how can I turn my linear regression model from univariate into the multvariate?

Question

I have built this univariate linear regression model from scratch and I am conceptually ok with how the multivariate version of it works.

the problem is that how can I code it ?

can you please show it to me by just changing and modifying my code.

thanks.

this is the model:

#THE HYPOTHESIS FUNCTION
def H0(theta0,theta1,X_norm):
  h = [(theta0 + (theta1 * X_norm[i]) ) for i in range(len(X_norm))]
  return h


#THE COST FUNCTION    
def cost_function(h,y_norm):
  cost = (1/(2*len(y_norm))) * sum([(h[i] - y_norm[i] )**2 for i in range(len(y_norm))])
  return cost



#THE GRADIENT DESCNET
def gradient_descent(theta0,theta1,h,X_norm,y_norm):
  a = 1
  theta0 = theta0 - a * (1/len(y_norm)) * sum([ (h[i] - y_norm[i]) for i in range(len(y_norm))])
  theta1 = theta1 - a * (1/len(y_norm)) * sum([ ( (h[i] - y_norm[i] ) * X_norm[i] ) for i in range(len(y_norm))])
  return theta0,theta1

#THE TRAINING STEP
def train(theta0,theta1,X_norm,y_norm,n):
  costs = []
  for i in range(n):
    h = H0(theta0,theta1,X_norm)
    cost = cost_function(h,y_norm)
    theta0,theta1 = gradient_descent(theta0,theta1,h,X_norm,y_norm)
    costs.append(cost)
  return theta0,theta1,costs