code: def plot_ellipse(mean, cov, color, ax):
      v, w = np.linalg.eigh(cov)
      u = w[0] / np.linalg.norm(w[0])
      angle = np.arctan(u[1] / u[0])
      angle = 180 * angle / np.pi  # convert to degrees
      # filled Gaussian at 2 standard deviation
      ell = mpl.patches.Ellipse(
          mean,
          2 * v[0] ** 0.5,
          2 * v[1] ** 0.5,
          angle=180 + angle,
          facecolor=color,
          edgecolor="black",
          linewidth=2,
      )
      ell.set_clip_box(ax.bbox)
      ell.set_alpha(0.4)
      ax.add_artist(ell)
  
file_path: scikit-learn/examples/classification/plot_lda_qda.py
imports: import numpy
  import matplotlib
  from matplotlib import colors
  from sklearn.inspection import DecisionBoundaryDisplay
  import matplotlib.pyplot
  from sklearn.discriminant_analysis import LinearDiscriminantAnalysis, QuadraticDiscriminantAnalysis
label_desc: Use image node_id 2 for calling a global function with example usage: plot_ellipse(mean, cov, color, ax) without return types