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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 var(self, values, axis=0, weights=None, dtype=None):
 """compute the variance over each group Parameters values to take variance of per group axis : int, optional alternative reduction axis for values Returns ------- unique: ndarray, [groups] unique keys value array, reduced over groups """

        values = np.asarray(values)
        unique, mean = self.mean(values, axis, weights, dtype)
        err = values - mean.take(self.inverse, axis)

        if weights is None:
            shape = [1] * values.ndim
            shape[axis] = self.groups
            group_weights = self.count.reshape(shape)
            var = self.reduce(err ** 2, axis=axis, dtype=dtype)
        else:
            weights = np.asarray(weights)
            group_weights = self.reduce(weights, axis=axis, dtype=dtype)
            var = self.reduce(weights * err ** 2, axis=axis, dtype=dtype)

        return unique, var / group_weights