code: def _interior_padding(
      operand, padding_value, padding_config, operand_shape
  ):
      # Used only when enable_xla=False
      # Applies only the interior padding from the padding_config.
      # We do this somewhat inefficiently, as a scatter.
      # For each dimension we compute the indices_by_dim as [0, f, 2f, 3f, ...] where
      # f is the dilation factor for the dimension, i.e., 1 + interior_padding.
      # Then we compute the cartesian production of the indices (using broadcast
      # and concat).
  
      # We could make this code more complex and do all the padding at once, but
      # we prefer to keep it simple.
      indices_by_dim = []
      indices_shape = operand_shape + (1,)
      output_shape = []  # considering only interior padding
      for d, (dsz, (_, _, i)) in enumerate(
          zip(operand_shape, padding_config)
      ):
          dilation_factor = i + 1
          output_shape.append(dsz * dilation_factor - i)
          indices = tf.range(dsz) * dilation_factor
          expansion = [None] * (1 + len(operand_shape))
          expansion[d] = slice(None, None, None)
          indices_by_dim.append(
              tf.broadcast_to(indices[expansion], indices_shape)
          )
  
      indices_cartesian = tf.concat(
          indices_by_dim, axis=len(operand_shape)
      )
      scattered = tf.scatter_nd(
          indices_cartesian, operand, output_shape
      )
      # What elements from the output array we use from
      mask = tf.scatter_nd(
          indices_cartesian,
          tf.ones_like(operand, dtype=np.bool_),
          output_shape,
      )
      return tf.where(mask, scattered, padding_value)
  
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 16 for calling a global function with example usage: _interior_padding(operand, padding_value, padding_config, operand_shape) and returns: tf