$1)$ Create a function to compute second derivative of a function using combination of Forward Finite Difference O$($h$2),$Backward Finite Difference O$($h$2)$ and Centered Finite Difference O$($h$2)$ formulations as follows:

function $[$ydd$,$newx,newy$]=$ myGradient$2($y$,$x$)$

$\%$ This function computes second derivative numerically

$\%$ Input:

$\%=====$

$\%$ y $=$ vector of dependent variable function values

$\%$ x $=$ vector of independent variable values

$\%$ Output:

$\%=======$

$\%$ ydd $=$ numerical $2$nd derivative

$\%$ newx $=$ interpolated x values $($if needed$)$

$\%$ new $=$ interpolated y values $($if needed$)$

$($a$)$ x and y must be equal size column vectors with at least $4$ data points in each

$($b$)$ Function determines the minimum difference $(\backslash$Delta x$)$ from the input x vector

$($c$)$ Function uses interp$1()$ to generate uniformly spaced x and y vectors with h $=\backslash$Delta x

$($d$)$ Function uses forward difference $((2$

$))$ for the first data point, backward difference $((2$

$))$ for the last data

points, and centered difference $((2$

$))$ for the interior points.