%8 = mhlo.divide %7, %2 : tensor<64xf32>
  %9 = mhlo.floor %8 : tensor<64xf32>
  %10 = mhlo.convert %9 : (tensor<64xf32>) -> tensor<64xi32>
  %11 = mhlo.compare  LT, %10, %1,  SIGNED : (tensor<64xi32>, tensor<64xi32>) -> tensor<64xi1>
  %12 = mhlo.add %10, %0 : tensor<64xi32>
  %13 = mhlo.select %11, %12, %10 : tensor<64xi1>, tensor<64xi32>
  %14 = mhlo.reshape %13 : (tensor<64xi32>) -> tensor<64x1xi32>

    if (failed(output_type)) {
      return failure();
    }

    auto input_quant = rewriter.create<mhlo::BitcastConvertOp>(
        op->getLoc(), *input_quant_type, input);
    auto result = rewriter.create<mhlo::UniformQuantizeOp>(
        op->getLoc(), *output_type, input_quant);
    rewriter.replaceOpWithNewOp<mhlo::BitcastConvertOp>(
        op,
        output_type->clone(

  if (lower_to_xla_hlo) {
    // This pass operates on MHLO control flow ops so it should be legalized
    // after the control flow ops are legalized.
    pm.addPass(mlir::mhlo::CreateLegalizeTFCommunicationPass());

    // Everything should be MHLO after this.
    if (!allow_partial_conversion) {
      pm.addNestedPass<mlir::func::FuncOp>(
          mlir::mhlo::CreateVerifyTFXLALegalizationPass(legalize_chlo));
    }
  }

  // Legalize MHLO to StableHLO should be moved closer to where it is needed
  // There are some entry points that start with HLO->MHLO like
  // jax_to_tfl_flatbuffer.cc which can likely be updated to emit StableHLO
  // to be consistent with other entrypoints.
  pass_manager.addPass(mlir::mhlo::createHloLegalizeToStablehloPass());

  // Decompose CHLO into StableHLO ops

#include "tensorflow/compiler/mlir/tf2xla/transforms/xla_legalize_targets.h"
#include "xla/mlir_hlo/mhlo/IR/hlo_ops.h"
#include "xla/mlir_hlo/mhlo/transforms/rewriters.h"
#include "xla/mlir_hlo/mhlo/utils/type_conversion.h"
#include "tensorflow/core/lib/monitoring/counter.h"

namespace mlir {
namespace mhlo {
namespace {

#define GEN_PASS_DEF_LEGALIZETF

        custom_legalization_passes);

// Compiles a Graph from TF to HLO and adds the resulting HLO to the
// XlaBuilder. This function adds HLO to a larger HLO computation, so
// HLO-level inputs are supplied, and HLO-level outputs are produced.
// xla_params is the HLO-level inputs and returns is the HLO-level outputs.
// If unconditionally_use_output_shapes is true then the unregistered

  module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 268 : i32}} {
  func.func @main(%arg0: tensor<?xi32, #mhlo.type_extensions<bounds = [3]>> ) -> (tensor<?xi32, #mhlo.type_extensions<bounds = [3]>>) {
    func.return %arg0 : tensor<?xi32, #mhlo.type_extensions<bounds = [3]>>
  }
})";

  auto compilation_result = CompileMlirModule(
      kMlirModuleWithBoundedDynamicArgStr,

#include "tensorflow/compiler/tf2xla/xla_compiler.h"
#include "tensorflow/compiler/tf2xla/xla_helpers.h"
#include "xla/client/compile_only_client.h"
#include "xla/hlo/ir/hlo_input_output_alias_config.h"
#include "xla/mlir_hlo/mhlo/IR/register.h"
#include "xla/shape.h"
#include "xla/shape_util.h"
#include "xla/status_macros.h"
#include "tensorflow/core/framework/function.h"
#include "tensorflow/core/framework/op.h"

  let dependentDialects = ["mlir::stablehlo::StablehloDialect"];
  let constructor = "mlir::quant::stablehlo::CreateQuantizeWeightPass()";
}

def UnfuseMhloBatchNormPass : Pass<"stablehlo-unfuse-mhlo-batch-norm", "mlir::func::FuncOp"> {
  let summary = "Unfuses batch normalization into arithmetic ops.";
}

def LiftQuantizableSpotsAsFunctionsPass : Pass<"stablehlo-lift-quantizable-spots-as-functions", "mlir::ModuleOp"> {

#include "tensorflow/compiler/mlir/tensorflow/utils/xla_call_module_attrs.h"
#include "tensorflow/compiler/tf2xla/kernels/xla_call_module_loader.h"
#include "xla/mlir_hlo/mhlo/IR/hlo_ops.h"  // IWYU pragma: keep
#include "tsl/platform/statusor.h"

namespace mlir {
namespace TF {
namespace {

#define GEN_PASS_DEF_XLACALLMODULEDESERIALIZATIONPASS