/*******************************************************************************
 * Copyright (c) 2015-2018 Skymind, Inc.
 *
 * 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.
 *
 * 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
 * under the License.
 *
 * SPDX-License-Identifier: Apache-2.0
 ******************************************************************************/

//
// @author raver119@gmail.com
//

#include "testlayers.h"
#include <Context.h>
#include <iomanip>
#include <Variable.h>
#include <VariableSpace.h>
#include <ops/declarable/OpRegistrator.h>
#include <ops/declarable/CustomOperations.h>
#include <helpers/helper_hash.h>
#include <NDArray.h>
#include <array/NDArrayList.h>
#include <NativeOps.h>
#include <ops/gemm.h>
#include <helpers/PointersManager.h>

using namespace nd4j;
using namespace nd4j::graph;

class DeclarableOpsTests1 : public testing::Test {
public:

    const int bS = 2;       // batch size
    const int iD = 1;       // input depth (number of picture channels, for example rgb=3)
    const int iH = 28;      // picture height in pixels
    const int iW = 28;      // picture width in pixels
    const int oD = 3;       // output depth (= N for dense layer)
    const int kH = 5;       // kernel height in pixels
    const int kW = 5;       // kernel width in pixels
    const int sH = 1;       // stride step in horizontal direction
    const int sW = 1;       // stride step in vertical direction
    const int pH = 0;       // padding height
    const int pW = 0;       // padding width
    const int dH = 2;       // dilation height
    const int dW = 2;       // dilation width
    const int oH = (iH - kH - (kH-1)*(dH-1) + 2*pH)/sH + 1;     // output height
    const int oW = (iW - kW - (kW-1)*(dW-1) + 2*pW)/sW + 1;     // output width

    DeclarableOpsTests1() {
        nd4j::memory::MemoryTracker::getInstance()->reset();
    }

    ~DeclarableOpsTests1() {
        nd4j::memory::MemoryTracker::getInstance()->summarize();
    }
};

template <typename T>
class TypedDeclarableOpsTests1 : public testing::Test {
public:

    const int bS = 2;       // batch size
    const int iD = 1;       // input depth (number of picture channels, for example rgb=3)
    const int iH = 28;      // picture height in pixels
    const int iW = 28;      // picture width in pixels
    const int oD = 3;       // output depth (= N for dense layer)
    const int kH = 5;       // kernel height in pixels
    const int kW = 5;       // kernel width in pixels
    const int sH = 1;       // stride step in horizontal direction
    const int sW = 1;       // stride step in vertical direction
    const int pH = 0;       // padding height
    const int pW = 0;       // padding width
    const int dH = 2;       // dilation height
    const int dW = 2;       // dilation width
    const int oH = (iH - kH - (kH-1)*(dH-1) + 2*pH)/sH + 1;     // output height
    const int oW = (iW - kW - (kW-1)*(dW-1) + 2*pW)/sW + 1;     // output width

    TypedDeclarableOpsTests1() {
        printf("\n");
    }
};

typedef ::testing::Types<double, float> TestingTypes;
TYPED_TEST_CASE(TypedDeclarableOpsTests1, TestingTypes);

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, BasicInitialization1) {
    auto concat = new nd4j::ops::concat();
    std::string expName("concat");
    ASSERT_EQ(expName, *(concat->getOpName()));

    auto x0 = NDArrayFactory::create_<float>('c', {1, 5});
    auto x1 = NDArrayFactory::create_<float>('c', {1, 5});
    auto x2 = NDArrayFactory::create_<float>('c', {1, 5});
    auto x3 = NDArrayFactory::create_<float>('c', {1, 5});
    auto x4 = NDArrayFactory::create_<float>('c', {1, 5});

    x0->assign(1.0f);
    x1->assign(1.0f);
    x2->assign(1.0f);
    x3->assign(1.0f);
    x4->assign(1.0f);

    auto variableSpace = new VariableSpace();

    variableSpace->putVariable(-1, x0);
    variableSpace->putVariable(-2, x1);
    variableSpace->putVariable(-3, x2);
    variableSpace->putVariable(-4, x3);
    variableSpace->putVariable(-5, x4);

    auto nodeVar = new Variable();

    variableSpace->putVariable(1, nodeVar);

    Context block(1, variableSpace);
    block.getIArguments()->push_back(1);
    block.fillInputs({-1, -2, -3, -4, -5});

    ASSERT_FALSE(nodeVar->hasNDArray());

    Nd4jStatus result = concat->execute(&block);

    ASSERT_TRUE(nodeVar->hasNDArray());

    ASSERT_EQ(25, nodeVar->getNDArray()->lengthOf());

    ASSERT_NEAR(25.0, nodeVar->getNDArray()->reduceNumber(reduce::Sum).e<double>(0), 1e-5);

    ASSERT_EQ(ND4J_STATUS_OK, result);


    delete variableSpace;
    delete concat;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, BasicInitialization2) {
    auto op = nd4j::ops::OpRegistrator::getInstance()->getOperation("concat");

    ASSERT_TRUE(op != nullptr);
    std::string expName("concat");
    ASSERT_EQ(expName, *(op->getOpName()));

    ASSERT_EQ(-1, op->getOpDescriptor()->getNumberOfInputs());
    ASSERT_EQ(1, op->getOpDescriptor()->getNumberOfOutputs());
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, ApplyGradientDescent_1) {
    auto x = NDArrayFactory::create<double>('c', {3,4}, {1,2,3,4,5,6,7,8,9,10,11,12});
    auto y = NDArrayFactory::create<double>('c', {3,4}, {0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0,1.1,1.2});
    auto exp = NDArrayFactory::create<double>('c', {3,4});
    exp.linspace(0.9, 0.9);
    nd4j::ops::apply_sgd op;
    auto result = op.execute({&x, &y}, {1.}, {}, {}, false, nd4j::DataType::DOUBLE);
    ASSERT_EQ(result->status(), ND4J_STATUS_OK);
    auto z = result->at(0);
//    result->at(0)->printIndexedBuffer("OUTPUT");
//    result->at(0)->printShapeInfo("OUTPUT Shape");
//    exp.printIndexedBuffer("EXPECT");
    ASSERT_TRUE(z->equalsTo(exp));
    delete result;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, AssignBroadcastTest_1) {
    auto x = NDArrayFactory::create<double>('c', {3,4}, {1,2,3,4,5,6,7,8,9,10,11,12});
    auto y = NDArrayFactory::create<double>('c', {1,4}, {0.1,0.2,0.3,0.4});
    auto exp = NDArrayFactory::create<double>('c', {3,4}, {0.1, 0.2, 0.3, 0.4, 0.1, 0.2, 0.3, 0.4, 0.1, 0.2, 0.3, 0.4});
    nd4j::ops::assign op;
    auto result = op.execute({&x, &y}, {}, {}, {}, false, nd4j::DataType::DOUBLE);
    ASSERT_EQ(result->status(), ND4J_STATUS_OK);
    auto z = result->at(0);
//    result->at(0)->printIndexedBuffer("OUTPUT");
//    result->at(0)->printShapeInfo("OUTPUT Shape");
//    exp.printIndexedBuffer("EXPECT");
    ASSERT_TRUE(z->equalsTo(exp));
    delete result;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, AssignBroadcastTest_2) {
    auto x = NDArrayFactory::create<double>('c', {3,4}, {1,2,3,4,5,6,7,8,9,10,11,12});
    auto y = NDArrayFactory::create<double>('c', {1,4}, {0.1,0.2,0.3,0.4});
    auto eps = NDArrayFactory::create<double>('c', {3,4}, {1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4});
    auto exp1 = NDArrayFactory::create<double>('c', {3,4}); // zero
    auto exp2 = NDArrayFactory::create<double>('c', {1,4}, {3, 6, 9, 12});
    nd4j::ops::assign_bp op;
    auto result = op.execute({&x, &y, &eps}, {}, {}, {}, false, nd4j::DataType::DOUBLE);
    ASSERT_EQ(result->status(), ND4J_STATUS_OK);
    auto z1 = result->at(0);
    auto z2 = result->at(1);
//    z1->printIndexedBuffer("OUTPUT");
//    z2->printIndexedBuffer("OUTPUT");
//
//    exp1.printIndexedBuffer("EXPECT");
//    exp2.printIndexedBuffer("EXPECT");

    ASSERT_TRUE(z1->equalsTo(exp1));
    ASSERT_TRUE(z2->equalsTo(exp2));
    delete result;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, AXpY_Test_1) {
    auto x = NDArrayFactory::create<double>('c', {3,4}, {1,2,3,4,5,6,7,8,9,10,11,12});
    auto y = NDArrayFactory::create<double>('c', {3,4}, {1,2,3,4,5,6,7,8,9,10,11,12});
    auto exp = NDArrayFactory::create<double>('c', {3,4});
    exp.linspace(3, 3);
    nd4j::ops::axpy op;
    auto result = op.execute({&x, &y}, {2.}, {}, {}, false, nd4j::DataType::DOUBLE);
    ASSERT_EQ(result->status(), ND4J_STATUS_OK);
    auto z = result->at(0);
//    result->at(0)->printIndexedBuffer("OUTPUT");
//    result->at(0)->printShapeInfo("OUTPUT Shape");
//    exp.printIndexedBuffer("EXPECT");
    ASSERT_TRUE(z->equalsTo(exp));
    delete result;
}

TEST_F(DeclarableOpsTests1, BasicInitialization3) {
    auto op1 = nd4j::ops::OpRegistrator::getInstance()->getOperation("concat");
    std::string expName("concat");
    auto hash = nd4j::ops::HashHelper::getInstance()->getLongHash(expName);

    auto op2 = nd4j::ops::OpRegistrator::getInstance()->getOperation(hash);

    ASSERT_TRUE(op1 == op2);
}


TEST_F(DeclarableOpsTests1, SynonymInitialization2) {
    auto op = nd4j::ops::OpRegistrator::getInstance()->getOperation("Mul");
    auto op2 = nd4j::ops::OpRegistrator::getInstance()->getOperation("multiply");

    ASSERT_TRUE(op != nullptr);
    std::string expName("multiply");
    ASSERT_EQ(expName, *(op->getOpName()));
    ASSERT_TRUE(op == op2);
}


TEST_F(DeclarableOpsTests1, TestTensorMmul1) {

    NDArray x('c', {2, 3, 4}, nd4j::DataType::FLOAT32);
    NDArray y('c', {2, 3, 4}, nd4j::DataType::FLOAT32);

    x.linspace(1);
    y.linspace(1);

    NDArray exp('c', {2, 2}, {650.0, 1586.0, 1586.0, 4250.0}, nd4j::DataType::FLOAT32);

    nd4j::ops::tensormmul op;
    auto results = op.execute({&x, &y}, {}, {2,1,2,2,1,2});

    ASSERT_EQ(ND4J_STATUS_OK, results->status());

    auto *out = results->at(0);
    // exp.printShapeInfo();
    // out->printShapeInfo();
    // exp.printBuffer();
    // out->printBuffer();

    // PointersManager manager(x.getContext(), "scatter");
    // manager.printDevContentOnHost<float>(out->getSpecialBuffer(), out->lengthOf());
    // manager.printDevContentOnHost<float>(exp.getSpecialBuffer(), exp.lengthOf());

    ASSERT_TRUE(exp.isSameShape(out));
    ASSERT_TRUE(exp.equalsTo(out));

    delete results;
}

TEST_F(DeclarableOpsTests1, TestTensorDot2) {

    NDArray x('f', {2, 3, 4}, {1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24.}, nd4j::DataType::FLOAT32);
    NDArray y('f', {2, 3, 4}, {1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24.}, nd4j::DataType::FLOAT32);

    NDArray exp('c', {2, 2}, {2300.0, 2444.0, 2444.0, 2600.0}, nd4j::DataType::FLOAT32);

    nd4j::ops::tensormmul op;
    auto results = op.execute({&x, &y}, {}, {2,1,2,2,1,2});

    ASSERT_EQ(ND4J_STATUS_OK, results->status());

    auto *out = results->at(0);
    // out->printBuffer();
    // out->printShapeInfo();

    ASSERT_TRUE(exp.isSameShape(out));
    ASSERT_TRUE(exp.equalsTo(out));

    delete results;
}

TEST_F(DeclarableOpsTests1, TestTensorDot3) {

    NDArray x('c', {2, 3, 4}, {1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24.}, nd4j::DataType::FLOAT32);
    NDArray y('f', {2, 3, 4}, {1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24.}, nd4j::DataType::FLOAT32);

    NDArray exp('f', {2, 2}, {1090.0, 2818.0, 1168.0, 3040.0}, nd4j::DataType::FLOAT32);

    nd4j::ops::tensormmul op;
    auto results = op.execute({&x, &y}, {}, {2,1,2,2,1,2});

    ASSERT_EQ(ND4J_STATUS_OK, results->status());

    auto *out = results->at(0);
    // out->printBuffer();
    // out->printShapeInfo();

    ASSERT_TRUE(exp.isSameShape(out));
    ASSERT_TRUE(exp.equalsTo(out));

    delete results;
}

TEST_F(DeclarableOpsTests1, TestTensorDot4) {

    NDArray x('f', {2, 3, 4}, {1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24.}, nd4j::DataType::FLOAT32);
    NDArray y('c', {2, 3, 4}, {1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24.}, nd4j::DataType::FLOAT32);

    NDArray exp('f', {2, 2}, {1090.0, 1168.0, 2818.0, 3040.0}, nd4j::DataType::FLOAT32);

    nd4j::ops::tensormmul op;
    auto results = op.execute({&x, &y}, {}, {2,1,2,2,1,2});

    ASSERT_EQ(ND4J_STATUS_OK, results->status());

    auto *out = results->at(0);
    // out->printBuffer();
    // out->printShapeInfo();

    ASSERT_TRUE(exp.isSameShape(out));
    ASSERT_TRUE(exp.equalsTo(out));

    delete results;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, DivergentCheck1) {
    auto op = nd4j::ops::OpRegistrator::getInstance()->getOperation("switch");

    ASSERT_TRUE(op != nullptr);
    std::string expName("Switch");
    ASSERT_EQ(expName, *(op->getOpName()));
    ASSERT_TRUE(op->getOpDescriptor()->isDivergent());
    ASSERT_EQ(2, op->getOpDescriptor()->getNumberOfOutputs());
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, AddMatrices1) {

    auto x = NDArrayFactory::create_<float>  ('c', {5, 3});
    auto y = NDArrayFactory::create_<float>  ('c', {5, 3});
    auto exp = NDArrayFactory::create_<float>('c', {5, 3});
    x->assign(2);
    y->assign(1);
    exp->assign(3);

    auto variableSpace = new VariableSpace();
    variableSpace->putVariable(-1, x);
    variableSpace->putVariable(-2, y);
    auto block = new Context(1, variableSpace, true);
    block->fillInputs({-1, -2});

    nd4j::ops::add addOp;

    addOp.execute(block);

    ASSERT_TRUE(x->equalsTo(exp));

    delete exp;
    delete block;
    delete variableSpace;

}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, AddVectorVector1) {

    auto x = NDArrayFactory::create_<float>  ('c', {1, 15});
    auto y = NDArrayFactory::create_<float>  ('c', {1, 15});
    auto exp = NDArrayFactory::create_<float>('c', {1, 15});
    x->assign(2);
    y->assign(1);
    exp->assign(3);

    auto variableSpace = new VariableSpace();
    variableSpace->putVariable(-1, x);
    variableSpace->putVariable(-2, y);
    auto block = new Context(1, variableSpace, true);
    block->fillInputs({-1, -2});

    nd4j::ops::add addOp;

    addOp.execute(block);

    ASSERT_TRUE(x->equalsTo(exp));

    delete exp;
    delete block;
    delete variableSpace;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, AddMatrixScalar1) {

    auto x = NDArrayFactory::create_<float>('c', {5, 3});
    auto y = NDArrayFactory::create_<float>('c', {1, 1});
    auto exp = NDArrayFactory::create<float>('c', {5, 3});
    x->assign(2);
    y->assign(1);
    exp.assign(3);

    auto variableSpace = new VariableSpace();
    variableSpace->putVariable(-1, x);
    variableSpace->putVariable(-2, y);
    auto block = new Context(1, variableSpace, true);
    block->fillInputs({-1, -2});

    nd4j::ops::add addOp;

    addOp.execute(block);

    ASSERT_TRUE(x->equalsTo(&exp));

    delete variableSpace;
    delete block;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, AddScalarScalar1) {

    auto x = NDArrayFactory::create_<float>('c', {1, 1});
    auto y = NDArrayFactory::create_<float>('c', {1, 1});
    auto exp = NDArrayFactory::create<float>('c', {1, 1});
    x->assign(2);
    y->assign(1);
    exp.assign(3);

    auto variableSpace = new VariableSpace();
    variableSpace->putVariable(-1, x);
    variableSpace->putVariable(-2, y);
    auto block = new Context(1, variableSpace, true);
    block->fillInputs({-1, -2});

    nd4j::ops::add addOp;

    addOp.execute(block);

    ASSERT_TRUE(x->equalsTo(&exp));

    delete variableSpace;
    delete block;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, SubtractMatrices1) {

    auto x = NDArrayFactory::create_<float>('c', {5, 3});
    auto y = NDArrayFactory::create_<float>('c', {5, 3});
    auto exp = NDArrayFactory::create<float>('c', {5, 3});
    x->assign(3);
    y->assign(1);
    exp.assign(2);

    auto variableSpace = new VariableSpace();
    variableSpace->putVariable(-1, x);
    variableSpace->putVariable(-2, y);
    auto block = new Context(1, variableSpace, true);
    block->fillInputs({-1, -2});

    nd4j::ops::subtract subOp;

    subOp.execute(block);

    ASSERT_TRUE(x->equalsTo(&exp));


    delete variableSpace;
    delete block;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, SubtractTest_1) {

    auto x = NDArrayFactory::create_<float>('c', {1, 6});
    auto y = NDArrayFactory::create_<float>('c', {1, 6});
    auto exp = NDArrayFactory::create<float>('c', {1, 6});
    x->assign(3);
    y->assign(1);
    exp.assign(2);

    auto variableSpace = new VariableSpace();
    variableSpace->putVariable(-1, x);
    variableSpace->putVariable(-2, y);
    auto block = new Context(1, variableSpace, true);
    block->fillInputs({-1, -2});

    nd4j::ops::subtract subOp;

    subOp.execute(block);

    ASSERT_TRUE(x->equalsTo(&exp));


    delete variableSpace;
    delete block;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, SubtractTest_2) {

    auto x = NDArrayFactory::create<float>('c', {3, 4, 5, 1});
    auto y = NDArrayFactory::create<float>('c', {1, 6});
//    auto y({6}, {1,1,1,1,1,1});
    auto exp = NDArrayFactory::create<float>('c', {3, 4, 5, 6});
    x.assign(3);
    y.assign(1);
    exp.assign(2);


    nd4j::ops::subtract subOp;

    auto res = subOp.execute({&x, &y}, {}, {});

    ASSERT_TRUE(res->status() == ND4J_STATUS_OK);

    ASSERT_TRUE(res->at(0)->equalsTo(&exp));

    delete res;
}

TEST_F(DeclarableOpsTests1, TestRng1) {
/*
    Nd4jLong *buffer = new Nd4jLong[100000];

    nd4j::random::RandomBuffer *rng = (nd4j::random::RandomBuffer *) initRandom(nullptr, 123, 100000, (Nd4jPointer) buffer);

    if (rng == nullptr)
        throw std::runtime_error("RNG initialization failed");

    auto x = NDArrayFactory::create_<float>('c', {5, 3});
    auto variableSpace = new VariableSpace();
    variableSpace->putVariable(-1, x);
    auto block = new Context(1, variableSpace, true);
    block->fillInputs({-1});
    block->setRNG(rng);
    block->getTArguments()->push_back(0.0f);
    block->getTArguments()->push_back(1.0f);

    nd4j::ops::randomuniform uniform;

    Nd4jStatus status = uniform.execute(block);

    ASSERT_EQ(ND4J_STATUS_OK, status);

    ASSERT_TRUE(x->sumNumber() > 0.0);

    destroyRandom((Nd4jPointer) rng);
    delete[] buffer;

    delete variableSpace;
    delete block;
    */
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, MergeSumTest1) {

    auto x = NDArrayFactory::create_<float>('c', {5, 5});
    auto y = NDArrayFactory::create_<float>('c', {5, 5});
    auto z = NDArrayFactory::create_<float>('c', {5, 5});
    auto exp = NDArrayFactory::create<float>('c', {5, 5});
    x->assign(3);
    y->assign(1);
    z->assign(2);
    exp.assign(6);

    auto variableSpace = new VariableSpace();
    variableSpace->putVariable(-1, x);
    variableSpace->putVariable(-2, y);
    variableSpace->putVariable(-3, z);
    variableSpace->putVariable(1, new Variable(NDArrayFactory::create_<float>('c', {5, 5})));
    auto block = new Context(1, variableSpace, false);
    block->fillInputs({-1, -2, -3});

    nd4j::ops::mergeadd merge;

    merge.execute(block);

    auto res = variableSpace->getVariable(1)->getNDArray();

    ASSERT_TRUE(res->equalsTo(&exp));

    delete variableSpace;
    delete block;
}


//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, ClipByValue1) {

    auto x = NDArrayFactory::create_<float>('c', {5, 5});
    auto exp = NDArrayFactory::create<float>('c', {5, 5});
    x->assign(4);
    x->p(0, -1);
    x->p(1, 2);
    exp.assign(3);
    exp.p(0, 0);
    exp.p(1, 2);

    auto variableSpace = new VariableSpace();
    variableSpace->putVariable(-1, x);
    variableSpace->putVariable(1, new Variable());
    auto block = new Context(1, variableSpace, true);
    block->getTArguments()->push_back(0.0f);
    block->getTArguments()->push_back(3.0f);
    block->fillInputs({-1});

    nd4j::ops::clipbyvalue clip;

    clip.execute(block);

    // x->printIndexedBuffer("Result");
    // exp.printIndexedBuffer("Expect");
    ASSERT_TRUE(x->equalsTo(&exp));


    delete variableSpace;
    delete block;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, MergeAvgTest1) {

    auto x = NDArrayFactory::create_<float>('c', {5, 5});
    auto y = NDArrayFactory::create_<float>('c', {5, 5});
    auto z = NDArrayFactory::create_<float>('c', {5, 5});
    auto exp = NDArrayFactory::create<float>('c', {5, 5});
    x->assign(3);
    y->assign(1);
    z->assign(2);
    exp.assign(2);

    auto zu = NDArrayFactory::create<float>('c', {5, 5});

    auto variableSpace = new VariableSpace();
    variableSpace->putVariable(-1, x);
    variableSpace->putVariable(-2, y);
    variableSpace->putVariable(-3, z);
    variableSpace->putVariable(1, new Variable(NDArrayFactory::create_<float>('c', {5, 5})));
    auto block = new Context(1, variableSpace, false);
    block->fillInputs({-1, -2, -3});

    nd4j::ops::mergeavg merge;

    merge.execute(block);

    auto res = variableSpace->getVariable(1)->getNDArray();

    ASSERT_TRUE(res->equalsTo(&exp));

    delete block;
    delete variableSpace;
}


//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, SubtractVectorVector1) {

    auto x = NDArrayFactory::create_<float>('c', {1, 15});
    auto y = NDArrayFactory::create_<float>('c', {1, 15});
    auto exp = NDArrayFactory::create<float>('c', {1, 15});
    x->assign(3);
    y->assign(1);
    exp.assign(2);

    auto variableSpace = new VariableSpace();
    variableSpace->putVariable(-1, x);
    variableSpace->putVariable(-2, y);
    auto block = new Context(1, variableSpace, true);
    block->fillInputs({-1, -2});

    nd4j::ops::subtract subOp;

    subOp.execute(block);

    ASSERT_TRUE(x->equalsTo(&exp));

    delete block;
    delete variableSpace;

}


//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, SubtractMatrixScalar1) {

    auto x = NDArrayFactory::create_<float>('c', {5, 3});
    auto y = NDArrayFactory::create_<float>('c', {1, 1});
    auto exp = NDArrayFactory::create<float>('c', {5, 3});
    x->assign(3);
    y->assign(1);
    exp.assign(2);

    auto variableSpace = new VariableSpace();
    variableSpace->putVariable(-1, x);
    variableSpace->putVariable(-2, y);
    auto block = new Context(1, variableSpace, true);
    block->fillInputs({-1, -2});

    nd4j::ops::subtract subOp;

    subOp.execute(block);

    ASSERT_TRUE(x->equalsTo(&exp));

    delete block;
    delete variableSpace;
}


//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, SubtractScalarScalar1) {

    auto x = NDArrayFactory::create_<float>('c', {1, 1});
    auto y = NDArrayFactory::create_<float>('c', {1, 1});
    auto exp = NDArrayFactory::create<float>('c', {1, 1});
    x->assign(3);
    y->assign(1);
    exp.assign(2);

    auto variableSpace = new VariableSpace();
    variableSpace->putVariable(-1, x);
    variableSpace->putVariable(-2, y);
    auto block = new Context(1, variableSpace, true);
    block->fillInputs({-1, -2});

    nd4j::ops::subtract subOp;

    subOp.execute(block);

    ASSERT_TRUE(x->equalsTo(&exp));

    delete block;
    delete variableSpace;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, ReverseSubtractMatrices1) {

    auto x = NDArrayFactory::create_<float>('c', {5, 3});
    auto y = NDArrayFactory::create_<float>('c', {5, 3});
    auto exp = NDArrayFactory::create<float>('c', {5, 3});
    x->assign(3.f);
    y->assign(1.f);
    exp.assign(-2.f);

    auto variableSpace = new VariableSpace();
    variableSpace->putVariable(-1, x);
    variableSpace->putVariable(-2, y);
    auto block = new Context(1, variableSpace, true);
    block->fillInputs({-1, -2});

    nd4j::ops::reversesubtract subOp;

    subOp.execute(block);
    // x->printIndexedBuffer("Output Subtract");
    ASSERT_TRUE(x->equalsTo(&exp));

    delete variableSpace;
    delete block;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, ReverseSubtractTest_1) {

    auto x = NDArrayFactory::create<float>('c', {1, 6});
    auto y = NDArrayFactory::create<float>('c', {1, 6});
    auto exp = NDArrayFactory::create<float>('c', {1, 6});
    x.assign(3.f);
    y.assign(1.f);
    exp.assign(-2.f);

    nd4j::ops::reversesubtract subOp;

    auto res = subOp.execute({&x, &y}, {}, {});

    ASSERT_TRUE(res->status() == ND4J_STATUS_OK);
    ASSERT_TRUE(res->at(0)->equalsTo(&exp));

    delete res;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, ReverseSubtractTest_2) {

//    auto x('c', {1, 6});
    auto x = NDArrayFactory::create<float>('c', {1, 6});
    auto y = NDArrayFactory::create<float>('c', {3, 4, 5, 1});
    auto exp = NDArrayFactory::create<float>('c', {3, 4, 5, 6});
    auto z(exp);
    x.assign(3.f);
    y.assign(1.f);
    exp.assign(-2.f);
    x.applyTrueBroadcast(BROADCAST(ReverseSubtract), &y, &z, true);
//    x.printIndexedBuffer("ReverseSubtract Legacy");
    ASSERT_TRUE(exp.equalsTo(&z));

    nd4j::ops::reversesubtract subOp;

    auto res = subOp.execute({&x, &y}, {}, {});

    ASSERT_TRUE(res->status() == ND4J_STATUS_OK);
    //res->at(0)->printIndexedBuffer("OUtput REVERSED SUB");
    ASSERT_TRUE(res->at(0)->equalsTo(&exp));

    delete res;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, ReverseSubtractTest_3) {

//    auto x('c', {1, 6});
    auto x = NDArrayFactory::create<float>('c', {6});
    auto y = NDArrayFactory::create<float>('c', {3, 4, 5, 1});
    auto exp = NDArrayFactory::create<float>('c', {3, 4, 5, 6});
    auto z(exp);
    x.assign(1);
    y.assign(3);
    exp.assign(2);
    x.applyTrueBroadcast(BROADCAST(ReverseSubtract), &y, &z, true);
    ASSERT_TRUE(z.equalsTo(&exp));
    nd4j::ops::reversesubtract subOp;

    auto res = subOp.execute({&x, &y}, {}, {});
    ASSERT_TRUE(res->status() == ND4J_STATUS_OK);
    ASSERT_TRUE(res->at(0)->equalsTo(&exp));

    delete res;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, ReverseModTest_1) {

//    auto x('c', {1, 6});
    auto x = NDArrayFactory::create<double>('c', {6});
    auto y = NDArrayFactory::create<double>('c', {3, 4, 5, 1});
    auto exp = NDArrayFactory::create<double>('c', {3, 4, 5, 6});
    auto z(exp);
    x.assign(2.);
    y.assign(9.f);
    exp.assign(1.f);
    y.applyTrueBroadcast(BROADCAST(Mod), &x, &z, true);
    // z.printIndexedBuffer("MOD1");
    ASSERT_TRUE(exp.equalsTo(&z));
    x.applyTrueBroadcast(BROADCAST(ReverseMod), &y, &exp, true);
    ASSERT_TRUE(exp.equalsTo(&z));

    nd4j::ops::reversemod subOp;

    auto res = subOp.execute({&x, &y}, {}, {});

    ASSERT_TRUE(res->status() == ND4J_STATUS_OK);
    ASSERT_TRUE(res->at(0)->equalsTo(&exp));
    ASSERT_TRUE(exp.equalsTo(&z));

    delete res;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, ReverseModTest_2) {

//    auto x('c', {1, 6});
    auto x = NDArrayFactory::create<float>('c', {3, 4, 5});
    auto y = NDArrayFactory::create<float>('c', {3, 4, 5});
    auto exp = NDArrayFactory::create<float>('c', {3, 4, 5});
    auto z(exp);
    x.assign(2.f);
    y.assign(9.f);
    exp.assign(1.f);
    x.applyTrueBroadcast(BROADCAST(ReverseMod), &y, &z, true);
    ASSERT_TRUE(z.equalsTo(&exp));
    x.applyTrueBroadcast(BROADCAST(ReverseMod), &y, &exp, true);
    ASSERT_TRUE(z.equalsTo(&exp));

    nd4j::ops::reversemod subOp;

    auto res = subOp.execute({&x, &y}, {}, {});

    ASSERT_TRUE(res->status() == ND4J_STATUS_OK);
//    res->at(0)->printIndexedBuffer("OUtput REVERSED MOD2");
    ASSERT_TRUE(res->at(0)->equalsTo(&exp));

    delete res;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, ReverseSubtractVectorVector1) {

    auto x = NDArrayFactory::create_<float>   ('c', {1, 15});
    auto y = NDArrayFactory::create_<float>   ('c', {1, 15});
    auto exp = NDArrayFactory::create_<float> ('c', {1, 15});
    x->assign(3);
    y->assign(1);
    exp->assign(-2);

    auto variableSpace = new VariableSpace();
    variableSpace->putVariable(-1, x);
    variableSpace->putVariable(-2, y);
    auto block = new Context(1, variableSpace, true);
    block->fillInputs({-1, -2});

    nd4j::ops::reversesubtract subOp;

    subOp.execute(block);

    ASSERT_TRUE(x->equalsTo(exp));

    delete variableSpace;
    delete block;
    delete exp;
}


//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, ReverseSubtractMatrixScalar1) {

    auto x = NDArrayFactory::create_<float>  ('c', {5, 3});
    auto y = NDArrayFactory::create_<float>  ('c', {1, 1});
    auto exp = NDArrayFactory::create_<float>('c', {5, 3});
    x->assign(3);
    y->assign(1);
    exp->assign(-2);

    auto variableSpace = new VariableSpace();
    variableSpace->putVariable(-1, x);
    variableSpace->putVariable(-2, y);
    auto block = new Context(1, variableSpace, true);
    block->fillInputs({-1, -2});

    nd4j::ops::reversesubtract subOp;

    subOp.execute(block);

    ASSERT_TRUE(x->equalsTo(exp));

    delete variableSpace;
    delete block;
    delete exp;
}


//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, ReverseSubtractScalarScalar1) {

    auto x = NDArrayFactory::create_<float>  ('c', {1, 1});
    auto y = NDArrayFactory::create_<float>  ('c', {1, 1});
    auto exp = NDArrayFactory::create_<float>('c', {1, 1});
    x->assign(3);
    y->assign(1);
    exp->assign(-2);

    auto variableSpace = new VariableSpace();
    variableSpace->putVariable(-1, x);
    variableSpace->putVariable(-2, y);
    auto block = new Context(1, variableSpace, true);
    block->fillInputs({-1, -2});

    nd4j::ops::reversesubtract subOp;

    subOp.execute(block);

    ASSERT_TRUE(x->equalsTo(exp));

    delete variableSpace;
    delete block;
    delete exp;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, MultiplyMatrices1) {

    auto x = NDArrayFactory::create_<float>  ('c', {5, 3});
    auto y = NDArrayFactory::create_<float>  ('c', {5, 3});
    auto exp = NDArrayFactory::create_<float>('c', {5, 3});
    x->assign(2);
    y->assign(3);
    exp->assign(6);

    auto variableSpace = new VariableSpace();
    variableSpace->putVariable(-1, x);
    variableSpace->putVariable(-2, y);
    auto block = new Context(1, variableSpace, true);
    block->fillInputs({-1, -2});

    nd4j::ops::multiply mul;

    mul.execute(block);

    ASSERT_TRUE(x->equalsTo(exp));

    delete variableSpace;
    delete block;
    delete exp;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, MultiplyVectorVector1) {

    auto x = NDArrayFactory::create_<float>  ('c', {1, 15});
    auto y = NDArrayFactory::create_<float>  ('c', {1, 15});
    auto exp = NDArrayFactory::create_<float>('c', {1, 15});
    x->assign(2);
    y->assign(3);
    exp->assign(6);

    auto variableSpace = new VariableSpace();
    variableSpace->putVariable(-1, x);
    variableSpace->putVariable(-2, y);
    auto block = new Context(1, variableSpace, true);
    block->fillInputs({-1, -2});

    nd4j::ops::multiply mul;

    mul.execute(block);

    ASSERT_TRUE(x->equalsTo(exp));

    delete variableSpace;
    delete block;
    delete exp;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, MultiplyMatrixScalar) {

    auto x = NDArrayFactory::create_<float>  ('c', {5, 3});
    auto y = NDArrayFactory::create_<float>  ('c', {1, 1});
    auto exp = NDArrayFactory::create_<float>('c', {5, 3});
    x->assign(2);
    y->assign(3);
    exp->assign(6);

    auto variableSpace = new VariableSpace();
    variableSpace->putVariable(-1, x);
    variableSpace->putVariable(-2, y);
    auto block = new Context(1, variableSpace, true);
    block->fillInputs({-1, -2});

    nd4j::ops::multiply mul;

    mul.execute(block);

    ASSERT_TRUE(x->equalsTo(exp));

    delete variableSpace;
    delete block;
    delete exp;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, MultiplyScalarScalar1) {

    auto x = NDArrayFactory::create_<float>  ('c', {1, 1});
    auto y = NDArrayFactory::create_<float>  ('c', {1, 1});
    auto exp = NDArrayFactory::create_<float>('c', {1, 1});
    x->assign(2);
    y->assign(3);
    exp->assign(6);

    auto variableSpace = new VariableSpace();
    variableSpace->putVariable(-1, x);
    variableSpace->putVariable(-2, y);
    auto block = new Context(1, variableSpace, true);
    block->fillInputs({-1, -2});

    nd4j::ops::multiply mul;

    mul.execute(block);

    ASSERT_TRUE(x->equalsTo(exp));

    delete block;
    delete variableSpace;
    delete exp;
}

TEST_F(DeclarableOpsTests1, TestMatMul1) {
    auto x = NDArrayFactory::create_<float>('c', {3, 5});
    x->linspace(1);

    auto y = NDArrayFactory::create_<float>('c', {5, 3});
    y->linspace(1);

    float _expB[]{135.0f, 310.0f, 485.0f, 150.0f, 350.0f, 550.0f, 165.0f, 390.0f, 615.0f};
    Nd4jLong _expS[] {2, 3, 3, 1, 3, 0, 1, 102}; // expected shape
    ArrayOptions::setDataType(_expS, nd4j::DataType::FLOAT32);
    NDArray exp(_expB, _expS);

    auto variableSpace = new VariableSpace();
    variableSpace->putVariable(-1, x);
    variableSpace->putVariable(-2, y);
    variableSpace->putVariable(1, new Variable());

    auto block = new Context(1, variableSpace, false);
    block->fillInputs({-1, -2});

    nd4j::ops::matmul op;

    Nd4jStatus status = op.execute(block);
    ASSERT_EQ(ND4J_STATUS_OK, status);
    ASSERT_TRUE(variableSpace->hasVariable(1));

    auto result = variableSpace->getVariable(1)->getNDArray();

    ASSERT_TRUE(result->equalsTo(&exp));

    delete block;
    delete variableSpace;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, TestSoftMax_bp_1) {

    auto input = NDArrayFactory::create_<double>('c', {2, 2});
    for (int e = 0; e < input->lengthOf(); e++)
        input->p(e, e+1);

    auto epsilon = NDArrayFactory::create_<double>('c', {2, 2});
    epsilon->p(0, 0.1f);
    epsilon->p(1, 0.2f);
    epsilon->p(2, 0.3f);
    epsilon->p(3, 0.4f);

    auto output = NDArrayFactory::create_<double>('c', {2, 2});
    output->assign(1.0f);

    auto exp = NDArrayFactory::create_<double>('c', {2, 2});
    exp->p(0, -0.019661194f);
    exp->p(1, 0.019661194f);
    exp->p(2, -0.019661194f);
    exp->p(3, 0.019661194f);

    auto variableSpace = new VariableSpace();
    variableSpace->putVariable(-1, input);
    variableSpace->putVariable(-2, epsilon);
    variableSpace->putVariable(1, output);
    //variableSpace->putVariable(42, exp);

    auto block = new Context(1, variableSpace, false);
    block->fillInputs({-1, -2});

    nd4j::ops::softmax_bp op;

    Nd4jStatus status = op.execute(block);
    ASSERT_EQ(ND4J_STATUS_OK, status);

    ASSERT_TRUE(output->equalsTo(exp));

    delete variableSpace;
    delete block;
    delete exp;

}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, BroadcastDivideTest_1) {

    auto  x = NDArrayFactory::create<float>('c', {3, 4, 5, 1});
    auto  y = NDArrayFactory::create<float>('c', {1, 6});
    auto  exp = NDArrayFactory::create<float>('c', {3, 4, 5, 6});
    x.assign(6);
    y.assign(2);
    exp.assign(3);

    nd4j::ops::divide div;

    auto res = div.execute({&x, &y}, {}, {});

    ASSERT_EQ(res->status(), ND4J_STATUS_OK);
    ASSERT_TRUE(res->at(0)->equalsTo(exp));

    delete res;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, BroadcastReverseDivideTest_1) {

    auto  x = NDArrayFactory::create<float>('c', {3, 4, 5, 1});
    auto  y = NDArrayFactory::create<float>('c', {1, 6});
    auto  exp = NDArrayFactory::create<float>('c', {3, 4, 5, 6});
    x.assign(3.f);
    y.assign(6.f);
    exp.assign(2.f);

    nd4j::ops::reversedivide div;

    auto res = div.execute({&x, &y}, {}, {});

    ASSERT_EQ(res->status(), ND4J_STATUS_OK);

    ASSERT_TRUE(res->at(0)->equalsTo(exp));
    auto z(exp);
    x.applyTrueBroadcast(BROADCAST(ReverseDivide), &y, &z, true);
    y.applyTrueBroadcast(BROADCAST(Divide), &x, &exp, true);

    ASSERT_TRUE(z.equalsTo(&exp));

    delete res;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, DivideMatrices1) {

    auto x = NDArrayFactory::create_<float>  ('c', {5, 3});
    auto y = NDArrayFactory::create_<float>  ('c', {5, 3});
    auto exp = NDArrayFactory::create_<float>('c', {5, 3});
    x->assign(6);
    y->assign(2);
    exp->assign(3);

    auto variableSpace = new VariableSpace();
    variableSpace->putVariable(-1, x);
    variableSpace->putVariable(-2, y);
    auto block = new Context(1, variableSpace, true);
    block->fillInputs({-1, -2});

    nd4j::ops::divide div;

    div.execute(block);

    ASSERT_TRUE(x->equalsTo(exp));

    delete variableSpace;
    delete block;
    delete exp;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, DivideVectorVector1) {

    auto x = NDArrayFactory::create_<float>('c', {1, 15});
    auto y = NDArrayFactory::create_<float>('c', {1, 15});
    auto exp = NDArrayFactory::create<float>('c', {1, 15});
    x->assign(6);
    y->assign(2);
    exp.assign(3);

    auto variableSpace = new VariableSpace();
    variableSpace->putVariable(-1, x);
    variableSpace->putVariable(-2, y);
    auto block = new Context(1, variableSpace, true);
    block->fillInputs({-1, -2});

    nd4j::ops::divide div;

    div.execute(block);

    ASSERT_TRUE(x->equalsTo(&exp));

    delete variableSpace;
    delete block;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, DivideMatrixScalar1) {

    auto x = NDArrayFactory::create_<float>('c', {5, 3});
    auto y = NDArrayFactory::create_<float>('c', {1, 1});
    auto exp = NDArrayFactory::create<float>('c', {5, 3});
    x->assign(6);
    y->assign(2);
    exp.assign(3);

    auto variableSpace = new VariableSpace();
    variableSpace->putVariable(-1, x);
    variableSpace->putVariable(-2, y);
    auto block = new Context(1, variableSpace, true);
    block->fillInputs({-1, -2});

    nd4j::ops::divide div;

    div.execute(block);

    ASSERT_TRUE(x->equalsTo(&exp));

    delete block;
    delete variableSpace;
}


//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, DivideScalarScalar1) {

    auto x = NDArrayFactory::create_<float>('c', {5, 1});
    auto y = NDArrayFactory::create_<float>('c', {5, 1});
    auto exp = NDArrayFactory::create<float>('c', {5, 1});
    x->assign(6);
    y->assign(2);
    exp.assign(3);

    auto variableSpace = new VariableSpace();
    variableSpace->putVariable(-1, x);
    variableSpace->putVariable(-2, y);
    auto block = new Context(1, variableSpace, true);
    block->fillInputs({-1, -2});

    nd4j::ops::divide div;

    div.execute(block);

    //x->printBuffer("x");
    ASSERT_TRUE(x->equalsTo(&exp));

    delete variableSpace;
    delete block;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, ReverseDivideMatrices1) {

    auto x = NDArrayFactory::create_<float>('c', {5, 3});
    auto y = NDArrayFactory::create_<float>('c', {5, 3});
    auto exp = NDArrayFactory::create<float>('c', {5, 3});
    x->assign(2);
    y->assign(6);
    exp.assign(3);

    auto variableSpace = new VariableSpace();
    variableSpace->putVariable(-1, x);
    variableSpace->putVariable(-2, y);
    auto block = new Context(1, variableSpace, true);
    block->fillInputs({-1, -2});

    nd4j::ops::reversedivide div;

    div.execute(block);

    ASSERT_TRUE(x->equalsTo(&exp));

    delete variableSpace;
    delete block;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, ReverseDivideVectorVector1) {

    auto x = NDArrayFactory::create_<float>('c', {1, 15});
    auto y = NDArrayFactory::create_<float>('c', {1, 15});
    auto exp = NDArrayFactory::create<float>('c', {1, 15});
    x->assign(2);
    y->assign(6);
    exp.assign(3);

    auto variableSpace = new VariableSpace();
    variableSpace->putVariable(-1, x);
    variableSpace->putVariable(-2, y);
    auto block = new Context(1, variableSpace, true);
    block->fillInputs({-1, -2});

    nd4j::ops::reversedivide div;

    div.execute(block);

    ASSERT_TRUE(x->equalsTo(&exp));

    delete variableSpace;
    delete block;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, ReverseDivideMatrixScalar1) {

    auto x = NDArrayFactory::create_<float>('c', {5, 3});
    auto y = NDArrayFactory::create_<float>('c', {1, 1});
    auto exp = NDArrayFactory::create<float>('c', {5, 3});
    x->assign(2);
    y->assign(6);
    exp.assign(3);

    auto variableSpace = new VariableSpace();
    variableSpace->putVariable(-1, x);
    variableSpace->putVariable(-2, y);
    auto block = new Context(1, variableSpace, true);
    block->fillInputs({-1, -2});

    nd4j::ops::reversedivide div;

    div.execute(block);

    ASSERT_TRUE(x->equalsTo(&exp));

    delete variableSpace;
    delete block;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, ReverseDivideScalarScalar1) {

    auto x = NDArrayFactory::create_<float>('c', {1, 1});
    auto y = NDArrayFactory::create_<float>('c', {1, 1});
    auto exp = NDArrayFactory::create<float>('c', {1, 1});
    x->assign(2);
    y->assign(6);
    exp.assign(3);

    auto variableSpace = new VariableSpace();
    variableSpace->putVariable(-1, x);
    variableSpace->putVariable(-2, y);
    auto block = new Context(1, variableSpace, true);
    block->fillInputs({-1, -2});

    nd4j::ops::reversedivide div;

    div.execute(block);

    ASSERT_TRUE(x->equalsTo(&exp));

    delete variableSpace;
    delete block;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, Reshapeas1) {
    const std::vector<Nd4jLong> xShape = {5,4,3};
    const std::vector<Nd4jLong> yShape = {3,5,4};

    auto x = NDArrayFactory::create_<float>('f', xShape);
    auto y = NDArrayFactory::create_<float>('f', yShape);


    auto variableSpace = new VariableSpace();
    variableSpace->putVariable(-1, x);
    variableSpace->putVariable(-2, y);
    auto block = new Context(1, variableSpace, true);
    block->fillInputs({-1, -2});

    nd4j::ops::reshapeas reshape;

    reshape.execute(block);

    ASSERT_TRUE(x->isSameShape(y));

    delete variableSpace;
    delete block;
}

TEST_F(DeclarableOpsTests1, Test_Cast_1) {
    // TODO: right now there's no real cast implementation, but genera idea should be the same: arrays equality to be expected
    auto x = NDArrayFactory::create<float>('c', {5, 5});
    auto yExp = NDArrayFactory::create<float16>('c', {5, 5});
    x.linspace(1);
    yExp.linspace(1);
    nd4j::ops::cast op;

    auto result = op.execute({&x}, {}, {3});
    ASSERT_EQ(ND4J_STATUS_OK, result->status());

    auto z = result->at(0);
    // z->printIndexedBuffer("OUtput");
    // yExp.printIndexedBuffer("Expect");
    // z->printShapeInfo("OUt shape");
    // yExp.printShapeInfo("Exp shape");
    ASSERT_TRUE(yExp.equalsTo(z));

    delete result;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, TestRegistrator1) {
    auto res = nd4j::ops::OpRegistrator::getInstance()->getAllCustomOperations();

    // nd4j_printf("Ops: %s\n", res)
}

// //////////////////////////////////////////////////////////////////////
// TEST_F(DeclarableOpsTests1, TestLegacyExecution1) {
//     auto x = NDArrayFactory::create_<float>('c', {10, 10});
//     x->assign(1.0f);

//     auto y = NDArrayFactory::create_<float>('c', {10, 10});
//     y->assign(2.0f);

//     auto z = NDArrayFactory::create_<float>('c', {10, 10});

//     auto exp = NDArrayFactory::create_<float>('c', {10, 10});
//     exp->assign(3.0f);
//     z->assign(120.0f);
//     std::string opName("add");

//     auto hash = nd4j::ops::HashHelper::getInstance()->getInstance()->getLongHash(opName);

//     auto inputBuffers = new Nd4jPointer[2];
//     auto inputShapes = new Nd4jPointer[2];

//     inputBuffers[0] = (Nd4jPointer) x->getBuffer();
//     inputBuffers[1] = (Nd4jPointer) y->getBuffer();

//     inputShapes[0] = (Nd4jPointer) x->getShapeInfo();
//     inputShapes[1] = (Nd4jPointer) y->getShapeInfo();

//     auto outputBuffers = new Nd4jPointer[1];
//     auto outputShapes = new Nd4jPointer[1];

//     outputBuffers[0] = (Nd4jPointer) z->getBuffer();
//     outputShapes[0] = (Nd4jPointer) z->getShapeInfo();


//     //auto status = execCustomOp(nullptr, hash, inputBuffers, inputShapes, 2, outputBuffers, outputShapes, 1, nullptr, 0, nullptr, 0, false);
//     auto status = execCustomOp(nullptr, hash, inputBuffers, inputShapes, 2, outputBuffers, outputShapes, 1, nullptr, 0, nullptr, 0, nullptr, 0, false);
//     ASSERT_EQ(ND4J_STATUS_OK, status);
//     // z->printIndexedBuffer("Output add");
//     ASSERT_NEAR(2.0f, y->meanNumber().e<float>(0), 1e-5);
//     ASSERT_NEAR(1.0f, x->meanNumber().e<float>(0), 1e-5);
//     ASSERT_NEAR(3.0f, z->meanNumber().e<float>(0), 1e-5);

//     delete x;
//     delete y;
//     delete z;
//     delete exp;
//     delete[] inputBuffers;
//     delete[] inputShapes;
//     delete[] outputBuffers;
//     delete[] outputShapes;
// }

// //////////////////////////////////////////////////////////////////////
// TEST_F(DeclarableOpsTests1, TestLegacyExecution2) {
//     auto x = NDArrayFactory::create_<float>('c', {10, 10});
//     x->assign(1.0f);

//     auto y = NDArrayFactory::create_<float>('c', {10, 10});
//     y->assign(2.0f);

//     auto z = NDArrayFactory::create_<float>('c', {10, 10});

//     auto exp = NDArrayFactory::create_<float>('c', {10, 10});
//     exp->assign(3.0);

//     std::string opName("add");

//     auto hash = nd4j::ops::HashHelper::getInstance()->getInstance()->getLongHash(opName);

//     auto inputBuffers = new Nd4jPointer[2];
//     auto inputShapes = new Nd4jPointer[2];

//     inputBuffers[0] = (Nd4jPointer) x->getBuffer();
//     inputBuffers[1] = (Nd4jPointer) y->getBuffer();

//     inputShapes[0] = (Nd4jPointer) x->getShapeInfo();
//     inputShapes[1] = (Nd4jPointer) y->getShapeInfo();

//     auto outputBuffers = new Nd4jPointer[1];
//     auto outputShapes = new Nd4jPointer[1];

//     execCustomOp(nullptr, hash, inputBuffers, inputShapes, 2, outputBuffers, outputShapes, 1, nullptr, 0, nullptr, 0, nullptr, 0, true);

//     ASSERT_NEAR(2.0, y->meanNumber().e<float>(0), 1e-5);
//     ASSERT_NEAR(3.0, x->meanNumber().e<float>(0), 1e-5);


//     delete x;
//     delete y;
//     delete z;
//     delete exp;
//     delete[] inputBuffers;
//     delete[] inputShapes;
//     delete[] outputBuffers;
//     delete[] outputShapes;
// }

#ifndef __CUDABLAS__
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, TestGemv1) {
    auto xBuffer = new float[15]{1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 10.f, 11.f, 12.f, 13.f, 14.f, 15.f};
    auto xShape = new Nd4jLong[8] {2, 5, 3, 3, 1, 0, 1, 99};
    ArrayOptions::setDataType(xShape, nd4j::DataType::FLOAT32);
    auto x = new NDArray(xBuffer, xShape);

    auto yBuffer = new float[3]{2.f, 4.f, 6.f};
    auto yShape = new Nd4jLong[8] {2, 3, 1, 1, 1, 0, 1, 99};
    ArrayOptions::setDataType(yShape, nd4j::DataType::FLOAT32);

    auto y = new NDArray(yBuffer, yShape);

    auto z = NDArrayFactory::create_<float>('f', {5, 1});

    auto expBuffer = new float[5]{28.00,64.00,100.00,136.00,172.00};
    auto exp = new NDArray(expBuffer, z->getShapeInfo());

     nd4j::blas::GEMV<float, float, float>::op('f',  x->rows(), x->columns(), 1.0f, x->getBuffer(), y->rows(), y->getBuffer(), 1, 0.0, z->getBuffer(), 1);

    //z->printBuffer();

    ASSERT_TRUE(z->equalsTo(exp));

    delete []xBuffer; delete []xShape; delete x; delete []yBuffer; delete []yShape; delete y; delete z; delete []expBuffer; delete exp;
}

#endif

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, Reshape1) {
    const std::vector<Nd4jLong> xShape = {5,4,3};
    const std::vector<Nd4jLong> yShape = {3,5,4};

    auto x = NDArrayFactory::create_<float>('f', xShape);
    auto y = NDArrayFactory::create_<float>('f', yShape);

    auto variableSpace = new VariableSpace();
    variableSpace->putVariable(-1, x);

    auto block = new Context(1, variableSpace, true);
    block->fillInputs({-1});
    std::vector<int>* arguments = block->getIArguments();
    arguments->push_back(-y->ordering());
    arguments->push_back(3);
    arguments->push_back(5);
    arguments->push_back(4);

    nd4j::ops::reshape reshape;

    reshape.execute(block);

    ASSERT_TRUE(x->isSameShape(y));

    delete y;
    delete block;
    delete variableSpace;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, Reshape2) {
    const std::vector<Nd4jLong> xShape = {5,4,3};
    const std::vector<Nd4jLong> yShape = {3,5,4};

    auto x = NDArrayFactory::create_<float>('c', xShape);
    auto y = NDArrayFactory::create_<float>('c', yShape);

    auto variableSpace = new VariableSpace();
    variableSpace->putVariable(-1, x);
    variableSpace->putVariable(1, new Variable());

    auto block = new Context(1, variableSpace, false);
    block->fillInputs({-1});
    std::vector<int>* arguments = block->getIArguments();
    arguments->push_back(-y->ordering());
    arguments->push_back(3);
    arguments->push_back(5);
    arguments->push_back(4);

    nd4j::ops::reshape reshape;

    Nd4jStatus status = reshape.execute(block);
    ASSERT_EQ(ND4J_STATUS_OK, status);
    auto result = variableSpace->getVariable(block->getNodeId())->getNDArray();

    ASSERT_TRUE(result->isSameShape(y));

    delete y;
    delete block;
    delete variableSpace;
}

TEST_F(DeclarableOpsTests1, Reshape3) {
    auto x = NDArrayFactory::create<float>('c', {3, 4, 5});

    nd4j::ops::reshape op;
    auto result = op.execute({&x}, {}, {-99, 3, 4, 5});

    ASSERT_EQ(ND4J_STATUS_OK, result->status());

    auto z = result->at(0);

    ASSERT_TRUE(x.isSameShape(z));

    delete result;
}

TEST_F(DeclarableOpsTests1, Reshape4) {
    auto x = NDArrayFactory::create<float>('c', {3, 4, 5});

    nd4j::ops::reshape op;
    auto result = op.execute({&x}, {}, {3, 4, 5});

    ASSERT_EQ(ND4J_STATUS_OK, result->status());

    auto z = result->at(0);

    ASSERT_TRUE(x.isSameShape(z));

    delete result;
}

TEST_F(DeclarableOpsTests1, Reshape5) {
    auto x = NDArrayFactory::create<float>('c', {3, 4, 5});

    nd4j::ops::reshape op;
    auto result = op.execute({&x}, {}, {5, 4, 3});

    ASSERT_EQ(ND4J_STATUS_OK, result->status());

    delete result;
}

TEST_F(DeclarableOpsTests1, Reshape6){
    auto x = NDArrayFactory::create<float>('c', {3, 4, 5});
    auto exp = NDArrayFactory::create<float>('c', {4, 15});

    nd4j::ops::reshape op;
    auto result = op.execute({&x}, {}, {4, -1});

    ASSERT_EQ(ND4J_STATUS_OK, result->status());

    auto z = result->at(0);

    ASSERT_TRUE(z->isSameShape(exp));

    delete result;

}

TEST_F(DeclarableOpsTests1, Reshape7){
    auto x = NDArrayFactory::create<float>('c', {3, 4, 5});
    auto exp = NDArrayFactory::create<float>('c', {60});

    nd4j::ops::reshape op;
    auto result = op.execute({&x}, {}, {-1});

    ASSERT_EQ(ND4J_STATUS_OK, result->status());

    auto z = result->at(0);

    ASSERT_TRUE(z->isSameShape(exp));

    delete result;

}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, Transpose1) {

    auto x = NDArrayFactory::create_<float>('c', {3,5,2});
    auto exp = NDArrayFactory::create_<float>('f', {2,5,3});

    auto variableSpace = new VariableSpace();
    variableSpace->putVariable(-1, x);

    auto block = new Context(1, variableSpace, true);  // in-place
    block->fillInputs({-1});
    nd4j::ops::transpose transpose;

    Nd4jStatus status = transpose.execute(block);
    ASSERT_EQ(ND4J_STATUS_OK, status);
    // ASSERT_TRUE(x.isSameShapeStrict(&exp));

    for (int e = 0; e < x->rankOf() * 2 + 2; e++) {
        ASSERT_EQ(x->getShapeInfo()[e], exp->getShapeInfo()[e]);
    }
//  ASSERT_EQ(x.getShapeInfo()[x.rankOf() * 2 + 2],-exp.getShapeInfo()[x.rankOf() * 2 + 2]);
    ASSERT_EQ(x->getShapeInfo()[x->rankOf() * 2 + 3], exp->getShapeInfo()[x->rankOf() * 2 + 3]);

    delete exp;
    delete block;
    delete variableSpace;

}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, Transpose2) {
    auto x = NDArrayFactory::create_<float>('c', {3,5,2});
    auto exp = NDArrayFactory::create_<float>('f', {2,5,3});

    auto variableSpace = new VariableSpace();
    variableSpace->putVariable(-1, x);
    variableSpace->putVariable(1, new Variable());

    auto block = new Context(1, variableSpace, false);  // not-in-place
    block->fillInputs({-1});
    nd4j::ops::transpose transpose;

    Nd4jStatus  status = transpose.execute(block);
    ASSERT_EQ(ND4J_STATUS_OK, status);

    auto result = variableSpace->getVariable(block->getNodeId())->getNDArray();
    // ASSERT_TRUE(result->isSameShapeStrict(&exp));
    for (int e = 0; e < result->rankOf() * 2 + 2; e++) {
        ASSERT_EQ(result->getShapeInfo()[e], exp->getShapeInfo()[e]);
    }
    //ASSERT_EQ(result->getShapeInfo()[x.rankOf() * 2 + 2],-exp.getShapeInfo()[x.rankOf() * 2 + 2]);
    ASSERT_EQ(result->getShapeInfo()[x->rankOf() * 2 + 3], exp->getShapeInfo()[x->rankOf() * 2 + 3]);

    delete exp;
    delete block;
    delete variableSpace;
}


//////////////////////////////////////////////////////////////////////
// in-place
TEST_F(DeclarableOpsTests1, Permute1) {

    Nd4jLong shapeX[]   = {3, 5, 10, 15, 150, 15, 1, 0, 1, 99};
    Nd4jLong shapeExp[] = {3, 15, 5, 10, 1, 150, 15, 0, 0, 99};
    const std::vector<int> perm = {2, 0, 1};
    ArrayOptions::setDataType(shapeX, nd4j::DataType::FLOAT32);
    ArrayOptions::setDataType(shapeExp, nd4j::DataType::FLOAT32);

    auto x = new NDArray(shapeX,true);
    auto exp = new NDArray(shapeExp,true);

    auto variableSpace = new VariableSpace();
    variableSpace->putVariable(-1, x);

    auto block = new Context(1, variableSpace, true);  // in-place
    block->fillInputs({-1});
    std::vector<int>* arguments = block->getIArguments();
    *arguments = perm;      // set dimensions to be permuted

    nd4j::ops::permute permute;
    Nd4jStatus status = permute.execute(block);
    ASSERT_EQ(ND4J_STATUS_OK, status);

    ASSERT_TRUE(x->isSameShapeStrict(exp));

    delete exp;
    delete block;
    delete variableSpace;
}

//////////////////////////////////////////////////////////////////////
// not-in-place
TEST_F(DeclarableOpsTests1, Permute2) {

    Nd4jLong shapeX[]   = {3, 5, 10, 15, 150, 15, 1, 0, 1, 99};
    Nd4jLong shapeExp[] = {3, 15, 5, 10, 1, 150, 15, 0, 0, 99};
    const std::vector<int> perm = {2, 0, 1};

    ArrayOptions::setDataType(shapeX, nd4j::DataType::FLOAT32);
    ArrayOptions::setDataType(shapeExp, nd4j::DataType::FLOAT32);

    auto x = new NDArray(shapeX, true);
    auto exp = new NDArray(shapeExp, true);

    auto variableSpace = new VariableSpace();
    variableSpace->putVariable(-1, x);
    variableSpace->putVariable(1, new Variable());

    auto block = new Context(1, variableSpace, false);  // not-in-place
    block->fillInputs({-1});
    auto arguments = block->getIArguments();
    *arguments = perm;      // set dimensions to be permuted

    nd4j::ops::permute permute;
    Nd4jStatus status = permute.execute(block);
    auto result = variableSpace->getVariable(block->getNodeId())->getNDArray();

    ASSERT_EQ(ND4J_STATUS_OK, status);
    ASSERT_TRUE(result->isSameShapeStrict(exp));

    delete block;
    delete variableSpace;
    delete exp;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, TestArgumentsValidation1) {
    Nd4jLong shapeX[]   = {3, 5, 10, 15, 150, 15, 1, 0, 1, 99};
    Nd4jLong shapeExp[] = {3, 15, 5, 10, 1, 150, 15, 0, -1, 99};

    ArrayOptions::setDataType(shapeX, nd4j::DataType::FLOAT32);
    ArrayOptions::setDataType(shapeExp, nd4j::DataType::FLOAT32);

    const std::vector<int> perm = {2, 0, 1};
    auto x = new NDArray(shapeX);
    auto exp = new NDArray(shapeExp);

    auto variableSpace = new VariableSpace();
    variableSpace->putVariable(-1, x);
    variableSpace->putVariable(1, new Variable());

    auto block = new Context(1, variableSpace, false);  // not-in-place
    block->fillInputs({-1});

    nd4j::ops::im2col permute;
    Nd4jStatus status = permute.execute(block);

    ASSERT_TRUE(status != 0);

    delete exp;
    delete block;
    delete variableSpace;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, TestReductionShape1) {
    auto input = NDArrayFactory::create_<float>('c', {4, 5, 5, 10, 10});

    auto variableSpace = new VariableSpace();
    variableSpace->putVariable(-1, input);

    auto block = new Context(1, variableSpace, false);  // not-in-place
    block->fillInputs({-1});

    // kernel params
    block->getIArguments()->push_back(MAX_INT);

    nd4j::ops::testreduction testop;

    auto inP = new Nd4jLong[shape::shapeInfoLength(input->getShapeInfo())];
    memcpy(inP, input->getShapeInfo(), shape::shapeInfoByteLength(input->rankOf()));

    auto inshape = new ShapeList(inP);

    auto shapes = testop.calculateOutputShape(inshape, *block);

    ASSERT_EQ(1,shapes->size());
    ASSERT_EQ(0,shapes->at(0)[0]); // scalar shape has rank 0
    ASSERT_EQ(8192,shapes->at(0)[1]);
    ASSERT_EQ(1,shapes->at(0)[2]);

    delete[] inP;
    delete variableSpace;
    delete block;
    delete inshape;
    delete shapes;

}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, TestReductionShape2) {
    auto input = NDArrayFactory::create_<float>('c', {4, 5, 5, 10, 10});

    auto variableSpace = new VariableSpace();
    variableSpace->putVariable(-1, input);

    auto block = new Context(1, variableSpace, false);  // not-in-place
    block->fillInputs({-1});

    // kernel params
    //block->getIArguments()->push_back(4);
    block->getIArguments()->push_back(1);
    block->getIArguments()->push_back(2);
    block->getIArguments()->push_back(3);
    block->getIArguments()->push_back(4);

    nd4j::ops::testreduction testop;

    auto inshapes = new ShapeList(input->getShapeInfo());
    auto shapes = testop.calculateOutputShape(inshapes, *block);
    ASSERT_EQ(1,shapes->size());
    ASSERT_EQ(1,shapes->at(0)[0]);
    ASSERT_EQ(4,shapes->at(0)[1]);
    ASSERT_EQ(1,shapes->at(0)[2]);

    delete variableSpace;
    delete block;
    delete shapes;
    delete inshapes;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, TestCustomShape1) {
    auto input = NDArrayFactory::create_<float>('c', {2, 3, 4});

    auto variableSpace = new VariableSpace();
    variableSpace->putVariable(-1, input);

    auto block = new Context(1, variableSpace, false);  // not-in-place
    block->fillInputs({-1});

    nd4j::ops::testcustom test;

    auto inshapes = new ShapeList(input->getShapeInfo());
    auto shapes = test.calculateOutputShape(inshapes, *block);

    //input.printShapeInfo("input");
    //shape::printShapeInfoLinear(shape);

    ASSERT_EQ(input->getShapeInfo()[0]    , shapes->at(0)[0]);
    ASSERT_EQ(input->getShapeInfo()[1] * 2, shapes->at(0)[1]);
    ASSERT_EQ(input->getShapeInfo()[2] * 2, shapes->at(0)[2]);
    ASSERT_EQ(input->getShapeInfo()[3] * 2, shapes->at(0)[3]);

    delete variableSpace;
    delete block;
    delete shapes;
    delete inshapes;
}


//////////////////////////////////////////////////////////////////////
/*
TEST_F(DeclarableOpsTests1, Sum1) {

    float xBuff[] = {1, 2, 3, 4, 5, 6, 7, 8};
    int xShape[]  = {2, 4, 2, 2, 1, 0, 1, 99};
    float expBuff[] = {16, 20};
    int expShape[]  = {2, 1, 2, 2, 1, 0, 1, 99};

    const std::vector<int> dimensions = {1,0};

    auto x = NDArrayFactory::create_<float>(xBuff, xShape);
    auto z = NDArrayFactory::create_<float>(1, 2, 'c');
    auto exp(expBuff, expShape);

    auto variableSpace = new VariableSpace();
    variableSpace->putVariable(-1, x);
    variableSpace->putVariable(1, z);

    auto block = new Context(1, variableSpace, false);  // not-in-place
    block->fillInputs({-1});
    std::vector<int>* arguments = block->getIArguments();
    *arguments = dimensions;

    nd4j::ops::sum<float> sum;
    Nd4jStatus status = sum.execute(block);

    auto result = variableSpace->getVariable(block->getNodeId())->getNDArray();

    ASSERT_EQ(ND4J_STATUS_OK, status);
    ASSERT_TRUE(result->equalsTo(&exp));

    delete block;
    delete variableSpace;
}
*/

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, Avgpool2d_test1) {

    auto x = NDArrayFactory::create_<float>('c', {bS,iD,iH,iW});
    auto exp = NDArrayFactory::create<float>('c',{bS,iD,oH,oW});
    // auto z('c',{bS,iD,oH,oW});

    auto variableSpace = new VariableSpace();
    variableSpace->putVariable(-1, x);
    // variableSpace->putVariable(1, &z);

    auto block = new Context(1, variableSpace, false);
    block->fillInputs({-1});
    std::vector<int>* argI = block->getIArguments();
    *argI = {kH,kW, sH,sW, pH,pW, dW,dH, 0, 0, 0};  // 0,1 - kernel Height/Width; 2,3 - stride Height/Width; 4,5 - pad Height/Width; 6,7 - dilation Height/Width; 8 - same mode;

    nd4j::ops::avgpool2d pooling;
    Nd4jStatus status = pooling.execute(block);
    ASSERT_EQ(ND4J_STATUS_OK, status);

    auto result = variableSpace->getVariable(block->getNodeId())->getNDArray();
    ASSERT_TRUE(exp.isSameShape(result));


    delete variableSpace;
    delete block;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, Avgpool2d_test2) {
    const int bS = 2;
    const int iD = 1;
    const int iH = 28;
    const int iW = 28;
    const int kH = 5;
    const int kW = 5;
    const int sH = 1;
    const int sW = 1;
    const int pH = 0;
    const int pW = 0;
    const int dH = 1;
    const int dW = 1;
    const int oH = (iH - kH - (kH-1)*(dH-1) + 2*pH)/sH + 1;     // output height
    const int oW = (iW - kW - (kW-1)*(dW-1) + 2*pW)/sW + 1;     // output width


    auto x = NDArrayFactory::create_<float>('c', {bS,iD,iH,iW});
    auto exp = NDArrayFactory::create<float>('c',{bS,iD,oH,oW});
    // auto z('c',{bS,iD,oH,oW});

    auto variableSpace = new VariableSpace();
    variableSpace->putVariable(-1, x);
    // variableSpace->putVariable(1, &z);

    auto block = new Context(1, variableSpace, false);
    block->fillInputs({-1});
    std::vector<int>* argI = block->getIArguments();
    *argI = {kH,kW, sH,sW, pH,pW, dW,dH, 0, 0, 0};  // 0,1 - kernel Height/Width; 2,3 - stride Height/Width; 4,5 - pad Height/Width; 6,7 - dilation Height/Width; 8 - same mode;

    nd4j::ops::avgpool2d pooling;
    Nd4jStatus status = pooling.execute(block);
    ASSERT_EQ(ND4J_STATUS_OK, status);

    auto result = variableSpace->getVariable(block->getNodeId())->getNDArray();
    // result->printShapeInfo();
    ASSERT_TRUE(exp.isSameShape(result));

    delete variableSpace;
    delete block;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, Avgpool2d_test3) {
    const int bS = 2;
    const int iD = 1;
    const int iH = 28;
    const int iW = 28;
    const int kH = 5;
    const int kW = 5;
    const int sH = 1;
    const int sW = 1;
    const int pH = 0;
    const int pW = 0;
    const int dH = 1;
    const int dW = 1;
    const int oH = (int) nd4j::math::nd4j_ceil<float, int>(iH * 1.f / sH);
    const int oW = (int) nd4j::math::nd4j_ceil<float, int>(iW * 1.f / sW);


    auto x = NDArrayFactory::create_<float>('c', {bS,iD,iH,iW});
    auto exp = NDArrayFactory::create<float>('c',{bS,iD,oH,oW});
    // auto z('c',{bS,iD,oH,oW});

    auto variableSpace = new VariableSpace();
    variableSpace->putVariable(-1, x);
    // variableSpace->putVariable(1, &z);

    auto block = new Context(1, variableSpace, false);
    block->fillInputs({-1});
    std::vector<int>* argI = block->getIArguments();
    *argI = {kH,kW, sH,sW, pH,pW, dW,dH, 1, 0, 0};  // 0,1 - kernel Height/Width; 2,3 - stride Height/Width; 4,5 - pad Height/Width; 6,7 - dilation Height/Width; 8 - same mode;

    nd4j::ops::avgpool2d pooling;
    Nd4jStatus status = pooling.execute(block);
    ASSERT_EQ(ND4J_STATUS_OK, status);

    auto result = variableSpace->getVariable(block->getNodeId())->getNDArray();
    // result->printShapeInfo();
    ASSERT_TRUE(exp.isSameShape(result));

    delete variableSpace;
    delete block;
}


//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, Pnormpool2d1) {

    auto x = NDArrayFactory::create_<float>('c', {bS,iD,iH,iW});
    auto exp = NDArrayFactory::create<float>('c',{bS,iD,oH,oW});
    // auto z('c',{bS,iD,oH,oW});

    auto variableSpace = new VariableSpace();
    variableSpace->putVariable(-1, x);
    // variableSpace->putVariable(1, &z);

    auto block = new Context(1, variableSpace, false);
    block->fillInputs({-1});
    std::vector<int>* argI = block->getIArguments();
    *argI = {kH,kW, sH,sW, pH,pW, dW,dH, 0, 1, 0};  // 0,1 - kernel Height/Width; 2,3 - stride Height/Width; 4,5 - pad Height/Width; 6,7 - dilation Height/Width; 8 - same mode; 9 - extraParam0 for pnorm case;

    nd4j::ops::pnormpool2d pooling;
    Nd4jStatus status = pooling.execute(block);
    ASSERT_EQ(ND4J_STATUS_OK, status);

    auto result = variableSpace->getVariable(block->getNodeId())->getNDArray();
    ASSERT_TRUE(exp.isSameShape(result));

    delete variableSpace;
    delete block;
}

/*/////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, IsMax1) {

    float xBuff[]   = {1,2,3,4,5,6,7,8,9};
    Nd4jLong xShape[]    = {2,3,3,3,1,0,1,99};
    bool expBuff[] = {0,0,1,0,0,1,0,0,1};
    ArrayOptions::setDataType(xShape, nd4j::DataType::BOOL);

    auto x = new NDArray(xBuff, xShape);
    NDArray exp(expBuff, xShape);

    auto variableSpace = new VariableSpace();
    variableSpace->putVariable(-1, x);

    auto block = new Context(1, variableSpace, false);
    block->fillInputs({-1});
    std::vector<int>* argI = block->getIArguments();
//    *argI = {1};                                        // dimensions
    argI->push_back(1); // = {1};                                        // dimensions

    nd4j::ops::ismax ismaxOp;
    Nd4jStatus status = ismaxOp.execute(block);
    ASSERT_EQ(ND4J_STATUS_OK, status);

    auto result = variableSpace->getVariable(block->getNodeId())->getNDArray();
    result->printIndexedBuffer("IS_MAX");
    ASSERT_TRUE(exp.equalsTo(result));

    delete variableSpace;
    delete block;
}
*/

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, IsMax1) {
    NDArray x('c', {3, 3}, nd4j::DataType::FLOAT32);
//    NDArray exp('c', {3, 3}, nd4j::DataType::BOOL);
    NDArray exp('c', {3, 3}, nd4j::DataType::FLOAT32);
    x.linspace(1);
    exp.p<bool>(0, 2, true);
    exp.p<bool>(1, 2, true);
    exp.p<bool>(2, 2, true);

    nd4j::ops::ismax ismaxOp;
    auto result = ismaxOp.execute({&x}, {}, {1});

    ASSERT_EQ(ND4J_STATUS_OK, result->status());

    auto res = result->at(0);
    //res->printIndexedBuffer("IS_MAX");
    ASSERT_TRUE(exp.equalsTo(res));

    delete result;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, IsMax2) {
    NDArray x('c', {3, 3}, nd4j::DataType::FLOAT32);
//    NDArray exp('c', {3, 3}, nd4j::DataType::BOOL);
    NDArray exp('c', {3, 3}, nd4j::DataType::FLOAT32);
    x.linspace(1);
    //exp.p<bool>(0, 2, true);
    //exp.p<bool>(1, 2, true);
    exp.p<bool>(2, 2, true);

    nd4j::ops::ismax ismaxOp;
    auto result = ismaxOp.execute({&x}, {}, {0, 1});

    ASSERT_EQ(ND4J_STATUS_OK, result->status());

    auto res = result->at(0);
    //res->printIndexedBuffer("IS_MAX");
    ASSERT_TRUE(exp.equalsTo(res));

    delete result;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, IsMax3) {
    NDArray x = NDArrayFactory::create<float>(120.f); //('c', {3, 3}, nd4j::DataType::FLOAT32);
//    NDArray exp('c', {3, 3}, nd4j::DataType::BOOL);
    NDArray exp = NDArrayFactory::create<float>(1.f);//, nd4j::DataType::FLOAT32); //'c', {3, 3}, nd4j::DataType::FLOAT32);
    x.linspace(1);
    //exp.p<bool>(0, 2, true);
    //exp.p<bool>(1, 2, true);
    //exp.p<bool>(2, 2, true);

    nd4j::ops::ismax ismaxOp;
    auto result = ismaxOp.execute({&x}, {}, {0});

    ASSERT_EQ(ND4J_STATUS_OK, result->status());

    auto res = result->at(0);
    //res->printIndexedBuffer("IS_MAX");
    ASSERT_TRUE(exp.equalsTo(res));

    delete result;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, IsMax4) {
    auto x = NDArrayFactory::create<double>('c', {6}, {0, 0, 0, 2, 2, 0});
    auto z = NDArrayFactory::create<bool>('c', {6});
    auto e = NDArrayFactory::create<bool>('c', {6}, {false, false, false, true, false, false});

    nd4j::ops::ismax op;
    auto result = op.execute({&x}, {&z}, {}, {}, {});
    ASSERT_EQ(Status::OK(), result);

    ASSERT_EQ(e, z);
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, CompactLaunchTests1) {

    NDArray input('c', {2, 3, 4, 4}, nd4j::DataType::FLOAT32);
    NDArray weights('c', {3, 3, 5, 5}, nd4j::DataType::FLOAT32);
    NDArray exp('c', {2,3,8,8}, {6276.0,12831.0,19668.0,26790.0,27012.0,20703.0,14100.0,7200.0,13719.0,28023.0,42918.0,58410.0,58902.0,45105.0,30693.0,15660.0,22389.0,45696.0,69930.0,95100.0,95910.0,73386.0,49899.0,25440.0,32346.0,65970.0,
                                100884.0,137100.0,138276.0,105726.0,71838.0,36600.0,33726.0,68790.0,105204.0,142980.0,144156.0,110226.0,74898.0,38160.0,27555.0,56154.0,85806.0,116520.0,117474.0,89748.0,60933.0,31020.0,19917.0,40557.0,61926.0,
                                84030.0,84714.0,64671.0,43875.0,22320.0,10752.0,21879.0,33384.0,45270.0,45636.0,34815.0,23604.0,12000.0,7551.0,15456.0,23718.0,32340.0,32562.0,24978.0,17025.0,8700.0,16569.0,33873.0,51918.0,70710.0,71202.0,
                                54555.0,37143.0,18960.0,27114.0,55371.0,84780.0,115350.0,116160.0,88911.0,60474.0,30840.0,39246.0,80070.0,122484.0,166500.0,167676.0,128226.0,87138.0,44400.0,40626.0,82890.0,126804.0,172380.0,173556.0,132726.0,
                                90198.0,45960.0,33180.0,67629.0,103356.0,140370.0,141324.0,107973.0,73308.0,37320.0,23967.0,48807.0,74526.0,101130.0,101814.0,77721.0,52725.0,26820.0,12927.0,26304.0,40134.0,54420.0,54786.0,41790.0,28329.0,14400.0,
                                8826.0,18081.0,27768.0,37890.0,38112.0,29253.0,19950.0,10200.0,19419.0,39723.0,60918.0,83010.0,83502.0,64005.0,43593.0,22260.0,31839.0,65046.0,99630.0,135600.0,136410.0,104436.0,71049.0,36240.0,46146.0,94170.0,
                                144084.0,195900.0,197076.0,150726.0,102438.0,52200.0,47526.0,96990.0,148404.0,201780.0,202956.0,155226.0,105498.0,53760.0,38805.0,79104.0,120906.0,164220.0,165174.0,126198.0,85683.0,43620.0,28017.0,57057.0,87126.0,
                                118230.0,118914.0,90771.0,61575.0,31320.0,15102.0,30729.0,46884.0,63570.0,63936.0,48765.0,33054.0,16800.0,17220.0,34863.0,52932.0,71430.0,72228.0,54831.0,36996.0,18720.0,36327.0,73527.0,111606.0,150570.0,152214.0,
                                115521.0,77925.0,39420.0,57381.0,116112.0,176202.0,237660.0,240198.0,182250.0,122907.0,62160.0,80442.0,162738.0,246900.0,332940.0,336420.0,255198.0,172062.0,87000.0,84702.0,171318.0,259860.0,350340.0,353820.0,
                                268338.0,180882.0,91440.0,66867.0,135210.0,205038.0,276360.0,279042.0,211572.0,142581.0,72060.0,46845.0,94701.0,143574.0,193470.0,195306.0,148047.0,99747.0,50400.0,24576.0,49671.0,75288.0,101430.0,102372.0,77583.0,
                                52260.0,26400.0,22095.0,44688.0,67782.0,91380.0,92178.0,69906.0,47121.0,23820.0,46377.0,93777.0,142206.0,191670.0,193314.0,146571.0,98775.0,49920.0,72906.0,147387.0,223452.0,301110.0,303648.0,230175.0,155082.0,
                                78360.0,101742.0,205638.0,311700.0,419940.0,423420.0,320898.0,216162.0,109200.0,106002.0,214218.0,324660.0,437340.0,440820.0,334038.0,224982.0,113640.0,83292.0,168285.0,254988.0,343410.0,346092.0,262197.0,176556.0,
                                89160.0,58095.0,117351.0,177774.0,239370.0,241206.0,182697.0,122997.0,62100.0,30351.0,61296.0,92838.0,124980.0,125922.0,95358.0,64185.0,32400.0,26970.0,54513.0,82632.0,111330.0,112128.0,84981.0,57246.0,28920.0,56427.0,114027.0,172806.0,232770.0,234414.0,177621.0,119625.0,60420.0,88431.0,178662.0,270702.0,364560.0,367098.0,278100.0,187257.0,94560.0,123042.0,248538.0,376500.0,506940.0,510420.0,386598.0,260262.0,131400.0,127302.0,257118.0,389460.0,524340.0,527820.0,399738.0,269082.0,135840.0,99717.0,201360.0,304938.0,410460.0,413142.0,312822.0,210531.0,106260.0,69345.0,140001.0,211974.0,285270.0,287106.0,217347.0,146247.0,73800.0,36126.0,72921.0,110388.0,148530.0,149472.0,113133.0,76110.0,38400.0}, nd4j::DataType::FLOAT32);

    input.linspace(1);
    weights.linspace(1);
    weights.permutei({2,3,1,0});

    nd4j::ops::deconv2d op;
    auto result = op.execute({&input, &weights}, {}, {5, 5, 1, 1, 0, 0, 1, 1, 0, 0});

    auto z = result->at(0);
    // z->printShapeInfo();
    // z->printBuffer();

    ASSERT_TRUE(exp.isSameShape(z));
    ASSERT_TRUE(exp.equalsTo(z));

    delete result;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, CompactLaunchTests2) {
    Nd4jLong _expS[] = {4, 2, 3, 8, 8, 192, 64, 8, 1, 16384, 1, 99};
    double _expB[] = {6276.0,12831.0,19668.0,26790.0,27012.0,20703.0,14100.0,7200.0,13719.0,28023.0,42918.0,58410.0,58902.0,45105.0,30693.0,15660.0,22389.0,45696.0,69930.0,95100.0,95910.0,73386.0,49899.0,25440.0,32346.0,65970.0,100884.0,137100.0,138276.0,105726.0,71838.0,36600.0,33726.0,68790.0,105204.0,142980.0,144156.0,110226.0,74898.0,38160.0,27555.0,56154.0,85806.0,116520.0,117474.0,89748.0,60933.0,31020.0,19917.0,40557.0,61926.0,84030.0,84714.0,64671.0,43875.0,22320.0,10752.0,21879.0,33384.0,45270.0,45636.0,34815.0,23604.0,12000.0,7551.0,15456.0,23718.0,32340.0,32562.0,24978.0,17025.0,8700.0,16569.0,33873.0,51918.0,70710.0,71202.0,54555.0,37143.0,18960.0,27114.0,55371.0,84780.0,115350.0,116160.0,88911.0,60474.0,30840.0,39246.0,80070.0,122484.0,166500.0,167676.0,128226.0,87138.0,44400.0,40626.0,82890.0,126804.0,172380.0,173556.0,132726.0,90198.0,45960.0,33180.0,67629.0,103356.0,140370.0,141324.0,107973.0,73308.0,37320.0,23967.0,48807.0,74526.0,101130.0,101814.0,77721.0,52725.0,26820.0,12927.0,26304.0,40134.0,54420.0,54786.0,41790.0,28329.0,14400.0,8826.0,18081.0,27768.0,37890.0,38112.0,29253.0,19950.0,10200.0,19419.0,39723.0,60918.0,83010.0,83502.0,64005.0,43593.0,22260.0,31839.0,65046.0,99630.0,135600.0,136410.0,104436.0,71049.0,36240.0,46146.0,94170.0,144084.0,195900.0,197076.0,150726.0,102438.0,52200.0,47526.0,96990.0,148404.0,201780.0,202956.0,155226.0,105498.0,53760.0,38805.0,79104.0,120906.0,164220.0,165174.0,126198.0,85683.0,43620.0,28017.0,57057.0,87126.0,118230.0,118914.0,90771.0,61575.0,31320.0,15102.0,30729.0,46884.0,63570.0,63936.0,48765.0,33054.0,16800.0,17220.0,34863.0,52932.0,71430.0,72228.0,54831.0,36996.0,18720.0,36327.0,73527.0,111606.0,150570.0,152214.0,115521.0,77925.0,39420.0,57381.0,116112.0,176202.0,237660.0,240198.0,182250.0,122907.0,62160.0,80442.0,162738.0,246900.0,332940.0,336420.0,255198.0,172062.0,87000.0,84702.0,171318.0,259860.0,350340.0,353820.0,268338.0,180882.0,91440.0,66867.0,135210.0,205038.0,276360.0,279042.0,211572.0,142581.0,72060.0,46845.0,94701.0,143574.0,193470.0,195306.0,148047.0,99747.0,50400.0,24576.0,49671.0,75288.0,101430.0,102372.0,77583.0,52260.0,26400.0,22095.0,44688.0,67782.0,91380.0,92178.0,69906.0,47121.0,23820.0,46377.0,93777.0,142206.0,191670.0,193314.0,146571.0,98775.0,49920.0,72906.0,147387.0,223452.0,301110.0,303648.0,230175.0,155082.0,78360.0,101742.0,205638.0,311700.0,419940.0,423420.0,320898.0,216162.0,109200.0,106002.0,214218.0,324660.0,437340.0,440820.0,334038.0,224982.0,113640.0,83292.0,168285.0,254988.0,343410.0,346092.0,262197.0,176556.0,89160.0,58095.0,117351.0,177774.0,239370.0,241206.0,182697.0,122997.0,62100.0,30351.0,61296.0,92838.0,124980.0,125922.0,95358.0,64185.0,32400.0,26970.0,54513.0,82632.0,111330.0,112128.0,84981.0,57246.0,28920.0,56427.0,114027.0,172806.0,232770.0,234414.0,177621.0,119625.0,60420.0,88431.0,178662.0,270702.0,364560.0,367098.0,278100.0,187257.0,94560.0,123042.0,248538.0,376500.0,506940.0,510420.0,386598.0,260262.0,131400.0,127302.0,257118.0,389460.0,524340.0,527820.0,399738.0,269082.0,135840.0,99717.0,201360.0,304938.0,410460.0,413142.0,312822.0,210531.0,106260.0,69345.0,140001.0,211974.0,285270.0,287106.0,217347.0,146247.0,73800.0,36126.0,72921.0,110388.0,148530.0,149472.0,113133.0,76110.0,38400.0,};
    NDArray exp(_expB, _expS);

    auto input = NDArrayFactory::create<double>('c', {2, 3, 4, 4});
    auto weights = NDArrayFactory::create<double>('c', {3, 3, 5, 5});
    auto z = NDArrayFactory::create<double>('c', {2, 3, 8, 8});

    input.linspace(1);
    weights.linspace(1);
    weights.permutei({2,3,1,0});

    nd4j::ops::deconv2d op;
    auto result = op.execute({&input, &weights}, {&z}, {}, {5, 5, 1, 1, 0, 0, 1, 1, 0, 0},{});

    ASSERT_EQ(ND4J_STATUS_OK, result);

    ASSERT_TRUE(exp.isSameShape(&z));
    ASSERT_TRUE(exp.equalsTo(&z));
}

////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, batchnorm_test1) {

    auto input    = NDArrayFactory::create<double>('c', {2,3,2,3,2});
    auto mean     = NDArrayFactory::create<double>('c', {2,3,2,3,2});
    auto variance = NDArrayFactory::create<double>('c', {2,3,2,3,2});
    auto gamma    = NDArrayFactory::create<double>('c', {2,3,2,3,2});
    auto beta     = NDArrayFactory::create<double>('c', {2,3,2,3,2});

    auto expected = NDArrayFactory::create<double>('c', {2,3,2,3,2}, {-0.52733537,-0.35763144,-0.18792751,-0.01822358, 0.15148035, 0.32118428, 0.49088821, 0.66059214, 0.83029607, 1., 1.16970393, 1.33940786, 1.50911179, 1.67881572, 1.84851965, 2.01822358, 2.18792751, 2.35763144, 2.52733537, 2.6970393 , 2.86674323, 3.03644717, 3.2061511 , 3.37585503, 3.54555896, 3.71526289, 3.88496682, 4.05467075, 4.22437468, 4.39407861, 4.56378254, 4.73348647, 4.9031904 , 5.07289433, 5.24259826, 5.41230219, 5.58200612, 5.75171005, 5.92141398, 6.09111791, 6.26082184, 6.43052577, 6.6002297 , 6.76993364, 6.93963757, 7.1093415 , 7.27904543, 7.44874936, 7.61845329, 7.78815722, 7.95786115, 8.12756508, 8.29726901, 8.46697294, 8.63667687, 8.8063808 , 8.97608473, 9.14578866, 9.31549259, 9.48519652, 9.65490045, 9.82460438, 9.99430831,10.16401224,10.33371617,10.50342011,10.67312404,10.84282797,11.0125319 ,11.18223583,11.35193976,11.52164369});

    input.linspace(0.1, 0.1);
    mean.assign(1.);
    variance.assign(0.5);
    gamma.assign(1.2);
    beta.assign(1.);

    nd4j::ops::batchnorm op;

    auto results = op.execute({&input, &mean, &variance, &gamma, &beta}, {1e-5}, {1,1});

    ASSERT_EQ(ND4J_STATUS_OK, results->status());

    auto output = results->at(0);

    ASSERT_TRUE(expected.isSameShapeStrict(output));
    ASSERT_TRUE(expected.equalsTo(output));

    delete results;
}


TEST_F(DeclarableOpsTests1, batchnorm_test2) {

    auto input    = NDArrayFactory::create<double>('c', {2,3,1,3,1});
    auto mean     = NDArrayFactory::create<double>('c', {1,3,2,1,2});
    auto variance = NDArrayFactory::create<double>('c', {2,1,2,3,2});
    auto gamma    = NDArrayFactory::create<double>('c', {2,3,2,3,1});
    auto beta     = NDArrayFactory::create<double>('c', {1,3,2,1,2});

    auto expected = NDArrayFactory::create<double>('c', {2,3,2,3,2}, {-0.52733537,-0.52733537,-0.35763144,-0.35763144,-0.18792751,-0.18792751, -0.52733537,-0.52733537,-0.35763144,-0.35763144,-0.18792751,-0.18792751, -0.01822358,-0.01822358, 0.15148035, 0.15148035, 0.32118428, 0.32118428, -0.01822358,-0.01822358, 0.15148035, 0.15148035, 0.32118428, 0.32118428, 0.49088821, 0.49088821, 0.66059214, 0.66059214, 0.83029607, 0.83029607, 0.49088821, 0.49088821, 0.66059214, 0.66059214, 0.83029607, 0.83029607, 1.        , 1.        , 1.16970393, 1.16970393, 1.33940786, 1.33940786, 1.        , 1.        , 1.16970393, 1.16970393, 1.33940786, 1.33940786, 1.50911179, 1.50911179, 1.67881572, 1.67881572, 1.84851965, 1.84851965, 1.50911179, 1.50911179, 1.67881572, 1.67881572, 1.84851965, 1.84851965, 2.01822358, 2.01822358, 2.18792751, 2.18792751, 2.35763144, 2.35763144, 2.01822358, 2.01822358, 2.18792751, 2.18792751, 2.35763144, 2.35763144});

    input.linspace(0.1, 0.1);
    mean.assign(1.);
    variance.assign(0.5);
    gamma.assign(1.2);
    beta.assign(1.);

    nd4j::ops::batchnorm op;

    auto results = op.execute({&input, &mean, &variance, &gamma, &beta}, {1e-5}, {1,1});

    ASSERT_EQ(ND4J_STATUS_OK, results->status());

    auto output = results->at(0);

    ASSERT_TRUE(expected.isSameShapeStrict(output));
    ASSERT_TRUE(expected.equalsTo(output));

    delete results;
}

////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, batchnorm_test3) {

    auto input    = NDArrayFactory::create<double>('c', {2,3,2,3,2});
    auto mean     = NDArrayFactory::create<double>('c', {2,3,2});
    auto variance = NDArrayFactory::create<double>('c', {2,3,1,3,1});
    auto gamma    = NDArrayFactory::create<double>('c', {1,1});
    auto beta     = NDArrayFactory::create<double>('c', {1,2});

    auto expected = NDArrayFactory::create<double>('c', {2,3,2,3,2}, {-0.52733537,-0.35763144,-0.18792751,-0.01822358, 0.15148035, 0.32118428, 0.49088821, 0.66059214, 0.83029607, 1., 1.16970393, 1.33940786, 1.50911179, 1.67881572, 1.84851965, 2.01822358, 2.18792751, 2.35763144, 2.52733537, 2.6970393 , 2.86674323, 3.03644717, 3.2061511 , 3.37585503, 3.54555896, 3.71526289, 3.88496682, 4.05467075, 4.22437468, 4.39407861, 4.56378254, 4.73348647, 4.9031904 , 5.07289433, 5.24259826, 5.41230219, 5.58200612, 5.75171005, 5.92141398, 6.09111791, 6.26082184, 6.43052577, 6.6002297 , 6.76993364, 6.93963757, 7.1093415 , 7.27904543, 7.44874936, 7.61845329, 7.78815722, 7.95786115, 8.12756508, 8.29726901, 8.46697294, 8.63667687, 8.8063808 , 8.97608473, 9.14578866, 9.31549259, 9.48519652, 9.65490045, 9.82460438, 9.99430831,10.16401224,10.33371617,10.50342011, 10.67312404,10.84282797,11.0125319 ,11.18223583,11.35193976,11.52164369});

    input.linspace(0.1, 0.1);
    mean.assign(1.);
    variance.assign(0.5);
    gamma.assign(1.2);
    beta.assign(1.);

    nd4j::ops::batchnorm op;

    auto results = op.execute({&input, &mean, &variance, &gamma, &beta}, {1e-5}, {1,1});

    ASSERT_EQ(ND4J_STATUS_OK, results->status());

    auto output = results->at(0);

    ASSERT_TRUE(expected.isSameShapeStrict(output));
    ASSERT_TRUE(expected.equalsTo(output));

    delete results;
}

////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, batchnorm_test4) {

    auto input    = NDArrayFactory::create<double>('c', {3,2});
    auto mean    = NDArrayFactory::create<double>('c', {2,3,2});
    auto variance= NDArrayFactory::create<double>('c', {2,3,1,3,2});
    auto gamma   = NDArrayFactory::create<double>('c', {1,1});
    auto beta    = NDArrayFactory::create<double>('c', {1,2});

    auto expected= NDArrayFactory::create<double>('c', {2,3,2,3,2}, {-0.52733537,-0.35763144,-0.18792751,-0.01822358, 0.15148035, 0.32118428, -0.52733537,-0.35763144,-0.18792751,-0.01822358, 0.15148035, 0.32118428, -0.52733537,-0.35763144,-0.18792751,-0.01822358, 0.15148035, 0.32118428, -0.52733537,-0.35763144,-0.18792751,-0.01822358, 0.15148035, 0.32118428, -0.52733537,-0.35763144,-0.18792751,-0.01822358, 0.15148035, 0.32118428, -0.52733537,-0.35763144,-0.18792751,-0.01822358, 0.15148035, 0.32118428, -0.52733537,-0.35763144,-0.18792751,-0.01822358, 0.15148035, 0.32118428, -0.52733537,-0.35763144,-0.18792751,-0.01822358, 0.15148035, 0.32118428, -0.52733537,-0.35763144,-0.18792751,-0.01822358, 0.15148035, 0.32118428, -0.52733537,-0.35763144,-0.18792751,-0.01822358, 0.15148035, 0.32118428, -0.52733537,-0.35763144,-0.18792751,-0.01822358, 0.15148035, 0.32118428, -0.52733537,-0.35763144,-0.18792751,-0.01822358, 0.15148035, 0.32118428});

    input.linspace(0.1, 0.1);
    mean.assign(1.);
    variance.assign(0.5);
    gamma.assign(1.2);
    beta.assign(1.);

    nd4j::ops::batchnorm op;

    auto results = op.execute({&input, &mean, &variance, &gamma, &beta}, {1e-5}, {1,1});

    ASSERT_EQ(ND4J_STATUS_OK, results->status());

    auto output = results->at(0);

    ASSERT_TRUE(expected.isSameShapeStrict(output));
    ASSERT_TRUE(expected.equalsTo(output));

    delete results;
}

////////////////////////////////////////////////////////////////////
// TEST_F(DeclarableOpsTests1, sru_old_test1) {

//     const int bS = 2;
//     const int K = 3;
//     const int N = 4;

//     NDArray input('c', {bS,K,N}, nd4j::DataType::DOUBLE);
//     NDArray weights('c', {3*K,K}, nd4j::DataType::DOUBLE);
//     NDArray bias('c', {1,2*K}, nd4j::DataType::DOUBLE);
//     NDArray init('c', {bS,K}, nd4j::DataType::DOUBLE);
//     NDArray mask('c', {bS,K}, nd4j::DataType::DOUBLE);
//     NDArray expState('c', {bS,K,N}, {0.847983, 0.874549, 0.896109, 0.913715, 0.847983, 0.874549, 0.896109, 0.913715, 0.847983, 0.874549, 0.896109, 0.913715, 0.847983, 0.874549, 0.896109, 0.913715, 0.847983, 0.874549, 0.896109, 0.913715, 0.847983, 0.874549, 0.896109, 0.913715}, nd4j::DataType::DOUBLE);
//     NDArray expOut('c', {bS,K,N}, {1.090533, 1.174509, 1.252403, 1.324656, 1.090533, 1.174509, 1.252403, 1.324656, 1.090533, 1.174509, 1.252403, 1.324656, 1.090533, 1.174509, 1.252403, 1.324656, 1.090533, 1.174509, 1.252403, 1.324656, 1.090533, 1.174509, 1.252403, 1.324656}, nd4j::DataType::DOUBLE);

//     input.assign(1.5);
//     weights.assign(0.5);
//     bias.assign(0.3) ;
//     init.assign(1.);
//     mask.assign(1.);

//     nd4j::ops::sru_old op;
//     auto  results = op.execute({&input, &weights, &bias, &init, &mask}, {}, {});
//     ASSERT_TRUE(results->size() == 2);

//     auto state  = results->at(0);
//     auto output = results->at(1);
//     // state->printBuffer();
//     // expState.printIndexedBuffer("EXP STATE");
//     // state->printIndexedBuffer("OUT STATE");
//     ASSERT_TRUE(expState.equalsTo(state));
//     ASSERT_TRUE(expOut.equalsTo(output));

//     delete results;
// }

//////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, sru_test1) {

    const int bS = 2;
    const int K = 3;
    const int N = 4;

    NDArray input('c', {bS,K,N}, nd4j::DataType::DOUBLE);
    NDArray weights('c', {3*K,K}, nd4j::DataType::DOUBLE);
    NDArray bias('c', {2*K}, nd4j::DataType::DOUBLE);
    NDArray init('c', {bS,K}, nd4j::DataType::DOUBLE);
    NDArray mask('c', {bS,K}, nd4j::DataType::DOUBLE);
    NDArray expState('c', {bS,K,N}, {1.090533, 1.174509, 1.252403, 1.324656, 1.090533, 1.174509, 1.252403, 1.324656, 1.090533, 1.174509, 1.252403, 1.324656, 1.090533, 1.174509, 1.252403, 1.324656, 1.090533, 1.174509, 1.252403, 1.324656, 1.090533, 1.174509, 1.252403, 1.324656}, nd4j::DataType::DOUBLE);
    NDArray expOut('c', {bS,K,N}, {0.847983, 0.874549, 0.896109, 0.913715, 0.847983, 0.874549, 0.896109, 0.913715, 0.847983, 0.874549, 0.896109, 0.913715, 0.847983, 0.874549, 0.896109, 0.913715, 0.847983, 0.874549, 0.896109, 0.913715, 0.847983, 0.874549, 0.896109, 0.913715}, nd4j::DataType::DOUBLE);

    input.assign(1.5);
    weights.assign(0.5);
    bias.assign(0.3) ;
    init.assign(1.);
    mask.assign(1.);

    nd4j::ops::sru op;
    auto results = op.execute({&input, &weights, &bias, &init, &mask}, {}, {});
    ASSERT_TRUE(results->size() == 2);

    auto output = results->at(0);
    auto state  = results->at(1);

    ASSERT_TRUE(expState.equalsTo(state));
    ASSERT_TRUE(expOut.equalsTo(output));

    delete results;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, sru_bp) {

    const int bS = 2;
    const int K = 3;
    const int N = 4;
    std::vector<double> expGradXBuff = {-0.0259303, -0.03869125, -0.0302272, -0.02299165, -0.0259303, -0.03869125, -0.0302272, -0.02299165, -0.0259303, -0.03869125, -0.0302272, -0.02299165, -0.0259303, -0.03869125, -0.0302272, -0.02299165, -0.0259303, -0.03869125, -0.0302272, -0.02299165, -0.0259303, -0.03869125, -0.0302272, -0.02299165};
    std::vector<double> expGradWBuff = {0.42526005,0.42526005,0.42526005, 0.42526005,0.42526005,0.42526005, 0.42526005,0.42526005,0.42526005, -0.5282811 , -0.5282811 , -0.5282811 , -0.5282811 , -0.5282811 , -0.5282811 , -0.5282811 , -0.5282811 , -0.5282811 , -0.15967215, -0.15967215, -0.15967215, -0.15967215, -0.15967215, -0.15967215, -0.15967215, -0.15967215, -0.15967215, 0.42526005,0.42526005,0.42526005, 0.42526005,0.42526005,0.42526005, 0.42526005,0.42526005,0.42526005, -0.5282811 , -0.5282811 , -0.5282811 , -0.5282811 , -0.5282811 , -0.5282811 , -0.5282811 , -0.5282811 , -0.5282811 , -0.15967215, -0.15967215, -0.15967215, -0.15967215, -0.15967215, -0.15967215, -0.15967215, -0.15967215, -0.15967215};
    std::vector<double> expGradBBuff = {-0.7043748, -0.7043748, -0.7043748, -0.2128962, -0.2128962, -0.2128962};
    std::vector<double> expGradInitBuff = {1.1421, 1.1421, 1.1421, 1.1421, 1.1421, 1.1421};
    std::vector<double> stateBuff = {0.847983, 0.874549, 0.896109, 0.913715, 0.847983, 0.874549, 0.896109, 0.913715, 0.847983, 0.874549, 0.896109, 0.913715, 0.847983, 0.874549, 0.896109, 0.913715, 0.847983, 0.874549, 0.896109, 0.913715, 0.847983, 0.874549, 0.896109, 0.913715};

    auto input = NDArrayFactory::create<double>('c', {bS,K,N});
    auto weights = NDArrayFactory::create<double>('c', {3*K,K});
    auto bias = NDArrayFactory::create<double>('c', {1,2*K});
    auto init = NDArrayFactory::create<double>('c', {bS,K});
    auto mask = NDArrayFactory::create<double>('c', {bS,K});
    auto state = NDArrayFactory::create<double>('c', {bS,K,N}, stateBuff);
    auto inGradCt = NDArrayFactory::create<double>('c', {bS,K});
    auto inGradH = NDArrayFactory::create<double>('c', {bS,K,N});

    auto expGradX = NDArrayFactory::create<double>('c', {bS,K,N}, expGradXBuff);
    auto expGradW = NDArrayFactory::create<double>('c', {bS,3*K,K}, expGradWBuff);
    auto expGradB = NDArrayFactory::create<double>('c', {1,2*K}, expGradBBuff);
    auto expGradInit = NDArrayFactory::create<double>('c', {bS,K}, expGradInitBuff);

    input.assign(1.5);
    weights.assign(0.5);
    bias.assign(0.3) ;
    mask.assign(1.);
    init.assign(1.);
    inGradCt.assign(0.5);
    inGradH.assign(0.5);

    nd4j::ops::sru_bp bp;
    auto resultsBP = bp.execute({&input, &weights, &bias, &init, &state, &inGradCt, &inGradH, &mask}, {}, {});
    ASSERT_TRUE(resultsBP->size() == 4);

    auto gradX    = resultsBP->at(0);
    auto gradW    = resultsBP->at(1);
    auto gradB    = resultsBP->at(2);
    auto gradInit = resultsBP->at(3);
    // expGradX.printBuffer("Exp GRAD");
    // gradX->printBuffer("Res GRAD");
    ASSERT_TRUE(expGradX.equalsTo(gradX,1e-4));
    ASSERT_TRUE(expGradW.equalsTo(gradW));
    ASSERT_TRUE(expGradB.equalsTo(gradB));
    ASSERT_TRUE(expGradInit.equalsTo(gradInit));

    delete resultsBP;
}

//////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, sru_bi_1) {

    const int bS = 2;
    const int K = 3;
    const int N = 4;

    NDArray input('c', {N,bS,2*K}, nd4j::DataType::DOUBLE);
    NDArray weights('c', {2*K,6*K}, nd4j::DataType::DOUBLE);
    NDArray bias('c', {4*K}, nd4j::DataType::DOUBLE);
    NDArray init('c', {bS,2*K}, nd4j::DataType::DOUBLE);
    NDArray mask('c', {bS,2*K}, nd4j::DataType::DOUBLE);
    NDArray expState('c', {N,bS,2*K}, {1.02857, 1.02857, 1.02857, 1.11288, 1.11288, 1.11288, 1.02857, 1.02857, 1.02857, 1.11288, 1.11288, 1.11288, 1.0569, 1.0569, 1.0569, 1.08501, 1.08501, 1.08501, 1.0569, 1.0569, 1.0569, 1.08501, 1.08501, 1.08501, 1.08501, 1.08501, 1.08501, 1.0569, 1.0569, 1.0569, 1.08501, 1.08501, 1.08501, 1.0569, 1.0569, 1.0569, 1.11288, 1.11288, 1.11288, 1.02857, 1.02857, 1.02857, 1.11288, 1.11288, 1.11288, 1.02857, 1.02857, 1.02857});
    NDArray expOut('c', {N,bS,2*K}, {0.779265, 0.779265, 0.779265, 0.810752, 0.810752, 0.810752, 0.779265, 0.779265, 0.779265, 0.810752, 0.810752, 0.810752, 0.790317, 0.790317, 0.790317, 0.800804, 0.800804, 0.800804, 0.790317, 0.790317, 0.790317, 0.800804, 0.800804, 0.800804, 0.800804, 0.800804, 0.800804, 0.790317, 0.790317, 0.790317, 0.800804, 0.800804, 0.800804, 0.790317, 0.790317, 0.790317, 0.810752, 0.810752, 0.810752, 0.779265, 0.779265, 0.779265, 0.810752, 0.810752, 0.810752, 0.779265, 0.779265, 0.779265});

    input.assign(1.5);
    weights.assign(0.5);
    bias.assign(0.3) ;
    init.assign(1.);
    mask.assign(1.);

    nd4j::ops::sru_bi op;
    auto results = op.execute({&input, &weights, &bias, &init, &mask}, {}, {});
    ASSERT_TRUE(results->size() == 2);

    auto output = results->at(0);
    auto state = results->at(1);
    // state->printBuffer();
    // output->printBuffer();

    ASSERT_TRUE(expState.equalsTo(state));
    ASSERT_TRUE(expOut.equalsTo(output));

    delete results;
}

TEST_F(DeclarableOpsTests1, sru_bi_bp_1) {

    const int bS = 2;
    const int K = 3;
    const int N = 3;
    std::vector<double> expGradXBuff = {0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129};
    std::vector<double> expGradInitBuff = {1.05121, 1.05121, 1.05121, 1.02676, 1.02676, 1.02676, 1.05121, 1.05121, 1.05121, 1.02676, 1.02676, 1.02676};
    std::vector<double> expGradWBuff = {0.02595354,-0.090096 ,-0.00882456,0.02595354,-0.090096 ,-0.0088245, 0.02595354,-0.090096 ,-0.00882456,0.01651665,-0.0559437,-0.0084390, 0.01651665,-0.0559437,-0.00843906,0.01651665,-0.0559437,-0.00843906, 0.02595354,-0.090096 ,-0.00882456,0.02595354,-0.090096 ,-0.0088245, 0.02595354,-0.090096 ,-0.00882456,0.01651665,-0.0559437,-0.0084390, 0.01651665,-0.0559437,-0.00843906,0.01651665,-0.0559437,-0.00843906, 0.02595354,-0.090096 ,-0.00882456,0.02595354,-0.090096 ,-0.0088245, 0.02595354,-0.090096 ,-0.00882456,0.01651665,-0.0559437,-0.0084390, 0.01651665,-0.0559437,-0.00843906,0.01651665,-0.0559437,-0.00843906, 0.02595354,-0.090096 ,-0.00882456,0.02595354,-0.090096 ,-0.0088245, 0.02595354,-0.090096 ,-0.00882456,0.01651665,-0.0559437,-0.0084390, 0.01651665,-0.0559437,-0.00843906,0.01651665,-0.0559437,-0.00843906, 0.02595354,-0.090096 ,-0.00882456,0.02595354,-0.090096 ,-0.0088245, 0.02595354,-0.090096 ,-0.00882456,0.01651665,-0.0559437,-0.0084390, 0.01651665,-0.0559437,-0.00843906,0.01651665,-0.0559437,-0.00843906, 0.02595354,-0.090096 ,-0.00882456,0.02595354,-0.090096 ,-0.0088245, 0.02595354,-0.090096 ,-0.00882456,0.01651665,-0.0559437,-0.0084390, 0.01651665,-0.0559437,-0.00843906,0.01651665,-0.0559437,-0.00843906, 0.02124567,-0.0731508,-0.00868926,0.02124567,-0.0731508,-0.0086892, 0.02124567,-0.0731508,-0.00868926,0.02084955,-0.0712011,-0.0085608, 0.02084955,-0.0712011,-0.00856086,0.02084955,-0.0712011,-0.00856086, 0.02124567,-0.0731508,-0.00868926,0.02124567,-0.0731508,-0.0086892, 0.02124567,-0.0731508,-0.00868926,0.02084955,-0.0712011,-0.0085608, 0.02084955,-0.0712011,-0.00856086,0.02084955,-0.0712011,-0.00856086, 0.02124567,-0.0731508,-0.00868926,0.02124567,-0.0731508,-0.0086892, 0.02124567,-0.0731508,-0.00868926,0.02084955,-0.0712011,-0.0085608, 0.02084955,-0.0712011,-0.00856086,0.02084955,-0.0712011,-0.00856086, 0.02124567,-0.0731508,-0.00868926,0.02124567,-0.0731508,-0.0086892, 0.02124567,-0.0731508,-0.00868926,0.02084955,-0.0712011,-0.0085608, 0.02084955,-0.0712011,-0.00856086,0.02084955,-0.0712011,-0.00856086, 0.02124567,-0.0731508,-0.00868926,0.02124567,-0.0731508,-0.0086892, 0.02124567,-0.0731508,-0.00868926,0.02084955,-0.0712011,-0.0085608, 0.02084955,-0.0712011,-0.00856086,0.02084955,-0.0712011,-0.00856086, 0.02124567,-0.0731508,-0.00868926,0.02124567,-0.0731508,-0.0086892, 0.02124567,-0.0731508,-0.00868926,0.02084955,-0.0712011,-0.0085608, 0.02084955,-0.0712011,-0.00856086,0.02084955,-0.0712011,-0.00856086, 0.01671156,-0.0570699,-0.00856086,0.01671156,-0.0570699,-0.0085608, 0.01671156,-0.0570699,-0.00856086,0.02534988,-0.0880002,-0.0086892, 0.02534988,-0.0880002,-0.00868926,0.02534988,-0.0880002,-0.00868926, 0.01671156,-0.0570699,-0.00856086,0.01671156,-0.0570699,-0.0085608, 0.01671156,-0.0570699,-0.00856086,0.02534988,-0.0880002,-0.0086892, 0.02534988,-0.0880002,-0.00868926,0.02534988,-0.0880002,-0.00868926, 0.01671156,-0.0570699,-0.00856086,0.01671156,-0.0570699,-0.0085608, 0.01671156,-0.0570699,-0.00856086,0.02534988,-0.0880002,-0.0086892, 0.02534988,-0.0880002,-0.00868926,0.02534988,-0.0880002,-0.00868926, 0.01671156,-0.0570699,-0.00856086,0.01671156,-0.0570699,-0.0085608, 0.01671156,-0.0570699,-0.00856086,0.02534988,-0.0880002,-0.0086892, 0.02534988,-0.0880002,-0.00868926,0.02534988,-0.0880002,-0.00868926, 0.01671156,-0.0570699,-0.00856086,0.01671156,-0.0570699,-0.0085608, 0.01671156,-0.0570699,-0.00856086,0.02534988,-0.0880002,-0.0086892, 0.02534988,-0.0880002,-0.00868926,0.02534988,-0.0880002,-0.00868926, 0.01671156,-0.0570699,-0.00856086,0.01671156,-0.0570699,-0.0085608, 0.01671156,-0.0570699,-0.00856086,0.02534988,-0.0880002,-0.0086892, 0.02534988,-0.0880002,-0.00868926,0.02534988,-0.0880002,-0.00868926};
    std::vector<double> expGradBBuff = {-0.0734389, -0.0734389, -0.0734389, -0.0717151, -0.0717151, -0.0717151, -0.0734389, -0.0734389, -0.0734389, -0.0717151, -0.0717151, -0.0717151, -0.00869156, -0.00869156, -0.00869156, -0.00856306, -0.00856306, -0.00856306, -0.00869156, -0.00869156, -0.00869156, -0.00856306, -0.00856306, -0.00856306};
    std::vector<double> stateBuff = {1.028569, 1.028569, 1.028569, 1.112884, 1.112884, 1.112884, 1.028569, 1.028569, 1.028569, 1.112884,1.112884, 1.112884, 1.056905, 1.056905, 1.056905, 1.085009, 1.085009, 1.085009, 1.056905, 1.056905,1.056905, 1.085009, 1.085009, 1.085009, 1.085009, 1.085009, 1.085009, 1.056905, 1.056905, 1.056905,1.085009, 1.085009, 1.085009, 1.056905, 1.056905, 1.056905};

    auto input = NDArrayFactory::create<double>('c', {N,bS,2*K});
    auto weights = NDArrayFactory::create<double>('c', {2*K,6*K});
    auto bias = NDArrayFactory::create<double>('c', {4*K});
    auto init = NDArrayFactory::create<double>('c', {bS,2*K});
    auto mask = NDArrayFactory::create<double>('c', {bS,2*K});
    NDArray state('c', {N,bS,2*K}, stateBuff);
    auto inGradCt = NDArrayFactory::create<double>('c', {bS,2*K});
    auto inGradH = NDArrayFactory::create<double>('c', {N,bS,2*K});

    NDArray gradBias('c', {bS,4*K}, expGradBBuff);

    NDArray expGradX('c', {N,bS,2*K}, expGradXBuff);
    NDArray expGradW('c', {N,2*K,6*K}, expGradWBuff);
    auto expGradB = NDArrayFactory::create<double>('c', {4*K});
    gradBias.reduceAlongDimension(reduce::Sum, &expGradB, {0});    // [bS, 4K] -> [4K]
    NDArray expGradInit('c', {bS,2*K}, expGradInitBuff);

    input.assign(1.5);
    weights.assign(0.5);
    bias.assign(0.3) ;
    mask.assign(1.);
    init.assign(1.);
    inGradCt.assign(0.5);
    inGradH.assign(0.5);

    nd4j::ops::sru_bi_bp bp;
    auto resultsBP = bp.execute({&input, &weights, &bias, &init, &state, &inGradCt, &inGradH, &mask}, {}, {});
    ASSERT_TRUE(resultsBP->size() == 4);

    auto gradX    = resultsBP->at(0);
    auto gradW    = resultsBP->at(1);
    auto gradB    = resultsBP->at(2);
    auto gradInit = resultsBP->at(3);

    ASSERT_TRUE(expGradX.equalsTo(gradX));
    ASSERT_TRUE(expGradW.equalsTo(gradW));
    ASSERT_TRUE(expGradB.equalsTo(gradB));
    ASSERT_TRUE(expGradInit.equalsTo(gradInit));

    delete resultsBP;
}

TEST_F(DeclarableOpsTests1, ArgMax1) {
    auto x = NDArrayFactory::create<float>('c', {3, 5});
    x.linspace(1);
    auto exp = NDArrayFactory::create<Nd4jLong>('c', {3});
    exp.assign(4);

    nd4j::ops::argmax op;

    auto result = op.execute({&x}, {}, {1});

    ASSERT_EQ(ND4J_STATUS_OK, result->status());

    auto z = result->at(0);

    ASSERT_TRUE(exp.isSameShape(z));
    ASSERT_TRUE(exp.equalsTo(z));

    delete result;
}


TEST_F(DeclarableOpsTests1, ArgMax2) {
    auto x = NDArrayFactory::create<float>('c', {3, 5});
    x.linspace(1);
    auto exp = NDArrayFactory::create<Nd4jLong>('c', {5});
    exp.assign(2);

    nd4j::ops::argmax op;

    auto result = op.execute({&x}, {}, {0});

    ASSERT_EQ(ND4J_STATUS_OK, result->status());

    auto z = result->at(0);

    ASSERT_TRUE(exp.isSameShape(z));
    ASSERT_TRUE(exp.equalsTo(z));

    delete result;
}


TEST_F(DeclarableOpsTests1, ArgMax3) {
    auto x = NDArrayFactory::create<float>('c', {3, 5});
    auto dim = NDArrayFactory::create<float>('c', {1, 1}, {0.});
    x.linspace(1);
    auto exp = NDArrayFactory::create<Nd4jLong>('c', {5});
    exp.assign(2);

    nd4j::ops::argmax op;

    auto result = op.execute({&x, &dim}, {}, {});

    ASSERT_EQ(ND4J_STATUS_OK, result->status());

    auto z = result->at(0);

    ASSERT_TRUE(exp.isSameShape(z));
    ASSERT_TRUE(exp.equalsTo(z));

    delete result;
}

TEST_F(DeclarableOpsTests1, ArgMax4) {
    auto x = NDArrayFactory::create<float>('c', {3, 5});
    auto dim = NDArrayFactory::create<float>('c', {1, 1}, {1});
    x.linspace(1);
    auto exp = NDArrayFactory::create<Nd4jLong>('c', {3});
    exp.assign(4);

    nd4j::ops::argmax op;

    auto result = op.execute({&x, &dim}, {}, {});

    ASSERT_EQ(ND4J_STATUS_OK, result->status());

    auto z = result->at(0);

    ASSERT_TRUE(exp.isSameShape(z));
    ASSERT_TRUE(exp.equalsTo(z));

    delete result;
}


TEST_F(DeclarableOpsTests1, ArgMax5) {
    auto x = NDArrayFactory::create<float>('c', {3, 5});
    auto dim = NDArrayFactory::create<float>('c', {1, 2}, {0, 1});
    x.linspace(1);
    auto exp = NDArrayFactory::create<Nd4jLong>(14);


    nd4j::ops::argmax op;

    auto result = op.execute({&x, &dim}, {}, {});

    ASSERT_EQ(ND4J_STATUS_OK, result->status());

    auto z = result->at(0);

    ASSERT_TRUE(exp.isSameShape(z));
    ASSERT_TRUE(exp.equalsTo(z));

    delete result;
}

TEST_F(DeclarableOpsTests1, ArgMax6) {
    auto x = NDArrayFactory::create<float>('c', {3, 4, 5});
    auto dim = NDArrayFactory::create<float>(-1.f);
    x.linspace(1);


    nd4j::ops::argmax op;

    auto expected = op.execute({&x}, {}, {2});
    ASSERT_EQ(Status::OK(), expected->status());
    auto exp = expected->at(0);


    auto result = op.execute({&x, &dim}, {}, {});
    ASSERT_EQ(Status::OK(), result->status());

    auto z = result->at(0);

    ASSERT_EQ(*exp, *z);

    delete result;
    delete expected;
}


TEST_F(DeclarableOpsTests1, ArgMin1) {
    auto x = NDArrayFactory::create<float>('c', {3, 5});
    x.linspace(1);
//    auto exp('c', {3, 1});
    auto exp = NDArrayFactory::create<Nd4jLong>('c', {3});
    exp.assign(0.0f);

    nd4j::ops::argmin op;

    auto result = op.execute({&x}, {}, {1});

    ASSERT_EQ(ND4J_STATUS_OK, result->status());

    auto z = result->at(0);

    ASSERT_TRUE(exp.isSameShape(z));
    ASSERT_TRUE(exp.equalsTo(z));

    delete result;
}


TEST_F(DeclarableOpsTests1, SquareTests1) {
    auto x = NDArrayFactory::create<float>('c', {3, 5});
    x.linspace(1);

    auto exp = NDArrayFactory::create<float>('c', {3, 5});
    exp.linspace(1);
    exp *= exp;

    nd4j::ops::square op;

    auto result = op.execute({&x}, {}, {});
    ASSERT_EQ(ND4J_STATUS_OK, result->status());

    auto z = result->at(0);

    ASSERT_TRUE(exp.equalsTo(z));

    delete result;
}

TEST_F(DeclarableOpsTests1, OneHotTests_1) {

    auto indices = NDArrayFactory::create<float>('c', {1, 4}, {0.0f, 2.0f, -1.0f, 1.0f});

    auto exp = NDArrayFactory::create<float>('c', {1, 4, 3}, {1.f, 0.f, 0.f, 0.f, 0.f, 1.f, 0.f, 0.f, 0.f, 0.f, 1.f, 0.f});

    nd4j::ops::onehot op;

    auto result = op.execute({&indices}, {1.0f, 0.0f}, {-1, 3});
    ASSERT_EQ(ND4J_STATUS_OK, result->status());

    auto z = result->at(0);
    // z->printBuffer();

    ASSERT_TRUE(exp.isSameShape(z));
    ASSERT_TRUE(exp.equalsTo(z));

    delete result;
}

TEST_F(DeclarableOpsTests1, OneHotTests_2) {
    auto indices = NDArrayFactory::create<float>('c', {2, 2}, {0.f, 2.f, 1.f, -1.f});

    auto exp = NDArrayFactory::create<float>('c', {2, 2, 3}, {1.f, 0.f, 0.f, 0.f, 0.f, 1.f, 0.f, 1.f, 0.f, 0.f, 0.f, 0.f});

    nd4j::ops::onehot op;
    auto result = op.execute({&indices}, {1.0f, 0.0f}, {-1, 3});

    ASSERT_EQ(ND4J_STATUS_OK, result->status());

    auto z = result->at(0);

    ASSERT_TRUE(exp.isSameShape(z));

    ASSERT_TRUE(exp.equalsTo(z));

    delete result;
}

TEST_F(DeclarableOpsTests1, OneHotTests_3) {
    auto indices = NDArrayFactory::create<float>('c', {4}, {0.0f, 2.0f, -1.0f, 1.0f});

    auto exp = NDArrayFactory::create<float>('c', {4, 3}, {1.f, 0.f, 0.f, 0.f, 0.f, 1.f, 0.f, 0.f, 0.f, 0.f, 1.f, 0.f});

    nd4j::ops::onehot op;

    auto result = op.execute({&indices}, {1.0f, 0.0f}, {-1, 3});
    ASSERT_EQ(ND4J_STATUS_OK, result->status());

    auto z = result->at(0);

    // z->printIndexedBuffer("z");

    ASSERT_TRUE(exp.isSameShape(z));
    ASSERT_TRUE(exp.equalsTo(z));

    delete result;
}

TEST_F(DeclarableOpsTests1, OneHotTests_4) {
    auto indices = NDArrayFactory::create<float>('c', {4}, {0.0f, 2.0f, -1.0f, 1.0f});
    auto depth = NDArrayFactory::create<float>(3.0f);

    auto exp = NDArrayFactory::create<float>('c', {4, 3}, {1.f, 0.f, 0.f, 0.f, 0.f, 1.f, 0.f, 0.f, 0.f, 0.f, 1.f, 0.f});

    nd4j::ops::onehot op;

    auto result = op.execute({&indices, &depth}, {1.0f, 0.0f}, {});
    ASSERT_EQ(ND4J_STATUS_OK, result->status());

    auto z = result->at(0);

    ASSERT_TRUE(exp.isSameShape(z));
    ASSERT_TRUE(exp.equalsTo(z));

    delete result;
}

TEST_F(DeclarableOpsTests1, OneHotTests_5) {
    auto indices = NDArrayFactory::create<float>('c', {4}, {0.0f, 2.0f, -1.0f, 1.0f});
    auto depth = NDArrayFactory::create<float>(3.0f);
    auto on = NDArrayFactory::create<float>(1.0f);
    auto off = NDArrayFactory::create<float>(0.0f);

    auto exp = NDArrayFactory::create<float>('c', {4, 3}, {1.f, 0.f, 0.f, 0.f, 0.f, 1.f, 0.f, 0.f, 0.f, 0.f, 1.f, 0.f});

    nd4j::ops::onehot op;

    auto result = op.execute({&indices, &depth, &on, &off}, {}, {});
    ASSERT_EQ(ND4J_STATUS_OK, result->status());

    auto z = result->at(0);

    ASSERT_TRUE(exp.isSameShape(z));
    ASSERT_TRUE(exp.equalsTo(z));

    delete result;
}

TEST_F(DeclarableOpsTests1, OneHotTests_6) {
    auto indices = NDArrayFactory::create<float>('c', {3}, {0., 1., 2.});
    auto e = NDArrayFactory::create<float>('c', {3, 3}, {1., 0., 0., 0., 1., 0., 0., 0., 1.});

    nd4j::ops::onehot op;
    auto result = op.execute({&indices}, {1.0, 0.0}, {0, 3});
    auto z = result->at(0);

    ASSERT_EQ(e, *z);

    delete result;
}

TEST_F(DeclarableOpsTests1, FillAs_1) {
    auto x = NDArrayFactory::create<float>('c', {2, 2});
    x.assign(117);

    float scalar = 119.f;

    nd4j::ops::fill_as op;
    auto result = op.execute({&x}, {scalar}, {});

    ASSERT_EQ(ND4J_STATUS_OK, result->status());

    ASSERT_TRUE(x.isSameShape(result->at(0)));

    ASSERT_NEAR(scalar, result->at(0)->meanNumber().e<float>(0), 1e-5f);

    delete result;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, LRN1) {
    nd4j::ops::lrn lrn;

    lrn.getOpName();
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, Stack_1) {

    float buff1[]   = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10,11,12};
    float buff2[]   = {13,14,16,16,17,18,19,20,21,22,23,24};
    float expBuff[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10,11,12,13,14,16,16,17,18,19,20,21,22,23,24};
    Nd4jLong shape1[]    = {2, 3, 4, 4, 1, 0, 1, 99};
    Nd4jLong shape2[]    = {2, 3, 4, 4, 1, 0, 1, 99};
    Nd4jLong expShape[]  = {3, 2, 3, 4, 12, 4, 1, 0, 1, 99};
    ArrayOptions::setDataType(shape1, nd4j::DataType::FLOAT32);
    ArrayOptions::setDataType(shape2, nd4j::DataType::FLOAT32);
    ArrayOptions::setDataType(expShape, nd4j::DataType::FLOAT32);

    NDArray input1(buff1, shape1);
    NDArray input2(buff2, shape2);
    NDArray expected(expBuff, expShape);

    nd4j::ops::stack op;
    auto results = op.execute({&input1, &input2}, {}, {0});
    auto output = results->at(0);

    ASSERT_TRUE(expected.isSameShapeStrict(output));
    ASSERT_TRUE(expected.equalsTo(output));

    delete results;

}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, Stack_2) {

    float buff1[]   = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10,11,12};
    float buff2[]   = {13,14,16,16,17,18,19,20,21,22,23,24};
    float expBuff[] = {1,2,3,4, 13, 14, 16, 16, 5,6,7,8, 17, 18, 19, 20, 9, 10, 11, 12, 21, 22, 23, 24};
    Nd4jLong shape1[]    = {2, 3, 4, 4, 1, 0, 1, 99};
    Nd4jLong shape2[]    = {2, 3, 4, 4, 1, 0, 1, 99};
    Nd4jLong expShape[]  = {3, 3, 2, 4, 8, 4, 1, 0, 1, 99};
    ArrayOptions::setDataType(shape1, nd4j::DataType::FLOAT32);
    ArrayOptions::setDataType(shape2, nd4j::DataType::FLOAT32);
    ArrayOptions::setDataType(expShape, nd4j::DataType::FLOAT32);

    NDArray input1(buff1, shape1);
    NDArray input2(buff2, shape2);
    NDArray expected(expBuff, expShape);

    nd4j::ops::stack op;
    auto results = op.execute({&input1, &input2}, {}, {1});
    auto output = results->at(0);

    ASSERT_TRUE(expected.isSameShapeStrict(output));
    ASSERT_TRUE(expected.equalsTo(output));

    delete results;
}


//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, Stack_3) {

    float buff1[]   = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10,11,12};
    float buff2[]   = {13,14,16,16,17,18,19,20,21,22,23,24};
    float expBuff[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10,11,12,13,14,16,16,17,18,19,20,21,22,23,24};
    Nd4jLong shape1[]    = {2, 1, 12, 12, 1, 0, 1, 99};
    Nd4jLong shape2[]    = {2, 1, 12, 12, 1, 0, 1, 99};
    Nd4jLong expShape[]  = {3, 2, 1, 12, 12, 12, 1, 0, 1, 99};
    ArrayOptions::setDataType(shape1, nd4j::DataType::FLOAT32);
    ArrayOptions::setDataType(shape2, nd4j::DataType::FLOAT32);
    ArrayOptions::setDataType(expShape, nd4j::DataType::FLOAT32);

    NDArray input1(buff1, shape1);
    NDArray input2(buff2, shape2);
    NDArray expected(expBuff, expShape);

    nd4j::ops::stack op;
    auto results = op.execute({&input1, &input2}, {}, {0});
    auto output = results->at(0);

    ASSERT_TRUE(expected.isSameShapeStrict(output));
    ASSERT_TRUE(expected.equalsTo(output));

    delete results;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, Stack_4) {

    float buff1[]   = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10,11,12};
    float buff2[]   = {13,14,16,16,17,18,19,20,21,22,23,24};
    float expBuff[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10,11,12,13,14,16,16,17,18,19,20,21,22,23,24};
    Nd4jLong shape1[]    = {2, 1, 12, 12, 1, 0, 1, 99};
    Nd4jLong shape2[]    = {2, 1, 12, 12, 1, 0, 1, 99};
    Nd4jLong expShape[]  = {3, 1, 2, 12, 24, 12, 1, 0, 1, 99};
    ArrayOptions::setDataType(shape1, nd4j::DataType::FLOAT32);
    ArrayOptions::setDataType(shape2, nd4j::DataType::FLOAT32);
    ArrayOptions::setDataType(expShape, nd4j::DataType::FLOAT32);

    NDArray input1(buff1, shape1);
    NDArray input2(buff2, shape2);
    NDArray expected(expBuff, expShape);

    nd4j::ops::stack op;
    auto results = op.execute({&input1, &input2}, {}, {1});
    auto output = results->at(0);

    ASSERT_TRUE(expected.isSameShapeStrict(output));
    ASSERT_TRUE(expected.equalsTo(output));

    delete results;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, Stack_5) {

    float buff1[]   = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10,11,12};
    float buff2[]   = {13,14,16,16,17,18,19,20,21,22,23,24};
    float expBuff[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10,11,12,13,14,16,16,17,18,19,20,21,22,23,24};
    Nd4jLong shape1[]    = {2, 12, 1, 1,1, 0, 1, 99};
    Nd4jLong shape2[]    = {2, 12, 1, 1,1, 0, 1, 99};
    Nd4jLong expShape[]  = {3, 2, 12, 1, 12, 1, 1, 0, 1, 99};
    ArrayOptions::setDataType(shape1, nd4j::DataType::FLOAT32);
    ArrayOptions::setDataType(shape2, nd4j::DataType::FLOAT32);
    ArrayOptions::setDataType(expShape, nd4j::DataType::FLOAT32);

    NDArray input1(buff1, shape1);
    NDArray input2(buff2, shape2);
    NDArray expected(expBuff, expShape);

    nd4j::ops::stack op;
    auto results = op.execute({&input1, &input2}, {}, {0});
    auto output = results->at(0);

    ASSERT_TRUE(expected.isSameShapeStrict(output));
    ASSERT_TRUE(expected.equalsTo(output));

    delete results;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, Stack_6) {

    float buff1[]   = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10,11,12};
    float buff2[]   = {13,14,16,16,17,18,19,20,21,22,23,24};
    float expBuff[] = {1 ,13 ,2 ,14 ,3 ,16 ,4 ,16 ,5 ,17 ,6 ,18 ,7 ,19 ,8 ,20 ,9 ,21 ,10 ,22 ,11 ,23 ,12 ,24};
    Nd4jLong shape1[]    = {2, 12, 1, 1, 12, 0, 1, 99};
    Nd4jLong shape2[]    = {2, 12, 1, 1, 12, 0, 1, 99};
    Nd4jLong expShape[]  = {3, 12, 2, 1, 2, 1, 1, 0, 1, 99};
    ArrayOptions::setDataType(shape1, nd4j::DataType::FLOAT32);
    ArrayOptions::setDataType(shape2, nd4j::DataType::FLOAT32);
    ArrayOptions::setDataType(expShape, nd4j::DataType::FLOAT32);

    NDArray input1(buff1, shape1);
    NDArray input2(buff2, shape2);
    NDArray expected(expBuff, expShape);

    nd4j::ops::stack op;
    auto results = op.execute({&input1, &input2}, {}, {1});
    auto output = results->at(0);

    ASSERT_TRUE(expected.isSameShapeStrict(output));
    ASSERT_TRUE(expected.equalsTo(output));

    delete results;
}


//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, Stack_7) {

    float buff1[]   = {1};
    float expBuff[] = {1, 1, 1};
    Nd4jLong shape1[]    = {2, 1, 1, 1, 1, 0, 1, 99};
    Nd4jLong expShape[]  = {3, 3, 1, 1, 1, 1, 1, 0, 1, 99};
    ArrayOptions::setDataType(shape1, nd4j::DataType::FLOAT32);
    ArrayOptions::setDataType(expShape, nd4j::DataType::FLOAT32);

    NDArray input1(buff1, shape1);
    NDArray expected(expBuff, expShape);

    nd4j::ops::stack op;
    auto results = op.execute({&input1, &input1, &input1}, {}, {0});
    auto output = results->at(0);

    ASSERT_TRUE(expected.isSameShapeStrict(output));
    ASSERT_TRUE(expected.equalsTo(output));

    delete results;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, Stack_8) {

    float buff1[]   = {1};
    float expBuff[] = {1, 1, 1};
    Nd4jLong shape1[]    = {1, 1, 1, 0, 1, 99};
    Nd4jLong expShape[]  = {2, 3, 1, 1, 1, 0, 1, 99};
    ArrayOptions::setDataType(shape1, nd4j::DataType::FLOAT32);
    ArrayOptions::setDataType(expShape, nd4j::DataType::FLOAT32);

    NDArray input1(buff1, shape1);
    NDArray expected(expBuff, expShape);

    nd4j::ops::stack op;
    auto results = op.execute({&input1, &input1, &input1}, {}, {0});
    auto output = results->at(0);

    ASSERT_TRUE(expected.isSameShapeStrict(output));
    ASSERT_TRUE(expected.equalsTo(output));

    delete results;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, Stack_9) {

    float buff1[]   = {1};
    float expBuff[] = {1, 1, 1};
    Nd4jLong shape1[]    = {2, 1, 1, 1, 1, 0, 1, 99};
    Nd4jLong expShape[]  = {3, 1, 3, 1, 3, 1, 1, 0, 1, 99};
    ArrayOptions::setDataType(shape1, nd4j::DataType::FLOAT32);
    ArrayOptions::setDataType(expShape, nd4j::DataType::FLOAT32);

    NDArray input1(buff1, shape1);
    NDArray expected(expBuff, expShape);

    nd4j::ops::stack op;
    auto results = op.execute({&input1, &input1, &input1}, {}, {1});
    auto output = results->at(0);

    ASSERT_TRUE(expected.isSameShapeStrict(output));
    ASSERT_TRUE(expected.equalsTo(output));

    delete results;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, Stack_10) {

    float buff1[]   = {1};
    float expBuff[] = {1, 1, 1};
    Nd4jLong shape1[]    = {1, 1, 1, 0, 1, 99};
    Nd4jLong expShape[]  = {2, 1, 3, 3, 1, 0, 1, 99};
    ArrayOptions::setDataType(shape1, nd4j::DataType::FLOAT32);
    ArrayOptions::setDataType(expShape, nd4j::DataType::FLOAT32);

    NDArray input1(buff1, shape1);
    NDArray expected(expBuff, expShape);

    nd4j::ops::stack op;
    auto results = op.execute({&input1, &input1, &input1}, {}, {1});
    auto output = results->at(0);

    //expected.printShapeInfo("exp");
    //output->printShapeInfo("out");

    ASSERT_TRUE(expected.isSameShapeStrict(output));
    ASSERT_TRUE(expected.equalsTo(output));

    delete results;
}

TEST_F(DeclarableOpsTests1, Stack_11) {

    float buff1[]   = {1};
    float expBuff[] = {1, 1, 1};
    Nd4jLong shape1[]    = {1, 1, 1, 0, 1, 99};
    Nd4jLong expShape[]  = {2, 3, 1, 1, 1, 0, 1, 99};
    ArrayOptions::setDataType(shape1, nd4j::DataType::FLOAT32);
    ArrayOptions::setDataType(expShape, nd4j::DataType::FLOAT32);

    NDArray input1(buff1, shape1);
    NDArray expected(expBuff, expShape);

    nd4j::ops::stack op;
    auto results = op.execute({&input1, &input1, &input1}, {}, {});
    auto output = results->at(0);

    ASSERT_TRUE(expected.isSameShapeStrict(output));
    ASSERT_TRUE(expected.equalsTo(output));

    delete results;
}


TEST_F(DeclarableOpsTests1, Test_Range_Integer_1) {
    auto exp = NDArrayFactory::create<int>('c', {4});
    exp.linspace(1);

    nd4j::ops::range op;

    auto result = op.execute({}, {}, {1, 5, 1});
    ASSERT_EQ(ND4J_STATUS_OK, result->status());

    ASSERT_EQ(1, result->size());

    auto array = result->at(0);
    // array->printIndexedBuffer("Range integer 1");
    ASSERT_TRUE(exp.isSameShape(array));
    ASSERT_TRUE(exp.equalsTo(array));

    delete result;
}


TEST_F(DeclarableOpsTests1, Test_Range_Integer_2) {
    auto exp = NDArrayFactory::create<float>('c', {4});
    exp.linspace(1);

    auto start = NDArrayFactory::create<float>('c', {1, 1});
    auto stop = NDArrayFactory::create<float>('c', {1, 1});
    auto step = NDArrayFactory::create<float>('c', {1, 1});
    start.p(0, 1.f);
    stop.p(0, 5.f);
    step.p(0, 1.f);

    nd4j::ops::range op;

    auto result = op.execute({&start, &stop, &step}, {}, {});
    ASSERT_EQ(ND4J_STATUS_OK, result->status());

    ASSERT_EQ(1, result->size());

    auto array = result->at(0);

    ASSERT_TRUE(exp.isSameShape(array));
    ASSERT_TRUE(exp.equalsTo(array));

    delete result;
}


TEST_F(DeclarableOpsTests1, Test_Range_Integer_3) {
    auto exp = NDArrayFactory::create<float>('c', {4});
    exp.linspace(1);

    nd4j::ops::range op;

    auto result = op.execute({}, {1.f, 5.f, 1.f}, {});
    ASSERT_EQ(ND4J_STATUS_OK, result->status());

    ASSERT_EQ(1, result->size());

    auto array = result->at(0);

    ASSERT_TRUE(exp.isSameShape(array));
    ASSERT_TRUE(exp.equalsTo(array));

    delete result;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, softmax_test1) {
    auto input = NDArrayFactory::create<double>('c', {3, 3}, {-1, 1, -2, 2, -3, 3, -4, 4, 5});
    auto expOutput = NDArrayFactory::create<double>('c', {3, 3}, {1.14195199e-01, 8.43794734e-01, 4.20100661e-02, 2.68454951e-01, 1.80883523e-03, 7.29736214e-01, 9.02116571e-05, 2.68917160e-01, 7.30992629e-01});

    nd4j::ops::softmax op;
    auto results = op.execute({&input}, {}, {}, {}, false, nd4j::DataType::DOUBLE);
    auto z = results->at(0);

    ASSERT_EQ(Status::OK(), results->status());
    ASSERT_TRUE(expOutput.isSameShape(z));
    ASSERT_TRUE(expOutput.equalsTo(z));

    delete results;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, softmax_test2) {
    auto input = NDArrayFactory::create<double>('c', {3, 3, 3}, {-1, 1, -2, 2, -3, 3, -4, 4, -5,5 ,-6,6, -7,7, -8,8, -9,9, -10,10, -11,11, -12,12, -13,13, 14});
    auto expOutput = NDArrayFactory::create<double>('c', {3, 3, 3}, {4.73142e-02,4.73847e-02,6.69062e-03, 9.50330e-01,8.67881e-04,9.92976e-01, 2.35563e-03,9.51747e-01,3.33106e-04, 4.74259e-02,2.26032e-06,4.74259e-02, 2.91395e-07,9.99998e-01,3.94360e-08, 9.52574e-01,1.12535e-07,9.52574e-01, 7.58256e-10,4.74259e-02,1.22325e-11, 1.00000e+00,1.32293e-11,1.19203e-01, 3.77513e-11,9.52574e-01,8.80797e-01});

    nd4j::ops::softmax op;
    auto results = op.execute({&input}, {}, {1}, {}, false, nd4j::DataType::DOUBLE);
    auto z = results->at(0);

    ASSERT_EQ(Status::OK(), results->status());
    ASSERT_TRUE(expOutput.isSameShape(z));
    ASSERT_TRUE(expOutput.equalsTo(z));

    delete results;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, softmax_test3) {
    auto input = NDArrayFactory::create<double>('c', {3, 3, 3}, {-1, 1, -2, 2, -3, 3, -4, 4, -5,5 ,-6,6, -7,7, -8,8, -9,9, -10,10, -11,11, -12,12, -13,13, 14});
    auto expOutput = NDArrayFactory::create<double>('c', {3, 3, 3}, {2.47262e-03,1.23395e-04,3.35350e-04, 1.23395e-04,4.53979e-05,1.23395e-04, 6.14417e-06,1.23395e-04,5.56530e-09, 9.97527e-01,1.12521e-07,9.99665e-01, 1.52281e-08,9.99955e-01,2.06090e-09, 9.99994e-01,2.78912e-10,6.69285e-03, 3.05146e-07,9.99876e-01,4.13855e-08, 9.99877e-01,5.60254e-09,9.99877e-01, 7.58251e-10,9.99877e-01,9.93307e-01});

    nd4j::ops::softmax op;
    auto results = op.execute({&input}, {}, {0}, {}, false, nd4j::DataType::DOUBLE);
    auto z = results->at(0);

    ASSERT_EQ(Status::OK(), results->status());
    ASSERT_TRUE(expOutput.isSameShape(z));
    ASSERT_TRUE(expOutput.equalsTo(z));

    delete results;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, softmax_test4) {
    auto input = NDArrayFactory::create<double>('c', {1, 5}, {-1, 1, -2, 2, 3});
    auto expOutput = NDArrayFactory::create<double>('c', {1, 5}, {0.01198,0.08855,0.00441,0.24072,0.65434});

    nd4j::ops::softmax op;
    auto results = op.execute({&input}, {}, {1}, {}, false, nd4j::DataType::DOUBLE);
    auto z = results->at(0);

    ASSERT_EQ(Status::OK(), results->status());
    ASSERT_TRUE(expOutput.isSameShape(z));
    ASSERT_TRUE(expOutput.equalsTo(z));

    delete results;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, softmax_test5) {
    auto input = NDArrayFactory::create<double>('c', {1, 5}, {-1, 1, -2, 2, 3});
    auto expOutput = NDArrayFactory::create<double>('c', {1, 5}, {1,1,1,1,1});

    nd4j::ops::softmax op;
    auto results = op.execute({&input}, {}, {0}, {}, false, nd4j::DataType::DOUBLE);
    auto z = results->at(0);

    ASSERT_EQ(Status::OK(), results->status());
    ASSERT_TRUE(expOutput.isSameShape(z));
    ASSERT_TRUE(expOutput.equalsTo(z));

    delete results;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, softmax_test6) {
    auto input = NDArrayFactory::create<double>('c', {5, 1}, {-1, 1, -2, 2, 3});
    auto expOutput = NDArrayFactory::create<double>('c', {5, 1}, {0.01198,0.08855,0.00441,0.24072,0.65434});

    nd4j::ops::softmax op;
    auto results = op.execute({&input}, {}, {0}, {}, false, nd4j::DataType::DOUBLE);
    auto z = results->at(0);

    ASSERT_EQ(Status::OK(), results->status());
    ASSERT_TRUE(expOutput.isSameShape(z));
    ASSERT_TRUE(expOutput.equalsTo(z));

    delete results;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, softmax_test7) {
    auto input = NDArrayFactory::create<double>('c', {5, 1}, {-1, 1, -2, 2, 3});
    auto expOutput = NDArrayFactory::create<double>('c', {5, 1}, {1,1,1,1,1});

    nd4j::ops::softmax op;
    auto results = op.execute({&input}, {}, {1}, {}, false, nd4j::DataType::DOUBLE);
    auto z = results->at(0);

    ASSERT_EQ(Status::OK(), results->status());
    ASSERT_TRUE(expOutput.isSameShape(z));
    ASSERT_TRUE(expOutput.equalsTo(z));

    delete results;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, softmax_test8) {
    auto input = NDArrayFactory::create<double>('c', {5}, {-1, 1, -2, 2, 3});
    auto expOutput = NDArrayFactory::create<double>('c', {5}, {0.01198,0.08855,0.00441,0.24072,0.65434});

    nd4j::ops::softmax op;
    auto results = op.execute({&input}, {}, {}, {}, false, nd4j::DataType::DOUBLE);
    auto z = results->at(0);

    ASSERT_EQ(Status::OK(), results->status());
    ASSERT_TRUE(expOutput.isSameShape(z));
    ASSERT_TRUE(expOutput.equalsTo(z));

    delete results;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, Test_Stack_Edge_1) {
    float inBuff[]  = {1.0f, 2.0f, 3.0f};
    float expBuff[] = {1.0f, 2.0f, 3.0f};

    auto input = NDArrayFactory::create<float>(inBuff, 'c', {1, 3});

    auto exp = NDArrayFactory::create<float>(expBuff, 'c', {1, 1, 3});

    nd4j::ops::stack op;

    auto result = op.execute({&input}, {}, {0});
    ASSERT_EQ(ND4J_STATUS_OK, result->status());

    auto z = result->at(0);

    ASSERT_TRUE(exp.isSameShape(z));
    ASSERT_TRUE(exp.equalsTo(z));

    delete result;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, Test_Stack_Edge_2) {
    float inBuff[]  = {1.0f, 2.0f, 3.0f};
    float expBuff[] = {1.0f, 2.0f, 3.0f};

    auto input = NDArrayFactory::create<float>(inBuff, 'c', {1, 1, 3});

    auto exp = NDArrayFactory::create<float>(expBuff, 'c', {1, 1, 1, 3});

    nd4j::ops::stack op;

    auto result = op.execute({&input}, {}, {0});
    ASSERT_EQ(ND4J_STATUS_OK, result->status());

    auto z = result->at(0);

    ASSERT_TRUE(exp.isSameShape(z));
    ASSERT_TRUE(exp.equalsTo(z));

    delete result;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, Test_Stack_Edge_3) {
    float inBuff[]  = {1.0f, 2.0f, 3.0f};
    float expBuff[] = {1.0f, 2.0f, 3.0f};

    auto input = NDArrayFactory::create<float>(inBuff, 'c', {1, 3});

    auto exp = NDArrayFactory::create<float>(expBuff, 'c', {1, 1, 3});

    nd4j::ops::stack op;

    auto result = op.execute({&input}, {}, {1});
    ASSERT_EQ(ND4J_STATUS_OK, result->status());

    auto z = result->at(0);

    //z->printShapeInfo();

    ASSERT_TRUE(exp.isSameShape(z));
    ASSERT_TRUE(exp.equalsTo(z));

    delete result;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, Reverse_1 ) {

    float inBuff[]  = {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24};
    float expBuff[] = {24., 23., 22., 21., 20., 19., 18., 17., 16., 15., 14., 13., 12., 11., 10., 9., 8., 7., 6., 5., 4., 3., 2., 1.};
    Nd4jLong shapeInfo[] = {3, 2, 3, 4, 12, 4, 1, 0, 1, 99};
    ArrayOptions::setDataType(shapeInfo, nd4j::DataType::FLOAT32);

    NDArray input(inBuff, shapeInfo);
    NDArray expected(expBuff, shapeInfo);
    NDArray output(shapeInfo);

    nd4j::ops::reverse op;
    auto results = op.execute({&input}, {}, {0,1,2});

    ASSERT_EQ(ND4J_STATUS_OK, results->status());

    auto result = results->at(0);

    ASSERT_TRUE(expected.isSameShapeStrict(result));
    ASSERT_TRUE(expected.equalsTo(result));

    delete results;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, Reverse_2 ) {

    float inBuff[]  = {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24};
    float expBuff[] = {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24};
    Nd4jLong shapeInfo[] = {3, 2, 3, 4, 12, 4, 1, 0, 1, 99};
    ArrayOptions::setDataType(shapeInfo, nd4j::DataType::FLOAT32);

    NDArray input(inBuff, shapeInfo);
    NDArray expected(expBuff, shapeInfo);
    NDArray output(shapeInfo);

    nd4j::ops::reverse op;
    auto results = op.execute({&input}, {}, {}, {}, true);

    ASSERT_EQ(ND4J_STATUS_OK, results->status());

    auto result = results->at(0);

    ASSERT_TRUE(expected.isSameShapeStrict(&input));
    ASSERT_TRUE(expected.equalsTo(&input));

    delete results;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, Reverse_3 ) {

    float inBuff[]  = {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24};
    float expBuff[] = {12., 11., 10., 9., 8., 7., 6., 5., 4., 3., 2., 1., 24., 23., 22., 21., 20., 19., 18., 17., 16., 15., 14., 13.};
    Nd4jLong shapeInfo[] = {3, 2, 3, 4, 12, 4, 1, 0, 1, 99};
    ArrayOptions::setDataType(shapeInfo, nd4j::DataType::FLOAT32);

    NDArray input(inBuff, shapeInfo);
    NDArray expected(expBuff, shapeInfo);
    NDArray output(shapeInfo);

    nd4j::ops::reverse op;
    auto results = op.execute({&input}, {}, {1,2});

    ASSERT_EQ(ND4J_STATUS_OK, results->status());

    auto result = results->at(0);
    // result->printBuffer();

    ASSERT_TRUE(expected.isSameShapeStrict(result));
    ASSERT_TRUE(expected.equalsTo(result));

    delete results;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, Reverse_4 ) {

    float inBuff[]  = {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24};
    float expBuff[] = {16,15,14,13,20,19,18,17,24,23,22,21,4,3,2,1,8,7,6,5,12,11,10,9,};
    Nd4jLong shapeInfo[] = {3, 2, 3, 4, 12, 4, 1, 0, 1, 99};
    ArrayOptions::setDataType(shapeInfo, nd4j::DataType::FLOAT32);

    NDArray input(inBuff, shapeInfo);
    NDArray expected(expBuff, shapeInfo);
    NDArray output(shapeInfo);

    nd4j::ops::reverse op;
    auto results = op.execute({&input}, {}, {0,2});

    ASSERT_EQ(ND4J_STATUS_OK, results->status());

    auto result = results->at(0);
    // result->printBuffer();

    ASSERT_TRUE(expected.isSameShapeStrict(result));
    ASSERT_TRUE(expected.equalsTo(result));

    delete results;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, Reverse_5 ) {

    float inBuff[]  = {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24};
    float expBuff[] = {21., 22., 23., 24., 17., 18., 19., 20., 13., 14., 15., 16., 9., 10., 11., 12., 5., 6., 7., 8., 1., 2., 3., 4.};
    Nd4jLong shapeInfo[] = {3, 2, 3, 4, 12, 4, 1, 0, 1, 99};
    ArrayOptions::setDataType(shapeInfo, nd4j::DataType::FLOAT32);

    NDArray input(inBuff, shapeInfo);
    NDArray expected(expBuff, shapeInfo);
    NDArray output(shapeInfo);

    nd4j::ops::reverse op;
    auto results = op.execute({&input}, {}, {0,1});

    ASSERT_EQ(ND4J_STATUS_OK, results->status());

    auto result = results->at(0);

    ASSERT_TRUE(expected.isSameShapeStrict(result));
    ASSERT_TRUE(expected.equalsTo(result));

    delete results;
}

////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, Reverse_6 ) {

    float inBuff[]  = {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24};
    float expBuff[] = {4., 3., 2., 1., 8., 7., 6., 5., 12., 11., 10., 9., 16., 15., 14., 13., 20., 19., 18., 17., 24., 23., 22., 21.};
    Nd4jLong shapeInfo[] = {3, 2, 3, 4, 12, 4, 1, 0, 1, 99};
    ArrayOptions::setDataType(shapeInfo, nd4j::DataType::FLOAT32);

    NDArray input(inBuff, shapeInfo);
    NDArray expected(expBuff, shapeInfo);
    NDArray output(shapeInfo);

    nd4j::ops::reverse op;
    auto results = op.execute({&input}, {}, {2}, {}, true);

    ASSERT_EQ(ND4J_STATUS_OK, results->status());

    auto result = results->at(0);
    // result->printBuffer();

    ASSERT_TRUE(expected.isSameShapeStrict(&input));
    ASSERT_TRUE(expected.equalsTo(&input));

    delete results;
}


////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, Reverse_7 ) {

    float inBuff[]  = {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24};
    float expBuff[] = {9., 10., 11., 12., 5., 6., 7., 8., 1., 2., 3., 4., 21., 22., 23., 24., 17., 18., 19., 20., 13., 14., 15., 16.};
    Nd4jLong shapeInfo[] = {3, 2, 3, 4, 12, 4, 1, 0, 1, 99};
    ArrayOptions::setDataType(shapeInfo, nd4j::DataType::FLOAT32);

    NDArray input(inBuff, shapeInfo);
    NDArray expected(expBuff, shapeInfo);
    NDArray output(shapeInfo);

    nd4j::ops::reverse op;
    auto results = op.execute({&input}, {}, {1});

    ASSERT_EQ(ND4J_STATUS_OK, results->status());

    auto result = results->at(0);
    // result->printBuffer();

    ASSERT_TRUE(expected.isSameShapeStrict(result));
    ASSERT_TRUE(expected.equalsTo(result));

    delete results;
}



//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, Reverse_8 ) {

    float inBuff[]  = {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24};
    float expBuff[] = {12., 11., 10., 9., 8., 7., 6., 5., 4., 3., 2., 1., 24., 23., 22., 21., 20., 19., 18., 17., 16., 15., 14., 13.};
    Nd4jLong shapeInfo[] = {3, 2, 3, 4, 12, 4, 1, 0, 1, 99};
    ArrayOptions::setDataType(shapeInfo, nd4j::DataType::FLOAT32);

    NDArray input(inBuff, shapeInfo);
    NDArray expected(expBuff, shapeInfo);
    NDArray output(shapeInfo);

    nd4j::ops::reverse op;
    auto results = op.execute({&input}, {}, {2,1});

    ASSERT_EQ(ND4J_STATUS_OK, results->status());

    auto result = results->at(0);
    // result->printBuffer();

    ASSERT_TRUE(expected.isSameShapeStrict(result));
    ASSERT_TRUE(expected.equalsTo(result));

    delete results;
}

////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, Reverse_9 ) {

    float inBuff[]  = {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24};
    float expBuff[] = {13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12.};
    Nd4jLong shapeInfo[] = {3, 2, 3, 4, 12, 4, 1, 0, 1, 99};
    ArrayOptions::setDataType(shapeInfo, nd4j::DataType::FLOAT32);

    NDArray input(inBuff, shapeInfo);
    NDArray expected(expBuff, shapeInfo);
    NDArray output(shapeInfo);

    nd4j::ops::reverse op;
    auto results = op.execute({&input}, {}, {0});

    ASSERT_EQ(ND4J_STATUS_OK, results->status());

    auto result = results->at(0);

    ASSERT_TRUE(expected.isSameShapeStrict(result));
    ASSERT_TRUE(expected.equalsTo(result));

    delete results;
}

TEST_F(DeclarableOpsTests1, Reverse_10 ) {
    auto x = NDArrayFactory::create<double>('c', {4, 3}, {1.5375735, 0.1592365, 0.09966054, 0.677872, 1.144433, -1.0355669, 0.48456487, -0.67863184, 0.85020787, 0.13950661, 0.20998026, -1.1660044});
    auto i = NDArrayFactory::create<int>('c', {1}, {-1});
    auto e = NDArrayFactory::create<double>('c', {4, 3}, {0.09966054, 0.1592365, 1.5375735,  -1.0355669, 1.144433, 0.677872,0.85020787, -0.67863184, 0.48456487,  -1.1660044, 0.20998026, 0.13950661});

    nd4j::ops::reverse op;
    auto result = op.execute({&x, &i}, {}, {}, {}, false, nd4j::DataType::DOUBLE);

    auto z = result->at(0);

    ASSERT_TRUE(e.isSameShape(z));
    ASSERT_TRUE(e.equalsTo(z));

    delete result;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, Reverse_11 ) {


    auto input = NDArrayFactory::create<float>('c', {2,3,4});
    auto expected = NDArrayFactory::create<float>('c', {2,3,4}, {24., 23., 22., 21., 20., 19., 18., 17., 16., 15., 14., 13., 12., 11., 10., 9., 8., 7., 6., 5., 4., 3., 2., 1.});

    input.linspace(1);
    nd4j::ops::reverse op;
    auto results = op.execute({&input}, {}, {0, 1, 2});

    ASSERT_EQ(ND4J_STATUS_OK, results->status());

    auto result = results->at(0);

    ASSERT_TRUE(expected.isSameShapeStrict(result));
    ASSERT_TRUE(expected.equalsTo(result));

    delete results;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, Reverse_12 ) {


    auto input = NDArrayFactory::create<float>({0.f, 1.f, 2.f, 3.f, 4.f});
    auto expected = NDArrayFactory::create<float>({4.f, 3.f, 2.f, 1.f, 0.f});

    //input.linspace(1);
    nd4j::ops::reverse op;
    auto results = op.execute({&input}, {}, {0});

    ASSERT_EQ(ND4J_STATUS_OK, results->status());

    auto result = results->at(0);
    //result->printIndexedBuffer("Result reverse");
    //expected.printIndexedBuffer("Expected reverse");
    ASSERT_TRUE(expected.isSameShapeStrict(result));
    ASSERT_TRUE(expected.equalsTo(result));

    delete results;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, Reverse_13 ) {


    auto input = NDArrayFactory::create<float>({0.f, 1.f, 2.f, 3.f, 4.f});
    auto expected = NDArrayFactory::create<float>({4.f, 3.f, 2.f, 1.f, 0.f});

    //input.linspace(1);
    nd4j::ops::reverse op;
    auto results = op.execute({&input}, {}, {-1});

    ASSERT_EQ(ND4J_STATUS_OK, results->status());

    auto result = results->at(0);

    ASSERT_TRUE(expected.isSameShapeStrict(result));
    ASSERT_TRUE(expected.equalsTo(result));

    delete results;
}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, Reverse_14 ) {


    auto input = NDArrayFactory::create<double>({0.f, 1.f, 2.f, 3.f, 4.f});
    auto expected = NDArrayFactory::create<double>({0.f, 1.f, 2.f, 3.f, 4.f});

    //input.linspace(1);
    nd4j::ops::reverse op;
    auto results = op.execute({&input}, {}, {}, {}, false, nd4j::DataType::DOUBLE);

    ASSERT_EQ(ND4J_STATUS_OK, results->status());

    auto result = results->at(0);

    ASSERT_TRUE(expected.isSameShapeStrict(result));
    ASSERT_TRUE(expected.equalsTo(result));

    delete results;
}

TEST_F(DeclarableOpsTests1, Test_Expose_1) {
    auto input0 = NDArrayFactory::create<float>('c', {2, 3}, {1, 2, 3, 6, 5, 4});
    auto input1 = NDArrayFactory::create<float>('c', {2, 3}, {3, 2, 1, 4, 5, 6});

    nd4j::ops::expose op;

    auto result = op.execute({&input0, &input1}, {}, {});

    ASSERT_EQ(ND4J_STATUS_OK, result->status());

    auto z0 = result->at(0);
    auto z1 = result->at(1);

    ASSERT_TRUE(input0.equalsTo(z0));
    ASSERT_TRUE(input1.equalsTo(z1));

    delete result;
}

TEST_F(DeclarableOpsTests1, Test_Expose_2) {
    auto list = new NDArrayList(0, true);

    auto var = new Variable(nullptr, "arraylist", -1, 0);
    var->setNDArrayList(list);

    VariableSpace variableSpace;
    variableSpace.putVariable(-1, var);
    variableSpace.trackList(list);

    Context block(1, &variableSpace);
    block.pickInput(-1);

    nd4j::ops::expose op;
    auto result = op.execute(&block);

    ASSERT_EQ(ND4J_STATUS_OK, result);
    ASSERT_TRUE(variableSpace.hasVariable(1));

    auto var1 = variableSpace.getVariable(1);

    ASSERT_EQ(var->variableType(), var1->variableType());

    auto list1 = var1->getNDArrayList();

    ASSERT_TRUE(list == list1);

}

TEST_F(DeclarableOpsTests1, Test_Release) {
    auto x = NDArrayFactory::create<float>('c', {8, 8});
    // x.printShapeInfo("x shape");
}

