
     gat_output = self.GAT(gcn_outputs)
-    mlp_output = self.MLP(gat_output)
+    mlp_output = self.MLP(gat_output).reshape(1,-1)
 
-    return output
+    return mlp_output