Add CNN class and integrate it into main.py and api.py (✓ Sandbox Passed)

src/cnn.py

@@ -0,0 +1,48 @@
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import DataLoader
+from torchvision import datasets, transforms
+
+
+class CNN(nn.Module):
+    def __init__(self):
+        super(CNN, self).__init__()
+        self.conv1 = nn.Conv2d(1, 32, kernel_size=5)
+        self.conv2 = nn.Conv2d(32, 64, kernel_size=5)
+        self.pool = nn.MaxPool2d(2, 2)
+        self.fc1 = nn.Linear(64 * 4 * 4, 128)
+        self.fc2 = nn.Linear(128, 10)
+
+    def forward(self, x):
+        x = self.pool(nn.functional.relu(self.conv1(x)))
+        x = self.pool(nn.functional.relu(self.conv2(x)))
+        x = x.view(-1, 64 * 4 * 4)
+        x = nn.functional.relu(self.fc1(x))
+        x = self.fc2(x)
+        return nn.functional.log_softmax(x, dim=1)
+
+    def train_cnn(self, trainloader, epochs=3):
+        optimizer = optim.SGD(self.parameters(), lr=0.01)
+        criterion = nn.NLLLoss()
+
+        for epoch in range(epochs):
+            for images, labels in trainloader:
+                optimizer.zero_grad()
+                output = self(images)
+                loss = criterion(output, labels)
+                loss.backward()
+                optimizer.step()
+
+        torch.save(self.state_dict(), "mnist_cnn_model.pth")
+
+
+transform = transforms.Compose(
+    [transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))]
+)
+
+trainset = datasets.MNIST(".", download=True, train=True, transform=transform)
+trainloader = DataLoader(trainset, batch_size=64, shuffle=True)
+
+cnn = CNN()
+cnn.train_cnn(trainloader)