def make_linearly_separable():
X, y = make_classification(n_samples=<NUM_LIT:200>, n_features=<NUM_LIT:2>, n_redundant=<NUM_LIT:0>, n_informative=<NUM_LIT:2>, random_state=<NUM_LIT:1>, n_clusters_per_class=<NUM_LIT:1>)<EOL>rng = np.random.RandomState(<NUM_LIT:2>)<EOL>X += <NUM_LIT:2> * rng.uniform(size=X.shape)<EOL>return (X, y)<EOL>
:return: