Use this code segment to create $2$D synthetic data

import numpy as np

from sklearn.datasets import make$\_$blobs

import matplotlib.pyplot as plt

transformation $=[[0\mathrm{.}60834549,-0\mathrm{.}63667341],[[-0\mathrm{.}40887718,0\mathrm{.}85253229]]$

D$,$ y $=$ make$\_$blobs$($n$\_$samples$=100,$ centers$=1,$ random$\_$state$=42)$

X $=$ np$.$dot$($D$,$ transformation$)$

plt$.$show$($plt$.$scatter$($X$[$: $,0],$ X$[$: $,1]))$

Using the data X which is not mean$-$centered, compute PCA. The recompute PCA after mean$-$centering X$.$ For both cases, plot data along with the first eigenvector. Additionally, plot the two eigenvectors in a single figure and comment on it$.$