code: def _pad_spatial_dims(x, x_shape, padding):
      """Pads `x` using `padding`, which specifies padding for the spatial dimensions."""
      padding = tuple(padding)
      if len(padding) == len(x_shape) - 2:
          # If necessary, add empty padding for batch and feature dimensions.
          no_pad = ((0, 0),)
          padding = no_pad + padding + no_pad
      x = tf.pad(x, padding)
      assert len(x.shape) == len(padding)
      x_shape = tuple(
          p0 + xs + p1 for xs, (p0, p1) in zip(x_shape, padding)
      )
      jax2tf._assert_matching_abstract_shape(x, x_shape)
      return x, x_shape
  
imports: from __future__ import annotations
  import builtins
  from collections.abc import Sequence
  import dataclasses
  from functools import partial, wraps
  import math
  import string
  from typing import Any, Callable, Optional
  from jax._src import core
  from jax import lax
  from jax._src.lax import slicing
  from jax._src import dtypes
  from jax._src import util
  from jax.experimental.jax2tf import jax2tf
  import numpy
  import tensorflow
label_desc: Use image node_id 7 for calling a global function with example usage: _pad_spatial_dims(x, x_shape, padding) and returns: x,  x_shape