Question about bias_variance_decomp return value

[qud0511]

hello. I am a student studying AI model engineering and working on a project. While I was struggling to figure out what bias and variance were, I received great help from mlxtend.
Meanwhile, I have a question that remains unresolved. Because of that, I'm sad that I can't use the bias_variance_decomp function even if I want to.

First question.
If you look at the "mlxtend.evalute.bias_variance_decomp" function, the return value is
avg_bias = np.sum((main_predictions - y_test) ** 2) / y_test.size
avg_var = np.sum((main_predictions - all_pred) ** 2) / all_pred.size
excluding avg_expected_loss.
Can anyone tell me the formula or basis for calculating average bias and average variance? I don't really understand it because it's called average.

Second question. This is more important to me.

Average expected loss: 0.062
Average bias: 0.022
Average variance: 0.040

https://machinelearningmastery.com/calculate-the-bias-variance-trade-off/
MSE: 22.417804
Bias: 20.743874
Variance: 1.673929

In some cases, small values ​​appear in the first and second decimal places, but in other cases, large numbers such as 22 appear.
Is the unit of bias_variance_decomp from 0 to 100?
How should I interpret it depending on the number of digits?
I'm going crazy because I lack the basics and knowledge. I would really appreciate it if you could show me the way.