code: def _normalize_padding_and_dilations(
      padding, lhs_dilation, rhs_dilation, is_conv1d
  ):
      if is_conv1d:
          lhs_dilation = list(lhs_dilation) + [1]
          rhs_dilation = list(rhs_dilation) + [1]
          # Empty padding in the dummy dimension.
          # Note that when kernel_size=stride=1, padding of (0, 0) is both 'VALID' and
          # 'SAME'. So the inferred padding type will still register according to the
          # first dimension padding.
          padding = list(padding) + [(0, 0)]
      return padding, lhs_dilation, rhs_dilation
  
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 11 for calling a global function with example usage: _normalize_padding_and_dilations(padding, lhs_dilation, rhs_dilation, is_conv1d) and returns: padding,  lhs_dilation,  rhs_dilation