def METHOD_NAME(self):
    ignore_order = True
    cache_size = 5000
    max_diffs = 30000
    max_passes = 40000
    t1 = list(range(1, 30))
    t2 = list(range(13, 33))
    diff = DeepDiff(t1, t2, ignore_order=ignore_order, max_passes=max_passes,
                    max_diffs=max_diffs, cache_size=cache_size, cache_tuning_sample_size=100)
    stats = diff.get_stats()
    # In this case an optimization happened to compare numbers. Behind the scene we converted the python lists
    # into Numpy arrays and cached the distances between numbers.
    # So the distance cache was heavily used and stayed ON but the level cache was turned off due to low cache hit
    expected_stats = {
        'PASSES COUNT': 1,
        'DIFF COUNT': 16,
        'DISTANCE CACHE HIT COUNT': 0,
        'MAX PASS LIMIT REACHED': False,
        'MAX DIFF LIMIT REACHED': False
    }
    assert expected_stats == stats
    expected = {
        'values_changed': {
            'root[11]': {
                'new_value': 30,
                'old_value': 12
            },
            'root[10]': {
                'new_value': 31,
                'old_value': 11
            },
            'root[9]': {
                'new_value': 32,
                'old_value': 10
            }
        },
        'iterable_item_removed': {
            'root[0]': 1,
            'root[1]': 2,
            'root[2]': 3,
            'root[3]': 4,
            'root[4]': 5,
            'root[5]': 6,
            'root[6]': 7,
            'root[7]': 8,
            'root[8]': 9
        }
    }
    assert expected == diff