code: def get_basic_ray_cr() -> dict:
      """Returns the example Ray CR included in the Ray documentation,
      modified to include a GPU worker group.
      """
      cr_path = str(
          Path(__file__).resolve().parents[2]
          / "autoscaler"
          / "kuberay"
          / "ray-cluster.complete.yaml"
      )
      config = yaml.safe_load(open(cr_path).read())
      gpu_group = copy.deepcopy(config["spec"]["workerGroupSpecs"][0])
      gpu_group["groupName"] = "gpu-group"
      gpu_group["template"]["spec"]["containers"][0]["resources"][
          "limits"
      ].setdefault("nvidia.com/gpu", 3)
      gpu_group["maxReplicas"] = 200
      config["spec"]["workerGroupSpecs"].append(gpu_group)
      return config
  
imports: import copy
  from pathlib import Path
  import requests
  from typing import Any, Dict, Optional
  from unittest import mock
  import platform
  import pytest
  import yaml
  from ray.autoscaler._private.kuberay.autoscaling_config import _derive_autoscaling_config_from_ray_cr, AutoscalingConfigProducer, _round_up_k8s_quantity
label_desc: Use image node_id 1 for calling a global function with example usage: get_basic_ray_cr() and returns: config