code: def log_likelihood(self, y, quiet=False):
          
          if not self.recompute(quiet=quiet):
              return -np.inf
          try:
              mu = self._call_mean(self._x)
          except ValueError:
              if quiet:
                  return -np.inf
              raise
          r = np.ascontiguousarray(self._check_dimensions(y) - mu,
                                   dtype=np.float64)
          ll = self._const - 0.5 * self.solver.dot_solve(r)
          return ll if np.isfinite(ll) else -np.inf
docs: Compute the logarithm of the marginalized likelihood of a set of
          observations under the Gaussian process model. You must call
          :func:`GP.compute` before this function.
  
          :param y: ``(nsamples, )``
              The observations at the coordinates provided in the ``compute``
              step.
  
          :param quiet:
              If ``True`` return negative infinity instead of raising an
              exception when there is an invalid kernel or linear algebra
              failure. (default: ``False``)