code: def test_interleaved_distributed_optimization(tmp_path):
      hps = hp_module.HyperParameters()
      hps.Float("a", -1, 1)
      hps.Float("b", -1, 1)
      hps.Float("c", -1, 1)
      hps.Float("d", -1, 1)
  
      def evaluate(hp):
          # Minimum at a=4, b=1, c=1e-3 with score=-1
          return (
              -1 * hp["a"] ** 3 + hp["b"] ** 3 + hp["c"] - abs(hp["d"])
          )
  
      oracle = bo_module.BayesianOptimizationOracle(
          objective=keras_tuner.Objective("score", "min"),
          hyperparameters=hps,
          max_trials=60,
          num_initial_points=2,
      )
      oracle._set_project_dir(tmp_path, "untitled")
  
      # Run 4 trials on 2 tuners
  
      # Start both tuners at the same time
      trial_1 = oracle.create_trial("tuner_0")
      trial_2 = oracle.create_trial("tuner_1")
  
      # tuner_0 finishes trial_1 before tuner_1 finishes
      oracle.update_trial(
          trial_1.trial_id, {"score": evaluate(trial_1.hyperparameters)}
      )
      trial_1.status = "COMPLETED"
      oracle.end_trial(trial_1)
  
      # tuner_0 request a new trial (trial_3)
      trial_3 = oracle.create_trial("tuner_0")
  
      # tuner_1 finishes trial_2
      oracle.update_trial(
          trial_2.trial_id, {"score": evaluate(trial_2.hyperparameters)}
      )
      trial_2.status = "COMPLETED"
      oracle.end_trial(trial_2)
  
      # tuner_1 requests the final new trial (trial_4)
      # the Bayesian optimizer will use ongoing trial_3 to hallucinate
      trial_4 = oracle.create_trial("tuner_1")
  
      # tuner_0 finishes trial_3
      oracle.update_trial(
          trial_3.trial_id, {"score": evaluate(trial_3.hyperparameters)}
      )
      trial_3.status = "COMPLETED"
      oracle.end_trial(trial_3)
  
      # tuner_1 finishes trial_4
      oracle.update_trial(
          trial_4.trial_id, {"score": evaluate(trial_4.hyperparameters)}
      )
      trial_4.status = "COMPLETED"
      oracle.end_trial(trial_4)
  
      assert True
  
imports: import math
  import numpy
  import pytest
  import keras_tuner
  from keras_tuner.backend import keras
  from keras_tuner.engine import hyperparameters
  from keras_tuner.engine import trial
  from keras_tuner.tuners import bayesian
label_desc: Use image node_id 16 for calling a global function with example usage: test_interleaved_distributed_optimization(tmp_path) and returns: unknown