code: def make_data(
      n_samples, n_features, cov_class_1, cov_class_2, seed=0
  ):
      rng = np.random.RandomState(seed)
      X = np.concatenate(
          [
              rng.randn(n_samples, n_features) @ cov_class_1,
              rng.randn(n_samples, n_features) @ cov_class_2
              + np.array([1, 1]),
          ]
      )
      y = np.concatenate([np.zeros(n_samples), np.ones(n_samples)])
      return X, y
  
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 1 for calling a global function with example usage: make_data(n_samples, n_features, cov_class_1, cov_class_2, seed) and returns: X,  y