code: def interpolate_positional_embeddings(restored_posemb_grid, n_tokens):
      """Interpolate positional embeddings from one size to another.
  
      Args:
        restored_posemb_grid: Positional embeddings from restored model. Shape is
          [n_restored_tokens, d]. It is assumed that the restored model used square
          image patches.
        n_tokens: Number of tokens in the target model. It is assumed that the input
          patches and image of the target model are square.
  
      Returns:
        positional embedding resized to match n_tokens. Shape is [1, n_tokens, d]
      """
  
      restored_gs = int(np.sqrt(len(restored_posemb_grid)))
      gs = int(np.sqrt(n_tokens))
      logging.info("Resizing grid-size from %s to %s.", restored_gs, gs)
      restored_posemb_grid = restored_posemb_grid.reshape(
          restored_gs, restored_gs, -1
      )
      zoom = (gs / restored_gs, gs / restored_gs, 1)
      restored_posemb_grid = scipy.ndimage.zoom(
          restored_posemb_grid, zoom, order=1
      )
      restored_posemb_grid = restored_posemb_grid.reshape(
          1, gs * gs, -1
      )
      return restored_posemb_grid
  
imports: from typing import Any, Optional, Tuple
  from absl import logging
  import flax
  from flax.linen import linear
  from flax.training import common_utils
  import jax
  import jax.numpy
  import ml_collections
  import numpy
  from scenic.common_lib import debug_utils
  from scenic.model_lib.base_models import model_utils
  import scipy
label_desc: Use image node_id 6 for calling a global function with example usage: interpolate_positional_embeddings(restored_posemb_grid, n_tokens) and returns: restored_posemb_grid