Unbiased Estimator of $\sigma^{2}$ from Error (disturbance) term

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How can I show that

$E[\sum_{i=1}^{n}u_{i}^{2}] = n\sigma^2$

to prove that

$ \sum u_{i}^{2} $ is an unbiased estimator of $n\sigma^{2}$?

Let $u$ denote the error (disturbance) term from the simple linear regression model $y_{i} = \beta_{0} + \beta_{1}x_{i} + u_{i} $