regression - How to compute a Fama-Macbeth R-Squared (R2)?

I'm reaching out regarding the R-Squared of a Fama-Macbeth regression. This is often reported in econometric results but I have yet to find a good explanation of how it is computed.

Specifically, if I consider the second stage of a Fama-Macbeth regression, where we are potentially running hundreds of regressions, how are the R-Squareds of these hundreds of regressions aggregated into a final R-Squared for the entire procedure? I understand that the coefficients are aggregated by a simple averaging, but was unclear about the R-Squareds.

I understand that there are codes to do this, but am trying to understand what's under the hood.

Thanks!

EDIT:

From the Fama-Macbeth regression we specify a model where each return $y_{i,t}$ of portfolio $i$ in time period $t$ can be priced by: $$ y_{i,t}=\gamma_0 + \gamma_1 \beta_{1, i}+ \gamma_2 \beta_{2, i} + \dots + \gamma_j \beta_{N,i} $$

where $N$ represents the total number of factors.

When calculating the predicted values to calculate our $R^2$, do we take the residuals of each time period or the mean portfolio returns, i.e. are our residuals $y_{i,t}-\hat{y}_{i,t}$ ($i \times t$ number of resids) or only $y_{i}-\hat{y}_{i}$ (i number of resids).