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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 _inferSchema(self, rdd, samplingRatio=None, names=None):
 """ Infer schema from an RDD of Row or tuple. :param rdd: an RDD of Row or tuple :param samplingRatio: sampling ratio, or no sampling (default) :return: :class:`pyspark.sql.types.StructType` """

        first = rdd.first()
        if not first:
            raise ValueError("The first row in RDD is empty, "
                             "can not infer schema")
        if type(first) is dict:
            warnings.warn("Using RDD of dict to inferSchema is deprecated. "
                          "Use pyspark.sql.Row instead")

        if samplingRatio is None:
            schema = _infer_schema(first, names=names)
            if _has_nulltype(schema):
                for row in rdd.take(100)[1:]:
                    schema = _merge_type(schema, _infer_schema(row, names=names))
                    if not _has_nulltype(schema):
                        break
                else:
                    raise ValueError("Some of types cannot be determined by the "
                                     "first 100 rows, please try again with sampling")
        else:
            if samplingRatio < 0.99:
                rdd = rdd.sample(False, float(samplingRatio))
            schema = rdd.map(lambda row: _infer_schema(row, names)).reduce(_merge_type)
        return schema