code: def from_inverse_noise_map(cls, pixel_scale, inverse_noise_map):
          
          noise_map = 1.0 / inverse_noise_map
          return NoiseMap(array=noise_map, pixel_scale=pixel_scale)
docs: Setup the noise-map from an root-mean square standard deviation map, which is a form of noise-map that \
          comes via HST image-reduction and the software package MultiDrizzle.
  
          The variance in each pixel is computed as:
  
          Variance = 1.0 / inverse_std_map.
  
          The weight map may contain zeros, in which cause the variances are converted to large values to omit them from \
          the analysis.
  
          Parameters
          -----------
          pixel_scale : float
              The size of each pixel in arc seconds.
          inverse_noise_map : ndarray
              The inverse noise_map value of each pixel which is converted to a variance.