code: def _apply_interpolation(
      query_points: TensorLike,
      train_points: TensorLike,
      w: TensorLike,
      v: TensorLike,
      order: int,
  ) -> TensorLike:
      """Apply polyharmonic interpolation model to data.
  
      Given coefficients w and v for the interpolation model, we evaluate
      interpolated function values at query_points.
  
      Args:
        query_points: `[b, m, d]` x values to evaluate the interpolation at.
        train_points: `[b, n, d]` x values that act as the interpolation centers
            (the c variables in the wikipedia article).
        w: `[b, n, k]` weights on each interpolation center.
        v: `[b, d, k]` weights on each input dimension.
        order: order of the interpolation.
  
      Returns:
        Polyharmonic interpolation evaluated at points defined in `query_points`.
      """
  
      # First, compute the contribution from the rbf term.
      pairwise_dists = _cross_squared_distance_matrix(
          query_points, train_points
      )
      phi_pairwise_dists = _phi(pairwise_dists, order)
  
      rbf_term = tf.matmul(phi_pairwise_dists, w)
  
      # Then, compute the contribution from the linear term.
      # Pad query_points with ones, for the bias term in the linear model.
      query_points_pad = tf.concat(
          [
              query_points,
              tf.ones_like(query_points[..., :1], train_points.dtype),
          ],
          2,
      )
      linear_term = tf.matmul(query_points_pad, v)
  
      return rbf_term + linear_term
  
file_path: addons/tensorflow_addons/image/interpolate_spline.py
imports: import tensorflow
  from tensorflow_addons.utils.types import FloatTensorLike, TensorLike
label_desc: Use image node_id 4 for calling a global function with example usage: _apply_interpolation(query_points, train_points, w, v, order) and returns: unknown