Ok so from my understanding, on this x = F.relu(self.self.fc_cnn_1(x)) ##[batch_size,1024], batch_size is static the output is (512,1024) 512 comes from flattening out the input. Same goes for graph, but here the number of nodes changes so the dimensions of the graph are not same always. So in one forward pass it may come as 200 in an other it may be 400 and so on. The output of the graph network is as follow (batch_size, 1024 (linear layer representation size)) for example it comes out to be like (200,1024) and in the next pass it is (400,1024) and maybe in the next pass it can become (350, 1024) and so on. I want to combine the output of the graph network with the output of the fasterrcnn. so we have two parallel netowrks that at one point combine thier output and process further as one network. Basically I want the faster rcnn to be able have textual features as well when deciding what the object is.
I hope it makes sense 
P.S i am also not sure about these things a lot so please help me out here