# Carlification of the MSE loss sum symbol

So I have a question regarding the MSE loss on the application of a Neural Network.
Loss function: $$\text{MSE} = \frac{1}{2} \sum_{i=1}^{n} (Y_i - \hat{Y_i}) ^ 2$$
I am wondering for what the $$\sum_{i=1}^{n}$$ stands.

1. Do I sum over the loss of all training examples for each output node in my Neural Network?
2. Or do I use a single training example and sum over all Neural network output nodes?
3. Or do I both and sum over all training examples and over all output nodes?

I want to use the MSE loss later than for updating my weights in the Neural Network. What would I do for that?