code: def inverse_distance_to_grid(xp, yp, variable, grid_x, grid_y, r, gamma=None, kappa=None,
                               min_neighbors=3, kind='cressman'):
      r
      # Handle grid-to-points conversion, and use function from `interpolation`
      points_obs = list(zip(xp, yp))
      points_grid = generate_grid_coords(grid_x, grid_y)
      img = inverse_distance_to_points(points_obs, variable, points_grid, r, gamma=gamma,
                                       kappa=kappa, min_neighbors=min_neighbors, kind=kind)
      return img.reshape(grid_x.shape)
docs: r"""Generate an inverse distance interpolation of the given points to a regular grid.
  
      Values are assigned to the given grid using inverse distance weighting based on either
      [Cressman1959]_ or [Barnes1964]_. The Barnes implementation used here based on [Koch1983]_.
  
      Parameters
      ----------
      xp: (N, ) ndarray
          x-coordinates of observations.
      yp: (N, ) ndarray
          y-coordinates of observations.
      variable: (N, ) ndarray
          observation values associated with (xp, yp) pairs.
          IE, variable[i] is a unique observation at (xp[i], yp[i]).
      grid_x: (M, 2) ndarray
          Meshgrid associated with x dimension.
      grid_y: (M, 2) ndarray
          Meshgrid associated with y dimension.
      r: float
          Radius from grid center, within which observations
          are considered and weighted.
      gamma: float
          Adjustable smoothing parameter for the barnes interpolation. Default None.
      kappa: float
          Response parameter for barnes interpolation. Default None.
      min_neighbors: int
          Minimum number of neighbors needed to perform barnes or cressman interpolation
          for a point. Default is 3.
      kind: str
          Specify what inverse distance weighting interpolation to use.
          Options: 'cressman' or 'barnes'. Default 'cressman'
  
      Returns
      -------
      img: (M, N) ndarray
          Interpolated values on a 2-dimensional grid
  
      See Also
      --------
      inverse_distance_to_points