class_docstr: Add a single layer of clouds to an image.
  
  **Supported dtypes**:
  
      * ``uint8``: yes; indirectly tested (1)
      * ``uint16``: no
      * ``uint32``: no
      * ``uint64``: no
      * ``int8``: no
      * ``int16``: no
      * ``int32``: no
      * ``int64``: no
      * ``float16``: yes; not tested
      * ``float32``: yes; not tested
      * ``float64``: yes; not tested
      * ``float128``: yes; not tested (2)
      * ``bool``: no
  
      - (1) Indirectly tested via tests for :class:`Clouds`` and :class:`Fog`
      - (2) Note that random values are usually sampled as ``int64`` or
            ``float64``, which ``float128`` images would exceed. Note also
            that random values might have to upscaled, which is done
            via :func:`~imgaug.imgaug.imresize_many_images` and has its own
            limited dtype support (includes however floats up to ``64bit``).
  
  Parameters
  ----------
  intensity_mean : number or tuple of number or list of number or imgaug.parameters.StochasticParameter
      Mean intensity of the clouds (i.e. mean color).
      Recommended to be in the interval ``[190, 255]``.
  
          * If a ``number``, then that value will always be used.
          * If a ``tuple`` ``(a, b)``, then a value will be uniformly
            sampled per image from the interval ``[a, b]``.
          * If a ``list``, then a random value will be sampled from that
            ``list`` per image.
          * If a ``StochasticParameter``, then a value will be sampled
            per image from that parameter.
  
  intensity_freq_exponent : number or tuple of number or list of number or imgaug.parameters.StochasticParameter
      Exponent of the frequency noise used to add fine intensity to the
      mean intensity.
      Recommended to be in the interval ``[-2.5, -1.5]``.
      See :func:`~imgaug.parameters.FrequencyNoise.__init__` for details.
  
  intensity_coarse_scale : number or tuple of number or list of number or imgaug.parameters.StochasticParameter
      Standard deviation of the gaussian distribution used to add more
      localized intensity to the mean intensity. Sampled in low resolution
      space, i.e. affects final intensity on a coarse level.
      Recommended to be in the interval ``(0, 10]``.
  
          * If a ``number``, then that value will always be used.
          * If a ``tuple`` ``(a, b)``, then a value will be uniformly sampled
            per image from the interval ``[a, b]``.
          * If a ``list``, then a random value will be sampled from that
            ``list`` per image.
          * If a ``StochasticParameter``, then a value will be sampled
            per image from that parameter.
  
  alpha_min : number or tuple of number or list of number or imgaug.parameters.StochasticParameter
      Minimum alpha when blending cloud noise with the image.
      High values will lead to clouds being "everywhere".
      Recommended to usually be at around ``0.0`` for clouds and ``>0`` for
      fog.
  
          * If a ``number``, then that value will always be used.
          * If a ``tuple`` ``(a, b)``, then a value will be uniformly sampled
            per image from the interval ``[a, b]``.
          * If a ``list``, then a random value will be sampled from that
            ``list`` per image.
          * If a ``StochasticParameter``, then a value will be sampled
            per image from that parameter.
  
  alpha_multiplier : number or tuple of number or list of number or imgaug.parameters.StochasticParameter
      Multiplier for the sampled alpha values. High values will lead to
      denser clouds wherever they are visible.
      Recommended to be in the interval ``[0.3, 1.0]``.
      Note that this parameter currently overlaps with `density_multiplier`,
      which is applied a bit later to the alpha mask.
  
          * If a ``number``, then that value will always be used.
          * If a ``tuple`` ``(a, b)``, then a value will be uniformly sampled
            per image from the interval ``[a, b]``.
          * If a ``list``, then a random value will be sampled from that
            ``list`` per image.
          * If a ``StochasticParameter``, then a value will be sampled
            per image from that parameter.
  
  alpha_size_px_max : number or tuple of number or list of number or imgaug.parameters.StochasticParameter
      Controls the image size at which the alpha mask is sampled.
      Lower values will lead to coarser alpha masks and hence larger
      clouds (and empty areas).
      See :func:`~imgaug.parameters.FrequencyNoise.__init__` for details.
  
  alpha_freq_exponent : number or tuple of number or list of number or imgaug.parameters.StochasticParameter
      Exponent of the frequency noise used to sample the alpha mask.
      Similarly to `alpha_size_max_px`, lower values will lead to coarser
      alpha patterns.
      Recommended to be in the interval ``[-4.0, -1.5]``.
      See :func:`~imgaug.parameters.FrequencyNoise.__init__` for details.
  
  sparsity : number or tuple of number or list of number or imgaug.parameters.StochasticParameter
      Exponent applied late to the alpha mask. Lower values will lead to
      coarser cloud patterns, higher values to finer patterns.
      Recommended to be somewhere around ``1.0``.
      Do not deviate far from that value, otherwise the alpha mask might
      get weird patterns with sudden fall-offs to zero that look very
      unnatural.
  
          * If a ``number``, then that value will always be used.
          * If a ``tuple`` ``(a, b)``, then a value will be uniformly sampled
            per image from the interval ``[a, b]``.
          * If a ``list``, then a random value will be sampled from that
            ``list`` per image.
          * If a ``StochasticParameter``, then a value will be sampled
            per image from that parameter.
  
  density_multiplier : number or tuple of number or list of number or imgaug.parameters.StochasticParameter
      Late multiplier for the alpha mask, similar to `alpha_multiplier`.
      Set this higher to get "denser" clouds wherever they are visible.
      Recommended to be around ``[0.5, 1.5]``.
  
          * If a ``number``, then that value will always be used.
          * If a ``tuple`` ``(a, b)``, then a value will be uniformly sampled
            per image from the interval ``[a, b]``.
          * If a ``list``, then a random value will be sampled from that
            ``list`` per image.
          * If a ``StochasticParameter``, then a value will be sampled
            per image from that parameter.
  
  seed : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional
      See :func:`~imgaug.augmenters.meta.Augmenter.__init__`.
  
  name : None or str, optional
      See :func:`~imgaug.augmenters.meta.Augmenter.__init__`.
  
  random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional
      Old name for parameter `seed`.
      Its usage will not yet cause a deprecation warning,
      but it is still recommended to use `seed` now.
      Outdated since 0.4.0.
  
  deterministic : bool, optional
      Deprecated since 0.4.0.
      See method ``to_deterministic()`` for an alternative and for
      details about what the "deterministic mode" actually does.
code: def _generate_intensity_map_fine(
      cls, height, width, intensity_mean, exponent, random_state
  ):
      intensity_details_generator = iap.FrequencyNoise(
          exponent=exponent,
          size_px_max=max(
              height, width, 1
          ),  # 1 here for case H, W being 0
          upscale_method="cubic",
      )
      intensity_details = intensity_details_generator.draw_samples(
          (height, width), random_state
      )
      return intensity_mean * ((2 * intensity_details - 1.0) / 5.0)
  
imports: from __future__ import print_function, division, absolute_import
  import numpy
  import imgaug
  from .None import meta, arithmetic, blur, contrast, color
  from ..None import parameters
  from ..None import dtypes
label_desc: Use image node_id 7 for calling the CloudLayer obj's underlying member method code with example usage: obj._generate_intensity_map_fine(cls, height, width, intensity_mean, exponent, random_state) and returns: unknown