auto* optimizer_options =
      config.mutable_graph_options()->mutable_optimizer_options();
  if (enable) {
    optimizer_options->set_global_jit_level(tensorflow::OptimizerOptions::ON_1);

    // These XLA flags are needed to trigger XLA properly from C (more generally
    // non-Python) clients. If this API is called again with `enable` set to
    // false, it is safe to keep these flag values as is.
    tensorflow::MarkForCompilationPassFlags* flags =

        "@com_google_absl//absl/strings",
        "@eigen_archive//:eigen3",
        "@local_xla//xla:shape_util",
        "@local_xla//xla:test",
        "@local_xla//xla:xla_data_proto_cc",
        "@local_xla//xla/service:hlo_profile_printer",
    ],
)

tf_cc_test(
    name = "tfcompile_test_mlir_bridge",
    srcs = ["tfcompile_test.cc"],
    extra_copts = ["-DENABLE_MLIR_BRIDGE_TEST"],
    tags = [

      }
    } else if (src_outside_compilation && !dst_outside_compilation) {
      // Case 1b: outside compilation to its XLA computation control edge.
      ReplaceAttr(e->src(), kXlaConnectedToXlaComputationAttrName, true);
    } else if (!src_outside_compilation && dst_outside_compilation) {
      // Case 1b: XLA computation to outside compilation in it control edge.
      ReplaceAttr(e->dst(), kXlaConnectedFromXlaComputationAttrName, true);

limitations under the License.
==============================================================================*/

// This is the legalization pattern definition file for TF to XLA.

include "mlir/IR/OpBase.td"
include "mlir/Dialect/Shape/IR/ShapeOps.td"
include "mlir/Dialect/Func/IR/FuncOps.td"
include "mlir/Dialect/Tensor/IR/TensorOps.td"
include "stablehlo/dialect/ChloOps.td"

#include "tensorflow/cc/ops/math_ops.h"
#include "tensorflow/compiler/jit/flags.h"
#include "tensorflow/compiler/jit/xla_cluster_util.h"
#include "tensorflow/compiler/tf2xla/cc/ops/xla_ops.h"
#include "xla/status_macros.h"
#include "tensorflow/core/common_runtime/shape_refiner.h"
#include "tensorflow/core/graph/algorithm.h"
#include "tensorflow/core/public/session_options.h"
#include "tensorflow/core/util/device_name_utils.h"

// RUN: tf-opt -split-input-file -verify-diagnostics -xla-legalize-tf-collective -xla-legalize-tf %s | FileCheck %s


// CHECK: module attributes
// CHECK-SAME{LITERAL}: tf2xla.collective_info.group_key = 0
// CHECK-SAME{LITERAL}: tf2xla.collective_info.group_size = 2
// CHECK-LABEL: func @all_reduce_cross_replica
func.func @all_reduce_cross_replica(%input: tensor<f32>) -> tensor<f32> {

#include "mlir/Transforms/DialectConversion.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "xla/mlir_hlo/mhlo/IR/hlo_ops.h"

namespace mlir {
namespace odml {

bool IsIotaAttr(ArrayRef<int64_t> arr, int64_t size) {
  if (arr.size() != size) return false;
  int64_t iota = 0;
  for (auto s : arr) {

// RUN: tf-opt "-xla-legalize-tf=device-type=XLA_CPU_JIT legalize-chlo=false use-tf2xla-fallback=true prefer-tf2xla=true" %s | FileCheck %s
// RUN: tf-opt "-xla-legalize-tf=device-type=XLA_CPU_JIT legalize-chlo=false prefer-tf2xla=true" %s | FileCheck --check-prefix NOFALLBACK %s

module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 268 : i32}} {

// CHECK-LABEL: @abs
func.func @abs(%arg0: tensor<2xf32>) -> tensor<2xf32> {

#include "mlir/Transforms/DialectConversion.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/lite/stablehlo/transforms/legalize_hlo_conversions/util.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "xla/mlir_hlo/mhlo/IR/hlo_ops.h"

namespace mlir {
namespace odml {

// Convert updates into canonical form as expected by tf.scatter ops.
//
// tf.scatter expects `update_window_dims` to be the trailing dimensions.
//

  // nested one.
  pm.addPass(mlir::tf_executor::CreateTFExecutorTPUV1IslandCoarseningPass());
  pm.addPass(mlir::tf_executor::CreateTFExecutorTPUV1IslandOutliningPass());
}

// Run the TF XLA Bridge based on the input pipeline, which can be either TPU
// bridge pipeline or non TPU bridge pipeline.
tensorflow::Status RunTFXLABridge(
    ModuleOp module,
    llvm::function_ref<void(OpPassManager &pm)> pipeline_builder,