org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.FloorModOp Maven / Gradle / Ivy
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* * terms of the Apache License, Version 2.0 which is available at
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* * information regarding copyright ownership.
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* * SPDX-License-Identifier: Apache-2.0
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package org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic;
import lombok.NonNull;
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 org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.bp.FloorModBpOp;
import org.nd4j.linalg.api.shape.Shape;
import java.util.Collections;
import java.util.List;
public class FloorModOp extends BaseDynamicTransformOp {
public FloorModOp() {}
public FloorModOp(SameDiff sameDiff, SDVariable x, SDVariable y) {
super(sameDiff, new SDVariable[]{x, y}, false);
}
public FloorModOp(@NonNull INDArray x, @NonNull INDArray y) {
this(new INDArray[]{x, y}, null);
}
public FloorModOp(INDArray[] inputs, INDArray[] outputs) {
super(inputs, outputs);
}
@Override
public String opName() {
return "floormod";
}
@Override
public String onnxName() {
return "FloorMod";
}
@Override
public String tensorflowName() {
return "FloorMod";
}
@Override
public List doDiff(List f1) {
return new FloorModBpOp(sameDiff, larg(), rarg(), f1.get(0)).outputs();
}
@Override
public List calculateOutputDataTypes(List dataTypes) {
Preconditions.checkState(dataTypes != null && dataTypes.size() == 2, "Expected exactly 2 input datatypes for %s, got input %s", getClass(), dataTypes);
DataType z = Shape.pickPairwiseDataType(dataTypes.get(0), dataTypes.get(1));
return Collections.singletonList(z);
}
}