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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def minimize(f, start=None, smooth=False, log=None, array=False, **vargs):
 """Minimize a function f of one or more arguments. Args: f: A function that takes numbers and returns a number start: A starting value or list of starting values smooth: Whether to assume that f is smooth and use first-order info log: Logging function called on the result of optimization (e.g. print) vargs: Other named arguments passed to scipy.optimize.minimize Returns either: (a) the minimizing argument of a one-argument function (b) an array of minimizing arguments of a multi-argument function """

    if start is None:
        assert not array, "Please pass starting values explicitly when array=True"
        arg_count = f.__code__.co_argcount
        assert arg_count > 0, "Please pass starting values explicitly for variadic functions"
        start = [0] * arg_count
    if not hasattr(start, '__len__'):
        start = [start]

    if array:
        objective = f
    else:
        @functools.wraps(f)
        def objective(args):
            return f(*args)

    if not smooth and 'method' not in vargs:
        vargs['method'] = 'Powell'
    result = optimize.minimize(objective, start, **vargs)
    if log is not None:
        log(result)
    if len(start) == 1:
        return result.x.item(0)
    else:
        return result.x