code: def test_inconsistent_number_of_trial_values() -> None:
      studies: list[Study] = []
      n_studies = 5
  
      for i in range(n_studies):
          study = prepare_study_with_trials()
          if i % 2 == 0:
              study.add_trial(create_trial(value=1.0))
          studies.append(study)
  
      edf_info = _get_edf_info(studies)
  
      x_values = edf_info.x_values
      min_objective = 0.0
      max_objective = 2.0
      assert np.min(x_values) == min_objective
      assert np.max(x_values) == max_objective
      assert len(x_values) == NUM_SAMPLES_X_AXIS
  
      lines = edf_info.lines
      assert len(lines) == n_studies
      for line, study in zip(lines, studies):
          assert line.study_name == study.study_name
          _validate_edf_values(line.y_values)
  
imports: from __future__ import annotations
  from io import BytesIO
  from typing import Any
  from typing import Callable
  import numpy
  import pytest
  import optuna
  from optuna import Study
  from optuna.study import create_study
  from optuna.testing.visualization import prepare_study_with_trials
  from optuna.trial import create_trial
  from optuna.visualization import plot_edf
  from optuna.visualization._edf import _EDFInfo
  from optuna.visualization._edf import _get_edf_info
  from optuna.visualization._edf import NUM_SAMPLES_X_AXIS
  from optuna.visualization._plotly_imports import _imports
  from optuna.visualization.matplotlib import plot_edf
  from optuna.visualization.matplotlib._matplotlib_imports import _imports
label_desc: Use image node_id 13 for calling a global function with example usage: test_inconsistent_number_of_trial_values() without return types