Gradient for starting Backpropagation
This is because of the loss assumption is the (Mean) Squared Error $\mathcal{L} = (\hat{y} - y)^2$ and the derivative is

$$
\frac{\partial}{\partial \hat{y}} \mathcal{L} = 2 (\hat{y} - y)
$$

which is then passed "backward" for use in the chain rule.