code: def fit_and_plot_model(X_plot, y, clf, ax):
      clf.fit(X_plot, y)
      disp = DecisionBoundaryDisplay.from_estimator(
          clf,
          X_plot,
          response_method="predict",
          alpha=0.5,
          ax=ax,
      )
      disp.ax_.scatter(
          X_plot["proline"], X_plot["hue"], c=y, s=20, edgecolor="k"
      )
      disp.ax_.set_xlim(
          (X_plot["proline"].min(), X_plot["proline"].max())
      )
      disp.ax_.set_ylim((X_plot["hue"].min(), X_plot["hue"].max()))
      return disp.ax_
  
file_path: scikit-learn/examples/preprocessing/plot_scaling_importance.py
imports: from sklearn.datasets import load_wine
  from sklearn.model_selection import train_test_split
  from sklearn.preprocessing import StandardScaler
  import matplotlib.pyplot
  from sklearn.inspection import DecisionBoundaryDisplay
  from sklearn.neighbors import KNeighborsClassifier
  import pandas
  from sklearn.decomposition import PCA
  import numpy
  from sklearn.linear_model import LogisticRegressionCV
  from sklearn.pipeline import make_pipeline
  from sklearn.metrics import accuracy_score, log_loss
label_desc: Use image node_id 1 for calling a global function with example usage: fit_and_plot_model(X_plot, y, clf, ax) and returns: disp