code: def test_plot_partial_dependence_passing_numpy_axes(
      pyplot, clf_diabetes, diabetes, kind, lines
  ):
      grid_resolution = 25
      feature_names = diabetes.feature_names
      disp1 = PartialDependenceDisplay.from_estimator(
          clf_diabetes,
          diabetes.data,
          ["age", "bmi"],
          kind=kind,
          grid_resolution=grid_resolution,
          feature_names=feature_names,
      )
      assert disp1.axes_.shape == (1, 2)
      assert disp1.axes_[0, 0].get_ylabel() == "Partial dependence"
      assert disp1.axes_[0, 1].get_ylabel() == ""
      assert len(disp1.axes_[0, 0].get_lines()) == lines
      assert len(disp1.axes_[0, 1].get_lines()) == lines
  
      lr = LinearRegression()
      lr.fit(diabetes.data, diabetes.target)
  
      disp2 = PartialDependenceDisplay.from_estimator(
          lr,
          diabetes.data,
          ["age", "bmi"],
          kind=kind,
          grid_resolution=grid_resolution,
          feature_names=feature_names,
          ax=disp1.axes_,
      )
  
      assert np.all(disp1.axes_ == disp2.axes_)
      assert len(disp2.axes_[0, 0].get_lines()) == 2 * lines
      assert len(disp2.axes_[0, 1].get_lines()) == 2 * lines
  
imports: import numpy
  import pytest
  from numpy.testing import assert_allclose
  from scipy.stats.mstats import mquantiles
  from sklearn.compose import make_column_transformer
  from sklearn.datasets import load_diabetes, load_iris, make_classification, make_regression
  from sklearn.ensemble import GradientBoostingClassifier, GradientBoostingRegressor
  from sklearn.inspection import PartialDependenceDisplay
  from sklearn.linear_model import LinearRegression
  from sklearn.pipeline import make_pipeline
  from sklearn.preprocessing import OneHotEncoder
  from sklearn.utils._testing import _convert_container
label_desc: Use image node_id 7 for calling a global function with example usage: test_plot_partial_dependence_passing_numpy_axes(pyplot, clf_diabetes, diabetes, kind, lines) without return types