In mechanical engineering, a team is designing a suspension system for a vehicle. They need to model the behavior of the system's shock absorbers under different loading conditions. To accurately predict the system's response, the team wants to fit a mathematical curve to the experimental data obtained from testing the shock absorbers$.$ Additionally, they need to interpolate intermediate values to estimate the system's behavior at specific load levels.

The team has collected discrete data points representing the relationship between the load applied to the shock absorbers and the resulting damping force. They need to perform curve fitting, interpolation, and approximation to analyze the system's behavior.

Your task:

Write a function that performs curve fitting using polynomial regression.

$3$ Inputs: x$\_$values, y$\_$values, and degree

$1$ Output: the coefficients

Write a function to evaluate a polynomial at a given x value $[$see hint above$]$

$2$ Inputs: coefficients, x

$2$ Outputs: coefficients, x

Write a main function that takes in two arguments and prints out the outputs of the above functions.

$3$ Inputs: load, damping$\_$force, polynomial$\_$regression$\_$degree

Use interp$1$d from scipy.interpolate to get each interpolation function, then use $250$ as the interpolation value.

All in python please