In a simple linear regression $Y_i = \beta_0 + \beta_1 X_i + \epsilon_i$, suppose the model is estimated using weighted least squares (WLS) where weights $w_i = \frac{1}{X_i^2}$. If the original OLS estimator $\hat{\beta}_1$ is biased due to heteroscedasticity, what is the primary purpose of this transformation?