code: def test_fit_predict_single_param() -> None:
      trials = [
          create_trial(
              value=1.0,
              distributions={
                  "spam": FloatDistribution(-1.0, 1.0),
              },
              params={
                  "spam": 1.0,
              },
          ),
      ]
  
      gp = _BoTorchGaussianProcess()
      gp.fit(trials)
      gp.predict_mean_std(trials)
  
imports: from optuna.distributions import FloatDistribution
  from optuna.terminator.improvement.gp.botorch import _BoTorchGaussianProcess
  from optuna.trial import create_trial
label_desc: Use image node_id 3 for calling a global function with example usage: test_fit_predict_single_param() without return types