code: def _get_beta(
      n_params: int, n_trials: int, delta: float = 0.1
  ) -> float:
      beta = 2 * np.log(
          n_params * n_trials**2 * np.pi**2 / 6 / delta
      )
  
      # The following div is according to the original paper: "We then further scale it down
      # by a factor of 5 as defined in the experiments in Srinivas et al. (2010)"
      beta /= 5
  
      return beta
  
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 3 for calling a global function with example usage: _get_beta(n_params, n_trials, delta) and returns: beta