def METHOD_NAME():
    """
    The test case initializes evo search with a batch of "bad" states and check whether
    the search algorithm can find "good" states by mutating the "bad" states.
    """
    class MockCostModel(PythonBasedModel):
        @staticmethod
        def is_good_state(state):
            for line in str(state).split("\n"):
                if (
                    line.find("parallel i.0@ (0") != -1
                    or line.find("parallel i.0@j.0@ (0") != -1
                    or line.find("parallel i.0@j.0@i.1@ (0") != -1
                ):
                    return True
            return False
        def predict(self, task, states):
            scores = []
            for state in states:
                scores.append(1 if self.is_good_state(state) else 0)
            return scores
    task = auto_scheduler.SearchTask(
        func=matmul_auto_scheduler_test, args=(1024, 1024, 1024), target="llvm"
    )
    policy = auto_scheduler.SketchPolicy(task, program_cost_model=MockCostModel(), verbose=0)
    found = False
    retry_ct = 0
    while retry_ct < 10 and not found:
        states = policy.sample_initial_population()[:100]
        bad_states = []
        for state in states:
            if not MockCostModel.is_good_state(state):
                bad_states.append(state)
        new_states = policy.evolutionary_search(bad_states, 50)
        for state in new_states:
            if MockCostModel.is_good_state(state):
                found = True
                break
        retry_ct += 1
    assert found