# Plot the adjacency matrix
### White Boxing of Deep Learning
GNNs have been applied to a wide range of domains, such as social networks, bioinformatics, and recommendation systems. The graph representation of a deep learning model can be converted into a GNN by defining the graph structure and the node features, and then using GNN layers to perform the computations. This allows for the integration of the graph structure into the model and can improve the performance and interpretability of the model on graph-structured data. While the use of GNNs can help improve the interpretability of deep learning models on graph-structured data, it is important to note that deep learning models, in general, can still be considered as black boxes. The reason for this is that deep learning models can have a large number of parameters and non-linear operations, making it difficult to fully understand how they arrive at their predictions or decisions. This is particularly true for tasks such as image or speech recognition, where the features learned by the model can be difficult to interpret. However, the use of techniques such as GNNs, attention mechanisms, and explainable AI can help improve the interpretability of deep learning models to some extent. These techniques can provide insights into which parts of the input data are most relevant for the model's decision and can help identify potential biases or errors in the model's predictions. In addition, the accountability of deep learning models can be improved through the use of transparency and regulation. This can include the use of open-source software, standardized evaluation metrics, and ethical guidelines for the use of AI. By promoting transparency and accountability in the development and use of deep learning models, we can help ensure that they are used in a responsible and trustworthy manner. Here is an example program code for training a deep learning model with a GNN using PyTorch:  
import torch  
from torch.nn import Linear  
import torch.nn.functional as F  
from torch_geometric.nn import GCNConv  
class GNN(torch.nn.Module):  
def_init_(self, num_features, num_classes):  
super(GNN, self)_init_()  
self.conv1 = GCNConv(num_features, 16)  
self.conv2 = GCNConv(16, num_classes)  
def forward(self, x, edge_index):  
x = F.relu(self.conv1(x, edge_index))  
x = F.dropout(x, training = self.training)  
x = self.conv2(x, edge_index)  
return F.log_softmax(x, dim = 1)  
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')  
model = GNN(num_features, num_classes).to(device)  
data = data.to(device)  
optimizer = torch.optim.Adam(model.parameters(), lr = 0.01, weight_decay = 5e\(-\)4)criterion = torch.nn.NULLLoss()  
def train():  
model.train()  
optimizer.zero_grad()  
out = model(data.x, data.edge_index)  
loss = criterion(out[data.train_mask], data.y[data.train_mask])  
loss.backward()  
optimizer.step()  
return loss  
def test():  
model.eval()  
with torch.no_grad():  
out = model(data.x, data.edge_index)  
pred = out.argmax(dim = 1)  
acc = pred.eq(data.y[data.test_mask]).sum().item()/data.test_mask.sum().item()  
return acc  
for epoch in range(1, 201):  
loss = train()  
acc = test()  
print(f'Epoch {epoch}, Loss: {loss:4f}, Test Accuracy: {acc:4f}')  
This code defines a GNN model with two GCNConv layers and trains it using the Adam optimizer and NLLLoss criterion. The train() and test() functions handle the training and testing of the model, respectively. Note that this code assumes that the graph data has already been loaded into a PyTorch Geometric Data object (called data in the code), which contains the node features, edge indices, and train/test masks. It is necessary to modify the code accordingly to suit the specific use case. One example of how a Graph Neural Network (GNN) can be used to improve the architecture of a Convolutional Neural Network (CNN) is through the representation of the CNN architecture as a graph. This can be achieved by treating each layer of the CNN as a node in the graph, and connecting the nodes based on their dependencies.