460.460. Graph Neural Network

Graph neural networks (GNNs) are a type of neural network designed to handle graph-structured data. They work by aggregating information from neighboring nodes and updating the node representations accordingly. In this problem, you will implement a basic GNN using the graph convolutional layer.

**Example:** Suppose we have a graph with three nodes and two edges. We can use the graph convolutional layer to update the node representations based on the neighboring nodes.

**Constraints:** You must use NumPy to implement the graph convolutional layer. You must also use a specific activation function to update the node representations.