text: """Fill holes in raster dataset by interpolation from the edges."""
  
  import rasterio
  from rasterio._fill import _fillnodata
  from rasterio.env import ensure_env
  
  
  @ensure_env
  def fillnodata(
          image,
          mask=None,
          max_search_distance=100.0,
          smoothing_iterations=0):
      """Fill holes in a raster dataset by interpolation from the edges.
  
      This algorithm will interpolate values for all designated nodata
      pixels (marked by zeros in `mask`). For each pixel a four direction
      conic search is done to find values to interpolate from (using
      inverse distance weighting). Once all values are interpolated, zero
      or more smoothing iterations (3x3 average filters on interpolated
      pixels) are applied to smooth out artifacts.
  
      This algorithm is generally suitable for interpolating missing
      regions of fairly continuously varying rasters (such as elevation
      models for instance). It is also suitable for filling small holes
      and cracks in more irregularly varying images (like aerial photos).
      It is generally not so great for interpolating a raster from sparse
      point data.
  
      Parameters
      ----------
      image : numpy ndarray
          The source containing nodata holes.
      mask : numpy ndarray or None
          A mask band indicating which pixels to interpolate. Pixels to
          interpolate into are indicated by the value 0. Values > 0
          indicate areas to use during interpolation. Must be same shape
          as image. If `None`, a mask will be diagnosed from the source
          data.
      max_search_distance : float, optional
          The maxmimum number of pixels to search in all directions to
          find values to interpolate from. The default is 100.
      smoothing_iterations : integer, optional
          The number of 3x3 smoothing filter passes to run. The default is
          0.
  
      Returns
      -------
      out : numpy ndarray
          The filled raster array.
      """
      max_search_distance = float(max_search_distance)
      smoothing_iterations = int(smoothing_iterations)
      return _fillnodata(
          image, mask, max_search_distance, smoothing_iterations)
  