self.dropout = dropout
```
In the above code, we begin by defining a single convolutional graph layer and then add two module list layers which allow us to pass in a variable number of layers. We then take our empty module list and append a variable number of GraphConv layers followed by a variable number of SAGPooling layers. We finish up our SAGPool definition by adding a JumpingKnowledge Layer, two linear layers, our activation function, and our dropout value. PyTorch’s intuitive syntax allows us to abstract away the complexity of working with state of the art methods like SAG Poolings while also maintaining the common approach to model development we are familiar with.