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Legacy API changes (#441)
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* Refactored buffer() and shapeInfo() methods usage with NDArray class.

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* Adopt Graph class methods to use const shapes.

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* Adopt choose op to use constant shapes.

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* Adopt where op shape method to use constant shapes.

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* Adopt lstsq op to use constant empty shapes.

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* Adopt matrix_diag_part op shape routine to use constant shapes.

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* Adopt determinant ops to use constant shapes.

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* Adopt mean_pairwssqerr_loss ops to use constant shapes.

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* Adopt ops shape methods.

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* Adopt shape methods for loss ops.

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* Adopt log_loss op shape method.

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* Adopt shape methods for ops.

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* Adopt dilation2d ops shape methods.

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* Adopted deconv2d ops shape methods.

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* Adopted dynamicRNN op shape method.

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* Adopted shape methods for ops.

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* Adopted shape methods for lstm layer ops.

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* few updates

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* first cuda tweak

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* Adopt constant shapes for sconv2d ops.

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* Adopt constant shapes for gru ops.

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* Adopt constant shapes with shape methods for segment ops and so on.

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* Adopted constant shapes with unsorted_segment_* ops.

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* Adopted constant shapes with gamma op shape method.

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* Adopted shape methods of reduce_stddev ops.

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* Adopted shape methods for reduce_* ops.

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* Adopt shape method for squeeze op.

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* Adopt strided_slice shape method.

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* Refactored concat op shape method to adopt constant shapes.

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* Adopted shape method for mirror_pad op.

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* Adopted split op shape method.

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* Adopted tile ops shape methods.

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* Added const cast for mkldnn routines handles.

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* Refactored logSoftMaxForVector_ routine to conform with proper data and shape pointer casts.

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* Cosmetic changes to proper usage of constant pointers.

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* Refactored a couple shape comparators for strides and addBias helpers to proper use data pointers with inplace option.

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* Refactored depthToSpace helpers.

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* Refactored histogram helpers.

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* Refactored im2col helpers.

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* Refactored gather and gatherND helpers.

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* Fixed buffer usage on percentile helper.

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* Fixed gather shape with helpers and range buffer usage.

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* Fixed buffer usage with space to depth helpers.

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* Fixed buffer usage and constant shapes.

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* Fixed buffer usage with LUP decomposition>

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* Refactored onehot_ helper.

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* Refactored pad and prefix to use constant shapes.

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* Refactoed softmax helpers.

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* Fixed space to batch helpers to use buffers properly.

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* Fixed stack and split helpers.

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* Fixed buffer usage with sparse to dense helpers.

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* Fixed buffer usage with mindistance_ helpers.

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* Fixed buffer usage with tile helper.

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* Fixed constant shape usage.

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* Fixed constant shape usage with legacy pairwise bool ops.

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* Refactored a couple of methods to adopt constant shape usage.

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* Fixed broadcasting with constant shape."

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* Fixed const usage with inplace reverse and constant shapes with legacy reduction.

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* Refactored legacy ops with const shapes.

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* Refactored sort to adopt constant shapes.

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* Corrected sort for constant shape usage.

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* Fixed constant shape usage with special methods.

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* Refactored Context to conform with constant shape usage.

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* CUDA broadcasting headers

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* pairwise/indexreduce/random headers

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* Refactored native ops to adopt constant shapes.

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* legacy reduce3/scalar headers

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* Corrected pullRow signature and tests.

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* Corrected routines to proper use of constant shapes.

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* Refactored tests to use constant shapes properly.

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* Refactored legacy ops tests to use constant shapes properly.

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* Refactored buffer usage with NDArray tests.

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* Fixed native ops tests.

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* Fixed special concat routine.

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* Fixed buffer usage with test.

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* Fixed buffer usage with a test.

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* Refactored TAD.h and tests.

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* Refactored calcStrides* routines to use constant shapes.

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* Fixed miscelaneous errors with constant shapes.

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* NativeOps const changes

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* Corrected definitions for declared functions.

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* NativeOps const changes

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* few more const changes

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* Fixed const shapes with shape routines.

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* few more const changes

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* Fixed shape method for broadcastable case.

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* few more const changes

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* xw_plus_b BP shape fn restored

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* Fixed signatures with broadcasting.

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* Repaired backprops shape methods for a set of operations.

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* Refactored broadcast bool for cuda.

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* Refactored methods for 3 args with const qualifier.

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* Fixed a couple of kernel signatures for broadcasting.

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* Fixed kernels signatures for const buffers and shapes.

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* Refactored pairwise methods to persistent buffers and shapes usage.

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* Adopt const to buffers and shapes with kernels.

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* Adopt const to buffers and shapes with scalar kernels.

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* Refactored indexreduce kernels signatures to use const buffers and shapes.

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* Refactored pairwise kernels to adopt cons shapes and buffers.

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* Refactored pairwise bool kernels to adopt cons shapes and buffers.

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* Refactored random special ops to conform with const shapes and buffers.

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* Refactored native ops to conform with const shapes and buffers under cuda platform.

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* Cosmetical changes only.

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* Fixed const shapes and buffers error.

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* Corrected start pos routine.

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* Refactored methods to conform with const shapes and buffers.

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* Refactored helpers to use proper methods instead.

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* bunch of changes

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* next bunch of changes

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* next bunch of changes

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* Fixed execScalar declaration.

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* Fixed execScalar declaration.

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* Corrected const shape cases with sort and so on.

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* Fixed const shapes for sort.

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* Refactored kernel declarations to adopt const shapes.

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* Fixed kernels declarations to adopt const shapes.

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* Corrected kernel declarations to adopt const shapes and buffers.

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* Fixed kernels declarations to adopt const shapes.

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* Fixed segment helpers kernels declarations and so on to adopt const shapes.

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* Fixed const shape usage with segment and solve helpers.

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* Fixed kernel declaration with adjustWeight helper.

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* Fixed cuda implementations for constant shape helpers.

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* Adopted const shape usage with kernels.

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* Adopted top_k kernels to use const shapes and buffers.

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* Corrected kernels declarations to adopt const shapes with helpers.

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* Refactored NDArray definitions to adopt const shapes and buffers.

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* Fixed const shapes with image suppression helpers.

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* Slight improvement with buffers.

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* Refactored buffer usage.

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* Refactored buffer usage with tests.

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* Fixed const shape usage with definitions.

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* minor updates on cpu side

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* Refactored const shape usage with ConstantDescritor and native ops with cuda platform.

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* Refactored tear and tile kernels to adopt with const shapes.

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* softmax_loop fix

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* update missing signature

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* softmax again

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* few more missing consts

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* new methods updated

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Co-authored-by: shugeo <sgazeos@gmail.com>
2020-05-09 08:06:14 +03:00

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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
//
// @author Yurii Shyrma (iuriish@yahoo.com), created on 17.05.2018
// @author raver119@gmail.com
//
#include <ops/declarable/helpers/percentile.h>
#include <array/NDArrayFactory.h>
#include <helpers/ConstantTadHelper.h>
#include <helpers/DebugHelper.h>
#include <array/ResultSet.h>
namespace sd {
namespace ops {
namespace helpers {
template <typename X>
static _CUDA_G void percentileKernel(void *vx, const Nd4jLong *xTadShapeInfo, const Nd4jLong *xTadOffsets,
const Nd4jLong numTads, const Nd4jLong tadLength,
void *vz, const Nd4jLong *zShapeInfo, const Nd4jLong zLength,
const Nd4jLong position) {
for (int t = blockIdx.x; t < numTads; t += gridDim.x) {
auto x = reinterpret_cast<X*>(vx) + xTadOffsets[t];
auto z = reinterpret_cast<X*>(vz);
// sort tad
if (tadLength > 1) {
for (int m = 0; m < tadLength; m++) {
if (m % 2 == 0) {
for (int tid = threadIdx.x; tid < tadLength; tid += blockDim.x) {
auto top = 2 * tid + 1;
if (top < tadLength) {
auto t0 = shape::getIndexOffset(top - 1, xTadShapeInfo);
auto t1 = shape::getIndexOffset(top, xTadShapeInfo);
if (x[t0] > x[t1]) {
//swap values
X dz0 = x[t0];
x[t0] = x[t1];
x[t1] = dz0;
}
}
}
} else {
for (int tid = threadIdx.x; tid < tadLength; tid += blockDim.x) {
auto top = 2 * tid + 2;
if (top < tadLength) {
auto t0 = shape::getIndexOffset(top - 1, xTadShapeInfo);
auto t1 = shape::getIndexOffset(top, xTadShapeInfo);
if (x[t0] > x[t1]) {
//swap values
X dz0 = x[t0];
x[t0] = x[t1];
x[t1] = dz0;
}
}
}
}
__syncthreads();
}
}
// saving final value
if (threadIdx.x == 0)
z[shape::getIndexOffset(t, zShapeInfo)] = x[shape::getIndexOffset(position, xTadShapeInfo)];
__syncthreads();
}
}
template <typename T>
static void _percentile(sd::LaunchContext * context, const NDArray& input, NDArray& output, std::vector<int>& axis, const float q, const int interpolation) {
const int inputRank = input.rankOf();
if(axis.empty())
for(int i=0; i<inputRank; ++i)
axis.push_back(i);
else
shape::checkDimensions(inputRank, axis);
auto tempArray = input.dup();
auto packX = ConstantTadHelper::getInstance()->tadForDimensions(tempArray.shapeInfo(), axis);
auto tadLength = shape::length(packX.primaryShapeInfo());
const float fraction = 1.f - q / 100.;
Nd4jLong position = 0;
switch(interpolation) {
case 0: // lower
position = static_cast<Nd4jLong>(math::nd4j_ceil<float,T>((tadLength - 1) * fraction));
break;
case 1: // higher
position = static_cast<Nd4jLong>(math::nd4j_floor<float,T>((tadLength - 1) * fraction));
break;
case 2: // nearest
position = static_cast<Nd4jLong>(math::nd4j_round<float,T>((tadLength - 1) * fraction));
break;
}
position = tadLength - position - 1;
percentileKernel<T><<<256, 512, 1024, *context->getCudaStream()>>>(tempArray.specialBuffer(), packX.platformShapeInfo(), packX.platformOffsets(), packX.numberOfTads(), tadLength, output.specialBuffer(), output.specialShapeInfo(), output.lengthOf(), position);
sd::DebugHelper::checkErrorCode(context->getCudaStream(), "percentile");
}
void percentile(sd::LaunchContext * context, const NDArray& input, NDArray& output, std::vector<int>& axises, const float q, const int interpolation) {
NDArray::prepareSpecialUse({&output}, {&input});
BUILD_SINGLE_SELECTOR(input.dataType(), _percentile, (context, input, output, axises, q, interpolation), LIBND4J_TYPES);
NDArray::registerSpecialUse({&output}, {&input});
}
BUILD_SINGLE_TEMPLATE(template void _percentile, (sd::LaunchContext * context, const NDArray& input, NDArray& output, std::vector<int>& axises, const float q, const int interpolation), LIBND4J_TYPES);
}
}
}