code: def _compute_color_lut(bins, sigma, max_value, *, dtype=float):
      """Helping function. Define a lookup table containing Gaussian filter
      values using the color distance sigma.
  
      Parameters
      ----------
      bins : int
          Number of discrete values for Gaussian weights of color filtering.
          A larger value results in improved accuracy.
      sigma : float
          Standard deviation for grayvalue/color distance (radiometric
          similarity). A larger value results in averaging of pixels with larger
          radiometric differences. Note, that the image will be converted using
          the `img_as_float` function and thus the standard deviation is in
          respect to the range ``[0, 1]``. If the value is ``None`` the standard
          deviation of the ``image`` will be used.
      max_value : float
          Maximum value of the input image.
      dtype : data type object, optional (default : float)
          The type and size of the data to be returned.
  
      Returns
      -------
      color_lut : ndarray
          Lookup table for the color distance sigma.
      """
      values = np.linspace(0, max_value, bins, endpoint=False)
      return _gaussian_weight(values, sigma**2, dtype=dtype)
  
imports: import functools
  from math import ceil
  import numbers
  import scipy.stats
  import numpy
  from ..util.dtype import img_as_float
  from .._shared import utils
  from .._shared.utils import _supported_float_type, warn
  from ._denoise_cy import _denoise_bilateral, _denoise_tv_bregman
  from ..None import color
  from ..color.colorconv import ycbcr_from_rgb
label_desc: Use image node_id 2 for calling a global function with example usage: _compute_color_lut(bins, sigma, max_value) and returns: _gaussian_weight