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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 quantile_for_single_value(self, **kwargs):
 """Returns quantile of each column or row. Returns: A new QueryCompiler object containing the quantile of each column or row. """

        if self._is_transposed:
            kwargs["axis"] = kwargs.get("axis", 0) ^ 1
            return self.transpose().quantile_for_single_value(**kwargs)
        axis = kwargs.get("axis", 0)
        q = kwargs.get("q", 0.5)
        assert type(q) is float

        def quantile_builder(df, **kwargs):
            try:
                return pandas.DataFrame.quantile(df, **kwargs)
            except ValueError:
                return pandas.Series()

        func = self._build_mapreduce_func(quantile_builder, **kwargs)
        result = self._full_axis_reduce(axis, func)
        if axis == 0:
            result.index = [q]
        else:
            result.columns = [q]
        return result