code: def _transpose_for_tf_conv(
      lhs,
      lhs_shape: core.Shape,
      rhs,
      rhs_shape: core.Shape,
      dimension_numbers,
  ):
      """Transposes lhs and rhs to respectively NHWC and HWIO so they can be passed to TF functions.
  
      The shapes passed in and returned may contain polynomials, and thus may
      be different than lhs.shape and rhs.shape.
      """
      # TODO(marcvanzee): Add tests for this ops for shape polymorphism.
      lhs_perm, rhs_perm, _ = dimension_numbers
  
      # TODO(marcvanzee): Consider merging transposes if we want to optimize.
      # For `lhs_perm` / `output_perm`, perm (0, 1, 2, 3) corresponds to "NCHW".
      lhs, lhs_shape = _transpose_with_shape(
          lhs, lhs_shape, lhs_perm
      )  # lhs --> "NCHW"
      if len(lhs_perm) == 3:
          # For 1D convolution, we add a trivial "W" dimension, so that 2D Convolution
          # logic can be applied downstream.
          lhs = lhs[:, :, :, np.newaxis]
          lhs_shape = tuple(lhs_shape) + (1,)
      # However, the TF ops only support "NHWC" on CPU, so we transpose again.
      lhs, lhs_shape = _transpose_with_shape(
          lhs, lhs_shape, (0, 2, 3, 1)
      )  # "NCHW" --> "NHWC"
  
      # For `rhs_perm`, perm (0, 1, 2, 3) corresponds to "OIHW".
      rhs, rhs_shape = _transpose_with_shape(
          rhs, rhs_shape, rhs_perm
      )  # rhs --> "OIHW"
      # Handle conv1d case.
      if len(rhs_perm) == 3:
          rhs = rhs[:, :, :, np.newaxis]
          rhs_shape = tuple(rhs_shape) + (1,)
      # For the tf ops, rhs is expected to be "OIHW".
      rhs, rhs_shape = _transpose_with_shape(
          rhs, rhs_shape, (2, 3, 1, 0)
      )  # "OIHW" --> "HWIO"
      jax2tf._assert_matching_abstract_shape(lhs, lhs_shape)
      jax2tf._assert_matching_abstract_shape(rhs, rhs_shape)
      return lhs, lhs_shape, rhs, rhs_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 5 for calling a global function with example usage: _transpose_for_tf_conv(lhs, lhs_shape, rhs, rhs_shape, dimension_numbers) and returns: lhs,  lhs_shape,  rhs,  rhs_shape