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 *  ******************************************************************************
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 *  * This program and the accompanying materials are made available under the
 *  * terms of the Apache License, Version 2.0 which is available at
 *  * https://www.apache.org/licenses/LICENSE-2.0.
 *  *
 *  *  See the NOTICE file distributed with this work for additional
 *  *  information regarding copyright ownership.
 *  * Unless required by applicable law or agreed to in writing, software
 *  * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
 *  * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
 *  * License for the specific language governing permissions and limitations
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package org.nd4j.linalg.api.ops.impl.loss;

import lombok.Getter;
import lombok.NonNull;
import lombok.Setter;
import org.nd4j.autodiff.loss.LossReduce;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.common.base.Preconditions;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import org.nd4j.linalg.factory.Nd4j;

import java.util.Collections;
import java.util.List;

public abstract class BaseLoss extends DynamicCustomOp {
    @Getter
    @Setter
    protected LossReduce lossReduce;

    public BaseLoss(@NonNull SameDiff sameDiff, @NonNull LossReduce lossReduce, @NonNull SDVariable predictions, SDVariable weights,
                    @NonNull SDVariable labels){
        super(null, sameDiff, new SDVariable[]{predictions, getWeights(sameDiff, weights, predictions), labels});
        this.lossReduce = lossReduce;
        addArgs();
    }

    public BaseLoss(@NonNull LossReduce lossReduce, @NonNull INDArray predictions, INDArray weights, @NonNull INDArray labels){
        super(new INDArray[]{predictions, getWeights(weights, predictions), labels}, null);
        this.lossReduce = lossReduce;
        addArgs();
    }

    protected static INDArray getWeights(INDArray weights, INDArray predictions) {
        return (weights != null) ? weights : Nd4j.scalar(predictions.dataType(), 1.0);
    }

    protected static SDVariable getWeights(SameDiff sd, SDVariable weights, SDVariable predictions){
        return weights != null ? weights : sd.constant(Nd4j.scalar(predictions.dataType(), 1.0));
    }

    protected BaseLoss(){ }

    protected void addArgs(){
        iArguments.clear();
        tArguments.clear();
        addIArgument(lossReduce.ordinal()); //Ops: 0 - "none"; 1 - "weighted_sum";  2 - "weighted_mean";  3 - "weighted_sum_by_nonzero_weights"
    }

    public abstract String opName();

    @Override
    public int getNumOutputs() {
        return 1;
    }

    @Override
    public List<DataType> calculateOutputDataTypes(List<DataType> inputDataTypes){
        Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() >= 2, "Expected exactly 2 or more input datatypes for %s, got %s", getClass(), inputDataTypes);
        return Collections.singletonList(inputDataTypes.get(0));    //Same as predictions
    }
}

@@ -2856,77 +2856,8 @@
 ;%0A%0A 
-   @Override%0A    public int getNumOutputs() %7B%0A        return 1;%0A    %7D
 %0A%0A  

Update BaseLoss.java