code: def validate_config(config: Dict[str, Any]) -> None:
      """Required Dicts indicate that no extra fields can be introduced."""
      if not isinstance(config, dict):
          raise ValueError(
              "Config {} is not a dictionary".format(config)
          )
  
      schema_path = os.path.join(
          os.path.dirname(ray.autoscaler.__file__), "ray-schema.json"
      )
      with open(schema_path) as f:
          schema = json.load(f)
  
      try:
          import jsonschema
      except (ModuleNotFoundError, ImportError) as e:
          # Don't log a warning message here. Logging be handled by upstream.
          raise e from None
  
      try:
          jsonschema.validate(config, schema)
      except jsonschema.ValidationError as e:
          raise e from None
  
      # Detect out of date defaults. This happens when the autoscaler that filled
      # out the default values is older than the version of the autoscaler that
      # is running on the cluster.
      if "cluster_synced_files" not in config:
          raise RuntimeError(
              "Missing 'cluster_synced_files' field in the cluster "
              "configuration. This is likely due to the Ray version running "
              "in the cluster {ray_version} is greater than the Ray version "
              "running on your laptop. Please try updating Ray on your local "
              "machine and make sure the versions match.".format(
                  ray_version=ray.__version__
              )
          )
  
      if "available_node_types" in config:
          if "head_node_type" not in config:
              raise ValueError(
                  "You must specify `head_node_type` if `available_node_types is set."
              )
          if (
              config["head_node_type"]
              not in config["available_node_types"]
          ):
              raise ValueError(
                  "`head_node_type` must be one of `available_node_types`."
              )
  
          sum_min_workers = sum(
              config["available_node_types"][node_type].get(
                  "min_workers", 0
              )
              for node_type in config["available_node_types"]
          )
          if sum_min_workers > config["max_workers"]:
              raise ValueError(
                  "The specified global `max_workers` is smaller than the "
                  "sum of `min_workers` of all the available node types."
              )
  
imports: import collections
  import copy
  import hashlib
  import json
  import logging
  import os
  import threading
  from dataclasses import dataclass
  from datetime import datetime
  from io import StringIO
  from numbers import Number, Real
  from typing import Any, Dict, List, Optional, Tuple, Union
  import ray
  import ray._private.services
  from ray._private.utils import PLACEMENT_GROUP_INDEXED_BUNDLED_RESOURCE_PATTERN, PLACEMENT_GROUP_WILDCARD_RESOURCE_PATTERN
  from ray.autoscaler._private import constants
  from ray.autoscaler._private.cli_logger import cli_logger
  from ray.autoscaler._private.docker import validate_docker_config
  from ray.autoscaler._private.local.config import prepare_local
  from ray.autoscaler._private.providers import _get_default_config
  from ray.autoscaler.tags import NODE_TYPE_LEGACY_HEAD, NODE_TYPE_LEGACY_WORKER
label_desc: Use image node_id 2 for calling a global function with example usage: validate_config(config) without return types