PushTransposeThroughEwisePass> {
 public:
  MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(PushTransposeThroughEwisePass)
  void runOnOperation() override;
};

// Compute the permutation that would take `arr` to the identity.
llvm::SmallVector<int32_t> InvertPermutation(llvm::SmallVector<int32_t> arr) {
  llvm::SmallVector<int32_t> inverse_arr(arr.size());
  for (int32_t i = 0; i < arr.size(); ++i) {

	for rev := range revisions {
		for _, base := range getObjectLabels() {
			base[label.IoIstioRev.Name] = rev
			objectLabels = append(objectLabels, base)
		}
	}

	// For each permutation, we check which webhooks it matches. It must match exactly 0 or 1!
	for _, nl := range namespaceLabels {
		for _, ol := range objectLabels {
			matches := sets.New[string]()
			for name, whs := range webhooks {

    return;
  }
  if (const auto* op = op_union.AsStablehloTransposeOptions()) {
    if (!op->permutation.empty()) {
      attributes.emplace_back(builder.getNamedAttr(
          "permutation",
          BuildVhloTensorV1Attr({static_cast<int64_t>(op->permutation.size())},
                                op->permutation, builder)));
    }
    return;
  }
  if (const auto* op = op_union.AsStablehloRngBitGeneratorOptions()) {

// CHECK: %[[TRANSPOSE_1:.+]] = stablehlo.transpose
// CHECK: return %[[TRANSPOSE_1]]

// -----

// [No change] Tests that the activation transpose whose permutation is not
// `[0, 3, 1, 2]` is not deferred.

// CHECK-LABEL: add_with_activation_transpose_permutation_mismatch
func.func @add_with_activation_transpose_permutation_mismatch(

	// Shuffle really ought not be called with n that doesn't fit in 32 bits.
	// Not only will it take a very long time, but with 2³¹! possible permutations,
	// there's no way that any PRNG can have a big enough internal state to
	// generate even a minuscule percentage of the possible permutations.
	// Nevertheless, the right API signature accepts an int n, so handle it as best we can.
	for i := n - 1; i > 0; i-- {

  //     ..., dn-1). For example, a 2-D matrix stored in row-major order would
  //     have traversal_order = (d0, d1).
  //   - if block sparse with a k-dimensional block (0 <= k <= n), the
  //     traversal_order has n + k elements. The first n elements are still a
  //     permutation of (d0, ..., dn-1). The lask k elements are a permutation

template <class Op>
static LogicalResult InferenceFoldOperandsPermutation(
    ArrayRef<int64_t> permutation, Op* op) {
  // FusedBatchNorm in training mode is a layout sentitive operation, and should
  // have already assigned an optimal data format.
  if (op->getIsTraining()) return failure();
  return ::mlir::TF::FoldOperandsPermutation(permutation, op);
}

template <class Op>

  //     ..., dn-1). For example, a 2-D matrix stored in row-major order would
  //     have traversal_order = (d0, d1).
  //   - if block sparse with a k-dimensional block (0 <= k <= n), the
  //     traversal_order has n + k elements. The first n elements are still a
  //     permutation of (d0, ..., dn-1). The lask k elements are a permutation

def : Pat<(TF_CastOp $arg, ConstBoolAttrFalse), (MHLO_ConvertOp $arg)>;

def : Pat<(TF_TransposeOp:$res $arg, (ConstantLikeMatcher ElementsAttr:$permutation)),
          (MHLO_TransposeOp $arg, (CastElementsToI64Elements $permutation))>;


// Lowering these ops with static shape to mhlo.reshape
foreach TfOp = [TF_ExpandDimsOp, TF_ReshapeOp, TF_SqueezeOp, ] in {

	// Shuffle really ought not be called with n that doesn't fit in 32 bits.
	// Not only will it take a very long time, but with 2³¹! possible permutations,
	// there's no way that any PRNG can have a big enough internal state to
	// generate even a minuscule percentage of the possible permutations.
	// Nevertheless, the right API signature accepts an int n, so handle it as best we can.
	i := n - 1
	for ; i > 1<<31-1-1; i-- {