}
	message.Range(func(descriptor protoreflect.FieldDescriptor, value protoreflect.Value) bool {
		m, isMessage := value.Interface().(protoreflect.Message)
		if isMessage {
			anyMessage, isAny := m.Interface().(*anypb.Any)
			if isAny {
				mt, err := protoregistry.GlobalTypes.FindMessageByURL(anyMessage.TypeUrl)
				if err != nil {
					topError = err
					return false
				}

    return failure();

  // Reshape recurrent weights to vectors if indy behaviour is enabled.
  // IndyLSTMs are a LSTM variant with diagonal recurrent weight
  // matrices. For optimization purposes these are provided as vectors.
  Value recurrent_to_input_weights =
      indy ? mlir::cast<Value>(
                 CreateFlattenOP(recurrent_weights_array->getResult(0),

    public val KaType.isCharSequence: Boolean get() = withValidityAssertion { isClassTypeWithClassId(DefaultTypeClassIds.CHAR_SEQUENCE) }
    public val KaType.isAny: Boolean get() = withValidityAssertion { isClassTypeWithClassId(DefaultTypeClassIds.ANY) }
    public val KaType.isNothing: Boolean get() = withValidityAssertion { isClassTypeWithClassId(DefaultTypeClassIds.NOTHING) }

    func.eraseBody();
    func.addEntryBlock();
    OpBuilder builder(func.getBody());
    if (failed(ConvertKerasLSTMLayer(func, &builder)))
      return signalPassFailure();
  }

  // LSTM `func::FuncOps` with indy behavior always have the `tf.api_implements`
  // function attribute prefixed with `"indy_lstm_"`.
  // IndyLSTMs have diagonal recurrent weight matrices and can benefit from

        }
    }

    /**
     * The bootstrap method for method calls from Groovy compiled code with indy enabled.
     * Gradle's bytecode processor replaces the Groovy's original {@link IndyInterface#bootstrap(MethodHandles.Lookup, String, MethodType, String, int)}