code: def _sigma_est_dwt(detail_coeffs, distribution="Gaussian"):
      """Calculate the robust median estimator of the noise standard deviation.
  
      Parameters
      ----------
      detail_coeffs : ndarray
          The detail coefficients corresponding to the discrete wavelet
          transform of an image.
      distribution : str
          The underlying noise distribution.
  
      Returns
      -------
      sigma : float
          The estimated noise standard deviation (see section 4.2 of [1]_).
  
      References
      ----------
      .. [1] D. L. Donoho and I. M. Johnstone. "Ideal spatial adaptation
         by wavelet shrinkage." Biometrika 81.3 (1994): 425-455.
         :DOI:`10.1093/biomet/81.3.425`
      """
      # Consider regions with detail coefficients exactly zero to be masked out
      detail_coeffs = detail_coeffs[np.nonzero(detail_coeffs)]
  
      if distribution.lower() == "gaussian":
          # 75th quantile of the underlying, symmetric noise distribution
          denom = scipy.stats.norm.ppf(0.75)
          sigma = np.median(np.abs(detail_coeffs)) / denom
      else:
          raise ValueError(
              "Only Gaussian noise estimation is currently " "supported"
          )
      return sigma
  
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 10 for calling a global function with example usage: _sigma_est_dwt(detail_coeffs, distribution) and returns: sigma