def AdditiveLaplaceNoise(loc=<NUM_LIT:0>, scale=<NUM_LIT:0>, per_channel=False, name=None, deterministic=False, random_state=None):
loc2 = iap.handle_continuous_param(loc, "<STR_LIT>", value_range=None, tuple_to_uniform=True, list_to_choice=True)<EOL>scale2 = iap.handle_continuous_param(scale, "<STR_LIT>", value_range=(<NUM_LIT:0>, None), tuple_to_uniform=True,<EOL>list_to_choice=True)<EOL>if name is None:<EOL><INDENT>name = "<STR_LIT>" % (ia.caller_name(),)<EOL><DEDENT>return AddElementwise(iap.Laplace(loc=loc2, scale=scale2), per_channel=per_channel, name=name,<EOL>deterministic=deterministic, random_state=random_state)<EOL>
Add laplace noise to images.

The laplace distribution is similar to the gaussian distribution, but has puts weight on the long tail.
Hence, this noise will add more outliers (very high/low values). It is somewhere between gaussian noise and
salt and pepper noise.

Values of around ``255 * 0.05`` for `scale` lead to visible noise (for uint8).
Values of around ``255 * 0.10`` for `scale` lead to very visible noise (for uint8).
It is recommended to usually set `per_channel` to True.

dtype support::

    See ``imgaug.augmenters.arithmetic.AddElementwise``.

Parameters
----------
loc : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional
    Mean of the laplace distribution that generates the noise.

        * If a number, exactly that value will be used.
        * If a tuple ``(a, b)``, a random value from the range ``a <= x <= b`` will
          be sampled per image.
        * If a list, then a random value will be sampled from that list per
          image.
        * If a StochasticParameter, a value will be sampled from the
          parameter per image.

scale : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional
    Standard deviation of the laplace distribution that generates the noise.
    Must be ``>= 0``. If 0 then only `loc` will be used.
    Recommended to be around ``255 * 0.05``.

        * If an int or float, exactly that value will be used.
        * If a tuple ``(a, b)``, a random value from the range ``a <= x <= b`` will
          be sampled per image.
        * If a list, then a random value will be sampled from that list per
          image.
        * If a StochasticParameter, a value will be sampled from the
          parameter per image.

per_channel : bool or float, optional
    Whether to use the same noise value per pixel for all channels (False)
    or to sample a new value for each channel (True).
    If this value is a float ``p``, then for ``p`` percent of all images
    `per_channel` will be treated as True, otherwise as False.

name : None or str, optional
    See :func:`imgaug.augmenters.meta.Augmenter.__init__`.

deterministic : bool, optional
    See :func:`imgaug.augmenters.meta.Augmenter.__init__`.

random_state : None or int or numpy.random.RandomState, optional
    See :func:`imgaug.augmenters.meta.Augmenter.__init__`.

Examples
--------
>>> aug = iaa.AdditiveLaplaceNoise(scale=0.1*255)

Adds laplace noise from the distribution ``Laplace(0, 0.1*255)`` to images.

>>> aug = iaa.AdditiveLaplaceNoise(scale=(0, 0.1*255))

Adds laplace noise from the distribution ``Laplace(0, s)`` to images,
where s is sampled per image from the range ``0 <= s <= 0.1*255``.

>>> aug = iaa.AdditiveLaplaceNoise(scale=0.1*255, per_channel=True)

Adds laplace noise from the distribution ``Laplace(0, 0.1*255)`` to images,
where the noise value is different per pixel *and* channel (e.g. a
different one for red, green and blue channels for the same pixel).

>>> aug = iaa.AdditiveLaplaceNoise(scale=0.1*255, per_channel=0.5)

Adds laplace noise from the distribution ``Laplace(0, 0.1*255)`` to images,
where the noise value is sometimes (50 percent of all cases) the same
per pixel for all channels and sometimes different (other 50 percent).