input: Write a python function to solve an optimization problem using scipy's minimize function, with given objective function and constraints.
  assert np.allclose(minimize_problem(), [1.0, 4.74299963, 3.82114998, 1.37940829])
output: def minimize_problem():
      import numpy as np
      from scipy.optimize import minimize
  
      # Define the objective function
      def objective(x):
          return x[0]**2 + x[1]**2 + x[2]**2 + x[3]**2
  
      # Define the constraints
      def constraint1(x):
          return x[0] + x[1] - 5
  
      def constraint2(x):
          return x[2] + x[3] - 5
  
      # Initial guess
      x0 = np.array([1, 1, 1, 1])
  
      # Define the constraints in dictionary format
      cons = (
          {'type': 'eq', 'fun': constraint1},
          {'type': 'eq', 'fun': constraint2}
      )
  
      # Optimize
      solution = minimize(objective, x0, constraints=cons)
  
      return solution.x