code: def _neg_bounded_fun(fun, bounds, x, args=()):
      
      if _check_bounds(x, bounds):
          return -fun(x, *args)
      else:
          return np.inf
docs: 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.