code: def _min_lcb(
      trials: List[FrozenTrial],
      gp: BaseGaussianProcess,
      n_params: int,
      n_trials: int,
  ) -> float:
      mean, std = gp.predict_mean_std(trials)
      lower = mean - std * np.sqrt(
          _get_beta(n_params=n_params, n_trials=n_trials)
      )
  
      return float(min(lower))
  
file_path: optuna/optuna/terminator/improvement/gp/base.py
imports: import abc
  from typing import List
  from typing import Tuple
  import numpy
  from optuna._experimental import experimental_class
  from optuna.trial._frozen import FrozenTrial
label_desc: Use image node_id 2 for calling a global function with example usage: _min_lcb(trials, gp, n_params, n_trials) and returns: float