code: def _deploy_bento_runners(
      svc: bentoml.Service,
      runners_deployment_config_map: dict | None = None,
      enable_batching: bool = False,
      batching_config: dict | None = None,
  ) -> dict[str, Deployment]:
      # Deploy BentoML Runners as Ray serve deployments
      runners_deployment_config_map = (
          runners_deployment_config_map or {}
      )
      batching_config = batching_config or {}
      runner_deployments = {}
      for runner in svc.runners:
          runner_deployment_config = (
              runners_deployment_config_map.get(runner.name)
              if runner.name in runners_deployment_config_map
              else {}
          )
          batching_config = (
              batching_config.get(runner.name)
              if runner.name in batching_config
              else {}
          )
          runner_deployment = _get_runner_deployment(
              svc,
              runner.name,
              runner_deployment_config,
              enable_batching,
              batching_config,
          ).bind()
          runner_deployments[runner.name] = runner_deployment
  
      return runner_deployments
  
file_path: BentoML/src/bentoml/_internal/ray/__init__.py
imports: from __future__ import annotations
  import typing
  from functools import partial
  import requests
  from ..._internal.utils import pkg
  import bentoml
  from bentoml import Tag
  from ...exceptions import MissingDependencyException
  from ..runner.container import AutoContainer
  from ..runner.utils import Params
  from .runner_handle import RayRunnerHandle
label_desc: Use image node_id 4 for calling a global function with example usage: _deploy_bento_runners(svc, runners_deployment_config_map, enable_batching, batching_config) and returns: runner_deployments