Calculate standard errors for Maximum Likelihood Estimates using scipy.minimize

Question

I am trying to use scipy.minimize constrained optimization to calculate maximum likelihood estimates in Python. However, I am not sure how to get the standard errors directly from the optimization results of scipy.minimize. As far as I understand, whether the Hessian (and therefore the standard errors) is returned depends on the method for optimization. Would anyone help me as to which "search method" I should use in order to obtain the Hessian?