CNN adding layers

class DeepCNN(nn.Module):
    def __init__(self, num_classes=5):
        super(DeepCNN, self).__init__()
        self.cnn_layers = nn.Sequential(
            nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1), # Conv Layer 1
            nn.ReLU(),
            nn.BatchNorm2d(32),
            nn.MaxPool2d(kernel_size=2, stride=2),  # Downsampling
            
            nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1), # Conv Layer 2
            nn.ReLU(),
            nn.BatchNorm2d(64),
            nn.MaxPool2d(kernel_size=2, stride=2),  # Downsampling

            nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1), # Conv Layer 3 (new)
            nn.ReLU(),
            nn.BatchNorm2d(128),
            nn.MaxPool2d(kernel_size=2, stride=2),  # Downsampling
            
            nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1), # Conv Layer 4 (new)
            nn.ReLU(),
            nn.BatchNorm2d(256),
            nn.MaxPool2d(kernel_size=2, stride=2),  # Downsampling
        )
        
        self.fc_layers = nn.Sequential(
            nn.Flatten(),
            nn.Linear(256 * 8 * 8, 512),  # Fully connected layer
            nn.ReLU(),
            nn.Dropout(0.5),
            nn.Linear(512, num_classes)
        )

    def forward(self, x):
        x = self.cnn_layers(x)
        x = self.fc_layers(x)
        return x