code: def generate(N, mean, cov, diff):
      # import ipdb;ipdb.set_trace()
      num_classes = len(diff)
      samples_per_class = int(N / 2)
  
      X0 = np.random.multivariate_normal(mean, cov, samples_per_class)
      Y0 = np.zeros(samples_per_class)
  
      for ci, d in enumerate(diff):
          X1 = np.random.multivariate_normal(
              mean + d, cov, samples_per_class
          )
          Y1 = (ci + 1) * np.ones(samples_per_class)
  
          X0 = np.concatenate((X0, X1))
          Y0 = np.concatenate((Y0, Y1))
  
      X, Y = shuffle(X0, Y0)
  
      return X, Y
  
imports: import numpy
  from sklearn.utils import shuffle
label_desc: Use image node_id 1 for calling a global function with example usage: generate(N, mean, cov, diff) and returns: X,  Y