org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.bp.SquaredDifferenceBpOp Maven / Gradle / Ivy
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* * terms of the Apache License, Version 2.0 which is available at
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package org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.bp;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.common.base.Preconditions;
import org.nd4j.imports.NoOpNameFoundException;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import java.util.Arrays;
import java.util.List;
public class SquaredDifferenceBpOp extends DynamicCustomOp {
public SquaredDifferenceBpOp() {}
public SquaredDifferenceBpOp(SameDiff sameDiff, SDVariable[] args) {
super("squaredsubtract_bp", sameDiff, args);
}
@Override
public String opName() {
return "squaredsubtract_bp";
}
@Override
public String onnxName() {
throw new NoOpNameFoundException("No onnx op opName found for " + opName());
}
@Override
public String tensorflowName() {
throw new NoOpNameFoundException("No TF opName found for " + opName());
}
@Override
public List doDiff(List i_v1) {
throw new UnsupportedOperationException("Not supported");
}
@Override
public List calculateOutputDataTypes(List dataTypes){
Preconditions.checkState(dataTypes != null && dataTypes.size() == 3, "Expected exactly 3 input datatypes for %s, got %s", getClass(), dataTypes);
Preconditions.checkState(dataTypes.get(0) == dataTypes.get(1), "Input datatypes 0 and 1 must be the same, got %s", dataTypes);
return Arrays.asList(dataTypes.get(0), dataTypes.get(1));
}
}