Please help code this in python. Thank you.

In this question, we are going to consider the simple linear mode; y=x where we are interested in estimating the value from some set of x and y values. To do this, we will first focus on the mean squared error: MSE=N1i=1N(yixi)2 First, let's start out by generating some data for this problem set. Sample n=30 values from a uniform distribution between 0 and 10 . From these x values, we will multiply by a true theta value true=1.2 and add 30 random draws from a standard normal to these x values to get y values. Then plot the results on a scatter plot. import numpy as np import matplotlib.pyplot as plt from scipy import stats from math import factorial np.random.seed( 121 ) \# Let's set some parameters theta =1.2 n_samples =30 \# Draw x from a uniform distribution over [0,10) and noise from a standard normal distribution, then calculate y \#ans here Now, let's create a function called mse, which will use inputs x,y, and theta_hat, and from those inputs calculate the mean squared error. Fill out the empty variables y_hat and mse to complete the function. Then write a loop to call the function for 3 theta_hat values [0.75,1.0,1.5]. Use the x and y samples from the cell above as your inputs to this function! def mse(x, y, theta_hat): "" "Compute the mean squared error Args: x (ndarray): An array of shape (samples, ) that contains the input values. y (ndarray): An array of shape (samples, ) that contains the corresponding measurement values to the inputs. theta hat (float): An estimate of the slope parameter Returns: float: The mean squared error of the data with the estimated parameter. "" \#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\# \#\# Enter your code below \#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\# \# Compute the estimated y \#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\# \# Compute mean squared error \#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\# return mse \#\#\#\# Call the function with the 3 given theta_hat values below \#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\# \#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#