removed some unnecessary code

src/main/java/de/monticore/lang/monticar/cnnarch/_symboltable/UnrollSymbol.java

@@ -202,12 +202,6 @@ public class UnrollSymbol extends ArchitectureElementSymbol {
 
     @Override
     public List<ArchTypeSymbol> computeOutputTypes() {
-        try {
-            System.err.println("############################" + this.getIntValue(AllPredefinedLayers.BEAMSEARCH_MAX_LENGTH).get());
-        }catch (Exception e){
-            System.err.println("44444444444444444444444444");
-            e.printStackTrace();
-        }
         if (getElements().isEmpty()){
             if (getInputElement().isPresent()){
                 return getInputElement().get().getOutputTypes();

src/test/java/de/monticore/lang/monticar/cnnarch/cocos/AllCoCoTest.java

@@ -66,7 +66,6 @@ public class AllCoCoTest extends AbstractCoCoTest {
         checkValid("valid_tests", "Alexnet_alt2");
         checkValid("valid_tests", "MultipleOutputs");
         checkValid("valid_tests", "MultipleStreams");
-        checkValid("valid_tests", "Alexnet_alt_OneHotOutput");
     }
 
     @Test

src/test/resources/valid_tests/Alexnet_alt_OneHotOutput.cnna

-architecture Alexnet_alt_OneHotOutput(img_height=224, img_width=224, img_channels=3, classes=10){
-    def input Z(0:255)^{img_channels, img_height, img_width} image
-    def output Q(0:1)^{classes} predictions
-
-    image ->
-    Convolution(kernel=(11,11), channels=96, stride=(4,4), padding="no_loss") ->
-    Lrn(nsize=5, alpha=0.0001, beta=0.75) ->
-    Pooling(pool_type="max", kernel=(3,3), stride=(2,2), padding="no_loss") ->
-    Relu() ->
-    Split(n=2) ->
-    (
-        [0] ->
-        Convolution(kernel=(5,5), channels=128) ->
-        Lrn(nsize=5, alpha=0.0001, beta=0.75) ->
-        Pooling(pool_type="max", kernel=(3,3), stride=(2,2), padding="no_loss") ->
-        Relu()
-    |
-        [1] ->
-        Convolution(kernel=(5,5), channels=128) ->
-        Lrn(nsize=5, alpha=0.0001, beta=0.75) ->
-        Pooling(pool_type="max", kernel=(3,3), stride=(2,2), padding="no_loss") ->
-        Relu()
-    ) ->
-    Concatenate() ->
-    Convolution(kernel=(3,3), channels=384) ->
-    Relu() ->
-    Split(n=2) ->
-    (
-        [0] ->
-        Convolution(kernel=(3,3), channels=192) ->
-        Relu() ->
-        Convolution(kernel=(3,3), channels=128) ->
-        Pooling(pool_type="max", kernel=(3,3), stride=(2,2), padding="no_loss") ->
-        Relu()
-    |
-        [1] ->
-        Convolution(kernel=(3,3), channels=192) ->
-        Relu() ->
-        Convolution(kernel=(3,3), channels=128) ->
-        Pooling(pool_type="max", kernel=(3,3), stride=(2,2), padding="no_loss") ->
-        Relu()
-    ) ->
-    Concatenate() ->
-    FullyConnected(units=4096) ->
-    Relu() ->
-    Dropout() ->
-    FullyConnected(units=4096) ->
-    Relu() ->
-    Dropout() ->
-    FullyConnected(units=classes) ->
-    Softmax() ->
-    predictions;
-}
\ No newline at end of file