How to use the diffprivlib.mechanisms.base.TruncationAndFoldingMixin function in diffprivlib

def __init__(self):
        super().__init__()
        TruncationAndFoldingMixin.__init__(self)

"""
        self.check_inputs(value)

        if self._scale is None:
            self._scale = - self._epsilon / self._sensitivity

        # Need to account for overlap of 0-value between distributions of different sign
        unif_rv = random() - 0.5
        unif_rv *= 1 + np.exp(self._scale)
        sgn = -1 if unif_rv < 0 else 1

        # Use formula for geometric distribution, with ratio of exp(-epsilon/sensitivity)
        return int(np.round(value + sgn * np.floor(np.log(sgn * unif_rv) / self._scale)))


class GeometricTruncated(Geometric, TruncationAndFoldingMixin):
    """
    The truncated geometric mechanism, where values that fall outside a pre-described range are mapped back to the
    closest point within the range.
    """
    def __init__(self):
        super().__init__()
        TruncationAndFoldingMixin.__init__(self)

    def __repr__(self):
        output = super().__repr__()
        output += TruncationAndFoldingMixin.__repr__(self)

        return output

    def set_bounds(self, lower, upper):
        """Sets the lower and upper bounds of the mechanism.

def __repr__(self):
        output = super().__repr__()
        output += TruncationAndFoldingMixin.__repr__(self)

        return output

def __init__(self):
        super().__init__()
        TruncationAndFoldingMixin.__init__(self)

return super().set_bounds(lower, upper)

    @copy_docstring(DPMechanism.get_bias)
    def get_bias(self, value):
        pass

    @copy_docstring(Geometric.randomise)
    def randomise(self, value):
        TruncationAndFoldingMixin.check_inputs(self, value)

        noisy_value = super().randomise(value)
        return int(np.round(self._truncate(noisy_value)))


class GeometricFolded(Geometric, TruncationAndFoldingMixin):
    """
    The folded geometric mechanism, where values outside a pre-described range are folded back toward the domain around
    the closest point within the domain.
    Half-integer bounds are permitted.
    """
    def __init__(self):
        super().__init__()
        TruncationAndFoldingMixin.__init__(self)

    def __repr__(self):
        output = super().__repr__()
        output += TruncationAndFoldingMixin.__repr__(self)

        return output

    def set_bounds(self, lower, upper):

def __repr__(self):
        output = super().__repr__()
        output += TruncationAndFoldingMixin.__repr__(self)

        return output

    @copy_docstring(Laplace.check_inputs)
    def check_inputs(self, value):
        super().check_inputs(value)
        TruncationAndFoldingMixin.check_inputs(self, value)

        return True

    @copy_docstring(Laplace.randomise)
    def randomise(self, value):
        TruncationAndFoldingMixin.check_inputs(self, value)

        noisy_value = super().randomise(value)
        return self._truncate(noisy_value)


class LaplaceFolded(Laplace, TruncationAndFoldingMixin):
    """
    The folded Laplace mechanism, where values outside a pre-described domain are folded around the domain until they
    fall within.
    """
    def __init__(self):
        super().__init__()
        TruncationAndFoldingMixin.__init__(self)

    def __repr__(self):
        output = super().__repr__()
        output += TruncationAndFoldingMixin.__repr__(self)

        return output

    @copy_docstring(Laplace.get_bias)
    def get_bias(self, value):