code: def test_is_log_scale() -> None:
      study = create_study()
      study.add_trial(
          create_trial(
              value=0.0,
              params={"param_linear": 1.0},
              distributions={
                  "param_linear": FloatDistribution(0.0, 3.0)
              },
          )
      )
      study.add_trial(
          create_trial(
              value=2.0,
              params={"param_linear": 2.0, "param_log": 1e-3},
              distributions={
                  "param_linear": FloatDistribution(0.0, 3.0),
                  "param_log": FloatDistribution(1e-5, 1.0, log=True),
              },
          )
      )
      assert _is_log_scale(study.trials, "param_log")
      assert not _is_log_scale(study.trials, "param_linear")
  
imports: import datetime
  import logging
  from textwrap import dedent
  from typing import cast
  import numpy
  import pytest
  from pytest import LogCaptureFixture
  import optuna
  from optuna.distributions import FloatDistribution
  from optuna.study import create_study
  from optuna.testing.visualization import prepare_study_with_trials
  from optuna.trial import create_trial
  from optuna.trial import FrozenTrial
  from optuna.trial import TrialState
  from optuna.visualization import is_available
  from optuna.visualization._utils import _check_plot_args
  from optuna.visualization._utils import _filter_nonfinite
  from optuna.visualization._utils import _is_log_scale
  from optuna.visualization._utils import _make_hovertext
label_desc: Use image node_id 1 for calling a global function with example usage: test_is_log_scale() without return types