cavis/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests13.cpp

/*******************************************************************************
 * Copyright (c) 2015-2019 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
 ******************************************************************************/


//
// Created by raver on 8/4/2018.
//

#include "testlayers.h"
#include <ops/declarable/CustomOperations.h>
#include <array/NDArray.h>
#include <ops/ops.h>
#include <helpers/GradCheck.h>
#include <memory>
#include <helpers/PointersManager.h>

using namespace sd;


class DeclarableOpsTests13 : public testing::Test {
public:

    DeclarableOpsTests13() {
        //printf("\n");
        //fflush(stdout);
    }
};

template <typename T>
class TypedDeclarableOpsTests13 : public testing::Test {
public:

    TypedDeclarableOpsTests13() {
        printf("\n");
        fflush(stdout);
    }
};

typedef ::testing::Types<double, float> TestingTypes;
TYPED_TEST_CASE(TypedDeclarableOpsTests13, TestingTypes);

TEST_F(DeclarableOpsTests13, test_pow_1) {
    auto x = NDArrayFactory::create<float>('c', {2, 2}, {2.f, 2.f, 2.f, 2.f});
    auto y = NDArrayFactory::create<int>('c', {2}, {3, 3});
    auto e = NDArrayFactory::create<float>('c', {2, 2}, {8.f, 8.f, 8.f, 8.f});

    sd::ops::Pow op;
    auto result = op.evaluate({&x, &y});
    ASSERT_EQ(Status::OK(), result.status());

    auto z = result.at(0);

    ASSERT_EQ(e, *z);
}

TEST_F(DeclarableOpsTests13, test_empty_range_1) {
    auto start = NDArrayFactory::create<int>(0);
    auto limit = NDArrayFactory::create<int>(0);

    sd::ops::range op;
    auto result = op.evaluate({&start, &limit});
    ASSERT_EQ(Status::OK(), result.status());

    auto z = result.at(0);
    ASSERT_TRUE(z->isEmpty());


}

TEST_F(DeclarableOpsTests13, test_empty_range_2) {

    sd::ops::range op;
    auto result = op.evaluate({}, {1.0, 1.0});
    ASSERT_EQ(Status::OK(), result.status());

    auto z = result.at(0);
    ASSERT_TRUE(z->isEmpty());
}

TEST_F(DeclarableOpsTests13, test_empty_range_3) {

    sd::ops::range op;
    auto result = op.evaluate({}, {1, 1});
    ASSERT_EQ(Status::OK(), result.status());

    auto z = result.at(0);
    ASSERT_TRUE(z->isEmpty());
}

TEST_F(DeclarableOpsTests13, test_argmax_edge_1) {
    auto ctx = new Context(1);
    auto arr = NDArrayFactory::create_<float>('c', {1024,1});

    ctx->setInputArray(0, arr, true);
    ctx->setOutputArray(0, NDArrayFactory::create_<Nd4jLong >('c', {1}), true);
    ctx->setInputArray(1, NDArrayFactory::create_<Nd4jLong >(0), true);   //Axis 0


    sd::ops::argmax op;
    auto result = op.execute(ctx);
    ASSERT_EQ(Status::OK(), result);

    //nd4j_printf("Done\n","");
    delete ctx;
}

TEST_F(DeclarableOpsTests13, test_add_1) {
    auto x = NDArrayFactory::create<float>('c', {1, 768});
    auto y = NDArrayFactory::create<float>('c', {768});
    auto e = NDArrayFactory::create<float>('c', {1, 768});;
    y. assign(1.0f);
    e.assign(1.0f);

    x += y;

    ASSERT_EQ(e, x);
}

TEST_F(DeclarableOpsTests13, test_listdiff_1) {
    auto x = NDArrayFactory::create<int>('c', {4}, {0, 1, 2, 3});
    auto y = NDArrayFactory::create<int>('c', {2}, {3, 1});

    auto od = NDArrayFactory::create<int>('c', {2});
    auto oi = NDArrayFactory::create<int>('c', {2});

    sd::ops::listdiff op;
    auto result = op.execute({&x, &y}, std::vector<NDArray*>{&od, &oi}, {}, {}, {});
    ASSERT_EQ(Status::OK(), result);
}

TEST_F(DeclarableOpsTests13, test_greater_1) {
    auto x = NDArrayFactory::create<float>('c', {3, 1});
    auto y = NDArrayFactory::create<float>('c', {1, 4});

    sd::ops::greater op;
    auto result = op.evaluate({&x, &y});
    ASSERT_EQ(Status::OK(), result.status());
}

TEST_F(DeclarableOpsTests13, test_eval_reduction_shape_1) {
    Nd4jLong axis = 0L;
    auto x = NDArrayFactory::create<Nd4jLong>('c', {2}, {4, 2});
    auto y = NDArrayFactory::create<Nd4jLong>('c', {1}, {axis});
    auto exp = NDArrayFactory::create<Nd4jLong>('c', {2}, {1, 2});

    sd::ops::evaluate_reduction_shape op;
    auto result = op.evaluate({&x, &y}, {true});
    ASSERT_EQ(Status::OK(), result.status());

    auto z = result.at(0);

    ASSERT_EQ(exp, *z);
}

TEST_F(DeclarableOpsTests13, test_or_1) {

    NDArray x('c', {4}, {false, true, false, true}, sd::DataType::BOOL);
    NDArray y('c', {4}, {false, false, true, true}, sd::DataType::BOOL);
    NDArray e('c', {4}, {false, true, true, true}, sd::DataType::BOOL);

    NDArray z('c', {4}, sd::DataType::BOOL);

    x.applyPairwiseTransform(pairwise::Or, y, z);

    ASSERT_EQ(e, z);
}

TEST_F(DeclarableOpsTests13, test_and_1) {
    auto x = NDArrayFactory::create<bool>('c', {4}, {false, true, false, true});
    auto y = NDArrayFactory::create<bool>('c', {4}, {false, false, true, true});
    auto e = NDArrayFactory::create<bool>('c', {4}, {false, false, false, true});

    auto z = NDArrayFactory::create<bool>('c', {4});

    x.applyPairwiseTransform(pairwise::And, y, z);

    ASSERT_EQ(e, z);
}

TEST_F(DeclarableOpsTests13, test_xor_1) {
    auto x = NDArrayFactory::create<bool>('c', {4}, {false, true, false, true});
    auto y = NDArrayFactory::create<bool>('c', {4}, {false, false, true, true});
    auto e = NDArrayFactory::create<bool>('c', {4}, {false, true, true, false});

    auto z = NDArrayFactory::create<bool>('c', {4});

    x.applyPairwiseTransform(pairwise::Xor, y, z);

    ASSERT_EQ(e, z);
}

TEST_F(DeclarableOpsTests13, BarnesHutTsne_GainsTest_1) {
    auto x = NDArrayFactory::create<double>('c', {2,3}, {1,2,3, 4, 5, 6});
    auto y = NDArrayFactory::create<double>('c', {2,3}, {1,-2,3, -4, 5, -6});
    auto eps = NDArrayFactory::create<double>('c', {2,3}, {-0.1, 0.2, -0.3, 0.4, -0.5, 0.6});
    auto exp = NDArrayFactory::create<double>('c', {2,3}, {1.2,2.2,3.2,4.2,5.2,6.2});
    sd::ops::barnes_gains op;
    auto result = op.evaluate({&x, &y, &eps});
    ASSERT_EQ(result.status(), Status::OK());
    //result.at(0)->printBuffer("Gains out");
    ASSERT_TRUE(exp.equalsTo(result.at(0)));
}

TEST_F(DeclarableOpsTests13, BarnesHutTsne_GainsTest_2) {
    auto x = NDArrayFactory::create<double>('c', {2,3}, {1, -2, 3, -4, 5, -6});
    auto y = NDArrayFactory::create<double>('c', {2,3}, {1, -2, 3, -4, 5, -6});
    auto eps = NDArrayFactory::create<double>('c', {2,3}, {-0.1, 0.2, -0.3, 0.4, -0.5, 0.6});
    auto exp = NDArrayFactory::create<double>('c', {2,3}, {1.2, 0.01, 3.2, 0.01, 5.2, 0.01});
    sd::ops::barnes_gains op;
    auto result = op.evaluate({&x, &y, &eps}, {}, {});
    ASSERT_EQ(result.status(), Status::OK());
    //result.at(0)->printBuffer("Gains out");
    ASSERT_TRUE(exp.equalsTo(result.at(0)));

    //ASSERT_EQ(e, z);
}

TEST_F(DeclarableOpsTests13, BarnesHutTsne_GainsTest_3) {
    auto x = NDArrayFactory::create<double>('c', {2,3}, {-1, 2, -3, 4, -5, 6});
    auto y = NDArrayFactory::create<double>('c', {2,3}, {-0.1,-2,3, -4, -0.5, -6});
    auto eps = NDArrayFactory::create<double>('c', {2,3}, {-0.1, 0.2, -0.3, 0.4, -0.5, 0.6});
    auto exp = NDArrayFactory::create<double>('c', {2,3}, {0.01, 2.2, 0.01, 4.2, 0.01, 6.2});
    sd::ops::barnes_gains op;
    auto result = op.evaluate({&x, &y, &eps}, {}, {});
    ASSERT_EQ(result.status(), Status::OK());
    //result.at(0)->printBuffer("Gains out");
    ASSERT_TRUE(exp.equalsTo(result.at(0)));

}

TEST_F(DeclarableOpsTests13, BarnesHutTsne_EdgeForceTest_1) {
    auto data = NDArrayFactory::create<double>('c', {5,4});
    auto rows = NDArrayFactory::create<int>('c', {2}, {2, 3});
    auto cols = NDArrayFactory::create<int>('c', {5}, {0, 2, 1, 4, 3});
    auto vals = NDArrayFactory::create<double>('c', {5}, {10., 20., 30., 40., 50.});
    //auto buf = NDArrayFactory::create<double>('c', {4});
    auto exp1 = NDArrayFactory::create<double>('c', {5,4}, {-1.846154, -1.846154, -1.846154, -1.846154, 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.});
    //auto exp2 = NDArrayFactory::create<double>({-4., -4., -4., -4.
    //std::vector<NDArray*> exp({&exp1, &exp2});
    data.linspace(1);

//    auto y = NDArrayFactory::create<double>('c', {2,3}, {-0.1,-2,3, -4, -0.5, -6});
//    auto eps = NDArrayFactory::create<double>('c', {2,3}, {-0.1, 0.2, -0.3, 0.4, -0.5, 0.6});
//    auto exp = NDArrayFactory::create<double>('c', {2,3}, {1, 2, 1, 2, 2, 2});
    sd::ops::barnes_edge_forces op;
    auto result = op.evaluate({&rows, &cols, &vals, &data}, {}, {1});


    ASSERT_EQ(result.status(), Status::OK());
    //result.at(0)->printBuffer("Output");
    ASSERT_TRUE(exp1.equalsTo(result.at(0)));

}

TEST_F(DeclarableOpsTests13, BarnesHutTsne_EdgeForceTest_2) {
    auto data = NDArrayFactory::create<double>('c', {5,4});
    auto rows = NDArrayFactory::create<int>('c', {3}, {1,2,3});
    auto cols = NDArrayFactory::create<int>('c', {5}, {1, 2, 0, 4, 3});
    auto vals = NDArrayFactory::create<double>('c', {5}, {10., 20., 30., 40., 50.});
    //auto buf = NDArrayFactory::create<double>('c', {4});
    auto exp = NDArrayFactory::create<double>('c', {5,4}, {-0.622568, -0.622568, -0.622568, -0.622568, 1.846154, 1.846154, 1.846154, 1.846154, 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.});
    //auto exp2 = NDArrayFactory::create<double>({-4., -4., -4., -4.
    //std::vector<NDArray*> exp({&exp1, &exp2});
    data.linspace(1);

//    auto y = NDArrayFactory::create<double>('c', {2,3}, {-0.1,-2,3, -4, -0.5, -6});
//    auto eps = NDArrayFactory::create<double>('c', {2,3}, {-0.1, 0.2, -0.3, 0.4, -0.5, 0.6});
//    auto exp = NDArrayFactory::create<double>('c', {2,3}, {1, 2, 1, 2, 2, 2});
    sd::ops::barnes_edge_forces op;
    auto result = op.evaluate({&rows, &cols, &vals, &data}, {}, {2});


    ASSERT_EQ(result.status(), Status::OK());
    //result.at(0)->printBuffer("Output");
    ASSERT_TRUE(exp.equalsTo(result.at(0)));

}

TEST_F(DeclarableOpsTests13, BarnesHutTsne_EdgeForceTest_3) {
    auto data = NDArrayFactory::create<double>('c', {11, 5}, {0.3, 0.2625, 0.2674, 0.8604, 0.4803, 0.1096, 0.795, 0.5918, 0.2738, 0.952, 0.969, 0.8586, 0.8088, 0.5338, 0.5961, 0.7187, 0.463, 0.0867, 0.7748, 0.4802, 0.2493, 0.3227, 0.3064, 0.698, 0.7977, 0.7674, 0.168, 0.3107, 0.0217, 0.138, 0.8619, 0.8413, 0.5285, 0.9703, 0.6774, 0.2624, 0.4374, 0.1569, 0.1107, 0.0601, 0.4094, 0.9564, 0.5994, 0.8279, 0.3859, 0.6202, 0.7604, 0.0788, 0.0865, 0.7445, 0.6548, 0.3385, 0.0582, 0.6249, 0.7432});
    auto rows = NDArrayFactory::create<int>({0, 9, 18, 27, 36, 45, 54, 63, 72, 81, 90, 99});
    auto cols = NDArrayFactory::create<int>({4, 3, 10, 8, 6, 7, 1, 5, 9, 4, 9, 8, 10, 2, 0, 6, 7, 3, 6, 8, 3, 9, 10, 1, 4, 0, 5, 10, 0, 4, 6, 8, 9, 2, 5, 7, 0, 10, 3, 1, 8, 9, 6, 7, 2, 7, 9, 3, 10, 0, 4, 2, 8, 1, 2, 8, 3, 10, 0, 4, 9, 1, 5, 5, 9, 0, 3, 10, 4, 8, 1, 2, 6, 2, 0, 3, 4, 1, 10, 9, 7, 10, 1, 3, 7, 4, 5, 2, 8, 6, 3, 4, 0, 9, 6, 5, 8, 7, 1});
    auto vals = NDArrayFactory::create<double>({0.6199614579042966, 0.19644097697184246, 0.13824979367331638, 0.01949900138247239, 0.008923198738222747, 0.008392793826291798, 0.0033348224714784204, 0.0026246189757042166, 0.0025733360563748838, 0.5877136110798608, 0.28250257562439585, 0.08098135424273815, 0.014862718272075049, 0.01219187321450782, 0.01152346362368888, 0.004243137936786281, 0.0034626999030188577, 0.0025185661029283168, 0.6777005651521399, 0.18321248222489303, 0.04018202465629351, 0.02941935889988646, 0.02164146250842832, 0.019898422145651618, 0.011683461395713935, 0.008439076090480863, 0.007823146926512332, 0.6770900431883232, 0.16617511239723026, 0.06039349887686468, 0.04650913399744179, 0.016886531410284355, 0.014591049666869658, 0.006407638669806174, 0.006074413005122801, 0.0058725787880570205, 0.6278185083409108, 0.235127797795446, 0.07023700015217448, 0.030885483448633774, 0.01229522088606573, 0.009238279699136107, 0.008219511168822047, 0.004303744819835723, 0.0018744536889749907, 0.7122603898978483, 0.07862620103245824, 0.07061257369349086, 0.06721483653169834, 0.028957853952131768, 0.01778978123182596, 0.01481713955181034, 0.005492728917348627, 0.0042284951913875955, 0.5266844101016999, 0.3304104787383107, 0.10930017433210941, 0.018514917515240075, 0.006969360999637938, 0.0063776901975396, 0.0010590388116165708, 6.526830884629785E-4, 3.1246215383067865E-5, 0.7176179284835663, 0.08741734015883978, 0.05927699083866909, 0.04663169573956976, 0.03287576269194147, 0.02993912340339554, 0.013365238657916641, 0.010616858763291145, 0.002259061262810172, 0.6891905160321706, 0.1397658294110526, 0.05438284759722162, 0.05437184733708826, 0.028683289714498808, 0.020986120697576355, 0.007218358114741088, 0.0032834770669826364, 0.002117714028667893, 0.6823873496503976, 0.1345267083671607, 0.08712863515505885, 0.04286621088946242, 0.02544804597749639, 0.01689343932533317, 0.007219134659004873, 0.0019232929717404616, 0.0016071830043453991, 0.6425809622897437, 0.18474464886441516, 0.10897036475298316, 0.03466939253836615, 0.013288054277817787, 0.005149178177380355, 0.0037974063158903518, 0.0037851733015991287, 0.0030148194818042273});
    //auto buf = NDArrayFactory::create<double>('c', {4});
    auto exp = NDArrayFactory::create<double>('c', {11, 5}, {-0.080205, -0.085862, 0.024045, 0.133551, -0.199896, -0.170597, 0.187301, 0.205824, -0.165268, 0.131228, 0.155135, 0.021446, 0.217583, -0.262873, -0.021075, 0.114537, 0.088023, -0.039205, 0.087984, -0.179565, -0.132683, 0.003677, 0.072081, -0.068737, 0.204481, 0.287223, -0.193989, 0.104569, -0.123401, -0.036368, 0.086745, 0.002961, -0.091327, 0.234853, 0.120270, -0.304006, 0.128305, -0.084867, -0.017550, -0.130837, -0.288569, 0.124679, 0.054078, -0.034187, -0.192599, 0.033196, 0.228182, -0.044972, -0.314217, 0.020287, 0.054427, -0.078887, -0.078246, -0.104543, 0.169803});
    //auto exp2 = NDArrayFactory::create<double>({-4., -4., -4., -4.
    //std::vector<NDArray*> exp({&exp1, &exp2});
    //data.assign(1.0); //linspace(1);

//    auto y = NDArrayFactory::create<double>('c', {2,3}, {-0.1,-2,3, -4, -0.5, -6});
//    auto eps = NDArrayFactory::create<double>('c', {2,3}, {-0.1, 0.2, -0.3, 0.4, -0.5, 0.6});
//    auto exp = NDArrayFactory::create<double>('c', {2,3}, {1, 2, 1, 2, 2, 2});
    sd::ops::barnes_edge_forces op;
    auto result = op.evaluate({&rows, &cols, &vals, &data}, {}, {11});

    //nd4j_printf("rows %lld, cols %lld, vals %lld, res full %lld\n", rows.lengthOf(), cols.lengthOf(), vals.lengthOf(), exp1.lengthOf());
    ASSERT_EQ(result.status(), Status::OK());
    //result.at(0)->printBuffer("Output");
    //exp.printBuffer("Expect");
    //result.at(0)->printShapeInfo("Shape output");
    ASSERT_TRUE(exp.equalsTo(result.at(0)));

}

TEST_F(DeclarableOpsTests13, BarnesHutTsne_symmetrized_1) {
//    auto data = NDArrayFactory::create<double>('c', {5,4});
    auto rows = NDArrayFactory::create<int>('c', {2}, {0, 1});
    auto cols = NDArrayFactory::create<int>('c', {4}, {0, 1, 1, 0});
    auto vals = NDArrayFactory::create<double>('c', {4}, {20., 30., 40., 50.});
    auto exp = NDArrayFactory::create<double>('c', {1,1}, {20.});
//    data.linspace(1);

//    auto y = NDArrayFactory::create<double>('c', {2,3}, {-0.1,-2,3, -4, -0.5, -6});
//    auto eps = NDArrayFactory::create<double>('c', {2,3}, {-0.1, 0.2, -0.3, 0.4, -0.5, 0.6});
//    auto exp = NDArrayFactory::create<double>('c', {2,3}, {1, 2, 1, 2, 2, 2});
    sd::ops::barnes_symmetrized op;
    auto result = op.evaluate({&rows, &cols, &vals}, {}, {1});
    ASSERT_EQ(result.status(), Status::OK());
    //result.at(2)->printBuffer("Symmetrized1");
    ASSERT_TRUE(exp.equalsTo(result.at(2)));
}

TEST_F(DeclarableOpsTests13, BarnesHutTsne_symmetrized_2) {
    auto rows = NDArrayFactory::create<int>('c', {4}, {0, 2, 2, 3});
    auto cols = NDArrayFactory::create<int>('c', {8}, {0, 1, 1, 0, 0, 1, 1, 1});
    auto vals = NDArrayFactory::create<double>('c', {8}, {20., 30., 40., 50., 120., 130., 140., 150.});
    auto exp = NDArrayFactory::create<double>('c', {1,5}, {20., 15., 15., 20., 20.});
//    data.linspace(1);

//    auto y = NDArrayFactory::create<double>('c', {2,3}, {-0.1,-2,3, -4, -0.5, -6});
//    auto eps = NDArrayFactory::create<double>('c', {2,3}, {-0.1, 0.2, -0.3, 0.4, -0.5, 0.6});
//    auto exp = NDArrayFactory::create<double>('c', {2,3}, {1, 2, 1, 2, 2, 2});
    sd::ops::barnes_symmetrized op;
    auto result = op.evaluate({&rows, &cols, &vals}, {}, {3});
    ASSERT_EQ(result.status(), Status::OK());
    //result.at(2)->printBuffer("Symmetrized2");
    //    ASSERT_TRUE(exp[i]->equalsTo(result.at(i)));
    ASSERT_TRUE(exp.equalsTo(result.at(2)));

}

TEST_F(DeclarableOpsTests13, BarnesHutTsne_symmetrized_3) {
    auto rows = NDArrayFactory::create<int>('c', {12}, {0, 2, 3, 5, 7, 8, 9, 11, 12, 14, 18, 21});
    auto cols = NDArrayFactory::create<int>('c', {24}, {0, 1, 2, 3, 4, 5, 4, 3, 2, 1, 0, 1, 0, 2, 4, 3, 2, 1, 0, 1, 2, 3, 4, 5});
    auto vals = NDArrayFactory::create<double>('c', {24}, {20., 30., 40., 50., 120., 130., 140., 150.,220., 230., 240., 250., 2120., 2130., 2140., 2150., 320., 330., 340., 350., 3120., 3130., 3140., 3150.});
    auto exp = NDArrayFactory::create<double>('c', {1, 39}, {15.000000, 0.000000, 0.000000, 65.000000, 60.000000, 145.000000, 20.000000, 25.000000, 65.000000, 145.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000});
//    data.linspace(1);

//    auto y = NDArrayFactory::create<double>('c', {2,3}, {-0.1,-2,3, -4, -0.5, -6});
//    auto eps = NDArrayFactory::create<double>('c', {2,3}, {-0.1, 0.2, -0.3, 0.4, -0.5, 0.6});
//    auto exp = NDArrayFactory::create<double>('c', {2,3}, {1, 2, 1, 2, 2, 2});
    sd::ops::barnes_symmetrized op;
    auto result = op.evaluate({&rows, &cols, &vals}, {}, {11});
    ASSERT_EQ(result.status(), Status::OK());
    //result.at(2)->printBuffer("Symmetrized3");
    //exp.printBuffer("EXPect symm3");
    //    ASSERT_TRUE(exp[i]->equalsTo(result.at(i)));
    //ASSERT_TRUE(exp.equalsTo(result.at(0)));

}

TEST_F(DeclarableOpsTests13, BarnesHutTsne_symmetrized_4) {
    auto rows = NDArrayFactory::create<int>({0,         9,        18,        27,        36,        45,        54,        63,        72,        81,        90,        99});
    auto cols = NDArrayFactory::create<int>({4,         3,        10,         8,         6,         7,         1,         5,         9,         4,         9,         8,        10,         2,         0,         6,         7,         3,         6,         8,         3,         9,        10,         1,         4,         0,         5,        10,         0,         4,         6,         8,         9,         2,         5,         7,         0,        10,         3,         1,         8,         9,         6,         7,         2,         7,         9,         3,        10,         0,         4,         2,         8,         1,         2,         8,         3,        10,         0,         4,         9,         1,         5,         5,         9,         0,         3,        10,         4,         8,         1,         2,         6,         2,         0,         3,         4,         1,        10,         9,         7,        10,         1,         3,         7,         4,         5,         2,         8,         6,         3,         4,         0,         9,         6,         5,         8,         7,         1});
    auto vals = NDArrayFactory::create<double>( {0.6200,    0.1964,    0.1382,    0.0195,    0.0089,    0.0084,    0.0033,    0.0026,    0.0026,    0.5877,    0.2825,    0.0810,    0.0149,    0.0122,    0.0115,    0.0042,    0.0035,    0.0025,    0.6777,    0.1832,    0.0402,    0.0294,    0.0216,    0.0199,    0.0117,    0.0084,    0.0078,    0.6771,    0.1662,    0.0604,    0.0465,    0.0169,    0.0146,    0.0064,    0.0061,    0.0059,    0.6278,    0.2351,    0.0702,    0.0309,    0.0123,    0.0092,    0.0082,    0.0043,    0.0019,    0.7123,    0.0786,    0.0706,    0.0672,    0.0290,    0.0178,    0.0148,    0.0055,    0.0042,    0.5267,    0.3304,    0.1093,    0.0185,    0.0070,    0.0064,    0.0011,    0.0007, 3.1246e-5,    0.7176,    0.0874,    0.0593,    0.0466,    0.0329,    0.0299,    0.0134,    0.0106,    0.0023,    0.6892,    0.1398,    0.0544,    0.0544,    0.0287,    0.0210,    0.0072,    0.0033,    0.0021,    0.6824,    0.1345,    0.0871,    0.0429,    0.0254,    0.0169,    0.0072,    0.0019,    0.0016,    0.6426,    0.1847,    0.1090,    0.0347,    0.0133,    0.0051,    0.0038,    0.0038,    0.0030});
    //auto exp = NDArrayFactory::create<double>('c', {1, 39}, {15.000000, 0.000000, 0.000000, 65.000000, 60.000000, 145.000000, 20.000000, 25.000000, 65.000000, 145.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000});
//    data.linspace(1);
    auto exp4 = NDArrayFactory::create<double>('c', {1, 108}, {0.6239, 0.1813, 0.1236, 0.03695, 0.00795, 0.03385, 0.0074, 0.0158, 0.0013, 0.0042, 0.0074, 0.3093, 0.2085, 0.051, 0.00895, 0.01605, 0.00245, 0.00705, 0.00125, 0.0021, 0.01605, 0.6022, 0.1615, 0.0233,
                                                0.0183, 0.0108, 0.0068, 0.0042, 0.0113, 0.00115, 0.1813, 0.00125, 0.0233, 0.65985, 0.0653, 0.0779, 0.03565, 0.05085, 0.03835, 0.02625, 0.6239, 0.3093, 0.0068, 0.0653, 0.2099, 0.0205, 0.0173, 0.0073,
                                                0.0171, 0.0089, 0.0158, 0.0113, 0.03835, 0.71495, 0.04775, 0.03615, 0.0089, 0.00275, 0.0021, 1.5623E-5, 0.00795, 0.00245, 0.6022, 0.0779, 0.0073, 0.5098, 0.0159, 0.00135, 1.5623E-5, 0.03385, 0.00705,
                                                0.02625, 0.0171, 0.71495, 0.06515, 0.01835, 0.00775, 0.00115, 0.03695, 0.051, 0.1615, 0.03565, 0.0205, 0.00275, 0.5098, 0.00775, 0.0055, 0.0026, 0.0013, 0.2085, 0.0183, 0.05085, 0.0173, 0.04775,
                                                0.00135, 0.06515, 0.0026, 0.35855, 0.1236, 0.00895, 0.0108, 0.65985, 0.2099, 0.03615, 0.0159, 0.01835, 0.0055, 0.35855});
//    auto y = NDArrayFactory::create<double>('c', {2,3}, {-0.1,-2,3, -4, -0.5, -6});
//    auto eps = NDArrayFactory::create<double>('c', {2,3}, {-0.1, 0.2, -0.3, 0.4, -0.5, 0.6});
//    auto exp = NDArrayFactory::create<double>('c', {2,3}, {1, 2, 1, 2, 2, 2});
    sd::ops::barnes_symmetrized op;
    auto result = op.evaluate({&rows, &cols, &vals}, {}, {11});
    ASSERT_EQ(result.status(), Status::OK());
    auto res = result.at(2);
  //  res->printBuffer("Symmetrized4");
  //  exp4.printBuffer("Expected sym");
  //  nd4j_printf("Total res is {1, %lld}\n", res->lengthOf());
  //  nd4j_printf("Expected is {1, %lld}\n", exp4.lengthOf());

    //exp.printBuffer("EXPect symm3");
    //    ASSERT_TRUE(exp[i]->equalsTo(result.at(i)));
    ASSERT_TRUE(exp4.equalsTo(res));

}

TEST_F(DeclarableOpsTests13, CellContains_test_1) {

    auto corners = NDArrayFactory::create<double>( {0.5384,    0.5640,    0.3449,    0.5257,    0.5505});
    auto width = NDArrayFactory::create<double>({0.4306,    0.3960,    0.4639,    0.5040,    0.4904});
    auto point = NDArrayFactory::create<double>({0.3000,    0.2625,    0.2674,    0.8604,    0.4803});
    //auto exp = NDArrayFactory::create<double>('c', {1, 39}, {15.000000, 0.000000, 0.000000, 65.000000, 60.000000, 145.000000, 20.000000, 25.000000, 65.000000, 145.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000});
    //    data.linspace(1);

    //    auto y = NDArrayFactory::create<double>('c', {2,3}, {-0.1,-2,3, -4, -0.5, -6});
    //    auto eps = NDArrayFactory::create<double>('c', {2,3}, {-0.1, 0.2, -0.3, 0.4, -0.5, 0.6});
    //    auto exp = NDArrayFactory::create<double>('c', {2,3}, {1, 2, 1, 2, 2, 2});
    sd::ops::cell_contains op;
    auto result = op.evaluate({&corners, &width, &point}, {}, {5});
    ASSERT_EQ(result.status(), Status::OK());
    ASSERT_TRUE(result.at(0)->e<bool>(0));
    //result.at(2)->printBuffer("Symmetrized3");
    //exp.printBuffer("EXPect symm3");
    //    ASSERT_TRUE(exp[i]->equalsTo(result.at(i)));
    //ASSERT_TRUE(exp.equalsTo(result.at(0)));

}

////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, adjustHue_1) {

    NDArray input('c', {2,2,3}, {0,100,56, 17,220,5,  150,97,230, 255,2,13}, sd::DataType::FLOAT32);
    NDArray factor = NDArrayFactory::create<float>(0.5);
    NDArray exp  ('c', {2,2,3}, {100,0,44, 208,5,220, 177,230,97,  2,255,244}, sd::DataType::FLOAT32);


    sd::ops::adjust_hue op;
    auto results (op.evaluate({&input, &factor}, {}, {2}));

    ASSERT_EQ(ND4J_STATUS_OK, results.status());

    auto result = results.at(0);
    // result.printIndexedBuffer();

    ASSERT_TRUE(exp.isSameShape(result));
    ASSERT_TRUE(exp.equalsTo(result));
}

////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, adjustHue_2) {

    NDArray input('c', { 2,2,3 }, { 0.f,100.f / 255.f,56.f / 255.f, 17.f / 255.f,220.f / 255.f,5.f / 255.f,  150.f / 255.f,97.f / 255.f,230.f / 255.f, 255.f / 255.f,2.f / 255.f,13.f / 255.f }, sd::DataType::FLOAT32);
    NDArray exp('c', { 2,2,3 }, { 4.f / 255.f,100.f / 255.f,0.f,  146.f / 255.f,220.f / 255.f,5.f / 255.f, 97.f / 255.f,123.8f / 255.f,230.f / 255.f, 255.f / 255.f,2.f / 255.f,164.8f / 255.f }, sd::DataType::FLOAT32);


    sd::ops::adjust_hue op;
    auto results(op.evaluate({&input}, {0.9}, {2}));

    ASSERT_EQ(ND4J_STATUS_OK, results.status());

    auto result = results.at(0);

    ASSERT_TRUE(exp.isSameShape(result));
    ASSERT_TRUE(exp.equalsTo(result));
}


////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, adjustHue_3) {

    NDArray input('c', {2,2,3}, {0,100,56,    17,220,5,          150,97,230,     255,2,13}, sd::DataType::FLOAT32);
    NDArray exp  ('c', {2,2,3}, {0.,84.,100., 5.,220.,122.0001,  229.8,97.,230., 255.,142.8002,2.}, sd::DataType::FLOAT32);

    sd::ops::adjust_hue op;
    auto results(op.evaluate({&input}, {-0.9}, {2}));

    ASSERT_EQ(ND4J_STATUS_OK, results.status());

    auto result = results.at(0);

    ASSERT_TRUE(exp.isSameShape(result));
    ASSERT_TRUE(exp.equalsTo(result));
}

////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, adjustHue_4) {

    NDArray input('c', {2,3,2}, {0,17,   100,220, 56,5,   150,255, 97,2,   230,13}, sd::DataType::FLOAT32);
    NDArray exp  ('c', {2,3,2}, {100,208, 0,5,   44,220,  177,2,   230,255, 97,244}, sd::DataType::FLOAT32);

    sd::ops::adjust_hue op;
    auto results(op.evaluate({&input}, {0.5}, {1}));

    ASSERT_EQ(ND4J_STATUS_OK, results.status());

    auto result = results.at(0);

    ASSERT_TRUE(exp.isSameShape(result));
    ASSERT_TRUE(exp.equalsTo(result));
}

////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, adjustHue_5) {

    NDArray input('c', {3,2,2}, {0,17, 150,255,   100,220, 97,2,  56,5, 230,13}, sd::DataType::FLOAT32);
    NDArray exp  ('c', {3,2,2}, {100,208, 177,2,  0,5, 230,255,   44,220, 97,244}, sd::DataType::FLOAT32);

    sd::ops::adjust_hue op;
    auto results(op.evaluate({&input}, {0.5}, {0}));

    ASSERT_EQ(ND4J_STATUS_OK, results.status());

    auto result = results.at(0);

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

}

////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, adjustSaturation_1) {

    NDArray input('c', {2,2,3}, {0,100,56,  17,220,5,         150,97,230,    255,2,13}, sd::DataType::FLOAT32);
    NDArray factor = NDArrayFactory::create<float>(0.5);
    NDArray exp  ('c', {2,2,3}, {50,100,78, 118.5,220,112.5,  190,163.5,230, 255,128.5,134}, sd::DataType::FLOAT32);

    sd::ops::adjust_saturation op;
    auto results = op.evaluate({&input, &factor}, {}, {2});

    ASSERT_EQ(ND4J_STATUS_OK, results.status());

    auto result = results.at(0);

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

}

////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, adjustSaturation_2) {

    NDArray input('c', {2,2,3}, {0,100,56,    17,220,5,          150,97,230,        255,2,13}, sd::DataType::DOUBLE);
    NDArray exp  ('c', {2,2,3}, {0.,100.,56., 12.279087,220.,0., 91.654228,0.,230., 255.,0.,11.087015}, sd::DataType::DOUBLE);

    sd::ops::adjust_saturation op;
    auto results = op.evaluate({&input}, {10}, {2});

    ASSERT_EQ(ND4J_STATUS_OK, results.status());

    auto result = results.at(0);
//    result.printIndexedBuffer("Result2");
//    exp.printIndexedBuffer("Expect2");

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

}

////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, adjustSaturation_3) {

    NDArray input('c', {2,2,3}, {0,100,56,       17,220,5,       150,97,230,     255,2,13}, sd::DataType::FLOAT32);
    NDArray exp  ('c', {2,2,3}, {100.,100.,100., 220.,220.,220., 230.,230.,230., 255., 255., 255.}, sd::DataType::FLOAT32);

    sd::ops::adjust_saturation op;
    auto results = op.evaluate({&input}, {-10}, {2});

    ASSERT_EQ(ND4J_STATUS_OK, results.status());

    auto result = results.at(0);

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

}


////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, adjustSaturation_4) {

    NDArray input('c', {2,3,2}, {0,17,   100,220,  56,5,   150,255,  97,2,   230,13}, sd::DataType::FLOAT32);
    NDArray exp  ('c', {2,3,2}, {50,118.5, 100,220, 78,112.5,  190,255, 163.5,128.5, 230,134}, sd::DataType::FLOAT32);

    sd::ops::adjust_saturation op;
    auto results = op.evaluate({&input}, {0.5}, {1});

    ASSERT_EQ(ND4J_STATUS_OK, results.status());

    auto result = results.at(0);
    // result.printIndexedBuffer();

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

}

////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, adjustSaturation_5) {

    NDArray input('c', {3,2,2}, {0,17,     150,255,  100,220,  97,2,        56,5,     230,13}, sd::DataType::FLOAT32);
    NDArray exp  ('c', {3,2,2}, {50,118.5, 190,255,  100,220,  163.5,128.5, 78,112.5, 230,134}, sd::DataType::FLOAT32);

    sd::ops::adjust_saturation op;
    auto results = op.evaluate({&input}, {0.5}, {0});

    ASSERT_EQ(ND4J_STATUS_OK, results.status());

    auto result = results.at(0);

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

}


TEST_F(DeclarableOpsTests13, shift_bits_1) {
    auto x = NDArrayFactory::create<int>('c', {5});
    auto y = NDArrayFactory::create<int>(4);
    auto e = x.ulike();
    x.assign(32);
    e.assign(512);

    sd::ops::shift_bits op;
    auto result = op.evaluate({&x, &y}, {}, {});
    ASSERT_EQ(Status::OK(), result.status());

    auto z = result.at(0);

    ASSERT_EQ(e, *z);

}

TEST_F(DeclarableOpsTests13, rshift_bits_1) {
    auto x = NDArrayFactory::create<int>('c', {5});
    auto y = NDArrayFactory::create<int>(4);
    auto e = x.ulike();
    x.assign(512);
    e.assign(32);

    sd::ops::rshift_bits op;
    auto result = op.evaluate({&x, &y}, {}, {});
    ASSERT_EQ(Status::OK(), result.status());

    auto z = result.at(0);

    ASSERT_EQ(e, *z);

}

TEST_F(DeclarableOpsTests13, cyclic_shift_bits_1) {
    auto x = NDArrayFactory::create<int>('c', {5});
    auto y = NDArrayFactory::create<int>(4);
    auto e = x.ulike();
    x.assign(32);
    e.assign(512);

    sd::ops::cyclic_shift_bits op;
    auto result = op.evaluate({&x, &y}, {}, {});
    ASSERT_EQ(Status::OK(), result.status());

    auto z = result.at(0);

    ASSERT_EQ(e, *z);

}

TEST_F(DeclarableOpsTests13, cyclic_rshift_bits_1) {
    auto x = NDArrayFactory::create<int>('c', {5});
    auto y = NDArrayFactory::create<int>(4);
    auto e = x.ulike();
    x.assign(512);
    e.assign(32);

    sd::ops::cyclic_rshift_bits op;
    auto result = op.evaluate({&x, &y}, {}, {});
    ASSERT_EQ(Status::OK(), result.status());

    auto z = result.at(0);

    ASSERT_EQ(e, *z);

}

TEST_F(DeclarableOpsTests13, shift_bits_2) {
    auto x = NDArrayFactory::create<int>('c', {5});
    auto y = NDArrayFactory::create<int>('c', {5});
    auto e = x.ulike();
    x.assign(32);
    y.assign(4);
    e.assign(512);

    sd::ops::shift_bits op;
    auto result = op.evaluate({&x, &y}, {}, {});
    ASSERT_EQ(Status::OK(), result.status());

    auto z = result.at(0);

    ASSERT_EQ(e, *z);

}

TEST_F(DeclarableOpsTests13, rshift_bits_2) {
    auto x = NDArrayFactory::create<int>('c', {5});
    auto y = NDArrayFactory::create<int>('c', {5});
    auto e = x.ulike();
    x.assign(512);
    y.assign(4);
    e.assign(32);

    sd::ops::rshift_bits op;
    auto result = op.evaluate({&x, &y}, {}, {});
    ASSERT_EQ(Status::OK(), result.status());

    auto z = result.at(0);

    ASSERT_EQ(e, *z);


}

TEST_F(DeclarableOpsTests13, cyclic_shift_bits_2) {
    auto x = NDArrayFactory::create<int>('c', {5});
    auto y = NDArrayFactory::create<int>('c', {5});
    auto e = x.ulike();
    x.assign(32);
    y.assign(4);
    e.assign(512);

    sd::ops::cyclic_shift_bits op;
    auto result = op.evaluate({&x, &y}, {}, {});
    ASSERT_EQ(Status::OK(), result.status());

    auto z = result.at(0);

    ASSERT_EQ(e, *z);

}

TEST_F(DeclarableOpsTests13, cyclic_rshift_bits_2) {
    auto x = NDArrayFactory::create<int>('c', {5});
    auto y = NDArrayFactory::create<int>('c', {5});
    auto e = x.ulike();
    x.assign(512);
    y.assign(4);
    e.assign(32);

    sd::ops::cyclic_rshift_bits op;
    auto result = op.evaluate({&x, &y}, {}, {});
    ASSERT_EQ(Status::OK(), result.status());

    auto z = result.at(0);

    ASSERT_EQ(e, *z);

}
TEST_F(DeclarableOpsTests13, shift_bits_3) {
    auto x = NDArrayFactory::create<int>('c', {5, 5});
    auto y = NDArrayFactory::create<int>('c', {1, 5});
    auto e = x.ulike();
    x.assign(32);
    y.assign(4);
    e.assign(512);

    sd::ops::shift_bits op;
    auto result = op.evaluate({&x, &y}, {}, {});
    ASSERT_EQ(Status::OK(), result.status());

    auto z = result.at(0);

    ASSERT_EQ(e, *z);

}

////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, space_to_batch_nd_1) {

    NDArray x('c', {1, 2, 2, 2, 3}, sd::DataType::FLOAT32);
    NDArray blockShape('c', {3}, {2, 2, 2} , sd::DataType::INT32);    // three spatial dimensions
    NDArray paddings('c', {3, 2}, std::vector<double>{0, 0, 0, 0, 0, 0} , sd::DataType::INT32);

    NDArray exp('c', {8, 1, 1, 1, 3}, sd::DataType::FLOAT32);

    x.linspace(1);
    exp.linspace(1);

    sd::ops::space_to_batch_nd op;
    auto result = op.evaluate({&x, &blockShape, &paddings}, {}, {});
    ASSERT_EQ(Status::OK(), result.status());

    auto z = result.at(0);

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

}

////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, space_to_batch_nd_2) {

    NDArray x('c', {2,  2,4,3,  1}, sd::DataType::FLOAT32);
    NDArray blockShape('c', {3}, {2, 2, 3} , sd::DataType::INT32);    // three spatial dimensions
    NDArray paddings('c', {3, 2}, {0,0,  0,2,  2,1} , sd::DataType::INT32);

    NDArray exp('c', {24, 1,3,2, 1}, { 0, 2, 0, 8, 0, 0, 0, 26, 0, 32, 0, 0, 0, 3, 0, 9, 0, 0, 0, 27, 0, 33, 0, 0, 1,
                                        0, 7, 0, 0, 0, 25, 0, 31, 0, 0, 0, 0, 5, 0, 11, 0, 0, 0, 29, 0, 35, 0, 0, 0, 6,
                                        0, 12, 0, 0, 0, 30, 0, 36, 0, 0, 4, 0, 10, 0, 0, 0, 28, 0, 34, 0, 0, 0, 0, 14,
                                        0, 20, 0, 0, 0, 38, 0, 44, 0, 0, 0, 15, 0, 21, 0, 0, 0, 39, 0, 45, 0, 0, 13, 0,
                                        19, 0, 0, 0, 37, 0, 43, 0, 0, 0, 0, 17, 0, 23, 0, 0, 0, 41, 0, 47, 0, 0, 0, 18,
                                        0, 24, 0, 0, 0, 42, 0, 48, 0, 0, 16, 0, 22, 0, 0, 0, 40, 0, 46, 0, 0, 0}, sd::DataType::FLOAT32);
    x.linspace(1);

    sd::ops::space_to_batch_nd op;
    auto result = op.evaluate({&x, &blockShape, &paddings}, {}, {});
    ASSERT_EQ(Status::OK(), result.status());

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

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

}

////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, space_to_batch_nd_3) {

    NDArray x('c', {2,  2,4,3,  1}, sd::DataType::FLOAT32);
    NDArray blockShape('c', {3}, {2, 2, 3} , sd::DataType::INT32);    // three spatial dimensions
    NDArray paddings('c', {3, 2}, {1,1,  0,2,  2,1} , sd::DataType::INT32);

    NDArray exp('c', {24, 2,3,2, 1}, { 0, 0, 0, 0, 0, 0, 0, 14, 0, 20, 0, 0, 0, 0, 0, 0, 0, 0, 0, 38, 0, 44, 0, 0, 0, 0, 0, 0, 0, 0, 0, 15,
                                        0, 21, 0, 0, 0, 0, 0, 0, 0, 0, 0, 39, 0, 45, 0, 0, 0, 0, 0, 0, 0, 0, 13, 0, 19, 0, 0, 0, 0, 0, 0, 0,
                                        0, 0, 37, 0, 43, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 17, 0, 23, 0, 0, 0, 0, 0, 0, 0, 0, 0, 41, 0, 47, 0, 0,
                                        0, 0, 0, 0, 0, 0, 0, 18, 0, 24, 0, 0, 0, 0, 0, 0, 0, 0, 0, 42, 0, 48, 0, 0, 0, 0, 0, 0, 0, 0, 16, 0,
                                        22, 0, 0, 0, 0, 0, 0, 0, 0, 0, 40, 0, 46, 0, 0, 0, 0, 2, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 26, 0, 32,
                                        0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 27, 0, 33, 0, 0, 0, 0, 0, 0, 0, 0, 1,
                                        0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 25, 0, 31, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 11, 0, 0, 0, 0, 0, 0,
                                        0, 0, 0, 29, 0, 35, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 12, 0, 0, 0, 0, 0, 0, 0, 0, 0, 30, 0, 36, 0, 0,
                                        0, 0, 0, 0, 0, 0, 4, 0, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 28, 0, 34, 0, 0, 0, 0, 0, 0, 0, 0, 0}, sd::DataType::FLOAT32);
    x.linspace(1);

    sd::ops::space_to_batch_nd op;
    auto result = op.evaluate({&x, &blockShape, &paddings}, {}, {});
    ASSERT_EQ(Status::OK(), result.status());

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

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

}

////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, batch_to_space_nd_1) {

    NDArray x('c', {8, 1, 1, 1, 3}, sd::DataType::FLOAT32);

    NDArray blockShape('c', {3}, {2., 2, 2} , sd::DataType::INT32);    // three spatial dimensions
    NDArray crop('c', {3, 2}, {0., 0, 0, 0, 0, 0} , sd::DataType::INT32);

    NDArray exp('c', {1, 2, 2, 2, 3}, sd::DataType::FLOAT32);

    x.linspace(1);
    exp.linspace(1);

    sd::ops::batch_to_space_nd op;
    auto result = op.evaluate({&x, &blockShape, &crop}, {}, {});
    ASSERT_EQ(Status::OK(), result.status());

    auto z = result.at(0);

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

}

////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, batch_to_space_nd_2) {

    NDArray x('c', {24, 1,3,2, 1}, sd::DataType::FLOAT32);
    NDArray blockShape('c', {3}, {2, 2, 3} , sd::DataType::INT32);    // three spatial dimensions
    NDArray crop('c', {3, 2}, {0,0,  0,2,  2,1} , sd::DataType::INT32);

    NDArray exp('c', {2,  2,4,3,  1}, {25, 2, 14, 61, 38, 50, 27, 4, 16, 63, 40, 52, 97, 74, 86, 133, 110, 122, 99, 76, 88, 135, 112, 124,
                                      31, 8, 20, 67, 44, 56, 33, 10, 22, 69, 46, 58, 103, 80, 92, 139, 116, 128, 105, 82, 94, 141, 118, 130}, sd::DataType::FLOAT32);
    x.linspace(1);

    sd::ops::batch_to_space_nd op;
    auto result = op.evaluate({&x, &blockShape, &crop}, {}, {});
    ASSERT_EQ(Status::OK(), result.status());

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

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

}

////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, batch_to_space_nd_3) {

    NDArray x('c', {24, 2,3,2, 1}, sd::DataType::FLOAT32);
    NDArray blockShape('c', {3}, {2, 2, 3} , sd::DataType::INT32);    // three spatial dimensions
    NDArray crop('c', {3, 2}, {1,1,  0,2,  2,1} , sd::DataType::INT32);

    NDArray exp('c', {2,  2,4,3,  1}, {193, 146, 170, 265, 218, 242, 195, 148, 172, 267, 220, 244, 55, 8, 32, 127, 80, 104, 57, 10, 34, 129, 82,
                                    106, 205, 158, 182, 277, 230, 254, 207, 160, 184, 279, 232, 256, 67, 20, 44, 139, 92, 116, 69, 22, 46, 141, 94, 118}, sd::DataType::FLOAT32);
    x.linspace(1);

    sd::ops::batch_to_space_nd op;
    auto result = op.evaluate({&x, &blockShape, &crop}, {}, {});
    ASSERT_EQ(Status::OK(), result.status());

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

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

}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, mergemax_1) {

    NDArray x1('c', {5, 5}, sd::DataType::FLOAT32);
    NDArray x2('c', {5, 5}, sd::DataType::FLOAT32);
    NDArray x3('c', {5, 5}, sd::DataType::FLOAT32);
    NDArray e('c', {5, 5}, sd::DataType::FLOAT32);
    x1.assign(3);
    x2.assign(1);
    x3.assign(2);
    e.assign(3);


    sd::ops::mergemax op;
    auto result = op.evaluate({&x1, &x2, &x3}, {}, {});
    ASSERT_EQ(Status::OK(), result.status());

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

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

}

//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, mergemax_2) {

    NDArray x1('c', {1, 3}, {0., 1, 2}, sd::DataType::FLOAT32);
    NDArray x2('c', {1, 1}, std::vector<double>{1.}, sd::DataType::FLOAT32);
    NDArray out('c', {1, 3}, {-1., -1, -1}, sd::DataType::FLOAT32);

    sd::ops::mergemax op;
    auto status = op.execute({&x1, &x2}, {&out}, {}, {}, {});

    ASSERT_EQ(20, status);
}
/////////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, mergemax_bp_1) {

    NDArray x1('c', { 5, 5 }, sd::DataType::FLOAT32);
    NDArray x2('c', { 5, 5 }, sd::DataType::FLOAT32);
    NDArray x3('c', { 5, 5 }, sd::DataType::FLOAT32);
    NDArray grad('c', { 5, 5 }, sd::DataType::FLOAT32);

    x1.assign(3);
    x2.assign(1);
    x3.assign(2);
    grad.linspace(.1, .1);


    sd::ops::mergemax_bp op;
    auto result = op.evaluate({ &x1, &x2, &x3, &grad }, {}, {});
    ASSERT_EQ(Status::OK(), result.status());
    ASSERT_EQ(3, result.size());

    auto z = result.at(0);

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

}
/////////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, mergemax_bp_2) {

    NDArray x1('c', { 2, 5 }, { 1,2,3,4,5,4,3,2,1,0 }, sd::DataType::FLOAT32);
    NDArray x2('c', { 2, 5 }, { 0,1,2,3,4,5,6,7,8,9 }, sd::DataType::FLOAT32);
    NDArray x3('c', { 2, 5 }, { 0,1,1,2,3,4,7,5,8,10 }, sd::DataType::FLOAT32);
    NDArray grad('c', { 2, 5 }, sd::DataType::FLOAT32);

    grad.linspace(.1, .1);

    NDArray exp1('c', { 2, 5 }, { 0.1, 0.2, 0.3, 0.4, 0.5, 0.0, 0.0, 0.0, 0.0, 0.0 }, sd::DataType::FLOAT32);
    NDArray exp2('c', { 2, 5 }, { 0.0, 0.0, 0.0, 0.0, 0.0, 0.6, 0.0, 0.8, 0.9, 0.0 }, sd::DataType::FLOAT32);
    NDArray exp3('c', { 2, 5 }, { 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.7, 0.0, 0.0, 1.0 }, sd::DataType::FLOAT32);

    sd::ops::mergemax_bp op;
    auto result = op.evaluate({ &x1, &x2, &x3, &grad }, {}, {});
    ASSERT_EQ(Status::OK(), result.status());
    ASSERT_EQ(3, result.size());

    auto z1 = result.at(0);
    auto z2 = result.at(1);
    auto z3 = result.at(2);

    ASSERT_TRUE(exp1.isSameShape(z1));
    ASSERT_TRUE(exp1.equalsTo(z1));
    ASSERT_TRUE(exp2.isSameShape(z2));
    ASSERT_TRUE(exp2.equalsTo(z2));
    ASSERT_TRUE(exp3.isSameShape(z3));
    ASSERT_TRUE(exp3.equalsTo(z3));

}
/////////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, mergemax_bp_3) {

    NDArray x1C('c', { 2, 5 }, { 1,2,3,4,5,4,3,2,1,0 }, sd::DataType::FLOAT32);
    NDArray x2C('c', { 2, 5 }, { 0,1,2,3,4,5,6,7,8,9 }, sd::DataType::FLOAT32);
    NDArray x3C('c', { 2, 5 }, { 0,1,1,2,3,4,7,5,8,10 }, sd::DataType::FLOAT32);
    NDArray grad('c', { 2, 5 }, sd::DataType::FLOAT32);

    grad.linspace(.1, .1);

    NDArray x1('f', { 2, 5 }, sd::DataType::FLOAT32);
    NDArray x2('f', { 2, 5 }, sd::DataType::FLOAT32);
    NDArray x3('f', { 2, 5 }, sd::DataType::FLOAT32);

    NDArray exp1C('c', { 2, 5 }, { 0.1, 0.2, 0.3, 0.4, 0.5, 0.0, 0.0, 0.0, 0.0, 0.0 }, sd::DataType::FLOAT32);
    NDArray exp2C('c', { 2, 5 }, { 0.0, 0.0, 0.0, 0.0, 0.0, 0.6, 0.0, 0.8, 0.9, 0.0 }, sd::DataType::FLOAT32);
    NDArray exp3C('c', { 2, 5 }, { 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.7, 0.0, 0.0, 1.0 }, sd::DataType::FLOAT32);

    NDArray exp1('f', { 2, 5 }, sd::DataType::FLOAT32);
    NDArray exp2('f', { 2, 5 }, sd::DataType::FLOAT32);
    NDArray exp3('f', { 2, 5 }, sd::DataType::FLOAT32);

    x1.assign(x1C);
    x2.assign(x2C);
    x3.assign(x3C);

    exp1.assign(exp1C);
    exp2.assign(exp2C);
    exp3.assign(exp3C);

    sd::ops::mergemax_bp op;
    auto result = op.evaluate({ &x1, &x2, &x3, &grad }, {}, {});
    ASSERT_EQ(Status::OK(), result.status());
    ASSERT_EQ(3, result.size());

    auto z1 = result.at(0);
    auto z2 = result.at(1);
    auto z3 = result.at(2);

    ASSERT_TRUE(exp1.isSameShape(z1));
    ASSERT_TRUE(exp1.equalsTo(z1));
    ASSERT_TRUE(exp2.isSameShape(z2));
    ASSERT_TRUE(exp2.equalsTo(z2));
    ASSERT_TRUE(exp3.isSameShape(z3));
    ASSERT_TRUE(exp3.equalsTo(z3));

}
/////////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, mergeadd_bp_1) {

    NDArray x1('c', { 5, 5 }, sd::DataType::FLOAT32);
    NDArray x2('c', { 5, 5 }, sd::DataType::FLOAT32);
    NDArray x3('c', { 5, 5 }, sd::DataType::FLOAT32);
    NDArray grad('c', { 5, 5 }, sd::DataType::FLOAT32);

    x1.assign(3);
    x2.assign(1);
    x3.assign(2);
    grad.linspace(.1, .1);

    sd::ops::mergeadd_bp op;
    auto result = op.evaluate({ &x1, &x2, &x3, &grad }, {}, {});
    ASSERT_EQ(Status::OK(), result.status());
    ASSERT_EQ(3, result.size());

    for (int i = 0; i < 3; i++) {
        auto z = result.at(0);
        ASSERT_TRUE(grad.isSameShape(z));
        ASSERT_TRUE(grad.equalsTo(z));
    }
}
/////////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, mergeavg_bp_1) {

    NDArray x1('c', { 5, 5 }, sd::DataType::FLOAT32);
    NDArray x2('c', { 5, 5 }, sd::DataType::FLOAT32);
    NDArray x3('c', { 5, 5 }, sd::DataType::FLOAT32);
    NDArray grad('c', { 5, 5 }, sd::DataType::FLOAT32);

    x1.assign(3);
    x2.assign(1);
    x3.assign(2);
    grad.linspace(.1, .1);

    sd::ops::mergeavg_bp op;
    auto result = op.evaluate({ &x1, &x2, &x3, &grad }, {}, {});
    ASSERT_EQ(Status::OK(), result.status());
    ASSERT_EQ(3, result.size());

    grad.applyScalar(sd::scalar::Divide, 3, grad);

    for (int i = 0; i < 3; i++) {
        auto z = result.at(i);
        ASSERT_TRUE(grad.isSameShape(z));
        ASSERT_TRUE(grad.equalsTo(z));
    }

}

///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, lstmLayer_1) {

    const int sL   = 5;
    const int bS   = 3;
    const int nIn  = 3;
    const int nOut = 3;

    // input arguments

    const int dataFormat = 0;       // [sL,bS,nIn]
    const int directionMode = 0;    // forward
    const int gateAct = 2;          // sigmoid activation for input (i), forget (f) and output (o) gates
    const int cellAct = 0;          // tanh activation for cell state
    const int outAct = 0;           // tanh activation for output

    const bool hasBiases  = true;   // biases array is provided
    const bool hasSeqLen  = false;  // seqLen array is not provided
    const auto hasInitH   = true;   // initial output is provided
    const auto hasInitC   = true;   // initial cell state is provided
    const auto hasPH      = false;  // peephole connections are absent
    const auto retFullSeq = true;   // return whole h {h_0, h_1, ... , h_sL-1}, [sL,bS,nOut]
    const auto retLastH   = true;  // do not return output at last time step
    const auto retLastC   = true;   // return cells state at last time step

    const double cellClip = 0;       // do not apply clipping

    NDArray x('c', {sL, bS, nIn}, sd::DataType::FLOAT32);
    NDArray Wx('c', {nIn, 4*nOut}, sd::DataType::FLOAT32);
    NDArray Wr('c', {nOut, 4*nOut}, sd::DataType::FLOAT32);
    NDArray b('c', {4*nOut}, sd::DataType::FLOAT32);
    NDArray hI('c', {bS, nOut}, sd::DataType::FLOAT32);
    NDArray cI('c', {bS, nOut}, sd::DataType::FLOAT32);

    x.linspace(0.5, 0.5);
    Wx = 0.003;
    Wr = 0.006;
    b = 0.5;
    hI = 1.;
    cI = 2.;

    std::vector<double>   tArgs = {cellClip};
    std::vector<Nd4jLong> iArgs = {dataFormat, directionMode, gateAct, cellAct, outAct};
    std::vector<bool>     bArgs = {hasBiases, hasSeqLen, hasInitH, hasInitC, hasPH, retFullSeq, retLastH, retLastC};

    auto expH = NDArrayFactory::create<float>('c', {sL, bS, nOut}, {0.57574f, 0.57574f, 0.57574f, 0.58006f, 0.58006f, 0.58006f, 0.58434f, 0.58434f, 0.58434f,
                                                           0.55114f, 0.55114f, 0.55114f, 0.55732f, 0.55732f, 0.55732f, 0.56338f, 0.56338f, 0.56338f,
                                                           0.53763f, 0.53763f, 0.53763f, 0.54534f, 0.54534f, 0.54534f, 0.55287f, 0.55287f, 0.55287f,
                                                           0.53626f, 0.53626f, 0.53626f, 0.54487f, 0.54487f, 0.54487f, 0.55327f, 0.55327f, 0.55327f,
                                                           0.54484f, 0.54484f, 0.54484f, 0.55379f, 0.55379f, 0.55379f, 0.5625f, 0.5625f, 0.5625f});

    auto expClast = NDArrayFactory::create<float>('c', {bS, nOut}, {1.1589154f, 1.1589154f, 1.1589154f, 1.1892855f, 1.1892855f, 1.1892855f, 1.219861f, 1.219861f, 1.219861f});

    sd::ops::lstmLayer op;
    auto results = op.evaluate({&x, &Wx, &Wr, &b, &hI, &cI}, tArgs, iArgs, bArgs);

    ASSERT_EQ(ND4J_STATUS_OK, results.status());

    auto *h  = results.at(0);
    auto *cL = results.at(2);

    ASSERT_TRUE(expH.isSameShape(h));
    ASSERT_TRUE(expH.equalsTo(h));

    ASSERT_TRUE(expClast.isSameShape(cL));
    ASSERT_TRUE(expClast.equalsTo(cL));

}

///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, lstmLayer_2) {

    const int sL   = 5;
    const int bS   = 3;
    const int nIn  = 3;
    const int nOut = 3;

    // input arguments

    const int dataFormat = 1;       // [bS,sL,nIn]
    const int directionMode = 0;    // forward
    const int gateAct = 2;          // sigmoid activation for input (i), forget (f) and output (o) gates
    const int cellAct = 0;          // tanh activation for cell state
    const int outAct = 0;           // tanh activation for output

    const bool hasBiases  = true;   // biases array is provided
    const bool hasSeqLen  = false;  // seqLen array is not provided
    const auto hasInitH   = true;   // initial output is provided
    const auto hasInitC   = true;   // initial cell state is provided
    const auto hasPH      = false;  // peephole connections are absent
    const auto retFullSeq = true;   // return whole h {h_0, h_1, ... , h_sL-1}, [sL,bS,nOut]
    const auto retLastH   = true;   // return output at last time step
    const auto retLastC   = true;   // return cells state at last time step

    const double cellClip = 0;       // do not apply clipping

    NDArray x('c', {bS, sL, nIn}, sd::DataType::FLOAT32);
    NDArray Wx('c', {nIn, 4*nOut}, sd::DataType::FLOAT32);
    NDArray Wr('c', {nOut, 4*nOut}, sd::DataType::FLOAT32);
    NDArray b('c', {4*nOut}, sd::DataType::FLOAT32);
    NDArray hI('c', {bS, nOut}, sd::DataType::FLOAT32);
    NDArray cI('c', {bS, nOut}, sd::DataType::FLOAT32);

    x.linspace(0.5, 0.5);
    Wx = 0.003;
    Wr = 0.006;
    b = 0.5;
    hI = 1.;
    cI = 2.;

    std::vector<double>   tArgs = {cellClip};
    std::vector<Nd4jLong> iArgs = {dataFormat, directionMode, gateAct, cellAct, outAct};
    std::vector<bool>     bArgs = {hasBiases, hasSeqLen, hasInitH, hasInitC, hasPH, retFullSeq, retLastH, retLastC};

    auto expH = NDArrayFactory::create<float>('c', {bS, sL, nOut}, {0.575735f, 0.575735f, 0.575735f, 0.541562f, 0.541562f, 0.541562f, 0.514003f, 0.514003f, 0.514003f, 0.495597f, 0.495597f, 0.495597f, 0.485999f, 0.485999f, 0.485999f,
                                            0.596965f, 0.596965f, 0.596965f, 0.571978f, 0.571978f, 0.571978f, 0.552888f, 0.552888f, 0.552888f, 0.540606f, 0.540606f, 0.540606f, 0.534764f, 0.534764f, 0.534764f,
                                            0.61725f, 0.61725f, 0.61725f, 0.599828f, 0.599828f, 0.599828f, 0.587627f, 0.587627f, 0.587627f, 0.580408f, 0.580408f, 0.580408f, 0.577735f, 0.577735f, 0.577735f});

    auto expClast = NDArrayFactory::create<float>('c', {bS, nOut}, {0.996965f, 0.996965f, 0.996965f, 1.146756f, 1.146756f, 1.146756f, 1.301922f, 1.301922f, 1.301922f});

    sd::ops::lstmLayer op;
    auto results = op.evaluate({&x, &Wx, &Wr, &b, &hI, &cI}, tArgs, iArgs, bArgs);

    ASSERT_EQ(ND4J_STATUS_OK, results.status());

    auto *h  = results.at(0);
    auto *cL = results.at(2);

    ASSERT_TRUE(expH.isSameShape(h));
    ASSERT_TRUE(expH.equalsTo(h));

    ASSERT_TRUE(expClast.isSameShape(cL));
    ASSERT_TRUE(expClast.equalsTo(cL));

}

///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, lstmLayer_3) {

    const int sL   = 5;
    const int bS   = 2;
    const int nIn  = 4;
    const int nOut = 3;

    // input arguments

    const int dataFormat = 0;       // [sL,bS,nIn]
    const int directionMode = 1;    // backward
    const int gateAct = 2;          // sigmoid activation for input (i), forget (f) and output (o) gates
    const int cellAct = 0;          // tanh activation for cell state
    const int outAct = 0;           // tanh activation for output

    const bool hasBiases  = true;   // biases array is provided
    const bool hasSeqLen  = false;  // seqLen array is not provided
    const auto hasInitH   = true;   // initial output is provided
    const auto hasInitC   = true;   // initial cell state is provided
    const auto hasPH      = false;  // peephole connections are absent
    const auto retFullSeq = true;   // return whole h {h_0, h_1, ... , h_sL-1}, [sL,bS,nOut]
    const auto retLastH   = true;  // do not return output at last time step
    const auto retLastC   = true;   // return cells state at last time step

    const double cellClip = 0;       // do not apply clipping

    NDArray x('c', {sL,bS, nIn}, sd::DataType::FLOAT32);
    NDArray Wx('c', {nIn, 4*nOut}, sd::DataType::FLOAT32);
    NDArray Wr('c', {nOut, 4*nOut}, sd::DataType::FLOAT32);
    NDArray b('c', {4*nOut}, sd::DataType::FLOAT32);
    NDArray hI('c', {bS, nOut}, sd::DataType::FLOAT32);
    NDArray cI('c', {bS, nOut}, sd::DataType::FLOAT32);

    x.linspace(0.5, 0.5);
    Wx = 0.003;
    Wr = 0.006;
    b = 0.5;
    hI = 1.;
    cI = 2.;

    std::vector<double>   tArgs = {cellClip};
    std::vector<Nd4jLong> iArgs = {dataFormat, directionMode, gateAct, cellAct, outAct};
    std::vector<bool>     bArgs = {hasBiases, hasSeqLen, hasInitH, hasInitC, hasPH, retFullSeq, retLastH, retLastC};

    NDArray expH('c', {sL, bS, nOut}, {0.493883f, 0.493883f, 0.493883f, 0.510990f, 0.510990f, 0.510990f, 0.534701f, 0.534701f, 0.534701f, 0.549139f,
                                        0.549139f, 0.549139f, 0.571900f, 0.571900f, 0.571900f, 0.583561f, 0.583561f, 0.583561f, 0.605106f, 0.605106f,
                                        0.605106f, 0.614114f, 0.614114f, 0.614114f, 0.635354f, 0.635354f, 0.635354f, 0.642045f, 0.642045f, 0.642045f}, sd::DataType::FLOAT32);

    NDArray expHL('c', {bS, nOut}, {0.493883f, 0.493883f, 0.493883f, 0.510990f, 0.510990f, 0.510990f}, sd::DataType::FLOAT32);
    NDArray expCL('c', {bS, nOut}, {1.061274f, 1.061274f, 1.061274f, 1.115888f, 1.115888f, 1.115888f}, sd::DataType::FLOAT32);

    sd::ops::lstmLayer op;
    auto results = op.evaluate({&x, &Wx, &Wr, &b, &hI, &cI}, tArgs, iArgs, bArgs);

    ASSERT_EQ(ND4J_STATUS_OK, results.status());

    auto h  = results.at(0);
    auto hL = results.at(1);
    auto cL = results.at(2);

    ASSERT_TRUE(expH.isSameShape(h));
    ASSERT_TRUE(expH.equalsTo(h));

    ASSERT_TRUE(expHL.isSameShape(hL));
    ASSERT_TRUE(expHL.equalsTo(hL));

    ASSERT_TRUE(expCL.isSameShape(cL));
    ASSERT_TRUE(expCL.equalsTo(cL));

}


///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, lstmLayer_4) {

    const int sL   = 5;
    const int bS   = 2;
    const int nIn  = 4;
    const int nOut = 3;

    // input arguments
    const int dataFormat = 0;       // [sL,bS,nIn]
    const int directionMode = 3;    // bidirectional concat
    const int gateAct = 2;          // sigmoid activation for input (i), forget (f) and output (o) gates
    const int cellAct = 0;          // tanh activation for cell state
    const int outAct = 0;           // tanh activation for output

    const bool hasBiases  = true;   // biases array is provided
    const bool hasSeqLen  = false;  // seqLen array is not provided
    const auto hasInitH   = true;   // initial output is provided
    const auto hasInitC   = true;   // initial cell state is provided
    const auto hasPH      = false;  // peephole connections are absent
    const auto retFullSeq = true;   // return whole h {h_0, h_1, ... , h_sL-1}, [sL,bS,nOut]
    const auto retLastH   = true;   // do not return output at last time step
    const auto retLastC   = true;   // return cells state at last time step

    const double cellClip = 0;       // do not apply clipping

    NDArray x('c', {sL, bS, nIn}, sd::DataType::FLOAT32);
    NDArray Wx('c', {2,nIn, 4*nOut}, sd::DataType::FLOAT32);
    NDArray Wr('c', {2,nOut, 4*nOut}, sd::DataType::FLOAT32);
    NDArray b('c', {2,4*nOut}, sd::DataType::FLOAT32);
    NDArray hI('c', {2,bS, nOut}, sd::DataType::FLOAT32);
    NDArray cI('c', {2,bS, nOut}, sd::DataType::FLOAT32);

    x.linspace(0.5, 0.5);
    Wx({0,1, 0,0, 0,0}) = 0.003f;
    Wx({1,2, 0,0, 0,0}) = -0.003f;
    Wr({0,1, 0,0, 0,0}) = 0.006f;
    Wr({1,2, 0,0, 0,0}) = -0.006f;
    b({0,1, 0,0})       = 0.5f;
    b({1,2, 0,0})       = -0.5f;
    hI({0,1, 0,0, 0,0}) = 1;
    hI({1,2, 0,0, 0,0}) = -1;
    cI({0,1, 0,0, 0,0}) = 2;
    cI({1,2, 0,0, 0,0}) = -2;

    std::vector<double>   tArgs = {cellClip};
    std::vector<Nd4jLong> iArgs = {dataFormat, directionMode, gateAct, cellAct, outAct};
    std::vector<bool>     bArgs = {hasBiases, hasSeqLen, hasInitH, hasInitC, hasPH, retFullSeq, retLastH, retLastC};

    NDArray expH('c', {sL, bS, 2 * nOut}, {
                                         0.577661f,  0.577661f,  0.577661f, -0.107642f, -0.107642f, -0.107642f,  0.585289f,  0.585289f,  0.585289f,
                                        -0.106937f, -0.106937f, -0.106937f,  0.556517f,  0.556517f,  0.556517f, -0.111647f, -0.111647f, -0.111647f,
                                         0.567274f,  0.567274f,  0.567274f, -0.110214f, -0.110214f, -0.110214f,  0.547395f,  0.547395f,  0.547395f,
                                        -0.123305f, -0.123305f, -0.123305f,  0.560640f,  0.560640f,  0.560640f, -0.120862f, -0.120862f, -0.120862f,
                                         0.550714f,  0.550714f,  0.550714f, -0.156223f, -0.156223f, -0.156223f,  0.565308f,  0.565308f,  0.565308f,
                                        -0.152313f, -0.152313f, -0.152313f,  0.563741f,  0.563741f,  0.563741f, -0.234128f, -0.234128f, -0.234128f,
                                         0.578676f,  0.578676f,  0.578676f, -0.228917f, -0.228917f, -0.228917f}, sd::DataType::FLOAT32);

    NDArray expHL('c', {2,bS, nOut}, {0.563741f, 0.563741f, 0.563741f, 0.578676f, 0.578676f, 0.578676f, -0.107642f,
                                    -0.107642f, -0.107642f, -0.106937f, -0.106937f, -0.106937f}, sd::DataType::FLOAT32);
    NDArray expCL('c', {2,bS, nOut}, {1.217757f, 1.217757f, 1.217757f, 1.272398f, 1.272398f, 1.272398f, -0.295768f,
                                    -0.295768f, -0.295768f, -0.298453f, -0.298453f, -0.298453f}, sd::DataType::FLOAT32);

    sd::ops::lstmLayer op;
    auto results = op.evaluate({&x, &Wx, &Wr, &b, &hI, &cI}, tArgs, iArgs, bArgs);

    ASSERT_EQ(ND4J_STATUS_OK, results.status());

    auto h  = results.at(0);
    auto hL = results.at(1);
    auto cL = results.at(2);

    ASSERT_TRUE(expH.isSameShape(h));
    ASSERT_TRUE(expH.equalsTo(h));

    ASSERT_TRUE(expHL.isSameShape(hL));
    ASSERT_TRUE(expHL.equalsTo(hL));

    ASSERT_TRUE(expCL.isSameShape(cL));
    ASSERT_TRUE(expCL.equalsTo(cL));

}

///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, lstmLayer_5) {

    const int sL   = 5;
    const int bS   = 2;
    const int nIn  = 4;
    const int nOut = 3;

    // input arguments
    const int dataFormat = 1;       // [bS,sL,nIn]
    const int directionMode = 3;    // bidirectional concat
    const int gateAct = 2;          // sigmoid activation for input (i), forget (f) and output (o) gates
    const int cellAct = 0;          // tanh activation for cell state
    const int outAct = 0;           // tanh activation for output

    const bool hasBiases  = true;   // biases array is provided
    const bool hasSeqLen  = false;  // seqLen array is not provided
    const auto hasInitH   = true;   // initial output is provided
    const auto hasInitC   = true;   // initial cell state is provided
    const auto hasPH      = false;  // peephole connections are absent
    const auto retFullSeq = true;   // return whole h {h_0, h_1, ... , h_sL-1}, [sL,bS,nOut]
    const auto retLastH   = true;   // do not return output at last time step
    const auto retLastC   = true;   // return cells state at last time step

    const double cellClip = 0;       // do not apply clipping

    NDArray x('c', {bS, sL, nIn}, sd::DataType::FLOAT32);
    NDArray Wx('c', {2,nIn, 4*nOut}, sd::DataType::FLOAT32);
    NDArray Wr('c', {2,nOut, 4*nOut}, sd::DataType::FLOAT32);
    NDArray b('c', {2,4*nOut}, sd::DataType::FLOAT32);
    NDArray hI('c', {2,bS, nOut}, sd::DataType::FLOAT32);
    NDArray cI('c', {2,bS, nOut}, sd::DataType::FLOAT32);

    x.linspace(0.5, 0.5);
    Wx({0,1, 0,0, 0,0}) = 0.003;
    Wx({1,2, 0,0, 0,0}) = -0.003;
    Wr({0,1, 0,0, 0,0}) = 0.006;
    Wr({1,2, 0,0, 0,0}) = -0.006;
    b({0,1, 0,0})       = 0.5;
    b({1,2, 0,0})       = -0.5;
    hI({0,1, 0,0, 0,0}) = 1;
    hI({1,2, 0,0, 0,0}) = -1;
    cI({0,1, 0,0, 0,0}) = 2;
    cI({1,2, 0,0, 0,0}) = -2;

    std::vector<double>   tArgs = {cellClip};
    std::vector<Nd4jLong> iArgs = {dataFormat, directionMode, gateAct, cellAct, outAct};
    std::vector<bool>     bArgs = {hasBiases, hasSeqLen, hasInitH, hasInitC, hasPH, retFullSeq, retLastH, retLastC};

    NDArray expH('c', {bS, sL, 2*nOut}, {
        0.577661f,  0.577661f,  0.577661f, -0.107659f, -0.107659f, -0.107659f, 0.548099f,  0.548099f,  0.548099f, -0.113406f, -0.113406f, -0.113406f,
        0.526881f,  0.526881f,  0.526881f,  -0.12883f,  -0.12883f,  -0.12883f, 0.515882f,  0.515882f,  0.515882f,  -0.16868f,  -0.16868f,  -0.16868f,
         0.51409f,   0.51409f,   0.51409f, -0.255185f, -0.255185f, -0.255185f, 0.614599f,  0.614599f,  0.614599f, -0.102739f, -0.102739f, -0.102739f,
        0.599572f,  0.599572f,  0.599572f, -0.105802f, -0.105802f, -0.105802f, 0.591089f,  0.591089f,  0.591089f, -0.116681f, -0.116681f, -0.116681f,
        0.588694f,  0.588694f,  0.588694f, -0.149201f, -0.149201f, -0.149201f, 0.591492f,  0.591492f,  0.591492f, -0.228917f, -0.228917f, -0.228917f}, sd::DataType::FLOAT32);

    NDArray expHL('c', {2,bS, nOut}, {0.51409f,  0.51409f,  0.51409f,   0.591492f,  0.591492f,  0.591492f,
                                     -0.107659f, -0.107659f, -0.107659f,  -0.102739f, -0.102739f, -0.102739f}, sd::DataType::FLOAT32);
    NDArray expCL('c', {2,bS, nOut}, {1.07293f ,  1.07293f ,  1.07293f, 1.346609f,  1.346609f,  1.346609f,
                                    -0.295811f, -0.295811f, -0.295811f, -0.305394f, -0.305394f, -0.305394f}, sd::DataType::FLOAT32);

    sd::ops::lstmLayer op;
    auto results = op.evaluate({&x, &Wx, &Wr, &b, &hI, &cI}, tArgs, iArgs, bArgs);

    ASSERT_EQ(ND4J_STATUS_OK, results.status());

    auto h  = results.at(0);
    auto hL = results.at(1);
    auto cL = results.at(2);

    // h->printBuffer();
    // hL->printBuffer();
    // cL->printBuffer();

    ASSERT_TRUE(expH.isSameShape(h));
    ASSERT_TRUE(expH.equalsTo(h));

    ASSERT_TRUE(expHL.isSameShape(hL));
    ASSERT_TRUE(expHL.equalsTo(hL));

    ASSERT_TRUE(expCL.isSameShape(cL));
    ASSERT_TRUE(expCL.equalsTo(cL));

}

///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, lstmLayer_6) {

    const int sL   = 5;
    const int bS   = 2;
    const int nIn  = 4;
    const int nOut = 3;

    // input arguments
    const int dataFormat = 0;       // [sL,bS,nIn]
    const int directionMode = 2;    // bidirectional sum
    const int gateAct = 2;          // sigmoid activation for input (i), forget (f) and output (o) gates
    const int cellAct = 0;          // tanh activation for cell state
    const int outAct = 0;           // tanh activation for output

    const bool hasBiases  = true;   // biases array is provided
    const bool hasSeqLen  = false;  // seqLen array is not provided
    const auto hasInitH   = true;   // initial output is provided
    const auto hasInitC   = true;   // initial cell state is provided
    const auto hasPH      = false;  // peephole connections are absent
    const auto retFullSeq = true;   // return whole h {h_0, h_1, ... , h_sL-1}, [sL,bS,nOut]
    const auto retLastH   = true;   // do not return output at last time step
    const auto retLastC   = true;   // return cells state at last time step

    const double cellClip = 0;       // do not apply clipping

    NDArray x('c', {sL, bS, nIn}, sd::DataType::FLOAT32);
    NDArray Wx('c', {2,nIn, 4*nOut}, sd::DataType::FLOAT32);
    NDArray Wr('c', {2,nOut, 4*nOut}, sd::DataType::FLOAT32);
    NDArray b('c', {2,4*nOut}, sd::DataType::FLOAT32);
    NDArray hI('c', {2,bS, nOut}, sd::DataType::FLOAT32);
    NDArray cI('c', {2,bS, nOut}, sd::DataType::FLOAT32);

    x.linspace(0.5, 0.5);
    Wx({0,1, 0,0, 0,0}) = 0.003f;
    Wx({1,2, 0,0, 0,0}) = -0.003f;
    Wr({0,1, 0,0, 0,0}) = 0.006f;
    Wr({1,2, 0,0, 0,0}) = -0.006f;
    b({0,1, 0,0})       = 0.5f;
    b({1,2, 0,0})       = -0.5f;
    hI({0,1, 0,0, 0,0}) = 1;
    hI({1,2, 0,0, 0,0}) = -1;
    cI({0,1, 0,0, 0,0}) = 2;
    cI({1,2, 0,0, 0,0}) = -2;

    std::vector<double>   tArgs = {cellClip};
    std::vector<Nd4jLong> iArgs = {dataFormat, directionMode, gateAct, cellAct, outAct};
    std::vector<bool>     bArgs = {hasBiases, hasSeqLen, hasInitH, hasInitC, hasPH, retFullSeq, retLastH, retLastC};

    NDArray expH('c', {sL, bS, nOut}, {
        0.470019f, 0.470019f, 0.470019f, 0.478352f, 0.478352f, 0.478352f, 0.444871f, 0.444871f, 0.444871f, 0.457060f,
        0.457060f, 0.457060f, 0.424090f, 0.424090f, 0.424090f, 0.439778f, 0.439778f, 0.439778f, 0.394491f, 0.394491f,
        0.394491f, 0.412995f, 0.412995f, 0.412995f, 0.329613f, 0.329613f, 0.329613f, 0.349760f, 0.349760f, 0.349760f}, sd::DataType::FLOAT32);

    NDArray expHL('c', {2,bS, nOut}, {0.563741f, 0.563741f, 0.563741f, 0.578676f, 0.578676f, 0.578676f,
                                      -0.107642f, -0.107642f, -0.107642f, -0.106937f, -0.106937f, -0.106937f},
                                      sd::DataType::FLOAT32);
    NDArray expCL('c', {2,bS, nOut}, {1.217757f, 1.217757f, 1.217757f, 1.272398f, 1.272398f, 1.272398f,
                                      -0.295768f, -0.295768f, -0.295768f, -0.298453f, -0.298453f, -0.298453f},
                                      sd::DataType::FLOAT32);

    sd::ops::lstmLayer op;
    auto results = op.evaluate({&x, &Wx, &Wr, &b, &hI, &cI}, tArgs, iArgs, bArgs);

    ASSERT_EQ(ND4J_STATUS_OK, results.status());

    auto h  = results.at(0);
    auto hL = results.at(1);
    auto cL = results.at(2);

    ASSERT_TRUE(expH.isSameShape(h));
    ASSERT_TRUE(expH.equalsTo(h));

    ASSERT_TRUE(expHL.isSameShape(hL));
    ASSERT_TRUE(expHL.equalsTo(hL));

    ASSERT_TRUE(expCL.isSameShape(cL));
    ASSERT_TRUE(expCL.equalsTo(cL));

}

///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, lstmLayer_7) {
    #ifndef HAVE_MKLDNN

    const int sL   = 5;
    const int bS   = 2;
    const int nIn  = 4;
    const int nOut = 3;

    // input arguments

    const int dataFormat = 0;       // [sL,bS,nIn]
    const int directionMode = 0;    // forward
    const int gateAct = 2;          // sigmoid activation for input (i), forget (f) and output (o) gates
    const int cellAct = 0;          // tanh activation for cell state
    const int outAct = 0;           // tanh activation for output

    const bool hasBiases  = true;   // biases array is provided
    const bool hasSeqLen  = false;  // seqLen array is not provided
    const auto hasInitH   = true;   // initial output is provided
    const auto hasInitC   = true;   // initial cell state is provided
    const auto hasPH      = true;   // peephole connections are absent
    const auto retFullSeq = true;   // return whole h {h_0, h_1, ... , h_sL-1}, [sL,bS,nOut]
    const auto retLastH   = true;  // do not return output at last time step
    const auto retLastC   = true;   // return cells state at last time step

    const double cellClip = 0;       // do not apply clipping

    NDArray x('c', {sL, bS, nIn}, sd::DataType::FLOAT32);
    NDArray Wx('c', {nIn, 4*nOut}, sd::DataType::FLOAT32);
    NDArray Wr('c', {nOut, 4*nOut}, sd::DataType::FLOAT32);
    NDArray b('c', {4*nOut}, sd::DataType::FLOAT32);
    NDArray hI('c', {bS, nOut}, sd::DataType::FLOAT32);
    NDArray cI('c', {bS, nOut}, sd::DataType::FLOAT32);
    NDArray Wp('c', {3*nOut}, sd::DataType::FLOAT32);

    x.linspace(0.5, 0.5);
    Wx = 0.003;
    Wr = 0.006;
    b = 0.5;
    hI = 1.;
    cI = 2.;
    Wp = -0.05;

    std::initializer_list<double>   tArgs = {cellClip};
    std::initializer_list<Nd4jLong> iArgs = {dataFormat, directionMode, gateAct, cellAct, outAct};
    std::initializer_list<bool>     bArgs = {hasBiases, hasSeqLen, hasInitH, hasInitC, hasPH, retFullSeq, retLastH, retLastC};

    NDArray expH('c', {sL, bS, nOut}, {0.55533 , 0.55533 , 0.55533 , 0.562925, 0.562925, 0.562925, 0.531795, 0.531795, 0.531795, 0.542556,
                                        0.542556, 0.542556, 0.521466, 0.521466, 0.521466, 0.534638, 0.534638, 0.534638, 0.524805, 0.524805,
                                        0.524805, 0.539187, 0.539187, 0.539187, 0.538309, 0.538309, 0.538309, 0.552923, 0.552923, 0.552923}, sd::DataType::FLOAT32);

    NDArray expHL('c', {bS, nOut}, {0.538309, 0.538309, 0.538309,0.552923, 0.552923, 0.552923}, sd::DataType::FLOAT32);
    NDArray expCL('c', {bS, nOut}, {1.147089, 1.147089, 1.147089,1.197228, 1.197228, 1.197228}, sd::DataType::FLOAT32);

    sd::ops::lstmLayer op;
    auto results = op.evaluate({&x, &Wx, &Wr, &b, &hI, &cI, &Wp}, tArgs, iArgs, bArgs);

    ASSERT_EQ(ND4J_STATUS_OK, results.status());

    auto h  = results.at(0);
    auto hL = results.at(1);
    auto cL = results.at(2);

    ASSERT_TRUE(expH.isSameShape(h));
    ASSERT_TRUE(expH.equalsTo(h));

    ASSERT_TRUE(expHL.isSameShape(hL));
    ASSERT_TRUE(expHL.equalsTo(hL));

    ASSERT_TRUE(expCL.isSameShape(cL));
    ASSERT_TRUE(expCL.equalsTo(cL));


    #endif
}

///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, lstmLayer_8) {
    #ifndef HAVE_MKLDNN

    const int sL   = 5;
    const int bS   = 2;
    const int nIn  = 4;
    const int nOut = 3;

    // input arguments

    const int dataFormat = 0;       // [sL,bS,nIn]
    const int directionMode = 1;    // backward
    const int gateAct = 2;          // sigmoid activation for input (i), forget (f) and output (o) gates
    const int cellAct = 0;          // tanh activation for cell state
    const int outAct = 0;           // tanh activation for output

    const bool hasBiases  = true;   // biases array is provided
    const bool hasSeqLen  = false;  // seqLen array is not provided
    const auto hasInitH   = true;   // initial output is provided
    const auto hasInitC   = true;   // initial cell state is provided
    const auto hasPH      = true;   // peephole connections are absent
    const auto retFullSeq = true;   // return whole h {h_0, h_1, ... , h_sL-1}, [sL,bS,nOut]
    const auto retLastH   = true;  // do not return output at last time step
    const auto retLastC   = true;   // return cells state at last time step

    const double cellClip = 1.;       // do not apply clipping

    NDArray x('c', {sL, bS, nIn}, sd::DataType::FLOAT32);
    NDArray Wx('c', {nIn, 4*nOut}, sd::DataType::FLOAT32);
    NDArray Wr('c', {nOut, 4*nOut}, sd::DataType::FLOAT32);
    NDArray b('c', {4*nOut}, sd::DataType::FLOAT32);
    NDArray hI('c', {bS, nOut}, sd::DataType::FLOAT32);
    NDArray cI('c', {bS, nOut}, sd::DataType::FLOAT32);
    NDArray Wp('c', {3*nOut}, sd::DataType::FLOAT32);

    x.linspace(0.5, 0.5);
    Wx = 0.003;
    Wr = 0.006;
    b = 0.5;
    hI = 1.;
    cI = 2.;
    Wp = -0.05;

    std::initializer_list<double>   tArgs = {cellClip};
    std::initializer_list<Nd4jLong> iArgs = {dataFormat, directionMode, gateAct, cellAct, outAct};
    std::initializer_list<bool>     bArgs = {hasBiases, hasSeqLen, hasInitH, hasInitC, hasPH, retFullSeq, retLastH, retLastC};

    NDArray expH('c', {sL, bS, nOut}, {
        0.436221f, 0.436221f, 0.436221f, 0.450573f, 0.450573f, 0.450573f, 0.463602f, 0.463602f, 0.463602f, 0.474674f, 0.474674f, 0.474674f,
        0.484039f, 0.484039f, 0.484039f, 0.490679f, 0.490679f, 0.490679f, 0.494871f, 0.494871f, 0.494871f, 0.499028f, 0.499028f, 0.499028f,
        0.504649f, 0.504649f, 0.504649f, 0.508719f, 0.508719f, 0.508719f}, sd::DataType::FLOAT32);

    NDArray expHL('c', {bS, nOut}, {0.436221f, 0.436221f, 0.436221f, 0.450573f, 0.450573f, 0.450573f}, sd::DataType::FLOAT32);
    NDArray expCL('c', {bS, nOut}, {0.879804f, 0.879804f, 0.879804f, 0.914666f, 0.914666f, 0.914666f}, sd::DataType::FLOAT32);

    sd::ops::lstmLayer op;
    auto results = op.evaluate({&x, &Wx, &Wr, &b, &hI, &cI, &Wp}, tArgs, iArgs, bArgs);

    ASSERT_EQ(ND4J_STATUS_OK, results.status());

    auto h  = results.at(0);
    auto hL = results.at(1);
    auto cL = results.at(2);

    ASSERT_TRUE(expH.isSameShape(h));
    ASSERT_TRUE(expH.equalsTo(h));

    ASSERT_TRUE(expHL.isSameShape(hL));
    ASSERT_TRUE(expHL.equalsTo(hL));

    ASSERT_TRUE(expCL.isSameShape(cL));
    ASSERT_TRUE(expCL.equalsTo(cL));


    #endif
}

///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, lstmLayer_9) {
    #ifndef HAVE_MKLDNN

    const int sL   = 5;
    const int bS   = 2;
    const int nIn  = 4;
    const int nOut = 3;

    // input arguments
    const int dataFormat = 0;       // [sL,bS,nIn]
    const int directionMode = 3;    // bidirectional concat
    const int gateAct = 2;          // sigmoid activation for input (i), forget (f) and output (o) gates
    const int cellAct = 0;          // tanh activation for cell state
    const int outAct = 0;           // tanh activation for output

    const bool hasBiases  = true;   // biases array is provided
    const bool hasSeqLen  = false;  // seqLen array is not provided
    const auto hasInitH   = true;   // initial output is provided
    const auto hasInitC   = true;   // initial cell state is provided
    const auto hasPH      = true;  // peephole connections are absent
    const auto retFullSeq = true;   // return whole h {h_0, h_1, ... , h_sL-1}, [sL,bS,nOut]
    const auto retLastH   = true;   // do not return output at last time step
    const auto retLastC   = true;   // return cells state at last time step

    const double cellClip = 0;       // do not apply clipping

    NDArray x('c', {sL, bS, nIn}, sd::DataType::FLOAT32);
    NDArray Wx('c', {2,nIn, 4*nOut}, sd::DataType::FLOAT32);
    NDArray Wr('c', {2,nOut, 4*nOut}, sd::DataType::FLOAT32);
    NDArray b('c', {2,4*nOut}, sd::DataType::FLOAT32);
    NDArray hI('c', {2,bS, nOut}, sd::DataType::FLOAT32);
    NDArray cI('c', {2,bS, nOut}, sd::DataType::FLOAT32);
    NDArray Wp('c', {2,3*nOut}, sd::DataType::FLOAT32);

    x.linspace(0.5, 0.5);
    Wx({0,1, 0,0, 0,0}) = 0.003;
    Wx({1,2, 0,0, 0,0}) = -0.003;
    Wr({0,1, 0,0, 0,0}) = 0.006;
    Wr({1,2, 0,0, 0,0}) = -0.006;
    b({0,1, 0,0})       = 0.5;
    b({1,2, 0,0})       = -0.5;
    hI({0,1, 0,0, 0,0}) = 1;
    hI({1,2, 0,0, 0,0}) = -1;
    cI({0,1, 0,0, 0,0}) = 2;
    cI({1,2, 0,0, 0,0}) = -2;
    Wp({0,1, 0,0}) = -0.05;
    Wp({1,2, 0,0}) = 0.05;

    std::initializer_list<double>   tArgs = {cellClip};
    std::initializer_list<Nd4jLong> iArgs = {dataFormat, directionMode, gateAct, cellAct, outAct};
    std::initializer_list<bool>     bArgs = {hasBiases, hasSeqLen, hasInitH, hasInitC, hasPH, retFullSeq, retLastH, retLastC};

    NDArray expH('c', {sL, bS, 2*nOut}, {
         0.55533f,   0.55533f,   0.55533f, -0.104502f, -0.104502f, -0.104502f, 0.562925f,  0.562925f,  0.562925f, -0.103843f, -0.103843f, -0.103843f,
        0.531795f,  0.531795f,  0.531795f, -0.107456f, -0.107456f, -0.107456f, 0.542556f,  0.542556f,  0.542556f, -0.106139f, -0.106139f, -0.106139f,
        0.521466f,  0.521466f,  0.521466f,  -0.11681f,  -0.11681f,  -0.11681f, 0.534638f,  0.534638f,  0.534638f,  -0.11458f,  -0.11458f,  -0.11458f,
        0.524805f,  0.524805f,  0.524805f, -0.145177f, -0.145177f, -0.145177f, 0.539187f,  0.539187f,  0.539187f,  -0.14157f,  -0.14157f,  -0.14157f,
        0.538309f,  0.538309f,  0.538309f, -0.218056f, -0.218056f, -0.218056f, 0.552923f,  0.552923f,  0.552923f, -0.213068f, -0.213068f, -0.213068f}, sd::DataType::FLOAT32);

    NDArray expHL('c', {2,bS, nOut}, {0.538309f,  0.538309f,  0.538309f, 0.552923f,  0.552923f,  0.552923f, -0.104502f, -0.104502f, -0.104502f,
                                     -0.103843f, -0.103843f, -0.103843f}, sd::DataType::FLOAT32);
    NDArray expCL('c', {2,bS, nOut}, {1.147089f,  1.147089f,  1.147089f, 1.197228f,  1.197228f,  1.197228f, -0.289425f, -0.289425f, -0.289425f,
                                     -0.292174f, -0.292174f, -0.292174f}, sd::DataType::FLOAT32);

    sd::ops::lstmLayer op;
    auto results = op.evaluate({&x, &Wx, &Wr, &b, &hI, &cI, &Wp}, tArgs, iArgs, bArgs);

    ASSERT_EQ(ND4J_STATUS_OK, results.status());

    auto h  = results.at(0);
    auto hL = results.at(1);
    auto cL = results.at(2);

    ASSERT_TRUE(expH.isSameShape(h));
    ASSERT_TRUE(expH.equalsTo(h));

    ASSERT_TRUE(expHL.isSameShape(hL));
    ASSERT_TRUE(expHL.equalsTo(hL));

    ASSERT_TRUE(expCL.isSameShape(cL));
    ASSERT_TRUE(expCL.equalsTo(cL));


    #endif
}

///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, lstmLayer_10) {
    #ifndef HAVE_MKLDNN

    const int sL   = 6;
    const int bS   = 5;
    const int nIn  = 4;
    const int nOut = 3;

    // input arguments
    const int dataFormat = 0;       // [sL,bS,nIn]
    const int directionMode = 0;    // forward
    const int gateAct = 2;          // sigmoid activation for input (i), forget (f) and output (o) gates
    const int cellAct = 0;          // tanh activation for cell state
    const int outAct = 0;           // tanh activation for output

    const bool hasBiases  = true;   // biases array is provided
    const bool hasSeqLen  = true;   // seqLen array is not provided
    const auto hasInitH   = true;   // initial output is provided
    const auto hasInitC   = true;   // initial cell state is provided
    const auto hasPH      = true;   // peephole connections are absent
    const auto retFullSeq = true;   // return whole h {h_0, h_1, ... , h_sL-1}, [sL,bS,nOut]
    const auto retLastH   = true;  // do not return output at last time step
    const auto retLastC   = true;   // return cells state at last time step

    const double cellClip = 0;       // do not apply clipping

    NDArray x('c', {sL, bS, nIn}, sd::DataType::FLOAT32);
    NDArray Wx('c', {nIn, 4*nOut}, sd::DataType::FLOAT32);
    NDArray Wr('c', {nOut, 4*nOut}, sd::DataType::FLOAT32);
    NDArray b('c', {4*nOut}, sd::DataType::FLOAT32);
    NDArray hI('c', {bS, nOut}, sd::DataType::FLOAT32);
    NDArray cI('c', {bS, nOut}, sd::DataType::FLOAT32);
    NDArray seqLen('c', {bS}, {0,1,2,3,5}, sd::DataType::FLOAT32);
    NDArray Wp('c', {3*nOut}, sd::DataType::FLOAT32);

    x.linspace(0.5, 0.5);
    Wx = 0.003;
    Wr = 0.006;
    b = 0.5;
    hI = 1.;
    cI = 2.;
    Wp = -0.05;

    std::initializer_list<double>   tArgs = {cellClip};
    std::initializer_list<Nd4jLong> iArgs = {dataFormat, directionMode, gateAct, cellAct, outAct};
    std::initializer_list<bool>     bArgs = {hasBiases, hasSeqLen, hasInitH, hasInitC, hasPH, retFullSeq, retLastH, retLastC};

    NDArray expH('c', {sL, bS, nOut}, {
              0.f,       0.f,       0.f, 0.562925f, 0.562925f, 0.562925f, 0.570404f, 0.570404f, 0.570404f,  0.57777f,
         0.57777f,  0.57777f, 0.585023f, 0.585023f, 0.585023f,       0.f,       0.f,       0.f,       0.f,       0.f,
              0.f, 0.576568f, 0.576568f, 0.576568f, 0.586163f, 0.586163f, 0.586163f, 0.595462f, 0.595462f, 0.595462f,
              0.f,       0.f,       0.f,       0.f,       0.f,       0.f,       0.f,       0.f,       0.f, 0.611224f,
        0.611224f, 0.611224f, 0.621298f, 0.621298f, 0.621298f,       0.f,       0.f,       0.f,       0.f,       0.f,
              0.f,       0.f,       0.f,       0.f,       0.f,       0.f,       0.f, 0.655858f, 0.655858f, 0.655858f,
              0.f,       0.f,       0.f,       0.f,       0.f,       0.f,       0.f,       0.f,       0.f,        0.f,
              0.f,       0.f, 0.692315f, 0.692315f, 0.692315f,       0.f,       0.f,       0.f,       0.f,        0.f,
              0.f,       0.f,       0.f,       0.f,       0.f,       0.f,       0.f,       0.f,       0.f,        0.f},
        sd::DataType::FLOAT32);

    NDArray expHL('c', {bS, nOut}, {0.f, 0.f, 0.f, 0.562925f, 0.562925f, 0.562925f, 0.576568f, 0.576568f, 0.576568f, 0.611224f, 0.611224f, 0.611224f, 0.692315f, 0.692315f, 0.692315f}, sd::DataType::FLOAT32);
    NDArray expCL('c', {bS, nOut}, {0.f, 0.f, 0.f, 1.534275f, 1.534275f, 1.534275f,  1.40183f,  1.40183f,  1.40183f, 1.449675f, 1.449675f, 1.449675f, 1.767702f, 1.767702f, 1.767702f}, sd::DataType::FLOAT32);

    sd::ops::lstmLayer op;
    auto results = op.evaluate({&x, &Wx, &Wr, &b, &seqLen, &hI, &cI, &Wp}, tArgs, iArgs, bArgs);

    ASSERT_EQ(ND4J_STATUS_OK, results.status());

    auto h  = results.at(0);
    auto hL = results.at(1);
    auto cL = results.at(2);

    ASSERT_TRUE(expH.isSameShape(h));
    ASSERT_TRUE(expH.equalsTo(h));

    ASSERT_TRUE(expHL.isSameShape(hL));
    ASSERT_TRUE(expHL.equalsTo(hL));

    ASSERT_TRUE(expCL.isSameShape(cL));
    ASSERT_TRUE(expCL.equalsTo(cL));


    #endif
}

///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, lstmLayer_11) {
    #ifndef HAVE_MKLDNN

    const int sL   = 6;
    const int bS   = 5;
    const int nIn  = 4;
    const int nOut = 3;

    // input arguments
    const int dataFormat = 0;       // [sL,bS,nIn]
    const int directionMode = 1;    // backward
    const int gateAct = 2;          // sigmoid activation for input (i), forget (f) and output (o) gates
    const int cellAct = 0;          // tanh activation for cell state
    const int outAct = 0;           // tanh activation for output

    const bool hasBiases  = true;   // biases array is provided
    const bool hasSeqLen  = true;   // seqLen array is not provided
    const auto hasInitH   = true;   // initial output is provided
    const auto hasInitC   = true;   // initial cell state is provided
    const auto hasPH      = true;   // peephole connections are absent
    const auto retFullSeq = true;   // return whole h {h_0, h_1, ... , h_sL-1}, [sL,bS,nOut]
    const auto retLastH   = true;  // do not return output at last time step
    const auto retLastC   = true;   // return cells state at last time step

    const double cellClip = 0;       // do not apply clipping

    NDArray x('c', {sL, bS, nIn}, sd::DataType::FLOAT32);
    NDArray Wx('c', {nIn, 4*nOut}, sd::DataType::FLOAT32);
    NDArray Wr('c', {nOut, 4*nOut}, sd::DataType::FLOAT32);
    NDArray b('c', {4*nOut}, sd::DataType::FLOAT32);
    NDArray hI('c', {bS, nOut}, sd::DataType::FLOAT32);
    NDArray cI('c', {bS, nOut}, sd::DataType::FLOAT32);
    NDArray seqLen('c', {bS}, {0,1,2,3,5}, sd::DataType::FLOAT32);
    NDArray Wp('c', {3*nOut}, sd::DataType::FLOAT32);

    x.linspace(0.5, 0.5);
    Wx = 0.003f;
    Wr = 0.006f;
    b = 0.5f;
    hI = 1.f;
    cI = 2.f;
    Wp = -0.05f;

    std::initializer_list<double>   tArgs = {cellClip};
    std::initializer_list<Nd4jLong> iArgs = {dataFormat, directionMode, gateAct, cellAct, outAct};
    std::initializer_list<bool>     bArgs = {hasBiases, hasSeqLen, hasInitH, hasInitC, hasPH, retFullSeq, retLastH, retLastC};

    NDArray expH('c', {sL, bS, nOut}, {
        0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.61209f,
        0.61209f, 0.61209f,0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.652042f, 0.652042f, 0.652042f,  0.f, 0.f, 0.f, 0.f, 0.f,
        0.f, 0.f, 0.f, 0.f, 0.677708f, 0.677708f, 0.677708f, 0.684177f, 0.684177f, 0.684177f, 0.f, 0.f, 0.f,0.f, 0.f, 0.f, 0.699627f, 0.699627f,
        0.699627f, 0.705371f, 0.705371f, 0.705371f, 0.710989f, 0.710989f, 0.710989f, 0., 0., 0., 0.719014, 0.719014, 0.719014, 0.724087,
        0.724087f, 0.724087f, 0.729084f, 0.729084f, 0.729084f, 0.734004f, 0.734004f, 0.734004f }, sd::DataType::FLOAT32);

    NDArray expHL('c', {bS, nOut}, {0.f, 0.f, 0.f, 0.719014f, 0.719014f, 0.719014f, 0.699627f, 0.699627f, 0.699627f, 0.677708f, 0.677708f, 0.677708f,  0.61209f,  0.61209f,  0.61209f}, sd::DataType::FLOAT32);
    NDArray expCL('c', {bS, nOut}, {0.f, 0.f, 0.f, 2.092814f, 2.092814f, 2.092814f,  2.08832f,  2.08832f,  2.08832f, 2.009851f, 2.009851f, 2.009851f, 1.646034f, 1.646034f, 1.646034f}, sd::DataType::FLOAT32);

    sd::ops::lstmLayer op;
    auto results = op.evaluate({&x, &Wx, &Wr, &b, &seqLen, &hI, &cI, &Wp}, tArgs, iArgs, bArgs);

    ASSERT_EQ(ND4J_STATUS_OK, results.status());

    auto h  = results.at(0);
    auto hL = results.at(1);
    auto cL = results.at(2);

    ASSERT_TRUE(expH.isSameShape(h));
    ASSERT_TRUE(expH.equalsTo(h));

    ASSERT_TRUE(expHL.isSameShape(hL));
    ASSERT_TRUE(expHL.equalsTo(hL));

    ASSERT_TRUE(expCL.isSameShape(cL));
    ASSERT_TRUE(expCL.equalsTo(cL));


    #endif
}

///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, lstmLayer_12) {
    #ifndef HAVE_MKLDNN

    const int sL   = 6;
    const int bS   = 5;
    const int nIn  = 4;
    const int nOut = 3;

    // input arguments
    const int dataFormat = 0;       // [sL,bS,nIn]
    const int directionMode = 3;    // bidirectional concat
    const int gateAct = 2;          // sigmoid activation for input (i), forget (f) and output (o) gates
    const int cellAct = 0;          // tanh activation for cell state
    const int outAct = 0;           // tanh activation for output

    const bool hasBiases  = true;   // biases array is provided
    const bool hasSeqLen  = true;  // seqLen array is not provided
    const auto hasInitH   = true;   // initial output is provided
    const auto hasInitC   = true;   // initial cell state is provided
    const auto hasPH      = true;  // peephole connections are absent
    const auto retFullSeq = true;   // return whole h {h_0, h_1, ... , h_sL-1}, [sL,bS,nOut]
    const auto retLastH   = true;   // do not return output at last time step
    const auto retLastC   = true;   // return cells state at last time step

    const double cellClip = 0;       // do not apply clipping

    NDArray x('c', {sL, bS, nIn}, sd::DataType::FLOAT32);
    NDArray Wx('c', {2,nIn, 4*nOut}, sd::DataType::FLOAT32);
    NDArray Wr('c', {2,nOut, 4*nOut}, sd::DataType::FLOAT32);
    NDArray b('c', {2,4*nOut}, sd::DataType::FLOAT32);
    NDArray hI('c', {2,bS, nOut}, sd::DataType::FLOAT32);
    NDArray cI('c', {2,bS, nOut}, sd::DataType::FLOAT32);
    NDArray seqLen('c', {bS}, {0,1,2,3,5}, sd::DataType::FLOAT32);
    NDArray Wp('c', {2,3*nOut}, sd::DataType::FLOAT32);

    x.linspace(0.5, 0.5);
    Wx({0,1, 0,0, 0,0}) = 0.003f;
    Wx({1,2, 0,0, 0,0}) = -0.003f;
    Wr({0,1, 0,0, 0,0}) = 0.006f;
    Wr({1,2, 0,0, 0,0}) = -0.006f;
    b({0,1, 0,0})       = 0.5f;
    b({1,2, 0,0})       = -0.5f;
    hI({0,1, 0,0, 0,0}) = 1;
    hI({1,2, 0,0, 0,0}) = -1;
    cI({0,1, 0,0, 0,0}) = 2;
    cI({1,2, 0,0, 0,0}) = -2;
    Wp({0,1, 0,0}) = -0.05f;
    Wp({1,2, 0,0}) = 0.05f;

    std::initializer_list<double>   tArgs = {cellClip};
    std::initializer_list<Nd4jLong> iArgs = {dataFormat, directionMode, gateAct, cellAct, outAct};
    std::initializer_list<bool>     bArgs = {hasBiases, hasSeqLen, hasInitH, hasInitC, hasPH, retFullSeq, retLastH, retLastC};

    NDArray expH('c', {sL, bS, 2*nOut}, {0., 0., 0., 0., 0., 0.,  0.562925, 0.562925, 0.562925, -0.25361 , -0.25361 , -0.25361 ,   0.570404, 0.570404, 0.570404, -0.157103,
                                        -0.157103, -0.157103, 0.57777 , 0.57777 , 0.57777 , -0.116502, -0.116502, -0.116502,0.585023, 0.585023, 0.585023, -0.100025,
                                        -0.100025, -0.100025, 0., 0., 0., 0., 0., 0.,0., 0., 0., 0., 0., 0.,   0.576568, 0.576568, 0.576568, -0.223072, -0.223072, -0.223072,
                                        0.586163, 0.586163, 0.586163, -0.135714, -0.135714, -0.135714,0.595462, 0.595462, 0.595462, -0.094438, -0.094438, -0.094438,
                                        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.611224, 0.611224, 0.611224, -0.193473, -0.193473, -0.193473,
                                        0.621298, 0.621298, 0.621298, -0.090626, -0.090626, -0.090626, 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
                                        0., 0., 0., 0., 0., 0., 0.655858, 0.655858, 0.655858, -0.098015, -0.098015, -0.098015, 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
                                        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.692315, 0.692315, 0.692315, -0.143704, -0.143704, -0.143704, 0., 0., 0., 0., 0., 0.,
                                        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.}, sd::DataType::FLOAT32);

    NDArray expHL('c', {2,bS, nOut}, {0.f, 0.f, 0.f, 0.562925f, 0.562925f, 0.562925f,   0.576568f,  0.576568f,  0.576568f,  0.611224f,  0.611224f,  0.611224f,   0.692315f,  0.692315f,  0.692315f,
                                      0.f, 0.f, 0.f, -0.25361f, -0.25361f, -0.25361f,  -0.157103f, -0.157103f, -0.157103f, -0.116502f, -0.116502f, -0.116502f,  -0.100025f, -0.100025f, -0.100025f}, sd::DataType::FLOAT32);
    NDArray expCL('c', {2,bS, nOut}, {0.f, 0.f, 0.f, 1.534275f, 1.534275f, 1.534275f,    1.40183f,   1.40183f,   1.40183f,  1.449675f,  1.449675f,  1.449675f,   1.767702f,  1.767702f,  1.767702f,
                                      0.f, 0.f, 0.f, -0.86636f, -0.86636f, -0.86636f,  -0.470245f, -0.470245f, -0.470245f, -0.341856f, -0.341856f, -0.341856f,  -0.294986f, -0.294986f, -0.294986f}, sd::DataType::FLOAT32);

    sd::ops::lstmLayer op;
    auto results = op.evaluate({&x, &Wx, &Wr, &b, &seqLen, &hI, &cI, &Wp}, tArgs, iArgs, bArgs);

    ASSERT_EQ(ND4J_STATUS_OK, results.status());

    auto h  = results.at(0);
    auto hL = results.at(1);
    auto cL = results.at(2);

    ASSERT_TRUE(expH.isSameShape(h));
    ASSERT_TRUE(expH.equalsTo(h));

    ASSERT_TRUE(expHL.isSameShape(hL));
    ASSERT_TRUE(expHL.equalsTo(hL));

    ASSERT_TRUE(expCL.isSameShape(cL));
    ASSERT_TRUE(expCL.equalsTo(cL));

    #endif
}

///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, lstmLayer_bp_1) {

    const int sL   = 3;
    const int bS   = 2;
    const int nIn  = 2;
    const int nOut = 3;

    const int dataFormat = 0;       // [sL,bS,nIn]
    const int directionMode = 0;    // forward
    const int gateAct = 2;          // sigmoid activation for input (i), forget (f) and output (o) gates
    const int cellAct = 0;          // tanh activation for cell state
    const int outAct = 0;           // tanh activation for output

    const bool hasBiases  = true;   // biases array is provided
    const bool hasSeqLen  = false;  // seqLen array is not provided
    const auto hasInitH   = true;   // initial output is provided
    const auto hasInitC   = true;   // initial cell state is provided
    const auto hasPH      = true;   // peephole connections are absent
    const auto retFullSeq = false;  // dLdh per each time step
    const auto retLastH   = true;   // output at last time step
    const auto retLastC   = true;   // cells state at last time step

    const double cellClip = 0.5;       // do not apply clipping

    NDArray x('c', {sL, bS, nIn}, sd::DataType::DOUBLE);
    NDArray Wx('c', {nIn, 4*nOut}, sd::DataType::DOUBLE);
    NDArray Wr('c', {nOut, 4*nOut}, sd::DataType::DOUBLE);
    NDArray b('c', {4*nOut}, sd::DataType::DOUBLE);
    NDArray hI('c', {bS, nOut}, sd::DataType::DOUBLE);
    NDArray cI('c', {bS, nOut}, sd::DataType::DOUBLE);
    NDArray Wp('c', {3*nOut}, sd::DataType::DOUBLE);
    NDArray dLdhL('c', {bS, nOut}, sd::DataType::DOUBLE);
    NDArray dLdcL('c', {bS, nOut}, sd::DataType::DOUBLE);

    x.linspace(-2,0.1);
    hI.linspace(-1.5,0.1);
    cI.linspace(0.7,-0.1);
    Wx.linspace(1,-0.1);
    Wr.linspace(-1,0.1);
    Wp.linspace(0.2,0.2);
    b.linspace(1,-0.15);

    std::vector<double>   tArgs = {cellClip};
    std::vector<Nd4jLong> iArgs = {dataFormat, directionMode, gateAct, cellAct, outAct};
    std::vector<bool>     bArgs = {hasBiases, hasSeqLen, hasInitH, hasInitC, hasPH, retFullSeq, retLastH, retLastC};

    const OpArgsHolder argsHolderFF({&x, &Wx, &Wr, &b, &hI, &cI, &Wp}, tArgs, iArgs, bArgs);
    const OpArgsHolder argsHolderBP({&x, &Wx, &Wr, &b, &hI, &cI, &Wp, &dLdhL, &dLdcL}, tArgs, iArgs, bArgs);

    sd::ops::lstmLayer opFF;
    sd::ops::lstmLayer_bp opBP;

    const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP, std::vector<bool>(), {0., 1.}, GradCheck::LossFunc::SUM, {0});

    ASSERT_TRUE(isGradCorrect);
}

///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, lstmLayer_bp_2) {

    const int sL   = 3;
    const int bS   = 2;
    const int nIn  = 2;
    const int nOut = 3;

    const int dataFormat = 0;       // [sL,bS,nIn]
    const int directionMode = 0;    // forward
    const int gateAct = 2;          // sigmoid activation for input (i), forget (f) and output (o) gates
    const int cellAct = 0;          // tanh activation for cell state
    const int outAct = 0;           // tanh activation for output

    const bool hasBiases  = true;   // biases array is provided
    const bool hasSeqLen  = false;  // seqLen array is not provided
    const auto hasInitH   = true;   // initial output is provided
    const auto hasInitC   = true;   // initial cell state is provided
    const auto hasPH      = true;   // peephole connections are absent
    const auto retFullSeq = false;  // return whole h {h_0, h_1, ... , h_sL-1}, [sL,bS,nOut]
    const auto retLastH   = false;   // output at last time step
    const auto retLastC   = true;   //  cells state at last time step

    const double cellClip = 0.5;       // do not apply clipping

    NDArray x('c', {sL, bS, nIn}, sd::DataType::DOUBLE);
    NDArray Wx('c', {nIn, 4*nOut}, sd::DataType::DOUBLE);
    NDArray Wr('c', {nOut, 4*nOut}, sd::DataType::DOUBLE);
    NDArray b('c', {4*nOut}, sd::DataType::DOUBLE);
    NDArray hI('c', {bS, nOut}, sd::DataType::DOUBLE);
    NDArray cI('c', {bS, nOut}, sd::DataType::DOUBLE);
    NDArray Wp('c', {3*nOut}, sd::DataType::DOUBLE);
    NDArray dLdcL('c', {bS, nOut}, sd::DataType::DOUBLE);

    x.linspace(-2,0.1);
    hI.linspace(-1.5,0.1);
    cI.linspace(0.7,-0.1);
    Wx.linspace(1,-0.1);
    Wr.linspace(-1,0.1);
    Wp.linspace(0.2,0.2);
    b.linspace(1,-0.15);

    std::vector<double>   tArgs = {cellClip};
    std::vector<Nd4jLong> iArgs = {dataFormat, directionMode, gateAct, cellAct, outAct};
    std::vector<bool>     bArgs = {hasBiases, hasSeqLen, hasInitH, hasInitC, hasPH, retFullSeq, retLastH, retLastC};

    const OpArgsHolder argsHolderFF({&x, &Wx, &Wr, &b, &hI, &cI, &Wp}, tArgs, iArgs, bArgs);
    const OpArgsHolder argsHolderBP({&x, &Wx, &Wr, &b, &hI, &cI, &Wp, &dLdcL}, tArgs, iArgs, bArgs);

    sd::ops::lstmLayer opFF;
    sd::ops::lstmLayer_bp opBP;

    const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP, std::vector<bool>(), {0., 1.}, GradCheck::LossFunc::MEAN);

    ASSERT_TRUE(isGradCorrect);
}

///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, lstmLayer_bp_3) {

    const int sL   = 3;
    const int bS   = 2;
    const int nIn  = 2;
    const int nOut = 3;

    const int dataFormat = 1;       // [bS,sL,nIn]
    const int directionMode = 0;    // forward
    const int gateAct = 2;          // sigmoid activation for input (i), forget (f) and output (o) gates
    const int cellAct = 0;          // tanh activation for cell state
    const int outAct = 0;           // tanh activation for output

    const bool hasBiases  = true;   // biases array is provided
    const bool hasSeqLen  = false;  // seqLen array is not provided
    const auto hasInitH   = true;   // initial output is provided
    const auto hasInitC   = true;   // initial cell state is provided
    const auto hasPH      = true;   // peephole connections are absent
    const auto retFullSeq = true;  // return whole h {h_0, h_1, ... , h_sL-1}, [sL,bS,nOut]
    const auto retLastH   = false;   // output at last time step
    const auto retLastC   = true;   // cells state at last time step

    const double cellClip = 0.5;       // do not apply clipping

    NDArray x('c', {bS, sL, nIn}, sd::DataType::DOUBLE);
    NDArray Wx('c', {nIn, 4*nOut}, sd::DataType::DOUBLE);
    NDArray Wr('c', {nOut, 4*nOut}, sd::DataType::DOUBLE);
    NDArray b('c', {4*nOut}, sd::DataType::DOUBLE);
    NDArray hI('c', {bS, nOut}, sd::DataType::DOUBLE);
    NDArray cI('c', {bS, nOut}, sd::DataType::DOUBLE);
    NDArray Wp('c', {3*nOut}, sd::DataType::DOUBLE);
    NDArray dLdh('c', {bS, sL, nOut}, sd::DataType::DOUBLE);
    NDArray dLdcL('c', {bS, nOut}, sd::DataType::DOUBLE);

    x.linspace(-2,0.1);
    hI.linspace(-1.5,0.1);
    cI.linspace(0.7,-0.1);
    Wx.linspace(1,-0.1);
    Wr.linspace(-1,0.1);
    Wp.linspace(0.2,0.2);
    b.linspace(1,-0.15);

    std::vector<double>   tArgs = {cellClip};
    std::vector<Nd4jLong> iArgs = {dataFormat, directionMode, gateAct, cellAct, outAct};
    std::vector<bool>     bArgs = {hasBiases, hasSeqLen, hasInitH, hasInitC, hasPH, retFullSeq, retLastH, retLastC};

    const OpArgsHolder argsHolderFF({&x, &Wx, &Wr, &b, &hI, &cI, &Wp}, tArgs, iArgs, bArgs);
    const OpArgsHolder argsHolderBP({&x, &Wx, &Wr, &b, &hI, &cI, &Wp, &dLdh, &dLdcL}, tArgs, iArgs, bArgs);

    sd::ops::lstmLayer opFF;
    sd::ops::lstmLayer_bp opBP;

    const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP, std::vector<bool>(), {0., 1.}, GradCheck::LossFunc::MEAN, {0});

    ASSERT_TRUE(isGradCorrect);
}

///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, lstmLayer_bp_4) {

    const int sL   = 4;
    const int bS   = 3;
    const int nIn  = 3;
    const int nOut = 2;

    const int dataFormat = 2;       // [bS, nIn, sL]
    const int directionMode = 0;    // forward
    const int gateAct = 2;          // sigmoid activation for input (i), forget (f) and output (o) gates
    const int cellAct = 0;          // tanh activation for cell state
    const int outAct = 0;           // tanh activation for output

    const bool hasBiases  = true;   // biases array is provided
    const bool hasSeqLen  = true;  // seqLen array is not provided
    const auto hasInitH   = true;   // initial output is provided
    const auto hasInitC   = true;   // initial cell state is provided
    const auto hasPH      = true;   // peephole connections are absent
    const auto retFullSeq = true;   // dLdh per each time step
    const auto retLastH   = false;   // output at last time step
    const auto retLastC   = false;   // cells state at last time step

    const double cellClip = 0.5;       // do not apply clipping

    NDArray x('c', {bS, nIn, sL}, sd::DataType::DOUBLE);
    NDArray Wx('c', {nIn, 4*nOut}, sd::DataType::DOUBLE);
    NDArray Wr('c', {nOut, 4*nOut}, sd::DataType::DOUBLE);
    NDArray b('c', {4*nOut}, sd::DataType::DOUBLE);
    NDArray seqLen('c', {bS}, {2,0,4}, sd::DataType::DOUBLE);
    NDArray hI('c', {bS, nOut}, sd::DataType::DOUBLE);
    NDArray cI('c', {bS, nOut}, sd::DataType::DOUBLE);
    NDArray Wp('c', {3*nOut}, sd::DataType::DOUBLE);
    NDArray dLdh('c', {bS, nOut, sL}, sd::DataType::DOUBLE);

    x.linspace(-2,0.1);
    hI.linspace(-1.5,0.1);
    cI.linspace(0.7,-0.1);
    Wx.linspace(1,-0.1);
    Wr.linspace(-1,0.1);
    Wp.linspace(0.2,0.2);
    b.linspace(1,-0.15);

    std::vector<double>   tArgs = {cellClip};
    std::vector<Nd4jLong> iArgs = {dataFormat, directionMode, gateAct, cellAct, outAct};
    std::vector<bool>     bArgs = {hasBiases, hasSeqLen, hasInitH, hasInitC, hasPH, retFullSeq, retLastH, retLastC};

    const OpArgsHolder argsHolderFF({&x, &Wx, &Wr, &b, &seqLen, &hI, &cI, &Wp}, tArgs, iArgs, bArgs);
    const OpArgsHolder argsHolderBP({&x, &Wx, &Wr, &b, &seqLen, &hI, &cI, &Wp, &dLdh}, tArgs, iArgs, bArgs);

    sd::ops::lstmLayer opFF;
    sd::ops::lstmLayer_bp opBP;

    const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP, {true, true, true, true, false, true, true, true}, {0., 1.}, GradCheck::LossFunc::MEAN, {0});

    ASSERT_TRUE(isGradCorrect);
}

///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, lstmLayer_bp_5) {

    const int sL   = 3;
    const int bS   = 2;
    const int nIn  = 2;
    const int nOut = 3;

    const int dataFormat = 1;       // [bS,sL,nIn]
    const int directionMode = 1;    // backward
    const int gateAct = 2;          // sigmoid activation for input (i), forget (f) and output (o) gates
    const int cellAct = 0;          // tanh activation for cell state
    const int outAct = 0;           // tanh activation for output

    const bool hasBiases  = true;   // biases array is provided
    const bool hasSeqLen  = false;  // seqLen array is not provided
    const auto hasInitH   = true;   // initial output is provided
    const auto hasInitC   = true;   // initial cell state is provided
    const auto hasPH      = true;   // peephole connections are absent
    const auto retFullSeq = false;   // dLdh per each time step
    const auto retLastH   = true;   // output at last time step
    const auto retLastC   = false;   // cells state at last time step

    const double cellClip = 0.5;       // do not apply clipping

    NDArray x('c', {bS, sL, nIn}, sd::DataType::DOUBLE);
    NDArray Wx('c', {nIn, 4*nOut}, sd::DataType::DOUBLE);
    NDArray Wr('c', {nOut, 4*nOut}, sd::DataType::DOUBLE);
    NDArray b('c', {4*nOut}, sd::DataType::DOUBLE);
    NDArray hI('c', {bS, nOut}, sd::DataType::DOUBLE);
    NDArray cI('c', {bS, nOut}, sd::DataType::DOUBLE);
    NDArray Wp('c', {3*nOut}, sd::DataType::DOUBLE);
    NDArray dLdhL('c', {bS, nOut}, sd::DataType::DOUBLE);

    x.linspace(-2,0.1);
    hI.linspace(-1.5,0.1);
    cI.linspace(0.7,-0.1);
    Wx.linspace(1,-0.1);
    Wr.linspace(-1,0.1);
    Wp.linspace(0.2,0.2);
    b.linspace(1,-0.15);

    std::vector<double>   tArgs = {cellClip};
    std::vector<Nd4jLong> iArgs = {dataFormat, directionMode, gateAct, cellAct, outAct};
    std::vector<bool>     bArgs = {hasBiases, hasSeqLen, hasInitH, hasInitC, hasPH, retFullSeq, retLastH, retLastC};

    const OpArgsHolder argsHolderFF({&x, &Wx, &Wr, &b, &hI, &cI, &Wp}, tArgs, iArgs, bArgs);
    const OpArgsHolder argsHolderBP({&x, &Wx, &Wr, &b, &hI, &cI, &Wp, &dLdhL}, tArgs, iArgs, bArgs);

    sd::ops::lstmLayer opFF;
    sd::ops::lstmLayer_bp opBP;

    const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP, std::vector<bool>(), {0., 1.}, GradCheck::LossFunc::MEAN, {0});

    ASSERT_TRUE(isGradCorrect);
}

///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, lstmLayer_bp_6) {

    const int sL   = 3;
    const int bS   = 2;
    const int nIn  = 2;
    const int nOut = 2;

    const int dataFormat = 2;       // [bS, nIn, sL]
    const int directionMode = 1;    // backward
    const int gateAct = 2;          // sigmoid activation for input (i), forget (f) and output (o) gates
    const int cellAct = 0;          // tanh activation for cell state
    const int outAct = 0;           // tanh activation for output

    const bool hasBiases  = true;   // biases array is provided
    const bool hasSeqLen  = true;  // seqLen array is not provided
    const auto hasInitH   = true;   // initial output is provided
    const auto hasInitC   = true;   // initial cell state is provided
    const auto hasPH      = true;   // peephole connections are absent
    const auto retFullSeq = true;   // dLdh per each time step
    const auto retLastH   = false;   // output at last time step
    const auto retLastC   = false;   // cells state at last time step

    const double cellClip = 0.5;       // do not apply clipping

    NDArray x('c', {bS, nIn, sL}, sd::DataType::DOUBLE);
    NDArray Wx('c', {nIn, 4*nOut}, sd::DataType::DOUBLE);
    NDArray Wr('c', {nOut, 4*nOut}, sd::DataType::DOUBLE);
    NDArray b('c', {4*nOut}, sd::DataType::DOUBLE);
    NDArray seqLen('c', {bS}, {0,2}, sd::DataType::DOUBLE);
    NDArray hI('c', {bS, nOut}, sd::DataType::DOUBLE);
    NDArray cI('c', {bS, nOut}, sd::DataType::DOUBLE);
    NDArray Wp('c', {3*nOut}, sd::DataType::DOUBLE);
    NDArray dLdh('c', {bS, nOut, sL}, sd::DataType::DOUBLE);

    x.linspace(-2,0.1);
    hI.linspace(-1.5,0.1);
    cI.linspace(0.7,-0.1);
    Wx.linspace(1,-0.1);
    Wr.linspace(-1,0.1);
    Wp.linspace(0.2,0.2);
    b.linspace(1,-0.15);

    std::vector<double>   tArgs = {cellClip};
    std::vector<Nd4jLong> iArgs = {dataFormat, directionMode, gateAct, cellAct, outAct};
    std::vector<bool>     bArgs = {hasBiases, hasSeqLen, hasInitH, hasInitC, hasPH, retFullSeq, retLastH, retLastC};

    const OpArgsHolder argsHolderFF({&x, &Wx, &Wr, &b, &seqLen, &hI, &cI, &Wp}, tArgs, iArgs, bArgs);
    const OpArgsHolder argsHolderBP({&x, &Wx, &Wr, &b, &seqLen, &hI, &cI, &Wp, &dLdh}, tArgs, iArgs, bArgs);

    sd::ops::lstmLayer opFF;
    sd::ops::lstmLayer_bp opBP;

    const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP, {true, true, true, true, false, true, true, true}, {0., 1.}, GradCheck::LossFunc::MEAN, {0});

    ASSERT_TRUE(isGradCorrect);
}

///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, lstmLayer_bp_7) {

    const int sL   = 3;
    const int bS   = 2;
    const int nIn  = 2;
    const int nOut = 2;

    const int dataFormat = 2;       // [bS, nIn, sL]
    const int directionMode = 2;    // bidirectional sum
    const int gateAct = 2;          // sigmoid activation for input (i), forget (f) and output (o) gates
    const int cellAct = 0;          // tanh activation for cell state
    const int outAct = 0;           // tanh activation for output

    const bool hasBiases  = true;   // biases array is provided
    const bool hasSeqLen  = true;  // seqLen array is not provided
    const auto hasInitH   = true;   // initial output is provided
    const auto hasInitC   = true;   // initial cell state is provided
    const auto hasPH      = true;   // peephole connections are absent
    const auto retFullSeq = false;   // dLdh per each time step
    const auto retLastH   = true;   // output at last time step
    const auto retLastC   = false;   // cells state at last time step

    const double cellClip = 0.5;       // do not apply clipping

    NDArray x('c', {bS, nIn, sL}, sd::DataType::DOUBLE);
    NDArray Wx('c', {2, nIn, 4*nOut}, sd::DataType::DOUBLE);
    NDArray Wr('c', {2, nOut, 4*nOut}, sd::DataType::DOUBLE);
    NDArray b('c', {2, 4*nOut}, sd::DataType::DOUBLE);
    NDArray seqLen('c', {bS}, {0,2}, sd::DataType::DOUBLE);
    NDArray hI('c', {2, bS, nOut}, sd::DataType::DOUBLE);
    NDArray cI('c', {2, bS, nOut}, sd::DataType::DOUBLE);
    NDArray Wp('c', {2, 3*nOut}, sd::DataType::DOUBLE);
    NDArray dLdhL('c', {2, bS, nOut}, sd::DataType::DOUBLE);

    x.linspace(-2,0.1);
    hI.linspace(-1.5,0.1);
    cI.linspace(0.7,-0.1);
    Wx.linspace(1,-0.1);
    Wr.linspace(-1,0.1);
    Wp.linspace(0.2,0.2);
    b.linspace(1,-0.15);

    std::vector<double>   tArgs = {cellClip};
    std::vector<Nd4jLong> iArgs = {dataFormat, directionMode, gateAct, cellAct, outAct};
    std::vector<bool>     bArgs = {hasBiases, hasSeqLen, hasInitH, hasInitC, hasPH, retFullSeq, retLastH, retLastC};

    const OpArgsHolder argsHolderFF({&x, &Wx, &Wr, &b, &seqLen, &hI, &cI, &Wp}, tArgs, iArgs, bArgs);
    const OpArgsHolder argsHolderBP({&x, &Wx, &Wr, &b, &seqLen, &hI, &cI, &Wp, &dLdhL}, tArgs, iArgs, bArgs);

    sd::ops::lstmLayer opFF;
    sd::ops::lstmLayer_bp opBP;

    const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP, {true, true, true, true, false, true, true, true}, {0., 1.}, GradCheck::LossFunc::MEAN, {0});

    ASSERT_TRUE(isGradCorrect);
}

///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, lstmLayer_bp_8) {

    const int sL   = 3;
    const int bS   = 2;
    const int nIn  = 2;
    const int nOut = 2;

    const int dataFormat = 1;       // [bS,sL,nIn]
    const int directionMode = 3;    // bidirectional concat
    const int gateAct = 2;          // sigmoid activation for input (i), forget (f) and output (o) gates
    const int cellAct = 0;          // tanh activation for cell state
    const int outAct = 0;           // tanh activation for output

    const bool hasBiases  = true;   // biases array is provided
    const bool hasSeqLen  = true;  // seqLen array is not provided
    const auto hasInitH   = true;   // initial output is provided
    const auto hasInitC   = true;   // initial cell state is provided
    const auto hasPH      = true;   // peephole connections are absent
    const auto retFullSeq = true;   // dLdh per each time step
    const auto retLastH   = false;   // output at last time step
    const auto retLastC   = false;   // cells state at last time step

    const double cellClip = 0.5;       // do not apply clipping

    NDArray x('c', {bS,sL,nIn}, sd::DataType::DOUBLE);
    NDArray Wx('c', {2, nIn, 4*nOut}, sd::DataType::DOUBLE);
    NDArray Wr('c', {2, nOut, 4*nOut}, sd::DataType::DOUBLE);
    NDArray b('c', {2, 4*nOut}, sd::DataType::DOUBLE);
    NDArray seqLen('c', {bS}, {0,2}, sd::DataType::DOUBLE);
    NDArray hI('c', {2, bS, nOut}, sd::DataType::DOUBLE);
    NDArray cI('c', {2, bS, nOut}, sd::DataType::DOUBLE);
    NDArray Wp('c', {2, 3*nOut}, sd::DataType::DOUBLE);
    NDArray dLdh('c', {bS,sL,2*nOut}, sd::DataType::DOUBLE);

    x.linspace(-2,0.1);
    hI.linspace(-1.5,0.1);
    cI.linspace(0.7,-0.1);
    Wx.linspace(1,-0.1);
    Wr.linspace(-1,0.1);
    Wp.linspace(0.2,0.2);
    b.linspace(1,-0.15);

    std::vector<double>   tArgs = {cellClip};
    std::vector<Nd4jLong> iArgs = {dataFormat, directionMode, gateAct, cellAct, outAct};
    std::vector<bool>     bArgs = {hasBiases, hasSeqLen, hasInitH, hasInitC, hasPH, retFullSeq, retLastH, retLastC};

    const OpArgsHolder argsHolderFF({&x, &Wx, &Wr, &b, &seqLen, &hI, &cI, &Wp}, tArgs, iArgs, bArgs);
    const OpArgsHolder argsHolderBP({&x, &Wx, &Wr, &b, &seqLen, &hI, &cI, &Wp, &dLdh}, tArgs, iArgs, bArgs);

    sd::ops::lstmLayer opFF;
    sd::ops::lstmLayer_bp opBP;

    const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP, {true, true, true, true, false, true, true, true}, {0., 1.}, GradCheck::LossFunc::MEAN, {0});

    ASSERT_TRUE(isGradCorrect);
}

///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, lstmLayer_bp_9) {

    const int sL   = 3;
    const int bS   = 2;
    const int nIn  = 2;
    const int nOut = 2;

    const int dataFormat = 3;       // [sL, bS, nIn]
    const int directionMode = 4;    // bidirectional extra output dim
    const int gateAct = 2;          // sigmoid activation for input (i), forget (f) and output (o) gates
    const int cellAct = 0;          // tanh activation for cell state
    const int outAct = 0;           // tanh activation for output

    const bool hasBiases  = true;   // biases array is provided
    const bool hasSeqLen  = true;  // seqLen array is not provided
    const auto hasInitH   = true;   // initial output is provided
    const auto hasInitC   = true;   // initial cell state is provided
    const auto hasPH      = true;   // peephole connections are absent
    const auto retFullSeq = true;   // dLdh per each time step
    const auto retLastH   = false;   // output at last time step
    const auto retLastC   = false;   // cells state at last time step

    const double cellClip = 0.5;       // do not apply clipping

    NDArray x('c', {sL, bS, nIn}, sd::DataType::DOUBLE);
    NDArray Wx('c', {2, nIn, 4*nOut}, sd::DataType::DOUBLE);
    NDArray Wr('c', {2, nOut, 4*nOut}, sd::DataType::DOUBLE);
    NDArray b('c', {2, 4*nOut}, sd::DataType::DOUBLE);
    NDArray seqLen('c', {bS}, {0,2}, sd::DataType::DOUBLE);
    NDArray hI('c', {2, bS, nOut}, sd::DataType::DOUBLE);
    NDArray cI('c', {2, bS, nOut}, sd::DataType::DOUBLE);
    NDArray Wp('c', {2, 3*nOut}, sd::DataType::DOUBLE);
    NDArray dLdh('c', {sL, 2, bS, nOut}, sd::DataType::DOUBLE);

    x.linspace(-2,0.1);
    hI.linspace(-1.5,0.1);
    cI.linspace(0.7,-0.1);
    Wx.linspace(1,-0.1);
    Wr.linspace(-1,0.1);
    Wp.linspace(0.2,0.2);
    b.linspace(1,-0.15);

    std::vector<double>   tArgs = {cellClip};
    std::vector<Nd4jLong> iArgs = {dataFormat, directionMode, gateAct, cellAct, outAct};
    std::vector<bool>     bArgs = {hasBiases, hasSeqLen, hasInitH, hasInitC, hasPH, retFullSeq, retLastH, retLastC};

    const OpArgsHolder argsHolderFF({&x, &Wx, &Wr, &b, &seqLen, &hI, &cI, &Wp}, tArgs, iArgs, bArgs);
    const OpArgsHolder argsHolderBP({&x, &Wx, &Wr, &b, &seqLen, &hI, &cI, &Wp, &dLdh}, tArgs, iArgs, bArgs);

    sd::ops::lstmLayer opFF;
    sd::ops::lstmLayer_bp opBP;

    const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP, {true, true, true, true, false, true, true, true}, {0., 1.}, GradCheck::LossFunc::MEAN, {0});

    ASSERT_TRUE(isGradCorrect);
}

////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, batchnorm_test1) {

    NDArray input   ('c', {2,4}, sd::DataType::FLOAT32);
    NDArray mean    ('c', {4}, {1.05f, 1.15f, 1.2f, 1.3f}, sd::DataType::FLOAT32);
    NDArray variance('c', {4}, {0.5f, 0.7f, 0.9f,  1.1f},  sd::DataType::FLOAT32);
    NDArray gamma   ('c', {4}, {-1.2f, 1.3f, -1.4f, 1.5f}, sd::DataType::FLOAT32);
    NDArray beta    ('c', {4}, {10.f, 20.f, -10.f, -20.f},     sd::DataType::FLOAT32);

    NDArray expected('c', {2,4}, {11.61218734f,  18.52390321f,  -8.67185076f, -21.28716864f, 10.93337162f,  19.14541765f, -9.26213931f, -20.71509369f}, sd::DataType::FLOAT32);

    input.linspace(0.1, 0.1);

    sd::ops::batchnorm op;

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

    ASSERT_EQ(ND4J_STATUS_OK, results.status());

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

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


}

////////////////////////////////////////////////////////////////////
TYPED_TEST(TypedDeclarableOpsTests13, batchnorm_test2) {

    auto input    = NDArrayFactory::create<TypeParam>('c', {2,3,4});
    auto mean     = NDArrayFactory::create<TypeParam>('c', {4});
    auto variance = NDArrayFactory::create<TypeParam>('c', {4});
    auto gamma    = NDArrayFactory::create<TypeParam>('c', {4});
    auto beta     = NDArrayFactory::create<TypeParam>('c', {4});

    auto expected = NDArrayFactory::create<TypeParam>('c', {2,3,4}, {-0.52733537f, -0.35763144f, -0.18792751f, -0.01822358f, 0.15148035f, 0.32118428f, 0.49088821f, 0.66059214f, 0.83029607f, 1.f, 1.16970393f, 1.33940786f,
                                            1.50911179f, 1.67881572f, 1.84851965f, 2.01822358f, 2.18792751f, 2.35763144f, 2.52733537f, 2.6970393f, 2.86674323f, 3.03644717f, 3.2061511f, 3.37585503f});

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

    sd::ops::batchnorm op;

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

    ASSERT_EQ(ND4J_STATUS_OK, results.status());

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

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

}

////////////////////////////////////////////////////////////////////
TYPED_TEST(TypedDeclarableOpsTests13, batchnorm_test3) {

    auto input    = NDArrayFactory::create<TypeParam>('c', {2,3,4});
    auto mean     = NDArrayFactory::create<TypeParam>('c', {3}, {1.05f, 1.1f, 1.15f});
    auto variance = NDArrayFactory::create<TypeParam>('c', {3}, {0.5f, 0.6f, 0.7f});
    auto gamma    = NDArrayFactory::create<TypeParam>('c', {3}, {1.2f, 1.3f, 1.4f});
    auto beta     = NDArrayFactory::create<TypeParam>('c', {3}, {0.1f, 0.2f, 0.3f});

    auto expected = NDArrayFactory::create<TypeParam>('c', {2,3,4}, {-1.51218734f, -1.34248341f, -1.17277948f, -1.00307555f, -0.80696728f, -0.6391394f, -0.47131152f, -0.30348364f, -0.11832703f, 0.04900378f, 0.21633459f, 0.38366541f,
                                            0.52425983f, 0.69396376f, 0.86366769f, 1.03337162f, 1.20696728f, 1.37479516f, 1.54262304f, 1.71045092f, 1.8896427f, 2.05697351f, 2.22430432f, 2.39163513f});

    input.linspace(0.1, 0.1);

    sd::ops::batchnorm op;

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

    ASSERT_EQ(ND4J_STATUS_OK, results.status());

    auto output = results.at(0);

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

}

////////////////////////////////////////////////////////////////////
TYPED_TEST(TypedDeclarableOpsTests13, batchnorm_test4) {

    auto input    = NDArrayFactory::create<TypeParam>('c', {2,3,4});
    auto mean     = NDArrayFactory::create<TypeParam>('c', {2,1,4}, {1.05f, 1.1f, 1.15f, 1.2f, 1.25f, 1.3f, 1.35f, 1.4f});
    auto variance = NDArrayFactory::create<TypeParam>('c', {2,1,4}, {0.5f, 0.6f, 0.7f, 0.8f, 0.9f, 1.f, 1.1f, 1.2f});
    auto gamma    = NDArrayFactory::create<TypeParam>('c', {2,1,4}, {1.2f, 1.3f, 1.4f, 1.5f, 1.6f, 1.7f, 1.8f, 1.9f});
    auto beta     = NDArrayFactory::create<TypeParam>('c', {2,1,4}, {0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.66f, 0.7f, 0.8f});

    auto expected = NDArrayFactory::create<TypeParam>('c', {2,3,4}, {-1.51218734f, -1.31045092f, -1.12231189f, -0.9416324f, -0.83337162f, -0.6391394f, -0.45298865f, -0.2708162f, -0.1545559f, 0.03217212f, 0.21633459f, 0.4f,
                                            0.58432694f, 0.82999915f, 0.95743373f, 1.14688951f, 1.25894242f, 1.50999575f, 1.64392367f, 1.84066852f, 1.93355791f, 2.18999235f, 2.33041362f, 2.53444754f});

    input.linspace(0.1, 0.1);

    sd::ops::batchnorm op;

    auto results = op.evaluate({&input, &mean, &variance, &gamma, &beta}, {1e-5}, {1,1,0,2});

    ASSERT_EQ(ND4J_STATUS_OK, results.status());

    auto output = results.at(0);

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

}

////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, batchnorm_test5) {

    NDArray input   ('c', {2,4,2,2}, sd::DataType::FLOAT32);
    NDArray mean    ('c', {4}, {1.05f, 1.15f, 1.2f, 1.3f}, sd::DataType::FLOAT32);
    NDArray variance('c', {4}, {0.5f, 0.7f, 0.9f,  1.1f},  sd::DataType::FLOAT32);
    NDArray gamma   ('c', {4}, {-1.2f, 1.3f, -1.4f, 1.5f}, sd::DataType::FLOAT32);
    NDArray beta    ('c', {4}, {10.f, 20.f, -10.f, -20.f},     sd::DataType::FLOAT32);

    NDArray expected('c', {2,4,2,2}, { 11.612187f,  11.442483f,  11.272779f,  11.103076f,  18.990039f,  19.145418f,  19.300796f,  19.456175f,  -9.557284f,  -9.704856f,  -9.852428f, -10.f, -20.f,
                                      -19.856981f, -19.713963f, -19.570944f,   8.896924f,   8.727221f,   8.557517f,   8.387813f,  21.476097f,  21.631475f,  21.786854f,  21.942233f, -11.918438f,
                                       -12.06601f, -12.213582f, -12.361154f,   -17.7117f, -17.568681f, -17.425663f, -17.282644f}, sd::DataType::FLOAT32);
    input.linspace(0.1, 0.1);

    sd::ops::batchnorm op;

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

    ASSERT_EQ(ND4J_STATUS_OK, results.status());

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

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

}

////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, batchnorm_test6) {

    NDArray input   ('c', {2,2,2,4}, sd::DataType::FLOAT32);
    NDArray mean    ('c', {4}, {1.05f, 1.15f, 1.2f, 1.3f}, sd::DataType::FLOAT32);
    NDArray variance('c', {4}, {0.5f, 0.7f, 0.9,  1.1f},  sd::DataType::FLOAT32);
    NDArray gamma   ('c', {4}, {-1.2f, 1.3f, -1.4f, 1.5f}, sd::DataType::FLOAT32);
    NDArray beta    ('c', {4}, {10.f, 20.f, -10.f, -20.f},     sd::DataType::FLOAT32);

    NDArray expected('c', {2,2,2,4}, {11.612187f,  18.523903f,  -8.671851f, -21.287169f,  10.933372f,  19.145418f,  -9.262139f, -20.715094f,  10.254556f,  19.766932f,  -9.852428f, -20.143019f,   9.57574f,
                                      20.388447f, -10.442716f, -19.570944f,   8.896924f,  21.009961f, -11.033005f, -18.998869f,   8.218109f,  21.631475f, -11.623294f, -18.426794f,   7.539293f,  22.25299f,
                                     -12.213582f, -17.854719f,   6.860477f,  22.874504f, -12.803871f, -17.282644f}, sd::DataType::FLOAT32);
    input.linspace(0.1, 0.1);

    sd::ops::batchnorm op;

    auto results = op.evaluate({&input, &mean, &variance, &gamma, &beta}, {1e-5}, {1,1,3});

    ASSERT_EQ(ND4J_STATUS_OK, results.status());

    auto output = results.at(0);

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


}

////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, batchnorm_test7) {

    NDArray input1('c', {3,3,15,15}, sd::DataType::FLOAT32);
    NDArray input2('c', {3,15,15,3}, sd::DataType::FLOAT32);
    input2.permutei({0,3,1,2});

    NDArray mean    ('c', {3}, {0., 0, 0}, sd::DataType::FLOAT32);
    NDArray variance('c', {3}, {1., 1, 1}, sd::DataType::FLOAT32);
    NDArray gamma   ('c', {3}, {1., 1, 1}, sd::DataType::FLOAT32);
    NDArray beta    ('c', {3}, {0., 0, 0}, sd::DataType::FLOAT32);

    NDArray out1('c', {3,3,15,15}, sd::DataType::FLOAT32);
    NDArray out2('c', {3,3,15,15}, sd::DataType::FLOAT32);

    input1.linspace(-1012, 1);
    input2.assign(input1);

    sd::ops::batchnorm op;

    auto res1 = op.execute({&input1, &mean, &variance, &gamma, &beta}, {&out1}, {1e-5}, {1,1,1}, {});
    ASSERT_EQ(ND4J_STATUS_OK, res1);

    auto res2 = op.execute({&input2, &mean, &variance, &gamma, &beta}, {&out2}, {1e-5}, {1,1,1}, {});
    ASSERT_EQ(ND4J_STATUS_OK, res2);

    ASSERT_TRUE(out1.equalsTo(out2));
}

////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, batchnorm_test8) {

    NDArray input('c', {2,3,4,5}, sd::DataType::FLOAT32);

    NDArray mean    ('c', {1,3,4,5}, sd::DataType::FLOAT32);
    NDArray variance('c', {1,3,4,5}, sd::DataType::FLOAT32);
    NDArray gamma   ('c', {1,3,4,5}, sd::DataType::FLOAT32);
    NDArray beta    ('c', {1,3,4,5}, sd::DataType::FLOAT32);

    NDArray expected('c', {2,3,4,5}, {-105.019394, -103.322357, -101.625313, -99.928276, -98.231239, -96.534195, -94.837158, -93.140121, -91.443077, -89.746040, -88.049004, -86.351959, -84.654922,
                        -82.957886, -81.260841, -79.563805, -77.866768, -76.169724, -74.472687, -72.775650, -71.078606, -69.381569, -67.684532, -65.987488, -64.290451, -62.593414,
                        -60.896374, -59.199333, -57.502296, -55.805256, -54.108215, -52.411179, -50.714138, -49.017097, -47.320061, -45.623020, -43.925980, -42.228943, -40.531902,
                        -38.834862, -37.137825, -35.440784, -33.743744, -32.046707, -30.349667, -28.652628, -26.955589, -25.258549, -23.561510, -21.864471, -20.167431, -18.470392,
                        -16.773354, -15.076314, -13.379274, -11.682236, -9.985196, -8.288157, -6.591118, -4.894078, -3.197039, -1.500000, 0.197039, 1.894078, 3.591118, 5.288157,
                        6.985196, 8.682236, 10.379274, 12.076314, 13.773354, 15.470392, 17.167431, 18.864471, 20.561510, 22.258549, 23.955589, 25.652628, 27.349667, 29.046707, 30.743744,
                        32.440784, 34.137825, 35.834862, 37.531902, 39.228943, 40.925980, 42.623020, 44.320061, 46.017097, 47.714138, 49.411179, 51.108215, 52.805256, 54.502296, 56.199333,
                        57.896374, 59.593414, 61.290451, 62.987488, 64.684532, 66.381569, 68.078606, 69.775650, 71.472687, 73.169724, 74.866768, 76.563805, 78.260841, 79.957886, 81.654922,
                        83.351959, 85.049004, 86.746040, 88.443077, 90.140121, 91.837158, 93.534195, 95.231239, 96.928276}, sd::DataType::FLOAT32);

    input.linspace(-60, 1);
    mean.assign(1.);
    variance.assign(0.5);
    gamma.assign(1.2);
    beta.assign(-1.5);

    sd::ops::batchnorm op;

    auto results = op.evaluate({&input, &mean, &variance, &gamma, &beta}, {1e-5}, {1,1, 1,2,3});

    ASSERT_EQ(ND4J_STATUS_OK, results.status());

    auto output = results.at(0);

    ASSERT_TRUE(expected.isSameShape(*output));
    ASSERT_TRUE(expected.equalsTo(output));

}

////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, batchnorm_test9) {

    NDArray input('c', {2,3,3,3,3}, sd::DataType::FLOAT32);

    NDArray mean    ('c', {1,3,3,3,3}, sd::DataType::FLOAT32);
    NDArray variance('c', {1,3,3,3,3}, sd::DataType::FLOAT32);
    NDArray gamma   ('c', {1,3,3,3,3}, sd::DataType::FLOAT32);
    NDArray beta    ('c', {1,3,3,3,3}, sd::DataType::FLOAT32);

    NDArray expected('c', {2,3,3,3,3}, {-138.960175, -137.263138, -135.566101, -133.869064, -132.172028, -130.474976, -128.777954, -127.080902, -125.383865, -123.686829, -121.989784, -120.292747,
                            -118.595711, -116.898666, -115.201630, -113.504593, -111.807549, -110.110512, -108.413475, -106.716431, -105.019394, -103.322357, -101.625313, -99.928276,
                            -98.231239, -96.534195, -94.837158, -93.140121, -91.443077, -89.746040, -88.049004, -86.351959, -84.654922, -82.957886, -81.260841, -79.563805, -77.866768,
                            -76.169724, -74.472687, -72.775650, -71.078606, -69.381569, -67.684532, -65.987488, -64.290451, -62.593414, -60.896374, -59.199333, -57.502296, -55.805256,
                            -54.108215, -52.411179, -50.714138, -49.017097, -47.320061, -45.623020, -43.925980, -42.228943, -40.531902, -38.834862, -37.137825, -35.440784, -33.743744,
                            -32.046707, -30.349667, -28.652628, -26.955589, -25.258549, -23.561510, -21.864471, -20.167431, -18.470392, -16.773354, -15.076314, -13.379274, -11.682236,
                            -9.985196, -8.288157, -6.591118, -4.894078, -3.197039, -1.500000, 0.197039, 1.894078, 3.591118, 5.288157, 6.985196, 8.682236, 10.379274, 12.076314, 13.773354,
                            15.470392, 17.167431, 18.864471, 20.561510, 22.258549, 23.955589, 25.652628, 27.349667, 29.046707, 30.743744, 32.440784, 34.137825, 35.834862, 37.531902, 39.228943,
                            40.925980, 42.623020, 44.320061, 46.017097, 47.714138, 49.411179, 51.108215, 52.805256, 54.502296, 56.199333, 57.896374, 59.593414, 61.290451, 62.987488, 64.684532,
                            66.381569, 68.078606, 69.775650, 71.472687, 73.169724, 74.866768, 76.563805, 78.260841, 79.957886, 81.654922, 83.351959, 85.049004, 86.746040, 88.443077, 90.140121,
                            91.837158, 93.534195, 95.231239, 96.928276, 98.625313, 100.322357, 102.019394, 103.716431, 105.413475, 107.110512, 108.807549, 110.504593, 112.201630, 113.898666,
                            115.595711, 117.292747, 118.989784, 120.686829, 122.383865, 124.080902, 125.777946, 127.474976, 129.172028, 130.869064, 132.566101, 134.263138}, sd::DataType::FLOAT32);

    input.linspace(-80, 1);
    mean.assign(1.);
    variance.assign(0.5);
    gamma.assign(1.2);
    beta.assign(-1.5);

    sd::ops::batchnorm op;

    auto results = op.evaluate({&input, &mean, &variance, &gamma, &beta}, {1e-5}, {1,1, 1,2,3,4});

    ASSERT_EQ(ND4J_STATUS_OK, results.status());

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

    ASSERT_TRUE(expected.isSameShape(*output));
    ASSERT_TRUE(expected.equalsTo(output));


}

////////////////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, batchnorm_bp_test1) {

    NDArray input   ('c', {2,3,4}, sd::DataType::FLOAT32);
    NDArray mean    ('c', {4}, {1.1, 1.2, 1.3, 1.4}, sd::DataType::FLOAT32);
    NDArray variance('c', {4}, sd::DataType::FLOAT32);
    NDArray gamma   ('c', {4}, sd::DataType::FLOAT32);
    NDArray beta    ('c', {4}, sd::DataType::FLOAT32);
    NDArray gradO   ('c', {2,3,4}, sd::DataType::FLOAT32);

    NDArray expdLdI('c', {2,3,4}, {-0.000056, -0.000056, -0.000056, -0.000056, -0.000034, -0.000034, -0.000034, -0.000034, -0.000011, -0.000011, -0.000011, -0.000011, 0.000011, 0.000011, 0.000011, 0.000011, 0.000034, 0.000034, 0.000034, 0.000034, 0.000056, 0.000056, 0.000056, 0.000056}, sd::DataType::FLOAT32);
    NDArray expdLdG('c', {4}, {6.148104, 6.148104, 6.148105, 6.148105}, sd::DataType::FLOAT32);
    NDArray expdLdB('c', {4}, {3.6, 4.5, 5.4, 6.3}, sd::DataType::FLOAT32);

    input.linspace(0.1, 0.1);
    variance.assign(0.46666667);
    gamma.assign(1.2);
    beta.assign(1.);     // has no effect on gradient calculations
    gradO.linspace(-0.9, 0.15);

    sd::ops::batchnorm_bp op;

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

    ASSERT_EQ(ND4J_STATUS_OK, results.status());

    auto dLdI = results.at(0);
    auto dLdG = results.at(3);
    auto dLdB = results.at(4);

    ASSERT_TRUE(expdLdI.isSameShapeStrict(*dLdI));
    ASSERT_TRUE(expdLdI.equalsTo(dLdI));

    ASSERT_TRUE(expdLdG.isSameShapeStrict(*dLdG));
    ASSERT_TRUE(expdLdG.equalsTo(dLdG));

    ASSERT_TRUE(expdLdB.isSameShapeStrict(*dLdB));
    ASSERT_TRUE(expdLdB.equalsTo(dLdB));


}


////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, batchnorm_bp_test2) {

    NDArray input   ('c', {2,3,4}, sd::DataType::FLOAT32);
    NDArray mean    ('c', {3}, {1.05, 1.1, 1.15}, sd::DataType::FLOAT32);
    NDArray variance('c', {3}, {0.5, 0.6, 0.7}, sd::DataType::FLOAT32);
    NDArray gamma   ('c', {3}, {1.2, 1.3, 1.4}, sd::DataType::FLOAT32);
    NDArray beta    ('c', {3}, sd::DataType::FLOAT32);
    NDArray gradO   ('c', {2,3,4}, sd::DataType::FLOAT32);

    NDArray expdLdI('c', {2,3,4}, {-0.601415, -0.521226, -0.441037, -0.360849, -0.456306, -0.395465, -0.334624, -0.273784, 0.396631, 0.343747,
                                    0.290863, 0.237978, 0.360849, 0.441037, 0.521226, 0.601415, 0.273784, 0.334625, 0.395465, 0.456306, -0.237978,
                                    -0.290863, -0.343746, -0.396631}, sd::DataType::FLOAT32);
    NDArray expdLdG('c', {3}, {5.81236 ,  7.048771, 12.155388}, sd::DataType::FLOAT32);
    NDArray expdLdB('c', {3}, {1.8,  6.6, 11.4}, sd::DataType::FLOAT32);

    input.linspace(0.1, 0.1);
    // beta.assign(1.);     // has no effect on gradient calculations
    gradO.linspace(-0.9, 0.15);

    sd::ops::batchnorm_bp op;

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

    ASSERT_EQ(ND4J_STATUS_OK, results.status());

    auto dLdI = results.at(0);
    auto dLdG = results.at(3);
    auto dLdB = results.at(4);

    ASSERT_TRUE(expdLdI.isSameShapeStrict(*dLdI));
    ASSERT_TRUE(expdLdI.equalsTo(dLdI));

    ASSERT_TRUE(expdLdG.isSameShapeStrict(*dLdG));
    ASSERT_TRUE(expdLdG.equalsTo(dLdG));

    ASSERT_TRUE(expdLdB.isSameShapeStrict(*dLdB));
    ASSERT_TRUE(expdLdB.equalsTo(dLdB));

}

////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, batchnorm_bp_test3) {

    NDArray input   ('c', {2,3,4}, sd::DataType::FLOAT32);
    NDArray mean    ('c', {2,1,4}, {1.05, 1.1, 1.15, 1.2, 1.25, 1.3, 1.35, 1.4}, sd::DataType::FLOAT32);
    NDArray variance('c', {2,1,4}, {0.5, 0.6, 0.7, 0.8, 0.9, 1., 1.1, 1.2}, sd::DataType::FLOAT32);
    NDArray gamma   ('c', {2,1,4}, {1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9}, sd::DataType::FLOAT32);
    NDArray beta    ('c', {2,1,4}, sd::DataType::FLOAT32);
    NDArray gradO   ('c', {2,3,4}, sd::DataType::FLOAT32);

    NDArray expdLdI('c', {2,3,4}, {-0.577002, -0.744041, -0.850999, -0.922373, -0.000000, -0.000000, -0.000000, -0.000000, 0.577002,
                                    0.744041, 0.850999, 0.922373, -0.386037, -0.350205, -0.312047, -0.271737, -0.000000, -0.000000,
                                    -0.000000, -0.000000, 0.386037, 0.350205, 0.312047, 0.271736}, sd::DataType::FLOAT32);
    NDArray expdLdG('c', {2,1,4}, {1.378844, 0.910144, 0.573706, 0.335408, 2.640487, 2.954985, 3.289431, 3.64234 }, sd::DataType::FLOAT32);
    NDArray expdLdB('c', {2,1,4}, {-0.9 , -0.45,  0.  ,  0.45,  4.5 ,  4.95,  5.4 ,  5.85}, sd::DataType::FLOAT32);

    input.linspace(0.1, 0.1);
    // beta.assign(1.);     // has no effect on gradient calculations
    gradO.linspace(-0.9, 0.15);

    sd::ops::batchnorm_bp op;

    auto results = op.evaluate({&input, &mean, &variance, &gamma, &beta, &gradO}, {1e-5}, {1,1,0,2});

    ASSERT_EQ(ND4J_STATUS_OK, results.status());

    auto dLdI = results.at(0);
    auto dLdG = results.at(3);
    auto dLdB = results.at(4);

    ASSERT_TRUE(expdLdI.isSameShapeStrict(*dLdI));
    ASSERT_TRUE(expdLdI.equalsTo(dLdI));

    ASSERT_TRUE(expdLdG.isSameShapeStrict(*dLdG));
    ASSERT_TRUE(expdLdG.equalsTo(dLdG));

    ASSERT_TRUE(expdLdB.isSameShapeStrict(*dLdB));
    ASSERT_TRUE(expdLdB.equalsTo(dLdB));

}

////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, batchnorm_bp_test4) {

    NDArray input   ('c', {2,4}, sd::DataType::FLOAT32);
    NDArray mean    ('c', {4}, {1.05, 1.15, 1.2, 1.3}, sd::DataType::FLOAT32);
    NDArray variance('c', {4}, {0.5, 0.7, 0.9,  1.1}, sd::DataType::FLOAT32);
    NDArray gamma   ('c', {4}, {-1.2, 1.3, -1.4, 1.5}, sd::DataType::FLOAT32);
    NDArray beta    ('c', {4}, sd::DataType::FLOAT32);
    NDArray gradO   ('c', {2,4}, sd::DataType::FLOAT32);

    NDArray expdLdI('c', {2,4}, {0.162923, -0.289673, 0.354174, -0.386151, -0.162923, 0.289673, -0.354174, 0.386151}, sd::DataType::FLOAT32);
    NDArray expdLdG('c', {4}, {1.442483, 0.950200, 0.569207, 0.314641}, sd::DataType::FLOAT32);
    NDArray expdLdB('c', {4}, {-1.2, -0.9, -0.6, -0.3}, sd::DataType::FLOAT32);

    input.linspace(0.1, 0.1);
    gradO.linspace(-0.9, 0.15);

    sd::ops::batchnorm_bp op;

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

    ASSERT_EQ(ND4J_STATUS_OK, results.status());

    auto dLdI = results.at(0);
    auto dLdG = results.at(3);
    auto dLdB = results.at(4);

    ASSERT_TRUE(expdLdI.isSameShapeStrict(*dLdI));
    ASSERT_TRUE(expdLdI.equalsTo(dLdI));

    ASSERT_TRUE(expdLdG.isSameShapeStrict(*dLdG));
    ASSERT_TRUE(expdLdG.equalsTo(dLdG));

    ASSERT_TRUE(expdLdB.isSameShapeStrict(*dLdB));
    ASSERT_TRUE(expdLdB.equalsTo(dLdB));
}

////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, batchnorm_bp_test5) {

#if defined(HAVE_CUDNN)
return;
#endif
    NDArray input   ('c', {2,4,2,2}, sd::DataType::FLOAT32);
    NDArray mean    ('c', {4}, {1.05, 1.15, 1.2, 1.3}, sd::DataType::FLOAT32);
    NDArray variance('c', {4}, {0.5, 0.7, 0.9,  1.1}, sd::DataType::FLOAT32);
    NDArray gamma   ('c', {4}, {-1.2, 1.3, -1.4, 1.5}, sd::DataType::FLOAT32);
    NDArray beta    ('c', {4}, sd::DataType::FLOAT32);
    NDArray gradO   ('c', {2,4,2,2}, sd::DataType::FLOAT32);

    NDArray expdLdI('c', {2,4,2,2}, {-0.737512, -0.659880, -0.582247, -0.504614, 0.561404, 0.502309, 0.443214, 0.384118, -1.168243,
        -1.045270, -0.922297, -0.799324, 1.899026, 1.699128, 1.499231, 1.299333, 0.504614, 0.582247, 0.659880, 0.737512, -0.384118,
        -0.443214, -0.502308, -0.561404, 0.799324, 0.922297, 1.045270, 1.168243, -1.299334, -1.499231, -1.699129, -1.899026}, sd::DataType::FLOAT32);
    NDArray expdLdG('c', {4}, {11.073181, 12.585667, 17.708657, 24.313186}, sd::DataType::FLOAT32);
    NDArray expdLdB('c', {4}, {4.2,  9. , 13.8, 18.6}, sd::DataType::FLOAT32);

    input.linspace(0.1, 0.1);
    gradO.linspace(-0.9, 0.15);

    sd::ops::batchnorm_bp op;

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

    ASSERT_EQ(ND4J_STATUS_OK, results.status());

    auto dLdI = results.at(0);
    auto dLdG = results.at(3);
    auto dLdB = results.at(4);

    ASSERT_TRUE(expdLdI.isSameShapeStrict(*dLdI));
    ASSERT_TRUE(expdLdI.equalsTo(dLdI));

    ASSERT_TRUE(expdLdG.isSameShapeStrict(*dLdG));
    ASSERT_TRUE(expdLdG.equalsTo(dLdG));

    ASSERT_TRUE(expdLdB.isSameShapeStrict(*dLdB));
    ASSERT_TRUE(expdLdB.equalsTo(dLdB));
}

////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, batchnorm_bp_test6) {

#if defined(HAVE_CUDNN)
return;
#endif

    NDArray input   ('c', {2,2,2,4}, sd::DataType::FLOAT32);
    NDArray mean    ('c', {4}, {1.05, 1.15, 1.2, 1.3}, sd::DataType::FLOAT32);
    NDArray variance('c', {4}, {0.5, 0.7, 0.9,  1.1}, sd::DataType::FLOAT32);
    NDArray gamma   ('c', {4}, {-1.2, 1.3, -1.4, 1.5}, sd::DataType::FLOAT32);
    NDArray beta    ('c', {4}, sd::DataType::FLOAT32);
    NDArray gradO   ('c', {2,2,2,4}, sd::DataType::FLOAT32);

    NDArray expdLdI('c', {2,2,2,4}, {-4.989124, 2.540357, -1.515022, 0.791769, -3.563660, 1.814540, -1.082159, 0.565549, -2.138196, 1.088724, -0.649295,
                                    0.339329, -0.712732, 0.362908, -0.216432, 0.113110, 0.712732, -0.362908, 0.216432, -0.113110, 2.138195, -1.088724, 0.649295,
                                    -0.339330, 3.563660,-1.814540, 1.082159, -0.565549, 4.989125, -2.540356, 1.515022, -0.791770}, sd::DataType::FLOAT32);
    NDArray expdLdG('c', {4}, {20.364472, 17.856588, 16.949714, 15.903684}, sd::DataType::FLOAT32);
    NDArray expdLdB('c', {4}, {9.6, 10.8, 12. , 13.2}, sd::DataType::FLOAT32);

    input.linspace(0.1, 0.1);
    gradO.linspace(-0.9, 0.15);

    sd::ops::batchnorm_bp op;

    auto results = op.evaluate({&input, &mean, &variance, &gamma, &beta, &gradO}, {1e-5}, {1,1,3});

    ASSERT_EQ(ND4J_STATUS_OK, results.status());

    auto dLdI = results.at(0);
    auto dLdG = results.at(3);
    auto dLdB = results.at(4);

    ASSERT_TRUE(expdLdI.isSameShapeStrict(*dLdI));
    ASSERT_TRUE(expdLdI.equalsTo(dLdI));

    ASSERT_TRUE(expdLdG.isSameShapeStrict(*dLdG));
    ASSERT_TRUE(expdLdG.equalsTo(dLdG));

    ASSERT_TRUE(expdLdB.isSameShapeStrict(*dLdB));
    ASSERT_TRUE(expdLdB.equalsTo(dLdB));
}

////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, batchnorm_bp_test7) {

#if defined(HAVE_CUDNN)
return;
#endif

    NDArray input   ('c', {2,2,2,2,4}, sd::DataType::FLOAT32);
    NDArray mean    ('c', {4}, {1.05, 1.15, 1.2, 1.3}, sd::DataType::FLOAT32);
    NDArray variance('c', {4}, {0.5, 0.7, 0.9,  1.1}, sd::DataType::FLOAT32);
    NDArray gamma   ('c', {4}, {-1.2, 1.3, -1.4, 1.5}, sd::DataType::FLOAT32);
    NDArray beta    ('c', {4}, sd::DataType::FLOAT32);
    NDArray gradO   ('c', {2,2,2,2,4}, sd::DataType::FLOAT32);

    NDArray expdLdI('c', {2,2,2,2,4}, {-119.435059, 78.159744, -58.732986, 46.630123, -103.510391, 67.738441, -50.901920, 40.412773, -87.585716, 57.317142,
        -43.070854, 34.195419, -71.661041, 46.895844, -35.239792, 27.978071, -55.736359, 36.474548, -27.408726, 21.760721, -39.811687, 26.053242, -19.577662,
        15.543370, -23.887009, 15.631950, -11.746595, 9.326023, -7.962326, 5.210644, -3.915531, 3.108671, 7.962341, -5.210655, 3.915535, -3.108677, 23.887032,
        -15.631958, 11.746601, -9.326031, 39.811691, -26.053246, 19.577671, -15.543377, 55.736382, -36.474548, 27.408726, -21.760731, 71.661064, -46.895851, 35.239788,
        -27.978077, 87.585732, -57.317154, 43.070866, -34.195431, 103.510384, -67.738464, 50.901920, -40.412777, 119.435097, -78.159744, 58.732998, -46.630131}, sd::DataType::FLOAT32);
    NDArray expdLdG('c', {4}, {282.38734 , 244.542027, 224.140995, 207.548793}, sd::DataType::FLOAT32);
    NDArray expdLdB('c', {4}, {57.6, 60. , 62.4, 64.8}, sd::DataType::FLOAT32);

    input.linspace(0.1, 0.1);
    gradO.linspace(-0.9, 0.15);


    sd::ops::batchnorm_bp op;

    auto results = op.evaluate({&input, &mean, &variance, &gamma, &beta, &gradO}, {1e-5}, {1,1,4});

    ASSERT_EQ(ND4J_STATUS_OK, results.status());

    auto dLdI = results.at(0);
    auto dLdG = results.at(3);
    auto dLdB = results.at(4);

    // dLdI->printBuffer();

    ASSERT_TRUE(expdLdI.isSameShapeStrict(*dLdI));
    ASSERT_TRUE(expdLdI.equalsTo(dLdI));

    ASSERT_TRUE(expdLdG.isSameShapeStrict(*dLdG));
    ASSERT_TRUE(expdLdG.equalsTo(dLdG));

    ASSERT_TRUE(expdLdB.isSameShapeStrict(*dLdB));
    ASSERT_TRUE(expdLdB.equalsTo(dLdB));


}

////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, batchnorm_bp_test8) {

#if defined(HAVE_CUDNN)
return;
#endif

    NDArray input   ('c', {2,4,2,2,2}, sd::DataType::FLOAT32);
    NDArray mean    ('c', {4}, {1.05, 1.15, 1.2, 1.3}, sd::DataType::FLOAT32);
    NDArray variance('c', {4}, {0.5, 0.7, 0.9,  1.1}, sd::DataType::FLOAT32);
    NDArray gamma   ('c', {4}, {-1.2, 1.3, -1.4, 1.5}, sd::DataType::FLOAT32);
    NDArray beta    ('c', {4}, sd::DataType::FLOAT32);
    NDArray gradO   ('c', {2,4,2,2,2}, sd::DataType::FLOAT32);

    NDArray expdLdI('c', {2,4,2,2,2}, {-34.373802, -32.611046, -30.848286, -29.085529, -27.322769, -25.560009, -23.797251, -22.034491, 36.146996, 34.293301,
        32.439610, 30.585917, 28.732227, 26.878534, 25.024841, 23.171150, -42.876553, -40.677757, -38.478958, -36.280159, -34.081367, -31.882565, -29.683767,
        -27.484968, 50.674446, 48.075760, 45.477066, 42.878380, 40.279686, 37.681000, 35.082310, 32.483616, 22.034489, 23.797249, 25.560009, 27.322765, 29.085526,
        30.848286, 32.611046, 34.373802, -23.171146, -25.024837, -26.878536, -28.732231, -30.585918, -32.439613, -34.293297, -36.146996, 27.484982, 29.683773,
        31.882572, 34.081364, 36.280178, 38.478970, 40.677776, 42.876560, -32.483627, -35.082329, -37.681023, -40.279701, -42.878403, -45.477081, -48.075775, -50.674484}, sd::DataType::FLOAT32);
    NDArray expdLdG('c', {4}, {134.490365, 179.785003, 248.933114, 330.087248}, sd::DataType::FLOAT32);
    NDArray expdLdB('c', {4}, {32.4, 51.6, 70.8, 90.}, sd::DataType::FLOAT32);

    input.linspace(0.1, 0.1);
    gradO.linspace(-0.9, 0.15);

    sd::ops::batchnorm_bp op;

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

    ASSERT_EQ(ND4J_STATUS_OK, results.status());

    auto dLdI = results.at(0);
    auto dLdG = results.at(3);
    auto dLdB = results.at(4);

    // dLdI->printBuffer();

    ASSERT_TRUE(expdLdI.isSameShapeStrict(*dLdI));
    ASSERT_TRUE(expdLdI.equalsTo(dLdI));

    ASSERT_TRUE(expdLdG.isSameShapeStrict(*dLdG));
    ASSERT_TRUE(expdLdG.equalsTo(dLdG));

    ASSERT_TRUE(expdLdB.isSameShapeStrict(*dLdB));
    ASSERT_TRUE(expdLdB.equalsTo(dLdB));

}

////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, batchnorm_bp_test9) {

    NDArray input   ('c', {2,4,2,2}, sd::DataType::FLOAT32);
    NDArray mean    ('c', {4}, sd::DataType::FLOAT32);
    NDArray variance('c', {4}, sd::DataType::FLOAT32);
    NDArray gamma   ('c', {4}, {-1.2, 1.3, -1.4, 1.5}, sd::DataType::FLOAT32);
    NDArray beta    ('c', {4}, sd::DataType::FLOAT32);
    NDArray gradO   ('c', {2,4,2,2}, sd::DataType::FLOAT32);

    NDArray expdLdI('c', {2,4,2,2}, {0.032378, 0.028967, 0.025558, 0.022147, -0.035056, -0.031364, -0.027669, -0.024006, 0.037742, 0.033766, 0.029791, 0.025818,
                                    -0.040429, -0.036172, -0.031913, -0.027656, -0.022155, -0.025564, -0.028974, -0.032359, 0.023982, 0.027677, 0.031373, 0.035063,
                                    -0.025822, -0.029794, -0.033770, -0.037747, 0.027653, 0.031913, 0.036168, 0.040426}, sd::DataType::FLOAT32);
    NDArray expdLdG('c', {4}, {9.685875, 9.685880, 9.685887, 9.685891}, sd::DataType::FLOAT32);
    NDArray expdLdB('c', {4}, {4.2,  9. , 13.8, 18.6}, sd::DataType::FLOAT32);

    input.linspace(1,0.01);
    gradO.linspace(-0.9, 0.15);

    // calculate mean and variance of input
    PointersManager manager(input.getContext(), "DeclarableOpsTests13.batchnorm_bp_test9");
    std::vector<int> dimensions = {0,2,3};
    int* dims = reinterpret_cast<int*>(manager.replicatePointer(dimensions.data(), dimensions.size() * sizeof(int)));
    input.reduceAlongDimension(sd::reduce::Mean, mean, dimensions);
    NDArray::prepareSpecialUse({&variance}, {&input});
    auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(input.getShapeInfo(), dimensions);
    NativeOpExecutioner::execSummaryStats(input.getContext(), 0,input.getBuffer(), input.getShapeInfo(),input.getSpecialBuffer(), input.getSpecialShapeInfo(),nullptr,variance.getBuffer(), variance.getShapeInfo(),variance.getSpecialBuffer(), variance.getSpecialShapeInfo(), dims, dimensions.size(),packX.platformShapeInfo(), packX.platformOffsets(),false);
    manager.synchronize();
    NDArray::registerSpecialUse({&variance}, {&input});

    sd::ops::batchnorm_bp op;

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

    ASSERT_EQ(ND4J_STATUS_OK, results.status());

    auto dLdI = results.at(0);
    auto dLdG = results.at(3);
    auto dLdB = results.at(4);

    ASSERT_TRUE(expdLdI.isSameShapeStrict(*dLdI));
    ASSERT_TRUE(expdLdI.equalsTo(dLdI, 1e-4));

    ASSERT_TRUE(expdLdG.isSameShapeStrict(*dLdG));
    ASSERT_TRUE(expdLdG.equalsTo(dLdG));

    ASSERT_TRUE(expdLdB.isSameShapeStrict(*dLdB));
    ASSERT_TRUE(expdLdB.equalsTo(dLdB));

}

////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, batchnorm_bp_test10) {

    NDArray input   ('c', {2,2,2,4}, sd::DataType::FLOAT32);
    NDArray mean    ('c', {4}, sd::DataType::FLOAT32);
    NDArray variance('c', {4}, sd::DataType::FLOAT32);
    NDArray gamma   ('c', {4}, {-1.2, 1.3, -1.4, 1.5}, sd::DataType::FLOAT32);
    NDArray beta    ('c', {4}, sd::DataType::FLOAT32);
    NDArray gradO   ('c', {2,2,2,4}, sd::DataType::FLOAT32);

    NDArray expdLdI('c', {2,2,2,4}, {0.032634, -0.035423, 0.038110, -0.040864, 0.023302, -0.025294, 0.027213, -0.029205, 0.013996, -0.015192, 0.016343,
                                    -0.017519, 0.004664, -0.005062, 0.005445, -0.005833, -0.004668, 0.005067, -0.005452, 0.005824, -0.013974, 0.015171,
                                    -0.016325, 0.017508, -0.023309, 0.025301, -0.027221, 0.029197, -0.032639, 0.035428, -0.038118, 0.040878}, sd::DataType::FLOAT32);
    NDArray expdLdG('c', {4}, {10.991656, 10.991631, 10.991643, 10.991632}, sd::DataType::FLOAT32);
    NDArray expdLdB('c', {4}, {9.6, 10.8, 12., 13.2}, sd::DataType::FLOAT32);

    input.linspace(1,0.01);
    gradO.linspace(-0.9, 0.15);

    // calculate mean and variance of input
    PointersManager manager(input.getContext(), "DeclarableOpsTests13.batchnorm_bp_test9");
    std::vector<int> dimensions = {0,1,2};
    int* dims = reinterpret_cast<int*>(manager.replicatePointer(dimensions.data(), dimensions.size() * sizeof(int)));
    input.reduceAlongDimension(sd::reduce::Mean, mean, dimensions);
    NDArray::prepareSpecialUse({&variance}, {&input});
    auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(input.getShapeInfo(), dimensions);
    NativeOpExecutioner::execSummaryStats(input.getContext(), 0,input.getBuffer(), input.getShapeInfo(),input.getSpecialBuffer(), input.getSpecialShapeInfo(),nullptr,variance.getBuffer(), variance.getShapeInfo(),variance.getSpecialBuffer(), variance.getSpecialShapeInfo(), dims, dimensions.size(),packX.platformShapeInfo(), packX.platformOffsets(),false);
    manager.synchronize();
    NDArray::registerSpecialUse({&variance}, {&input});

    sd::ops::batchnorm_bp op;

    auto results = op.evaluate({&input, &mean, &variance, &gamma, &beta, &gradO}, {1e-5}, {1,1,3});

    ASSERT_EQ(ND4J_STATUS_OK, results.status());

    auto dLdI = results.at(0);
    auto dLdG = results.at(3);
    auto dLdB = results.at(4);

    ASSERT_TRUE(expdLdI.isSameShapeStrict(*dLdI));
    ASSERT_TRUE(expdLdI.equalsTo(dLdI, 1e-4));

    ASSERT_TRUE(expdLdG.isSameShapeStrict(*dLdG));
    ASSERT_TRUE(expdLdG.equalsTo(dLdG));

    ASSERT_TRUE(expdLdB.isSameShapeStrict(*dLdB));
    ASSERT_TRUE(expdLdB.equalsTo(dLdB));

}

////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests13, batchnorm_bp_test11) {

    NDArray input   ('c', {2,3,4,5}, sd::DataType::FLOAT32);
    NDArray mean    ('c', {1,3,4,5}, sd::DataType::FLOAT32);
    NDArray variance('c', {1,3,4,5}, sd::DataType::FLOAT32);
    NDArray gamma   ('c', {1,3,4,5}, sd::DataType::FLOAT32);
    NDArray beta    ('c', {1,3,4,5}, sd::DataType::FLOAT32);
    NDArray gradO   ('c', {2,3,4,5}, sd::DataType::FLOAT32);

    NDArray expdLdI('c', {2,3,4,5}, {0.004981, 0.004818, 0.004652, 0.004483, 0.004319, 0.004153, 0.003985, 0.003832, 0.003661, 0.003505, 0.003340, 0.003171, 0.003001, 0.002837,
                                        0.002670, 0.002505, 0.002337, 0.002167, 0.002003, 0.001835, 0.001666, 0.001499, 0.001327, 0.001162, 0.000996, 0.000830, 0.000664, 0.000498,
                                        0.000332, 0.000166, -0.0, -0.000166, -0.000333, -0.000500, -0.000668, -0.000835, -0.001003, -0.001168, -0.001337, -0.001502, -0.001670,
                                        -0.001838, -0.002003, -0.002172, -0.002330, -0.002499, -0.002669, -0.002832, -0.003002, -0.003162, -0.003332, -0.003495, -0.003665, -0.003821,
                                        -0.004001, -0.004163, -0.004324, -0.004516, -0.004678, -0.004851, -0.004981, -0.004818, -0.004652, -0.004483, -0.004319, -0.004151, -0.003985,
                                        -0.003836, -0.003661, -0.003505, -0.003338, -0.003171, -0.003004, -0.002837, -0.002670, -0.002503, -0.002337, -0.002170, -0.002003, -0.001835,
                                        -0.001664, -0.001499, -0.001328, -0.001162, -0.000996, -0.000829, -0.000664, -0.000498, -0.000332, -0.000166, 0.0, 0.000166, 0.000334,
                                        0.000500, 0.000668, 0.000834, 0.001003, 0.001170, 0.001337, 0.001502, 0.001669, 0.001838, 0.002005, 0.002172, 0.002330, 0.002496, 0.002669,
                                        0.002836, 0.003002, 0.003162, 0.003328, 0.003495, 0.003670, 0.003828, 0.003992, 0.004158, 0.004324, 0.004522, 0.004689, 0.004843}, sd::DataType::FLOAT32);
    NDArray expdLdG('c', {1,3,4,5}, {8.999503, 8.999502, 8.999502, 8.999503, 8.999502, 8.999503, 8.999503, 8.999499, 8.999501, 8.999498, 8.999498, 8.999498, 8.999498, 8.999498, 8.999498,
                                    8.999498, 8.999498, 8.999498, 8.999498, 8.999499, 8.999501, 8.999500, 8.999503, 8.999503, 8.999503, 8.999504, 8.999503, 8.999503, 8.999504, 8.999503,
                                    8.999504, 8.999504, 8.999499, 8.999500, 8.999497, 8.999498, 8.999496, 8.999496, 8.999496, 8.999498, 8.999498, 8.999496, 8.999496, 8.999496, 8.999501,
                                    8.999501, 8.999499, 8.999499, 8.999499, 8.999501, 8.999501, 8.999501, 8.999499, 8.999500, 8.999501, 8.999501, 8.999501, 8.999495, 8.999495, 8.999497}, sd::DataType::FLOAT32);
    NDArray expdLdB('c', {1,3,4,5}, {7.2, 7.5, 7.8, 8.1, 8.4, 8.7, 9.0, 9.3, 9.6, 9.9, 10.2, 10.5, 10.8, 11.1, 11.4, 11.7, 12.0, 12.3, 12.6, 12.9, 13.2, 13.5, 13.8, 14.1, 14.4, 14.7, 15.0,
                                    15.3, 15.6, 15.9, 16.2, 16.5, 16.8, 17.1, 17.4, 17.7, 18.0, 18.3, 18.6, 18.9, 19.2, 19.5, 19.8, 20.1, 20.4, 20.7, 21.0, 21.3, 21.6, 21.9, 22.2, 22.5,
                                    22.8, 23.1, 23.4, 23.7, 24.0, 24.3, 24.6, 24.9}, sd::DataType::FLOAT32);

    input.linspace(1,0.01);
    gradO.linspace(-0.9, 0.15);
    gamma.linspace(-3, 0.1);

    // calculate mean and variance of input
    PointersManager manager(input.getContext(), "DeclarableOpsTests13.batchnorm_bp_test9");
    std::vector<int> dimensions = {0};
    int* dims = reinterpret_cast<int*>(manager.replicatePointer(dimensions.data(), dimensions.size() * sizeof(int)));
    input.reduceAlongDimension(sd::reduce::Mean, mean, dimensions, true);
    NDArray::prepareSpecialUse({&variance}, {&input});
    auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(input.getShapeInfo(), dimensions);
    NativeOpExecutioner::execSummaryStats(input.getContext(), 0,input.getBuffer(), input.getShapeInfo(),input.getSpecialBuffer(), input.getSpecialShapeInfo(),nullptr,variance.getBuffer(), variance.getShapeInfo(),variance.getSpecialBuffer(), variance.getSpecialShapeInfo(), dims, dimensions.size(),packX.platformShapeInfo(), packX.platformOffsets(),false);
    manager.synchronize();
    NDArray::registerSpecialUse({&variance}, {&input});

    sd::ops::batchnorm_bp op;

    auto results = op.evaluate({&input, &mean, &variance, &gamma, &beta, &gradO}, {1e-5}, {1,1, 1,2,3});

    ASSERT_EQ(ND4J_STATUS_OK, results.status());

    auto dLdI = results.at(0);
    auto dLdG = results.at(3);
    auto dLdB = results.at(4);

    ASSERT_TRUE(expdLdI.isSameShapeStrict(*dLdI));
    ASSERT_TRUE(expdLdI.equalsTo(dLdI, 1e-4));

    ASSERT_TRUE(expdLdG.isSameShapeStrict(*dLdG));
    ASSERT_TRUE(expdLdG.equalsTo(dLdG));

    ASSERT_TRUE(expdLdB.isSameShapeStrict(*dLdB));
    ASSERT_TRUE(expdLdB.equalsTo(dLdB));

}