code: def regular_grid_interpolation(
      grid, pts, min_grid_value=(0, 0, 0), max_grid_value=(1, 1, 1)
  ):
      """Regular grid interpolator, returns inpterpolation values.
  
      Args:
        grid: `[batch_size, *size, features]` tensor, input feature grid.
        pts: `[batch_size, num_points, dim]` tensor, coordinates of points that
        in each dim are within the range (min_grid_value[dim], max_grid_value[dim]).
        min_grid_value: tuple, minimum value in each dimension corresponding to the
          grid.
        max_grid_value: tuple, maximum values in each dimension corresponding to the
          grid.
      Returns:
        vals: `[batch_size, num_points, features]` tensor, values
      """
      lats, weights, _ = get_interp_coefficients(
          grid, pts, min_grid_value, max_grid_value
      )
      vals = tf.reduce_sum(lats * weights[..., tf.newaxis], axis=-2)
      return vals
  
label_desc: Use image node_id 2 for calling a global function with example usage: regular_grid_interpolation(grid, pts, min_grid_value, max_grid_value) and returns: vals