code: def _is_log_scale(trials: list[FrozenTrial], param: str) -> bool:
      for trial in trials:
          if param in trial.params:
              dist = trial.distributions[param]
  
              if isinstance(dist, (FloatDistribution, IntDistribution)):
                  if dist.log:
                      return True
  
      return False
  
imports: from __future__ import annotations
  import json
  from typing import Any
  from typing import Callable
  from typing import cast
  from typing import Sequence
  import warnings
  import numpy
  import optuna
  from optuna.distributions import CategoricalDistribution
  from optuna.distributions import FloatDistribution
  from optuna.distributions import IntDistribution
  from optuna.study import Study
  from optuna.study._study_direction import StudyDirection
  from optuna.trial import FrozenTrial
  from optuna.visualization import _plotly_imports
label_desc: Use image node_id 3 for calling a global function with example usage: _is_log_scale(trials, param) and returns: False, True