input: Write a python function to solve an optimization problem with a quadratic objective function and a single quadratic inequality constraint using scipy.optimize. Verify the solution with assert statements.
  assert round(solution.x[0], 2) == 0.0
  assert round(solution.x[1], 2) == 0.0
  assert round(solution.x[2], 2) == 0.0
output: def solve_quadratic_optimization():
      from scipy.optimize import minimize, NonlinearConstraint
      import numpy as np
  
      # Define the quadratic objective function
      def objective(x):
          return x[0]**2 + x[1]**2 + x[2]**2
  
      # Define the quadratic inequality constraint
      def constraint(x):
          return 1 - (x[0]**2 + x[1]**2 + x[2]**2)
  
      # Initial guess
      x0 = np.array([0.5, 0.5, 0.5])
  
      # Nonlinear constraint
      nonlinear_constraint = NonlinearConstraint(constraint, 0, np.inf)
  
      # Minimize the objective function with the constraint
      solution = minimize(objective, x0, constraints=[nonlinear_constraint])
  
      return solution
  
  # Verify the solution
  solution = solve_quadratic_optimization()
  assert round(solution.x[0], 2) == 0.0
  assert round(solution.x[1], 2) == 0.0
  assert round(solution.x[2], 2) == 0.0