// (x - mean) * scale / sqrt(variance + epsilon) + offset,
    // is then to compute (x * multiplier) + (offset - mean * multiplier).

    auto epsilon = materializeEpsilon(
        bn_op.getOperation(), bn_op.getEpsilonAttr(), fp_type,
        bn_op.getVariance(), variance_type, rewriter);
    if (!epsilon) {
      return failure();
    }

    // Compute multiplier = scale / sqrt(variance + epsilon)

// tf_device.parallel_execute
//===----------------------------------------------------------------------===//

LogicalResult ParallelExecuteOp::verify() {
  ParallelExecuteOp op = *this;
  const auto& regions = op.getOperation()->getRegions();
  if (regions.empty()) {
    return op.emitOpError() << "must have at least one region.";
  }

  int output_index = 0;
  for (const auto& region_and_index : llvm::enumerate(regions)) {

                             Operation* new_yield_op) {
  Block* block = new_yield_op->getBlock();

  auto move_inner_ops = [block, new_yield_op](IslandOp island) {
    auto& island_body = island.GetBody().getOperations();
    block->getOperations().splice(new_yield_op->getIterator(), island_body,
                                  island_body.begin(),
                                  std::prev(island_body.end()));
  };

					// Ignore context cancelled from webhook metrics
					if !errors.Is(err.Reason, context.Canceled) {
						admissionmetrics.Metrics.ObserveWebhookRejection(ctx, hook.Name, "admit", string(versionedAttr.Attributes.GetOperation()), admissionmetrics.WebhookRejectionCallingWebhookError, int(err.Status.ErrStatus.Code))
					}
				}

  using OpRewritePattern::OpRewritePattern;

  LogicalResult matchAndRewrite(TF::AddNOp op,
                                PatternRewriter& rewriter) const override {
    if (!HasVariantInputOrOutput(op.getOperation())) {
      return failure();
    }
    auto converted = rewriter.create<TFL::CustomOp>(
        op->getLoc(), op->getResultTypes(), op->getOperands(), "VariantAddN",

    StrAttr:$device
  );

  let results = (outs
    Variadic<AnyType>:$results
  );

  let regions = (region SizedRegion<1>:$body);

  let extraClassDeclaration = [{
    Block &GetBody() { return getOperation()->getRegion(0).front(); }
    bool WrapsSingleOp();
  }];

  let builders = [
    OpBuilder<(ins "StringAttr":$device, "TypeRange":$result_types),
    [{
      $_state.addAttribute("device", device);

          "target device.");
    }

    SmallVector<Location, 3> locations{contraction.getLoc(), bias_add.getLoc()};
    SmallVector<Attribute, 2> fused_ops{StringAttr::get(
        context, bias_add.getOperation()->getName().stripDialect())};

    // BiasAdd may or may not feed into an activation function.
    auto activation = GetActivation(bias_add);

}

}  // namespace

struct LegalizeStablehloToVhloPass
    : public impl::LegalizeStablehloToVhloPassBase<
          LegalizeStablehloToVhloPass> {
  void runOnOperation() override {
    ModuleOp module = getOperation();
    std::string target_version = tflite_supported_stablehlo_version;
    VhloToStablehloTypeConverter to_builtin_converter;

    // StableHLO --> VHLO (allow funcs)

void TPURewritePass::runOnOperation() {
  TF::RuntimeDevices devices;
  if (failed(tensorflow::GetDevicesFromOp(getOperation(), &devices)))
    return signalPassFailure();

  // Collect compilation results.
  llvm::DenseMap<Attribute, SmallVector<TF::TPUCompilationResultOp, 1>>
      compilation_results;
  auto result_init = getOperation().walk([&](TF::TPUCompilationResultOp op) {

      OpBuilder::InsertionGuard guard(rewriter);
      rewriter.setInsertionPointAfter(op->getParentOfType<func::FuncOp>());
      cloned_func = llvm::dyn_cast_or_null<func::FuncOp>(
          rewriter.clone(*func.getOperation()));
      manager.insert(cloned_func);
      rewriter.setInsertionPointToStart(&cloned_func.getBody().front());
      auto result_type =
          RankedTensorType::get({3}, rewriter.getType<TF::StringType>());