class_docstr: This is piggybacked into existing scalability tests.
code: def __init__(self, addr: ray._private.worker.RayContext):
      self.addr = addr
  
      # Schedule the actor on the current node (which is a head node).
      current_node_ip = (
          ray._private.worker.global_worker.node_ip_address
      )
      nodes = list_nodes(filters=[("node_ip", "=", current_node_ip)])
      assert (
          len(nodes) > 0
      ), f"{current_node_ip} not found in the cluster"
      node = nodes[0]
      # Schedule on a head node.
      self.tester = DashboardTester.options(
          scheduling_strategy=NodeAffinitySchedulingStrategy(
              node_id=node["node_id"], soft=False
          )
      ).remote()
  
      self.tester.run.remote()
  
imports: import asyncio
  import time
  import urllib
  from typing import Dict, Optional, List
  from pprint import pprint
  import requests
  import ray
  import logging
  from collections import defaultdict
  from ray.util.state import list_nodes
  from ray._private.test_utils import fetch_prometheus_metrics
  from ray.util.scheduling_strategies import NodeAffinitySchedulingStrategy
  from pydantic import BaseModel
  from ray.dashboard.consts import DASHBOARD_METRIC_PORT
  from ray.dashboard.utils import get_address_for_submission_client
label_desc: Use image node_id 1 to create a new DashboardTestAtScale object  with example: obj = DashboardTestAtScale(addr)