code: def compute_mu(L_aug, Y, k, p):
      
      n, d = L_aug.shape
      assert Y.shape[0] == n
  
      # Compute mu
      mu = np.zeros((d, k))
      for y in range(1, k + 1):
          L_y = L_aug[Y == y]
          mu[:, y - 1] = L_y.sum(axis=0) / L_y.shape[0]
      return mu
docs: Given label matrix L_aug and labels Y, compute the true mu params.
  
      Args:
          L: (np.array {0,1}) [n, d] The augmented (indicator) label matrix
          Y: (np.array int) [n] The true labels in {1,...,k}
          k: (int) Cardinality
          p: (np.array float) [k] The class balance