code: def _get_y_range(
      info: _ImprovementInfo, min_n_trials: int
  ) -> tuple[float, float]:
      min_value = min(info.improvements)
      if info.errors is not None:
          min_value = min(min_value, min(info.errors))
  
      # Determine the display range based on trials after min_n_trials.
      if len(info.trial_numbers) > min_n_trials:
          max_value = max(info.improvements[min_n_trials:])
      # If there are no trials after min_trials, determine the display range based on all trials.
      else:
          max_value = max(info.improvements)
  
      if info.errors is not None:
          max_value = max(max_value, max(info.errors))
  
      padding = (max_value - min_value) * PADDING_RATIO_Y
      return (min_value - padding, max_value + padding)
  
imports: from __future__ import annotations
  from typing import NamedTuple
  import tqdm
  import optuna
  from optuna._experimental import experimental_func
  from optuna.logging import get_logger
  from optuna.study.study import Study
  from optuna.terminator import BaseErrorEvaluator
  from optuna.terminator import BaseImprovementEvaluator
  from optuna.terminator import CrossValidationErrorEvaluator
  from optuna.terminator import RegretBoundEvaluator
  from optuna.terminator.improvement.evaluator import DEFAULT_MIN_N_TRIALS
  from optuna.visualization._plotly_imports import _imports
label_desc: Use image node_id 5 for calling a global function with example usage: _get_y_range(info, min_n_trials) and returns: unknown,  unknown