class_docstr: Test convergence in gaussian process.
class_docstr_tok: ["Test","convergence","in","gaussian","process","."]
code: def _testConvergence(self, searcher, top=3, patience=20):
      # This is the space of parameters to explore
      space = {"x": tune.uniform(0, 20)}
  
      resources_per_trial = {"cpu": 1, "gpu": 0}
  
      analysis = tune.run(
          loss,
          metric="loss",
          mode="min",
          stop=ExperimentPlateauStopper(
              metric="loss", top=top, patience=patience
          ),
          search_alg=searcher,
          config=space,
          num_samples=max(100, patience),  # Number of iterations
          resources_per_trial=resources_per_trial,
          raise_on_failed_trial=False,
          fail_fast=True,
          reuse_actors=True,
          verbose=1,
      )
      print(
          f"Num trials: {len(analysis.trials)}. "
          f"Best result: {analysis.best_config['x']}"
      )
  
      return analysis
  
imports: import math
  import numpy
  import pytest
  import ray
  from ray import train, tune
  from ray.tune.stopper import ExperimentPlateauStopper
  from ray.tune.search import ConcurrencyLimiter
  import unittest
label_desc: Use image node_id 3 for calling the ConvergenceTest obj's underlying member method code with example usage: obj._testConvergence(searcher, top, patience) and returns: analysis