func.getAllResultAttrs(tmp);

    for (const auto& res : tmp) {
      result_attrs.push_back(res.getValue().vec());
    }
  }

  mlir::Builder builder(func.getOperation());

  for (OpOperand& operand : fetch.getOperation()->getOpOperands()) {
    if (mlir::isa<tf_executor::ControlType>(operand.get().getType())) break;
    auto it = value_to_device.find(operand.get());
    if (it != value_to_device.end()) {

  auto parallel_execute = llvm::dyn_cast<tf_device::ParallelExecuteOp>(
      execute_launch->getParentOp());
  Operation* execute_parent =
      parallel_execute ? parallel_execute.getOperation() : execute_launch;
  // Collect all operands of `execute` whose defining ops are variable reads
  // that might get merged, and add relevant information to `var_access_info`.

    llvm::SmallDenseMap<ResourceHandle, int64_t> &resource_handle_id_map,
    int64_t &next_id) {
  llvm::SmallVector<ResourceHandleValueAndId, 4> resource_vec;
  llvm::StringRef device = GetDeviceOrEmpty(getOperation());

  for (const auto &iter : llvm::enumerate(getResults())) {
    auto index = iter.index();
    if (mlir::isa<TF::ResourceType>(
            getElementTypeOrSelf(iter.value().getType()))) {

  MLIRContext* ctx = &getContext();
  RewritePatternSet patterns(ctx);
  mhlo::populateUnfuseBatchNormPatterns(ctx, &patterns);

  if (failed(
          applyPatternsAndFoldGreedily(getOperation(), std::move(patterns)))) {
    return signalPassFailure();
  }
}
}  // namespace

    }
    // Use the min/max from the operands and the num_bits and narrow_range
    // attribute to create the quantization parameter for the new quantize op.
    rewriter.setInsertionPointAfter(tf_op.getOperation());
    IntegerAttr num_bits = rewriter.getI64IntegerAttr(tf_op.getNumBits());
    BoolAttr narrow_range = rewriter.getBoolAttr(tf_op.getNarrowRange());
    Type res_type = tf_op.getType();

}

void ClusterOutliningPass::runOnOperation() {
  ModuleOp module = getOperation();
  SymbolTable symbol_table(module);
  OpBuilder builder(module.getContext());
  module.walk([&](tf_device::ClusterOp cluster) {
    OutlineCluster(cluster, &symbol_table, &builder);
  });
}

void LaunchOutliningPass::runOnOperation() {
  ModuleOp module = getOperation();
  SymbolTable symbol_table(module);

#include "tensorflow/compiler/mlir/quantization/stablehlo/passes/bridge/optimize.inc"

void OptimizeIntGraph::runOnOperation() {
  RewritePatternSet patterns(&getContext());
  populateWithGenerated(patterns);
  auto func = getOperation();
  if (failed(applyPatternsAndFoldGreedily(func, std::move(patterns)))) {
    signalPassFailure();
  }
}

}  // namespace

std::unique_ptr<OperationPass<func::FuncOp>> CreateOptimizeIntGraphPass() {

 public:
  void runOnOperation() override;
};

void InferenceMetricsPass::runOnOperation() {
  bool has_tpu_partitioned_call = false;
  ModuleOp module = getOperation();

  for (auto func_op : module.getOps<func::FuncOp>()) {
    func_op->walk(
        [&](TF::TPUPartitionedCallOp op) { has_tpu_partitioned_call = true; });

    if (has_tpu_partitioned_call) break;
  }

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

        new_ewise_op->getLoc(), op->getResult(0).getType(),
        new_ewise_op->getResults()[0], perm);
    rewriter.replaceOp(op, out_tpose_op.getOperation());
    return success();
  }
};

void PushTransposeThroughEwisePass::runOnOperation() {
  auto module = getOperation();
  RewritePatternSet patterns(&getContext());

  patterns.add<CommuteTransposeWithEwiseOps>(&getContext());