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 *  *  information regarding copyright ownership.
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package org.nd4j.linalg.api.ops.impl.transforms.same;

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.impl.transforms.BaseDynamicTransformOp;

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

public class Identity extends BaseDynamicTransformOp {

    public Identity(SameDiff sd, SDVariable input){
        super(sd, new SDVariable[]{input}, false);
    }

    public Identity(INDArray x, INDArray z){
        super(new INDArray[]{x}, new INDArray[]{z});
    }

    public Identity(INDArray x){
        super(new INDArray[]{x}, null);
    }

    public Identity(){ }

    @Override
    public String opName() {
        return "identity";
    }

    @Override
    public String onnxName() {
        return "Constant";
    }

    @Override
    public String tensorflowName() {
        return "Identity";
    }

    @Override
    public String[] tensorflowNames() {
        return new String[]{"Identity", "DeepCopy", "CopyHost"};
    }

    @Override
    public List doDiff(List i_v) {
        //Eventually we'll optimize this out
        return Collections.singletonList(sameDiff.identity(i_v.get(0)));
    }

    @Override
    public List calculateOutputDataTypes(List dataTypes) {
        Preconditions.checkState(dataTypes != null && dataTypes.size() == 1, "Expected exactly 1 input datatype for %s, got input %s", getClass(), dataTypes);
        if(!dArguments.isEmpty())
            return Arrays.asList(dArguments.get(0));
        return dataTypes;
    }

}




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