<component name="ProjectDictionaryState">
  <dictionary name="sergej.jaskiewicz">
    <words>
      <w>lowerings</w>
      <w>unmuted</w>
    </words>
  </dictionary>

  EXPECT_EQ(non_categorized_count, 428);
}

// Just a counter test to see which ops have duplicate lowerings. This isn't a
// correctness test versus a test to easily ensure the op registry is kept
// in sync.
TEST_F(LegalizationOpConfigTest, CountTypesWhichHaveBothMlirAndTf2xlaFallback) {
  int double_lowering_count = 0;

  DialectRegistry dialect_registry;
  dialect_registry.insert<mlir::TF::TensorFlowDialect>();

import org.jetbrains.kotlin.resolve.BindingContext

@OptIn(KaAnalysisNonPublicApi::class)
fun MutableSet<KtFile>.collectReachableInlineDelegatedPropertyAccessors() {
    if (isEmpty()) return

    // One of the compiler lowerings, namely `PropertyReferenceLowering`,
    // optimizes usages of property references in some cases,
    // and if a containing delegated property accessor is inline,
    // it might need this accessor's bytecode.
    //

std::unique_ptr<OperationPass<ModuleOp>> createLegalizeTFPass(
    bool legalize_chlo = true,
    std::optional<StringRef> tf2xla_fallback_device_type = std::nullopt,
    bool prefer_tf2xla = false);

/// Adds the TF to TF lowerings and TF to XLA rewrite patterns to the pattern
/// list.
void PopulateLegalizeTfPatterns(MLIRContext* context,
                                RewritePatternSet* patterns);

  target.addLegalDialect<arith::ArithDialect>();

  if (failed(applyPartialConversion(getOperation(), target,
                                    std::move(patterns)))) {
    getOperation().emitError("Composite lowering pass failed.");
    signalPassFailure();
  }
}

}  // namespace

// Creates an instance of the pass.
std::unique_ptr<OperationPass<ModuleOp>> CreateCompositeLoweringPass() {

See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

// Pattern definition file for direct lowering of mhlo composites to tflite ops.
include "mlir/IR/OpBase.td"
include "mlir/Dialect/Arith/IR/ArithOps.td"
include "mlir/Dialect/Func/IR/FuncOps.td"
include "mhlo/IR/hlo_ops.td"

                    // generated, which requires a container source to be provided. Without a facade class, function IR symbols will have
                    // an `IrExternalPackageFragment` parent, which trips up code generation during IR lowering.
                    val psiSourceFile =
                        descriptor.toSourceElement.containingFile as? PsiSourceFile ?: return super.getContainerSource(descriptor)

	}
	*fn.NWBRCalls = append(*fn.NWBRCalls, ir.SymAndPos{Sym: to, Pos: pos})
}

func (c *nowritebarrierrecChecker) check() {
	// We walk the call graph as late as possible so we can
	// capture all calls created by lowering, but this means we
	// only get to see the obj.LSyms of calls. symToFunc lets us
	// get back to the ODCLFUNCs.
	symToFunc := make(map[*obj.LSym]*ir.Func)
	// funcs records the back-edges of the BFS call graph walk. It

def TF_IfrtCallOp : TF_Op<"IfrtCall", []> {
  let summary = "Invokes a program via IFRT on a device";

  let description = [{
    This op calls an IFRT program uniquely identified by the given program id.

    During lowering from a `tf_device.cluster_func` op to a `tf.IfrtCall` op,
    the region owned by the former will be outlined to a function with a
    `tfrt_ifrt_serving.program_id` attribute. After that, the runtime ensures