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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 logtrick_minimizer(minimizer):
 r""" Log-Trick decorator for optimizers. This decorator implements the "log trick" for optimizing positive bounded variables. It will apply this trick for any variables that correspond to a Positive() bound. Examples -------- Here is an example where we may want to enforce a particular parameter or parameters to be strictly greater than zero, Now let's enforce that the `w` are positive, Initial values array([ True, True, True], dtype=bool) Note ---- This decorator only works on unstructured optimizers. However, it can be use with structured_minimizer, so long as it is the inner wrapper. """

    @wraps(minimizer)
    def new_minimizer(fun, x0, jac=True, bounds=None, **minimizer_kwargs):

        if bounds is None:
            return minimizer(fun, x0, jac=jac, bounds=bounds,
                             **minimizer_kwargs)

        logx, expx, gradx, bounds = _logtrick_gen(bounds)

        # Intercept gradient
        if callable(jac):
            def new_jac(x, *fargs, **fkwargs):
                return gradx(jac(expx(x), *fargs, **fkwargs), x)
        else:
            new_jac = jac

        # Intercept objective
        if (not callable(jac)) and bool(jac):
            def new_fun(x, *fargs, **fkwargs):
                o, g = fun(expx(x), *fargs, **fkwargs)
                return o, gradx(g, x)
        else:
            def new_fun(x, *fargs, **fkwargs):
                return fun(expx(x), *fargs, **fkwargs)

        # Transform the final result
        result = minimizer(new_fun, logx(x0), jac=new_jac, bounds=bounds,
                           **minimizer_kwargs)
        result['x'] = expx(result['x'])
        return result

    return new_minimizer