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 *  *  information regarding copyright ownership.
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package org.nd4j.linalg.api.ops.impl.layers.convolution;

import lombok.Builder;
import lombok.extern.slf4j.Slf4j;
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.impl.layers.convolution.config.DeConv2DConfig;

import java.util.ArrayList;
import java.util.List;


@Slf4j
public class DeConv2DDerivative extends DeConv2D {

    public DeConv2DDerivative() {}

    @Builder(builderMethodName = "derivativeBuilder")
    public DeConv2DDerivative(SameDiff sameDiff, SDVariable[] inputs, DeConv2DConfig config) {
        super(sameDiff, inputs, config);
    }

    @Override
    public String opName() {
        return "deconv2d_bp";
    }



    @Override
    public String onnxName() {
        throw new NoOpNameFoundException("No op name found for backwards.");
    }

    @Override
    public String tensorflowName() {
        throw new NoOpNameFoundException("No op name found for backwards");
    }

    @Override
    public List doDiff(List f1) {
        throw new UnsupportedOperationException("Unable to take derivative of derivative.");

    }

    @Override
    public int getNumOutputs(){
        //Inputs: in, weights, optional bias, gradOut                      3 req, 1 optional
        //Outputs: gradAtInput, gradW, optional gradB                      2 req, 1 optional
        SDVariable[] args = args();
        return args.length - 1;
    }

    @Override
    public List calculateOutputDataTypes(List inputDataTypes){
        int n = args().length;  //Original inputs + gradient at
        Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == n, "Expected %s input data types for %s, got %s", n, getClass(), inputDataTypes);
        List out = new ArrayList<>(n-1);
        for( int i=0; i




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