code: def generate_data(n_samples, n_features):
      """Generate random blob-ish data with noisy features.
  
      This returns an array of input data with shape `(n_samples, n_features)`
      and an array of `n_samples` target labels.
  
      Only one feature contains discriminative information, the other features
      contain only noise.
      """
      X, y = make_blobs(
          n_samples=n_samples, n_features=1, centers=[[-2], [2]]
      )
  
      # add non-discriminative features
      if n_features > 1:
          X = np.hstack([X, np.random.randn(n_samples, n_features - 1)])
      return X, y
  
imports: import matplotlib.pyplot
  import numpy
  from sklearn.covariance import OAS
  from sklearn.datasets import make_blobs
  from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
label_desc: Use image node_id 1 for calling a global function with example usage: generate_data(n_samples, n_features) and returns: X,  y