<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def iter_tuple_from_csv(path, iterator=False, chunksize=None, skiprows=None, nrows=None, **kwargs):
 """A high performance, low memory usage csv file row iterator function. :param path: csv file path. :param iterator: :param chunksize: :param skiprows: :param nrows: :yield tuple: **中文文档** 对dataframe进行tuple风格的高性能行遍历。 对用pandas从csv文件读取的dataframe进行逐行遍历时, iterrows和itertuple 都不是性能最高的方法。这是因为iterrows要生成Series对象, 而itertuple 也要对index进行访问。所以本方法是使用内建zip方法对所有的column进行打包 解压, 所以性能上是最佳的。 """

    kwargs["iterator"] = iterator
    kwargs["chunksize"] = chunksize
    kwargs["skiprows"] = skiprows
    kwargs["nrows"] = nrows

    if iterator is True:
        for df in pd.read_csv(path, **kwargs):
            for tp in itertuple(df):
                yield tp
    else:
        df = pd.read_csv(path, **kwargs)
        for tp in itertuple(df):
            yield tp