def METHOD_NAME(self, x):
  # Pass data through conv1
  x = self.conv1(x)
  # Use the rectified-linear activation function over x
  x = F.relu(x)
  x = self.conv2(x)
  x = F.relu(x)
  # Run max pooling over x
  x = F.max_pool2d(x, 2)
  # Pass data through dropout1
  x = self.dropout1(x)
  # Flatten x with start_dim=1
  x = torch.flatten(x, 1)
  # Pass data through ``fc1``
  x = self.fc1(x)
  x = F.relu(x)
  x = self.dropout2(x)
  x = self.fc2(x)
  # Apply softmax to x 
  output = F.log_softmax(x, dim=1)
  return output