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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 _neg_bounded_fun(fun, bounds, x, args=()):
 """ Wrapper for bounding and taking the negative of `fun` for the Nelder-Mead algorithm. JIT-compiled in `nopython` mode using Numba. Parameters fun : callable The objective function to be minimized. `fun(x, *args) -> float` where x is an 1-D array with shape (n,) and args is a tuple of the fixed parameters needed to completely specify the function. This function must be JIT-compiled in `nopython` mode using Numba. bounds: ndarray(float, ndim=2) Sequence of (min, max) pairs for each element in x. x : ndarray(float, ndim=1) 1-D array with shape (n,) of independent variables at which `fun` is to be evaluated. args : tuple, optional Extra arguments passed to the objective function. Returns scalar `-fun(x, *args)` if x is within `bounds`, `np.inf` otherwise. """

    if _check_bounds(x, bounds):
        return -fun(x, *args)
    else:
        return np.inf