code: def get_max_return(self, weights, returns):
          
  
          def func(weights):
              """The objective function that maximizes returns."""
              return np.dot(weights, returns.values) * -1
  
          constraints = ({'type': 'eq', 'fun': lambda weights: (weights.sum() - 1)})
          solution = self.solve_minimize(func, weights, constraints)
          max_return = solution.fun * -1
  
          # NOTE: `max_risk` is not used anywhere, but may be helpful in the future.
          # allocation = solution.x
          # max_risk = np.matmul(
          #     np.matmul(allocation.transpose(), cov_matrix), allocation
          # )
  
          return max_return
docs: Maximizes the returns of a portfolio.