finish the code in c$++$:

$//$ TODO$3$

float regression$\_$predict$($const float variable$\_$array$[],$ const float regression$\_$weight$[],$ int length$)\{$

$//-------------------------------------------------------$

$//$ This function computes a prediction based on a linear regression model.

$//$

$//$ Inputs:

$//-$ variable$\_$array: a $1$D array of size 'length', containing the input variables.

$//-$ regression$\_$weight: a $1$D array of size 'length', containing the weights for the regression model.

$//-$ length: an integer representing the number of variables in the variable$\_$array and the corresponding regression weights.

$//$

$//$ Outputs:

$//-$ The function returns a float representing the predicted value based on the linear regression model.

$//$

$//$ Functionality:

$//-$ The function computes the prediction as a weighted sum of the input variables and their corresponding regression weights.

$//-$ This is done using the formula:

$//$ prediction $=\backslash$Sigma $($variableArray$\_$i $*$ regressionWeight$\_$i$)$ for i from $0$ to length$-1.$

$//$

$//$ Example:

$//$ If variable$\_$array $=\{2,4,6\}$ and regression$\_$weight $=\{0\mathrm{.}5,1\mathrm{.}0,1\mathrm{.}5\},$

$//-$ The function will compute:

$//$ prediction $=(2*0\mathrm{.}5)+(4*1\mathrm{.}0)+(6*1\mathrm{.}5)=1+4+9=14.$

$//$

$//**$ Your TODO: Implement this function by looping through the variable$\_$array and regression$\_$weight arrays,

$//**$ multiplying each element, and accumulating the result to obtain the final prediction. $**$

$\}$