class_docstr: This is piggybacked into existing scalability tests.
docstr: Get the result from the test.
  
  Returns:
      A tuple of success, and the result (Result object).
docstr_tok: ["Get","the","result","from","the","test",".","Returns",":","A","tuple","of","success",",","and","the","result","(","Result","object",")","."]
code: def get_result(self):
      """Get the result from the test.
  
      Returns:
          A tuple of success, and the result (Result object).
      """
      try:
          result = ray.get(self.tester.get_result.remote(), timeout=60)
      except ray.exceptions.GetTimeoutError:
          return Result(success=False)
  
      # Get the memory usage.
      dashboard_export_addr = "{}:{}".format(
          self.addr["raylet_ip_address"], DASHBOARD_METRIC_PORT
      )
      metrics = fetch_prometheus_metrics([dashboard_export_addr])
      memories = []
      for name, samples in metrics.items():
          if name == "ray_component_uss_mb":
              for sample in samples:
                  if sample.labels["Component"] == "dashboard":
                      memories.append(sample.value)
  
      return Result(
          success=True,
          result=result,
          memory_mb=max(memories) if memories else None,
      )
  
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 2 for calling the DashboardTestAtScale obj's underlying member method code with example usage: obj.get_result() and returns: Result, Result