def METHOD_NAME():
    formulas = [
        ("23.*x", lambda x: 23.0 * x),
        ("23.*log(max(x, 0.1))", lambda x: 23.0 * math.log(max(x, 0.1))),
    ]
    cset = {
        "schema_version": 1,
        "corrections": [
            {
                "name": "test",
                "version": 1,
                "inputs": [
                    {"name": "index", "type": "int"},
                    {"name": "x", "type": "real"},
                ],
                "output": {"name": "f", "type": "real"},
                "data": {
                    "nodetype": "category",
                    "keys": list(range(len(formulas))),
                    "content": [
                        {"expression": expr, "parser": "TFormula", "parameters": [1]}
                        for expr, _ in formulas
                    ],
                },
            }
        ],
    }
    schemav1.CorrectionSet.parse_obj(cset)
    corr = core.CorrectionSet.from_string(json.dumps(cset))["test"]
    test_values = [1.0, 32.0, -3.0, 1550.0]
    for i, (_, expected) in enumerate(formulas):
        for x in test_values:
            assert corr.evaluate(i, x) == expected(x)