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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 has_resources(self, resources):
 """Returns whether this runner has at least the specified resources. This refreshes the Ray cluster resources if the time since last update has exceeded self._refresh_period. This also assumes that the cluster is not resizing very frequently. """

        if time.time() - self._last_resource_refresh > self._refresh_period:
            self._update_avail_resources()

        currently_available = Resources.subtract(self._avail_resources,
                                                 self._committed_resources)

        have_space = (
            resources.cpu_total() <= currently_available.cpu
            and resources.gpu_total() <= currently_available.gpu and all(
                resources.get_res_total(res) <= currently_available.get(res)
                for res in resources.custom_resources))

        if have_space:
            return True

        can_overcommit = self._queue_trials

        if (resources.cpu_total() > 0 and currently_available.cpu <= 0) or \
           (resources.gpu_total() > 0 and currently_available.gpu <= 0) or \
           any((resources.get_res_total(res_name) > 0
                and currently_available.get(res_name) <= 0)
               for res_name in resources.custom_resources):
            can_overcommit = False  # requested resource is already saturated

        if can_overcommit:
            logger.warning(
                "Allowing trial to start even though the "
                "cluster does not have enough free resources. Trial actors "
                "may appear to hang until enough resources are added to the "
                "cluster (e.g., via autoscaling). You can disable this "
                "behavior by specifying `queue_trials=False` in "
                "ray.tune.run().")
            return True

        return False