code: def _get_tick_info(target_values: np.ndarray) -> _TickInfo:
      sorted_target_values = np.sort(target_values)
      coloridxs = [0, 0.25, 0.5, 0.75, 1]
      values = np.quantile(sorted_target_values, coloridxs)
      rank_text = ["min.", "25%", "50%", "75%", "max."]
      text = [
          f"{rank_text[i]} ({values[i]:3g})" for i in range(len(values))
      ]
      return _TickInfo(coloridxs=coloridxs, text=text)
  
imports: from __future__ import annotations
  import math
  import typing
  from typing import Any
  from typing import Callable
  from typing import NamedTuple
  import numpy
  from optuna._experimental import experimental_func
  from optuna.logging import get_logger
  from optuna.samplers._base import _CONSTRAINTS_KEY
  from optuna.study import Study
  from optuna.trial import FrozenTrial
  from optuna.trial import TrialState
  from optuna.visualization._plotly_imports import _imports
  from optuna.visualization._utils import _check_plot_args
  from optuna.visualization._utils import _is_log_scale
  from optuna.visualization._utils import _is_numerical
  from optuna.visualization.matplotlib._matplotlib_imports import _imports
label_desc: Use image node_id 7 for calling a global function with example usage: _get_tick_info(target_values) and returns: _TickInfo