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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def structured_minimizer(minimizer):
 r""" Allow an optimizer to accept nested sequences of Parameters to optimize. This decorator can intepret the :code:`Parameter` objects in `btypes.py`, and can accept nested sequences of *any* structure of these objects to optimise! It can also optionally evaluate *random starts* (i.e. random starting candidates) if the parameter objects have been initialised with distributions. For this, two additional parameters are exposed in the :code:`minimizer` interface. Parameters fun : callable objective function that takes in arbitrary ndarrays, floats or nested sequences of these. parameters : (nested) sequences of Parameter objects Initial guess of the parameters of the objective function nstarts : int, optional The number random starting candidates for optimisation to evaluate. This will only happen for :code:`nstarts > 0` and if at least one :code:`Parameter` object is random. random_state : None, int or RandomState, optional random seed Examples -------- Define a cost function that returns a pair. The first element is the cost value and the second element is the gradient represented by a tuple. Even if the cost is a function of a single variable, the gradient must be a tuple containing one element. Augment the Scipy optimizer to take structured inputs Constant Initial values Random Initial values """

    @wraps(minimizer)
    def new_minimizer(fun, parameters, jac=True, args=(), nstarts=0,
                      random_state=None, **minimizer_kwargs):

        (array1d, fbounds), shapes = flatten(
            parameters,
            hstack=bt.hstack,
            shape=bt.shape,
            ravel=partial(bt.ravel, random_state=random_state)
        )

        # Find best random starting candidate if we are doing random starts
        if nstarts > 0:
            array1d = _random_starts(
                fun=fun,
                parameters=parameters,
                jac=jac,
                args=args,
                nstarts=nstarts,
                random_state=random_state
            )

        # Wrap function calls to work with wrapped minimizer
        flatten_args_dec = flatten_args(shapes)
        new_fun = flatten_args_dec(fun)

        # Wrap gradient calls to work with wrapped minimizer
        if callable(jac):
            new_jac = flatten_args_dec(jac)
        else:
            new_jac = jac
            if bool(jac):
                new_fun = flatten_func_grad(new_fun)

        result = minimizer(new_fun, array1d, jac=new_jac, args=args,
                           bounds=fbounds, **minimizer_kwargs)
        result['x'] = tuple(unflatten(result['x'], shapes))

        if bool(jac):
            result['jac'] = tuple(unflatten(result['jac'], shapes))

        return result

    return new_minimizer