Optimization theory and gradient descent (for Uni- and bi-variate functions). Can you please complete below python code

def find_root(df, eta, max_iter, tol, root): """ This function will return the root and number of gradient descent iterations to solve f(x) Parameters: df (func) : first derivative function eta (float) : learning rate max_iter (int) : maximum number of iterations tol (float) : tolerance root (float) : initial guess for root of the gradient function Returns: root (float) : root of function i (int) : the number of iterations """ # initialize delta delta = float('inf') i = 0 while delta > tol and i < max_iter: #==================================================# # Place your code between here # new_root = root root = ... delta = abs(...) i+=1 #==================================================# return root, i