// B: 1. NamedValueChecker 2. Column Converter 3. Default
		// C: 1. NamedValueChecker 3. Default
		// D: 1. Column Converter 2. Default
		//
		// The only time a Column Converter is called is first
		// or after NamedValueConverter. If first it is handled before
		// the nextCheck label. Thus for repeats tries only when the
		// NamedValueConverter is selected should the Column Converter
		// be used in the retry.

        &result_types);

    // Create a new if op with new operands and updated result types.
    auto converted = rewriter.create<TF::IfOp>(
        op.getLoc(), result_types, adaptor.getOperands(), op->getAttrs());
    converted->removeAttr("T");
    (void)UpdateFunctionTypesForIfOp(rewriter, converted, adaptor.getOperands(),
                                     tensor_list_args, resized_tensor_lists,

					f.Fuzz(patch)
					converted := test.converterFn(patch)

					b, err := json.Marshal(converted)
					if err != nil {
						t.Fatalf("failed to marshal converted object (%v): %v", err, converted)
					}
					got := &metav1.PatchOptions{}
					err = json.Unmarshal(b, &got)
					if err != nil {
						t.Fatalf("failed to unmarshal converted object: %v", err)
					}

  public void testStringConverter_convert() {
    Converter<String, TestEnum> converter = Enums.stringConverter(TestEnum.class);
    assertEquals(TestEnum.CHEETO, converter.convert("CHEETO"));
    assertEquals(TestEnum.HONDA, converter.convert("HONDA"));
    assertEquals(TestEnum.POODLE, converter.convert("POODLE"));
    assertNull(converter.convert(null));
    assertNull(converter.reverse().convert(null));
  }

        is This -> ExprConversion.Failed(listOf(UnsupportedSyntax(UnsupportedSyntaxCause.UnsupportedThisValue)))
    }.also { (it as? ExprConversion.Converted)?.let { converted -> valueMapping[converted.valueNode] = expr } }

    private
    fun Expr.asChainedNameOrNull(): String? {
        fun recurse(e: Expr): StringBuilder? = when (e) {
            is PropertyAccess -> {

            for (expr in this@convertVarargsExpression) {
                val converted = expr.convertConstantExpression(builder) ?: continue

                if ((expr is FirSpreadArgumentExpression || expr is FirNamedArgumentExpression) && converted is KaArrayAnnotationValue) {
                    addAll(converted.values)
                } else {
                    add(converted)
                }

namespace mlir {

namespace func {
class FuncOp;
}
class ModuleOp;
class Operation;
template <typename T>
class OperationPass;
class Pass;

namespace mhlo {

/// Converter to be used along with the fallback Tf2Xla patterns below.
class Tf2XlaTypeConverter : public TypeConverter {
 public:
  Tf2XlaTypeConverter();
};

/// Adds the TF to XLA via TF2XLA rewrite patterns to the pattern list.

# limitations under the License.
# ==============================================================================
"""Wraps TFLite Converter interface with python lazy loader."""
# We need to import pywrap_tensorflow prior to the converter wrapper.
# pylint: disable=invalid-import-order,g-bad-import-order
from tensorflow.python import pywrap_tensorflow  # pylint: disable=unused-import

}

class Tf2XlaRewritePattern : public ConversionPattern {
 public:
  explicit Tf2XlaRewritePattern(MLIRContext* ctx, TypeConverter& converter,
                                const std::string& device_type,
                                bool prefer_tf2xla)
      : ConversionPattern(converter, MatchAnyOpTypeTag(), /*benefit=*/1, ctx),
        device_type_(device_type),
        prefer_tf2xla_(prefer_tf2xla) {}

      T defaultType = (T) getFirstTypeParameter(type.getType());
      return defaultType;
    }
    if (type.getRawType() == Converter.class) {
      TypeToken<?> convertFromType = type.resolveType(Converter.class.getTypeParameters()[0]);
      TypeToken<?> convertToType = type.resolveType(Converter.class.getTypeParameters()[1]);
      @SuppressWarnings("unchecked") // returns default for both F and T