//
// Copyright © 2017 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//

#include "Deserializer.hpp"

#include <armnn/Descriptors.hpp>
#include <armnn/Exceptions.hpp>
#include <armnn/TypesUtils.hpp>
#include <armnn/LstmParams.hpp>
#include <armnn/QuantizedLstmParams.hpp>
#include <armnn/Logging.hpp>

#include <armnnUtils/Permute.hpp>
#include <armnnUtils/Transpose.hpp>
#include <armnn/utility/Assert.hpp>
#include <armnn/utility/IgnoreUnused.hpp>
#include <armnn/utility/NumericCast.hpp>

#include <ParserHelper.hpp>
#include <VerificationHelpers.hpp>

#include <fmt/format.h>

#include <fstream>
#include <algorithm>
#include <limits>
#include <numeric>

using armnn::ParseException;
using namespace armnn;
using namespace armnnSerializer;

namespace armnnDeserializer
{

IDeserializer::IDeserializer() : pDeserializerImpl(new DeserializerImpl()){}

IDeserializer::~IDeserializer() = default;

IDeserializer *IDeserializer::CreateRaw()
{
    return new IDeserializer();
}

IDeserializerPtr IDeserializer::Create()
{
    return IDeserializerPtr(CreateRaw(), &IDeserializer::Destroy);
}

void IDeserializer::Destroy(IDeserializer *parser)
{
    delete parser;
}

armnn::INetworkPtr IDeserializer::CreateNetworkFromBinary(const std::vector<uint8_t> &binaryContent)
{
    return pDeserializerImpl->CreateNetworkFromBinary(binaryContent);
}

armnn::INetworkPtr IDeserializer::CreateNetworkFromBinary(std::istream &binaryContent)
{
    return pDeserializerImpl->CreateNetworkFromBinary(binaryContent);
}

BindingPointInfo IDeserializer::GetNetworkInputBindingInfo(unsigned int layerId, const std::string &name) const
{
    return pDeserializerImpl->GetNetworkInputBindingInfo(layerId, name);
}

BindingPointInfo IDeserializer::GetNetworkOutputBindingInfo(unsigned int layerId, const std::string &name) const
{
    return pDeserializerImpl->GetNetworkOutputBindingInfo(layerId, name);
}

namespace
{

const uint32_t VIRTUAL_LAYER_ID = std::numeric_limits<uint32_t>::max();

 void CheckGraph(const GraphPtr& graph,
                 unsigned int layersIndex,
                 const CheckLocation& location)
{
    if (graph->layers() == nullptr)
    {
        throw ParseException(fmt::format("{0} was called with invalid (null) graph. "
                                         "Possible reason is that the graph is not yet loaded and Unpack(ed). "
                                         "layers:{1} at {2}",
                                         location.m_Function,
                                         layersIndex,
                                         location.FileLine()));
    }
    else if (layersIndex >= graph->layers()->size())
    {
        throw ParseException(fmt::format("{0} was called with an invalid layers index. layers:{1} at {2}",
                                         location.m_Function,
                                         layersIndex,
                                         location.FileLine()));
    }
}

void CheckLayers(const GraphPtr& graph,
                 unsigned int layersIndex,
                 unsigned int layerIndex,
                 const CheckLocation& location)
{
    if (graph->layers() == nullptr)
    {
        throw ParseException(fmt::format("{0} was called with invalid (null) graph. "
                                         "Possible reason is that the graph is not yet loaded and Unpack(ed). "
                                         "layers:{1} at {2}",
                                         location.m_Function,
                                         layersIndex,
                                         location.FileLine()));
    }
    else if (layersIndex >= graph->layers()->size())
    {
        throw ParseException(fmt::format("{0} was called with an invalid layers index. "
                                         "layers:{1} at {2}",
                                         location.m_Function,
                                         layersIndex,
                                         location.FileLine()));
    }
    else if (layerIndex >= graph->layers()[layersIndex].size()
            && layerIndex != VIRTUAL_LAYER_ID)
    {
        throw ParseException(fmt::format("{0} was called with an invalid layer index. "
                                         "layers:{1} layer:{2} at {3}",
                                         location.m_Function,
                                         layersIndex,
                                         layerIndex,
                                         location.FileLine()));
    }
}

void CheckTensorPtr(TensorRawPtr rawPtr,
                    const CheckLocation& location)
{
    if (rawPtr == nullptr)
    {
        throw ParseException(fmt::format("{0} was called with a null tensor pointer. at {1}",
                                         location.m_Function,
                                         location.FileLine()));
    }
}

void CheckConstTensorPtr(ConstTensorRawPtr rawPtr,
                         const CheckLocation& location)
{
    if (rawPtr == nullptr)
    {
        throw ParseException(fmt::format("{0} was called with a null const tensor pointer. at {1}",
                                         location.m_Function,
                                         location.FileLine()));
    }
}

void CheckConstTensorSize(const unsigned int constTensorSize,
                          const unsigned int tensorSize,
                          const CheckLocation& location)
{
    if (constTensorSize != tensorSize)
    {
        throw ParseException(fmt::format("{0} wrong number of components supplied to tensor. at:{1}",
                                         location.m_Function,
                                         location.FileLine()));
    }
}

#define CHECK_TENSOR_PTR(TENSOR_PTR) \
    CheckTensorPtr(TENSOR_PTR, CHECK_LOCATION())

#define CHECK_CONST_TENSOR_SIZE(CONST_TENSOR_SIZE, TENSOR_SIZE) \
    CheckConstTensorSize(CONST_TENSOR_SIZE, TENSOR_SIZE, CHECK_LOCATION())

#define CHECK_CONST_TENSOR_PTR(TENSOR_PTR) \
    CheckConstTensorPtr(TENSOR_PTR, CHECK_LOCATION())

#define CHECK_LAYERS(GRAPH, LAYERS_INDEX, LAYER_INDEX) \
    CheckLayers(GRAPH, LAYERS_INDEX, LAYER_INDEX, CHECK_LOCATION())

#define CHECK_GRAPH(GRAPH, LAYERS_INDEX) \
    CheckGraph(GRAPH, LAYERS_INDEX, CHECK_LOCATION())
}

bool CheckShape(const armnn::TensorShape& actual, const std::vector<uint32_t>& expected)
{
    const unsigned int actualSize = actual.GetNumDimensions();
    if (actualSize != expected.size())
    {
        return false;
    }

    for (unsigned int i = 0u; i < actualSize; i++)
    {
        if (actual[i] != static_cast<unsigned int>(expected[i]))
        {
            return false;
        }
    }

    return true;
}

IDeserializer::DeserializerImpl::DeserializerImpl()
: m_Network(nullptr, nullptr),
//May require LayerType_Max to be included
m_ParserFunctions(Layer_MAX+1, &IDeserializer::DeserializerImpl::ParseUnsupportedLayer)
{
    // register supported layers
    m_ParserFunctions[Layer_AbsLayer]                    = &DeserializerImpl::ParseAbs;
    m_ParserFunctions[Layer_ActivationLayer]             = &DeserializerImpl::ParseActivation;
    m_ParserFunctions[Layer_AdditionLayer]               = &DeserializerImpl::ParseAdd;
    m_ParserFunctions[Layer_ArgMinMaxLayer]              = &DeserializerImpl::ParseArgMinMax;
    m_ParserFunctions[Layer_BatchMatMulLayer]            = &DeserializerImpl::ParseBatchMatMul;
    m_ParserFunctions[Layer_BatchToSpaceNdLayer]         = &DeserializerImpl::ParseBatchToSpaceNd;
    m_ParserFunctions[Layer_BatchNormalizationLayer]     = &DeserializerImpl::ParseBatchNormalization;
    m_ParserFunctions[Layer_CastLayer]                   = &DeserializerImpl::ParseCast;
    m_ParserFunctions[Layer_ChannelShuffleLayer]         = &DeserializerImpl::ParseChannelShuffle;
    m_ParserFunctions[Layer_ComparisonLayer]             = &DeserializerImpl::ParseComparison;
    m_ParserFunctions[Layer_ConcatLayer]                 = &DeserializerImpl::ParseConcat;
    m_ParserFunctions[Layer_ConstantLayer]               = &DeserializerImpl::ParseConstant;
    m_ParserFunctions[Layer_Convolution2dLayer]          = &DeserializerImpl::ParseConvolution2d;
    m_ParserFunctions[Layer_Convolution3dLayer]          = &DeserializerImpl::ParseConvolution3d;
    m_ParserFunctions[Layer_DepthToSpaceLayer]           = &DeserializerImpl::ParseDepthToSpace;
    m_ParserFunctions[Layer_DepthwiseConvolution2dLayer] = &DeserializerImpl::ParseDepthwiseConvolution2d;
    m_ParserFunctions[Layer_DequantizeLayer]             = &DeserializerImpl::ParseDequantize;
    m_ParserFunctions[Layer_DetectionPostProcessLayer]   = &DeserializerImpl::ParseDetectionPostProcess;
    m_ParserFunctions[Layer_DivisionLayer]               = &DeserializerImpl::ParseDivision;
    m_ParserFunctions[Layer_ElementwiseUnaryLayer]       = &DeserializerImpl::ParseElementwiseUnary;
    m_ParserFunctions[Layer_EqualLayer]                  = &DeserializerImpl::ParseEqual;
    m_ParserFunctions[Layer_FullyConnectedLayer]         = &DeserializerImpl::ParseFullyConnected;
    m_ParserFunctions[Layer_FillLayer]                   = &DeserializerImpl::ParseFill;
    m_ParserFunctions[Layer_FloorLayer]                  = &DeserializerImpl::ParseFloor;
    m_ParserFunctions[Layer_GatherLayer]                 = &DeserializerImpl::ParseGather;
    m_ParserFunctions[Layer_GatherNdLayer]               = &DeserializerImpl::ParseGatherNd;
    m_ParserFunctions[Layer_GreaterLayer]                = &DeserializerImpl::ParseGreater;
    m_ParserFunctions[Layer_InstanceNormalizationLayer]  = &DeserializerImpl::ParseInstanceNormalization;
    m_ParserFunctions[Layer_L2NormalizationLayer]        = &DeserializerImpl::ParseL2Normalization;
    m_ParserFunctions[Layer_LogicalBinaryLayer]          = &DeserializerImpl::ParseLogicalBinary;
    m_ParserFunctions[Layer_LogSoftmaxLayer]             = &DeserializerImpl::ParseLogSoftmax;
    m_ParserFunctions[Layer_LstmLayer]                   = &DeserializerImpl::ParseLstm;
    m_ParserFunctions[Layer_MaximumLayer]                = &DeserializerImpl::ParseMaximum;
    m_ParserFunctions[Layer_MeanLayer]                   = &DeserializerImpl::ParseMean;
    m_ParserFunctions[Layer_MinimumLayer]                = &DeserializerImpl::ParseMinimum;
    m_ParserFunctions[Layer_MergeLayer]                  = &DeserializerImpl::ParseMerge;
    m_ParserFunctions[Layer_MergerLayer]                 = &DeserializerImpl::ParseConcat;
    m_ParserFunctions[Layer_MultiplicationLayer]         = &DeserializerImpl::ParseMultiplication;
    m_ParserFunctions[Layer_NormalizationLayer]          = &DeserializerImpl::ParseNormalization;
    m_ParserFunctions[Layer_PadLayer]                    = &DeserializerImpl::ParsePad;
    m_ParserFunctions[Layer_PermuteLayer]                = &DeserializerImpl::ParsePermute;
    m_ParserFunctions[Layer_Pooling2dLayer]              = &DeserializerImpl::ParsePooling2d;
    m_ParserFunctions[Layer_Pooling3dLayer]              = &DeserializerImpl::ParsePooling3d;
    m_ParserFunctions[Layer_PreluLayer]                  = &DeserializerImpl::ParsePrelu;
    m_ParserFunctions[Layer_QLstmLayer]                  = &DeserializerImpl::ParseQLstm;
    m_ParserFunctions[Layer_QuantizeLayer]               = &DeserializerImpl::ParseQuantize;
    m_ParserFunctions[Layer_QuantizedLstmLayer]          = &DeserializerImpl::ParseQuantizedLstm;
    m_ParserFunctions[Layer_RankLayer]                   = &DeserializerImpl::ParseRank;
    m_ParserFunctions[Layer_ReduceLayer]                 = &DeserializerImpl::ParseReduce;
    m_ParserFunctions[Layer_ReshapeLayer]                = &DeserializerImpl::ParseReshape;
    m_ParserFunctions[Layer_ResizeBilinearLayer]         = &DeserializerImpl::ParseResizeBilinear;
    m_ParserFunctions[Layer_ResizeLayer]                 = &DeserializerImpl::ParseResize;
    m_ParserFunctions[Layer_RsqrtLayer]                  = &DeserializerImpl::ParseRsqrt;
    m_ParserFunctions[Layer_ShapeLayer]                  = &DeserializerImpl::ParseShape;
    m_ParserFunctions[Layer_SliceLayer]                  = &DeserializerImpl::ParseSlice;
    m_ParserFunctions[Layer_SoftmaxLayer]                = &DeserializerImpl::ParseSoftmax;
    m_ParserFunctions[Layer_SpaceToBatchNdLayer]         = &DeserializerImpl::ParseSpaceToBatchNd;
    m_ParserFunctions[Layer_SpaceToDepthLayer]           = &DeserializerImpl::ParseSpaceToDepth;
    m_ParserFunctions[Layer_SplitterLayer]               = &DeserializerImpl::ParseSplitter;
    m_ParserFunctions[Layer_StackLayer]                  = &DeserializerImpl::ParseStack;
    m_ParserFunctions[Layer_StandInLayer]                = &DeserializerImpl::ParseStandIn;
    m_ParserFunctions[Layer_StridedSliceLayer]           = &DeserializerImpl::ParseStridedSlice;
    m_ParserFunctions[Layer_SubtractionLayer]            = &DeserializerImpl::ParseSubtraction;
    m_ParserFunctions[Layer_SwitchLayer]                 = &DeserializerImpl::ParseSwitch;
    m_ParserFunctions[Layer_TransposeConvolution2dLayer] = &DeserializerImpl::ParseTransposeConvolution2d;
    m_ParserFunctions[Layer_TransposeLayer]              = &DeserializerImpl::ParseTranspose;
    m_ParserFunctions[Layer_UnidirectionalSequenceLstmLayer] = &DeserializerImpl::ParseUnidirectionalSequenceLstm;
}

LayerBaseRawPtr IDeserializer::DeserializerImpl::GetBaseLayer(const GraphPtr& graphPtr, unsigned int layerIndex)
{
    auto layerType = graphPtr->layers()->Get(layerIndex)->layer_type();

    switch(layerType)
    {
        case Layer::Layer_AbsLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_AbsLayer()->base();
        case Layer::Layer_ActivationLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_ActivationLayer()->base();
        case Layer::Layer_AdditionLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_AdditionLayer()->base();
        case Layer::Layer_ArgMinMaxLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_ArgMinMaxLayer()->base();
        case Layer::Layer_BatchMatMulLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_BatchMatMulLayer()->base();
        case Layer::Layer_BatchToSpaceNdLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_BatchToSpaceNdLayer()->base();
        case Layer::Layer_BatchNormalizationLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_BatchNormalizationLayer()->base();
        case Layer::Layer_CastLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_CastLayer()->base();
        case Layer::Layer_ChannelShuffleLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_ChannelShuffleLayer()->base();
        case Layer::Layer_ComparisonLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_ComparisonLayer()->base();
        case Layer::Layer_ConcatLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_ConcatLayer()->base();
        case Layer::Layer_ConstantLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_ConstantLayer()->base();
        case Layer::Layer_Convolution2dLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_Convolution2dLayer()->base();
        case Layer::Layer_Convolution3dLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_Convolution3dLayer()->base();
        case Layer::Layer_DepthToSpaceLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_DepthToSpaceLayer()->base();
        case Layer::Layer_DepthwiseConvolution2dLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_DepthwiseConvolution2dLayer()->base();
        case Layer::Layer_DequantizeLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_DequantizeLayer()->base();
        case Layer::Layer_DetectionPostProcessLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_DetectionPostProcessLayer()->base();
        case Layer::Layer_DivisionLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_DivisionLayer()->base();
        case Layer::Layer_EqualLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_EqualLayer()->base();
        case Layer::Layer_ElementwiseUnaryLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_ElementwiseUnaryLayer()->base();
        case Layer::Layer_FullyConnectedLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_FullyConnectedLayer()->base();
        case Layer::Layer_FillLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_FillLayer()->base();
        case Layer::Layer_FloorLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_FloorLayer()->base();
        case Layer::Layer_GatherLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_GatherLayer()->base();
        case Layer::Layer_GatherNdLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_GatherNdLayer()->base();
        case Layer::Layer_GreaterLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_GreaterLayer()->base();
        case Layer::Layer_InputLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_InputLayer()->base()->base();
        case Layer::Layer_InstanceNormalizationLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_InstanceNormalizationLayer()->base();
        case Layer::Layer_L2NormalizationLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_L2NormalizationLayer()->base();
        case Layer::Layer_LogicalBinaryLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_LogicalBinaryLayer()->base();
        case Layer::Layer_LogSoftmaxLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_LogSoftmaxLayer()->base();
        case Layer::Layer_LstmLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_LstmLayer()->base();
        case Layer::Layer_MeanLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_MeanLayer()->base();
        case Layer::Layer_MinimumLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_MinimumLayer()->base();
        case Layer::Layer_MaximumLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_MaximumLayer()->base();
        case Layer::Layer_MergeLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_MergeLayer()->base();
        case Layer::Layer_MergerLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_MergerLayer()->base();
        case Layer::Layer_MultiplicationLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_MultiplicationLayer()->base();
        case Layer::Layer_NormalizationLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_NormalizationLayer()->base();
        case Layer::Layer_OutputLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_OutputLayer()->base()->base();
        case Layer::Layer_PadLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_PadLayer()->base();
        case Layer::Layer_PermuteLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_PermuteLayer()->base();
        case Layer::Layer_Pooling2dLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_Pooling2dLayer()->base();
        case Layer::Layer_Pooling3dLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_Pooling3dLayer()->base();
        case Layer::Layer_PreluLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_PreluLayer()->base();
        case Layer::Layer_QLstmLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_QLstmLayer()->base();
        case Layer::Layer_QuantizeLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_QuantizeLayer()->base();
        case Layer::Layer_QuantizedLstmLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_QuantizedLstmLayer()->base();
        case Layer::Layer_RankLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_RankLayer()->base();
        case Layer::Layer_ReduceLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_ReduceLayer()->base();
        case Layer::Layer_ReshapeLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_ReshapeLayer()->base();
        case Layer::Layer_ResizeBilinearLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_ResizeBilinearLayer()->base();
        case Layer::Layer_ResizeLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_ResizeLayer()->base();
        case Layer::Layer_RsqrtLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_RsqrtLayer()->base();
        case Layer::Layer_ShapeLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_ShapeLayer()->base();
        case Layer::Layer_SliceLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_SliceLayer()->base();
        case Layer::Layer_SoftmaxLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_SoftmaxLayer()->base();
        case Layer::Layer_SpaceToBatchNdLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_SpaceToBatchNdLayer()->base();
        case Layer::Layer_SpaceToDepthLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_SpaceToDepthLayer()->base();
        case Layer::Layer_SplitterLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_SplitterLayer()->base();
        case Layer::Layer_StackLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_StackLayer()->base();
        case Layer::Layer_StandInLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_StandInLayer()->base();
        case Layer::Layer_StridedSliceLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_StridedSliceLayer()->base();
        case Layer::Layer_SubtractionLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_SubtractionLayer()->base();
        case Layer::Layer_SwitchLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_SwitchLayer()->base();
        case Layer::Layer_TransposeConvolution2dLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_TransposeConvolution2dLayer()->base();
        case Layer::Layer_TransposeLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_TransposeLayer()->base();
        case Layer::Layer_UnidirectionalSequenceLstmLayer:
            return graphPtr->layers()->Get(layerIndex)->layer_as_UnidirectionalSequenceLstmLayer()->base();
        case Layer::Layer_NONE:
        default:
            throw ParseException(fmt::format("Layer type {} not recognized", layerType));
    }
}

std::string IDeserializer::DeserializerImpl::GetLayerName(const GraphPtr& graph, unsigned int index)
{
    auto layer = GetBaseLayer(graph, index);
    assert(layer);
    return layer->layerName()->str();
}

int32_t IDeserializer::DeserializerImpl::GetBindingLayerInfo(const GraphPtr& graphPtr, unsigned int layerIndex)
{
    auto layerType = graphPtr->layers()->Get(layerIndex)->layer_type();

    if (layerType == Layer::Layer_InputLayer)
    {
        return graphPtr->layers()->Get(layerIndex)->layer_as_InputLayer()->base()->layerBindingId();
    }
    else if ( layerType == Layer::Layer_OutputLayer )
    {
        return graphPtr->layers()->Get(layerIndex)->layer_as_OutputLayer()->base()->layerBindingId();
    }
    return 0;
}

armnn::DataLayout ToDataLayout(armnnSerializer::DataLayout dataLayout)
{
    switch (dataLayout)
    {
        case armnnSerializer::DataLayout::DataLayout_NHWC:
            return armnn::DataLayout::NHWC;
        case armnnSerializer::DataLayout::DataLayout_NDHWC:
            return armnn::DataLayout::NDHWC;
        case armnnSerializer::DataLayout::DataLayout_NCDHW:
            return armnn::DataLayout::NCDHW;
        case armnnSerializer::DataLayout::DataLayout_NCHW:
        default:
            return armnn::DataLayout::NCHW;
    }
}

armnn::ActivationFunction ToActivationFunction(armnnSerializer::ActivationFunction function)
{
    switch (function)
    {
        case armnnSerializer::ActivationFunction_Sigmoid:
            return armnn::ActivationFunction::Sigmoid;
        case armnnSerializer::ActivationFunction_TanH:
            return armnn::ActivationFunction::TanH;
        case armnnSerializer::ActivationFunction_Linear:
            return armnn::ActivationFunction::Linear;
        case armnnSerializer::ActivationFunction_ReLu:
            return armnn::ActivationFunction::ReLu;
        case armnnSerializer::ActivationFunction_BoundedReLu:
            return armnn::ActivationFunction::BoundedReLu;
        case armnnSerializer::ActivationFunction_LeakyReLu:
            return armnn::ActivationFunction::LeakyReLu;
        case armnnSerializer::ActivationFunction_Abs:
            return armnn::ActivationFunction::Abs;
        case armnnSerializer::ActivationFunction_Sqrt:
            return armnn::ActivationFunction::Sqrt;
        case armnnSerializer::ActivationFunction_Square:
            return armnn::ActivationFunction::Square;
        case armnnSerializer::ActivationFunction_Elu:
            return armnn::ActivationFunction::Elu;
        case armnnSerializer::ActivationFunction_HardSwish:
            return armnn::ActivationFunction::HardSwish;
        default:
            return armnn::ActivationFunction::Sigmoid;
    }
}

armnn::ArgMinMaxFunction ToArgMinMaxFunction(armnnSerializer::ArgMinMaxFunction function)
{
    switch (function)
    {
        case armnnSerializer::ArgMinMaxFunction::ArgMinMaxFunction_Max:
            return armnn::ArgMinMaxFunction::Max;
        case armnnSerializer::ArgMinMaxFunction::ArgMinMaxFunction_Min:
        default:
            return armnn::ArgMinMaxFunction::Min;
    }
}

armnn::ComparisonOperation ToComparisonOperation(armnnSerializer::ComparisonOperation operation)
{
    switch (operation)
    {
        case armnnSerializer::ComparisonOperation::ComparisonOperation_Equal:
            return armnn::ComparisonOperation::Equal;
        case armnnSerializer::ComparisonOperation::ComparisonOperation_Greater:
            return armnn::ComparisonOperation::Greater;
        case armnnSerializer::ComparisonOperation::ComparisonOperation_GreaterOrEqual:
            return armnn::ComparisonOperation::GreaterOrEqual;
        case armnnSerializer::ComparisonOperation::ComparisonOperation_Less:
            return armnn::ComparisonOperation::Less;
        case armnnSerializer::ComparisonOperation::ComparisonOperation_LessOrEqual:
            return armnn::ComparisonOperation::LessOrEqual;
        case armnnSerializer::ComparisonOperation::ComparisonOperation_NotEqual:
        default:
            return armnn::ComparisonOperation::NotEqual;
    }
}

armnn::ReduceOperation ToReduceOperation(armnnSerializer::ReduceOperation operation)
{
    switch (operation)
    {
        case armnnSerializer::ReduceOperation::ReduceOperation_Sum:
            return armnn::ReduceOperation::Sum;
        case armnnSerializer::ReduceOperation::ReduceOperation_Max:
            return armnn::ReduceOperation::Max;
        case armnnSerializer::ReduceOperation::ReduceOperation_Mean:
            return armnn::ReduceOperation::Mean;
        case armnnSerializer::ReduceOperation::ReduceOperation_Min:
            return armnn::ReduceOperation::Min;
        case armnnSerializer::ReduceOperation::ReduceOperation_Prod:
            return armnn::ReduceOperation::Prod;
        default:
            return armnn::ReduceOperation::Sum;
    }
}

armnn::LogicalBinaryOperation ToLogicalBinaryOperation(armnnSerializer::LogicalBinaryOperation operation)
{
    switch (operation)
    {
        case armnnSerializer::LogicalBinaryOperation::LogicalBinaryOperation_LogicalAnd:
            return armnn::LogicalBinaryOperation::LogicalAnd;
        case armnnSerializer::LogicalBinaryOperation::LogicalBinaryOperation_LogicalOr:
            return armnn::LogicalBinaryOperation::LogicalOr;
        default:
            throw armnn::InvalidArgumentException("Logical Binary operation unknown");
    }
}

armnn::UnaryOperation ToUnaryOperation(armnnSerializer::UnaryOperation operation)
{
    switch (operation)
    {
        case armnnSerializer::UnaryOperation::UnaryOperation_Abs:
            return armnn::UnaryOperation::Abs;
        case armnnSerializer::UnaryOperation::UnaryOperation_Rsqrt:
            return armnn::UnaryOperation::Rsqrt;
        case armnnSerializer::UnaryOperation::UnaryOperation_Sqrt:
            return armnn::UnaryOperation::Sqrt;
        case armnnSerializer::UnaryOperation::UnaryOperation_Exp:
            return armnn::UnaryOperation::Exp;
        case armnnSerializer::UnaryOperation::UnaryOperation_Neg:
            return armnn::UnaryOperation::Neg;
        case armnnSerializer::UnaryOperation::UnaryOperation_LogicalNot:
            return armnn::UnaryOperation::LogicalNot;
        case armnnSerializer::UnaryOperation::UnaryOperation_Log:
            return armnn::UnaryOperation::Log;
        case armnnSerializer::UnaryOperation::UnaryOperation_Sin:
            return armnn::UnaryOperation::Sin;
        default:
            throw armnn::InvalidArgumentException("Unary operation unknown");
    }
}

armnn::PaddingMode ToPaddingMode(armnnSerializer::PaddingMode paddingMode)
{
    switch (paddingMode)
    {
        case armnnSerializer::PaddingMode::PaddingMode_Reflect:
            return armnn::PaddingMode::Reflect;
        case armnnSerializer::PaddingMode::PaddingMode_Symmetric:
            return armnn::PaddingMode::Symmetric;
        default:
            return armnn::PaddingMode::Constant;
    }
}

armnn::ResizeMethod ToResizeMethod(armnnSerializer::ResizeMethod method)
{
    switch (method)
    {
        case armnnSerializer::ResizeMethod_NearestNeighbor:
            return armnn::ResizeMethod::NearestNeighbor;
        case armnnSerializer::ResizeMethod_Bilinear:
            return armnn::ResizeMethod::Bilinear;
        default:
            return armnn::ResizeMethod::NearestNeighbor;
    }
}

armnn::TensorInfo ToTensorInfo(TensorRawPtr tensorPtr)
{
    armnn::DataType type;
    CHECK_TENSOR_PTR(tensorPtr);

    switch (tensorPtr->dataType())
    {
        case DataType_QAsymmS8:
            type = armnn::DataType::QAsymmS8;
            break;
        case DataType_QSymmS8:
            type = armnn::DataType::QSymmS8;
            break;
        case DataType_QuantisedAsymm8:
        case DataType_QAsymmU8:
            type = armnn::DataType::QAsymmU8;
            break;
        case DataType_QSymmS16:
        case DataType_QuantisedSymm16:
            type = armnn::DataType::QSymmS16;
            break;
        case DataType_Signed32:
            type = armnn::DataType::Signed32;
            break;
        case DataType_Signed64:
            type = armnn::DataType::Signed64;
            break;
        case DataType_Float32:
            type = armnn::DataType::Float32;
            break;
        case DataType_Float16:
            type = armnn::DataType::Float16;
            break;
        case DataType_Boolean:
            type = armnn::DataType::Boolean;
            break;
        default:
        {
            CheckLocation location = CHECK_LOCATION();
            throw ParseException(fmt::format("Unsupported data type {0} = {1}. {2}",
                                             tensorPtr->dataType(),
                                             EnumNameDataType(tensorPtr->dataType()),
                                             location.AsString()));
        }
    }

    float quantizationScale = tensorPtr->quantizationScale();
    int32_t quantizationOffset = tensorPtr->quantizationOffset();

    if (tensorPtr->dimensionality() == static_cast<unsigned int>(Dimensionality::Scalar))
    {
        return armnn::TensorInfo(TensorShape{armnn::Dimensionality::Scalar},
                                 type,
                                 quantizationScale,
                                 quantizationOffset);
    }
    else if (tensorPtr->dimensionality() == static_cast<unsigned int>(Dimensionality::NotSpecified))
    {
        armnn::TensorInfo result(TensorShape{Dimensionality::NotSpecified},
                                 type,
                                 quantizationScale,
                                 quantizationOffset);
        return result;
    }

    auto dimensions = tensorPtr->dimensions();
    unsigned int size = dimensions->size();
    std::vector<unsigned int> outputDims(dimensions->begin(), dimensions->begin() + size);
    bool dimensionsSpecificity[armnn::MaxNumOfTensorDimensions];
    std::fill_n(dimensionsSpecificity, armnn::MaxNumOfTensorDimensions, true);
    // For backwards compatibility check if the dimensionSpecificity vector is present first.
    // The default is to have dimensionSpecificity set to all true's anyway.
    if (tensorPtr->dimensionSpecificity() != nullptr)
    {
        auto dimensionSpecificity = tensorPtr->dimensionSpecificity();
        size = dimensionSpecificity->size();
        for (unsigned int i = 0; i < size; ++i)
        {
            dimensionsSpecificity[i] = dimensionSpecificity->Get(i);
        }
    }
    // Construct a TensorShape
    TensorShape shape(size, outputDims.data(), dimensionsSpecificity);

    auto quantizationScales = tensorPtr->quantizationScales();
    if (quantizationScales)
    {
        unsigned int quantizationScalesSize = quantizationScales->size();
        std::vector<float> scales(quantizationScales->begin(), quantizationScales->begin() + quantizationScalesSize);
        unsigned int quantizationDim = tensorPtr->quantizationDim();
        armnn::TensorInfo result(shape,
                                 type,
                                 scales,
                                 quantizationDim);
        return result;
    }

    // two statements (on purpose) for easier debugging:
    armnn::TensorInfo result(shape,
                             type,
                             quantizationScale,
                             quantizationOffset);
    
    return result;
}

armnn::ConstTensor ToConstTensor(ConstTensorRawPtr constTensorPtr)
{
    CHECK_CONST_TENSOR_PTR(constTensorPtr);
    armnn::TensorInfo tensorInfo = ToTensorInfo(constTensorPtr->info());
    tensorInfo.SetConstant();

    switch (constTensorPtr->data_type())
    {
        case ConstTensorData_ByteData:
        {
            auto byteData = constTensorPtr->data_as_ByteData()->data();
            CHECK_CONST_TENSOR_SIZE(byteData->size(), tensorInfo.GetNumElements());
            return armnn::ConstTensor(tensorInfo, byteData->data());
        }
        case ConstTensorData_ShortData:
        {
            auto shortData = constTensorPtr->data_as_ShortData()->data();
            CHECK_CONST_TENSOR_SIZE(shortData->size(), tensorInfo.GetNumElements());
            return armnn::ConstTensor(tensorInfo, shortData->data());
        }
        case ConstTensorData_IntData:
        {
            auto intData = constTensorPtr->data_as_IntData()->data();
            CHECK_CONST_TENSOR_SIZE(intData->size(), tensorInfo.GetNumElements());
            return armnn::ConstTensor(tensorInfo, intData->data());
        }
        case ConstTensorData_LongData:
        {
            auto longData = constTensorPtr->data_as_LongData()->data();
            CHECK_CONST_TENSOR_SIZE(longData->size(), tensorInfo.GetNumElements());
            return armnn::ConstTensor(tensorInfo, longData->data());
        }
        default:
        {
            CheckLocation location = CHECK_LOCATION();
            throw ParseException(fmt::format("Unsupported data type {0} = {1}. {2}",
                                             constTensorPtr->data_type(),
                                             EnumNameConstTensorData(constTensorPtr->data_type()),
                                             location.AsString()));
        }
    }
}

TensorRawPtrVector IDeserializer::DeserializerImpl::GetInputs(const GraphPtr& graphPtr, unsigned int layerIndex)
{
    CHECK_LAYERS(graphPtr, 0, layerIndex);
    auto layer = GetBaseLayer(graphPtr, layerIndex);
    const auto& numInputs = layer->inputSlots()->size();

    TensorRawPtrVector result(numInputs);

   for (unsigned int i=0; i<numInputs; ++i)
   {
       auto inputId = CHECKED_NON_NEGATIVE(static_cast<int32_t>
                                          (layer->inputSlots()->Get(i)->connection()->sourceLayerIndex()));
       result[i] = GetBaseLayer(graphPtr, inputId)->outputSlots()->Get(0)->tensorInfo();
   }
   return result;
}

TensorRawPtrVector IDeserializer::DeserializerImpl::GetOutputs(const GraphPtr& graphPtr, unsigned int layerIndex)
{
    CHECK_LAYERS(graphPtr, 0, layerIndex);
    auto layer = GetBaseLayer(graphPtr, layerIndex);
    const auto& numOutputs = layer->outputSlots()->size();

    TensorRawPtrVector result(numOutputs);

    for (unsigned int i=0; i<numOutputs; ++i)
    {
        result[i] = layer->outputSlots()->Get(i)->tensorInfo();
    }
    return result;
}

void IDeserializer::DeserializerImpl::ParseUnsupportedLayer(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);
    const auto layerName = GetBaseLayer(graph, layerIndex)->layerName()->c_str();
    throw ParseException(fmt::format("Layer not supported. layerIndex: {0} "
                                     "layerName: {1} / {2}",
                                     layerIndex,
                                     layerName,
                                     CHECK_LOCATION().AsString()));
}

void IDeserializer::DeserializerImpl::ResetParser()
{
    m_Network = armnn::INetworkPtr(nullptr, nullptr);
    m_InputBindings.clear();
    m_OutputBindings.clear();
}


INetworkPtr IDeserializer::DeserializerImpl::CreateNetworkFromBinary(const std::vector<uint8_t>& binaryContent)
{
     ResetParser();
     GraphPtr graph = LoadGraphFromBinary(binaryContent.data(), binaryContent.size());
     return CreateNetworkFromGraph(graph);
}

armnn::INetworkPtr IDeserializer::DeserializerImpl::CreateNetworkFromBinary(std::istream& binaryContent)
{
    ResetParser();
    if (binaryContent.fail()) {
        ARMNN_LOG(error) << (std::string("Cannot read input"));
        throw ParseException("Unable to read Input stream data");
    }
    binaryContent.seekg(0, std::ios::end);
    const std::streamoff size = binaryContent.tellg();
    std::vector<char> content(static_cast<size_t>(size));
    binaryContent.seekg(0);
    binaryContent.read(content.data(), static_cast<std::streamsize>(size));
    GraphPtr graph = LoadGraphFromBinary(reinterpret_cast<uint8_t*>(content.data()), static_cast<size_t>(size));
    return CreateNetworkFromGraph(graph);
}

GraphPtr IDeserializer::DeserializerImpl::LoadGraphFromBinary(const uint8_t* binaryContent, size_t len)
{
    if (binaryContent == nullptr)
    {
        throw InvalidArgumentException(fmt::format("Invalid (null) binary content {}",
                                                   CHECK_LOCATION().AsString()));
    }
    flatbuffers::Verifier verifier(binaryContent, len);
    if (verifier.VerifyBuffer<SerializedGraph>() == false)
    {
        throw ParseException(fmt::format("Buffer doesn't conform to the expected Armnn "
                                         "flatbuffers format. size:{0} {1}",
                                         len,
                                         CHECK_LOCATION().AsString()));
    }
    return GetSerializedGraph(binaryContent);
}

INetworkPtr IDeserializer::DeserializerImpl::CreateNetworkFromGraph(GraphPtr graph)
{
    m_Network = INetwork::Create();
    ARMNN_ASSERT(graph != nullptr);
    unsigned int layerIndex = 0;
    for (AnyLayer const* layer : *graph->layers())
    {
        if (layer->layer_type() != Layer_InputLayer &&
            layer->layer_type() != Layer_OutputLayer)
        {
            // lookup and call the parser function
            auto& parserFunction = m_ParserFunctions[layer->layer_type()];
            (this->*parserFunction)(graph, layerIndex);
        }
        ++layerIndex;
    }

    SetupInputLayers(graph);
    SetupOutputLayers(graph);

    // establish the connections from the layer outputs to the inputs of the subsequent layers
    for (auto&& graphIt : m_GraphConnections)
    {
        Connections& connections = graphIt.second;
        for (auto&& outputIt : connections.outputSlots)
        {
            const unsigned int outputSlotIndex = outputIt.first;
            IOutputSlot* outputSlot = outputIt.second;
            if (connections.inputSlots.find(outputSlotIndex) != connections.inputSlots.end())
            {
                for (IInputSlot* inputSlot : connections.inputSlots[outputSlotIndex])
                {
                    outputSlot->Connect(*inputSlot);
                }
            }
        }
    }

    return std::move(m_Network);
}

BindingPointInfo IDeserializer::DeserializerImpl::GetNetworkInputBindingInfo(unsigned int layerIndex,
                                                          const std::string& name) const
{
    IgnoreUnused(layerIndex);
    for (auto inputBinding : m_InputBindings)
    {
        if (inputBinding.first == name)
        {
            return inputBinding.second;
        }
    }
    throw ParseException(fmt::format("No input binding found for layer:{0} / {1}",
                                     name,
                                     CHECK_LOCATION().AsString()));
}

BindingPointInfo IDeserializer::DeserializerImpl::GetNetworkOutputBindingInfo(unsigned int layerIndex,
                                                                const std::string& name) const
{
    IgnoreUnused(layerIndex);
    for (auto outputBinding : m_OutputBindings)
    {
        if (outputBinding.first == name)
        {
            return outputBinding.second;
        }
    }
    throw ParseException(fmt::format("No output binding found for layer:{0} / {1}",
                                     name,
                                     CHECK_LOCATION().AsString()));
}

unsigned int IDeserializer::DeserializerImpl::GetInputLayerInVector(GraphPtr graph, int targetId)
{
    for (unsigned int i = 0; i < graph->layers()->size(); i++)
    {
        auto layer = graph->layers()->Get(i);
        if (layer->layer_type() == Layer::Layer_InputLayer)
        {
            auto layerBindingId = layer->layer_as_InputLayer()->base()->layerBindingId();
            if (layerBindingId == targetId)
            {
                return i;
            }
        }
    }
    throw ParseException("Input layer with given layerBindingId not found");
}

unsigned int IDeserializer::DeserializerImpl::GetOutputLayerInVector(GraphPtr graph, int targetId)
{
    for (unsigned int i = 0; i < graph->layers()->size(); i++)
    {
        auto layer = graph->layers()->Get(i);
        if (layer->layer_type() == Layer::Layer_OutputLayer)
        {
            auto layerBindingId = layer->layer_as_OutputLayer()->base()->layerBindingId();
            if (layerBindingId == targetId)
            {
                return i;
            }
        }
    }
    throw ParseException("Output layer with given layerBindingId not found");
}

unsigned int IDeserializer::DeserializerImpl::GetLayerIndexInVector(GraphPtr graph, unsigned int targetIndex)
{
    for (unsigned int i = 0; i < graph->layers()->size(); i++)
    {
        LayerBaseRawPtr layer = GetBaseLayer(graph, i);
        if (layer->index() == targetIndex)
        {
            return i;
        }
    }
    throw ParseException("Layer with given index not found");
}

IDeserializer::DeserializerImpl::FeatureVersions IDeserializer::DeserializerImpl::GetFeatureVersions(GraphPtr graph)
{
    IDeserializer::DeserializerImpl::FeatureVersions versions;

    if (graph->featureVersions())
    {
        versions.m_BindingIdScheme = graph->featureVersions()->bindingIdsScheme();
        versions.m_WeightsLayoutScheme = graph->featureVersions()->weightsLayoutScheme();
        versions.m_ConstTensorsAsInputs = graph->featureVersions()->constantTensorsAsInputs();
    }

    return versions;
}

void IDeserializer::DeserializerImpl::SetupInputLayers(GraphPtr graph)
{
    CHECK_GRAPH(graph, 0);
    const unsigned int numInputs = graph->inputIds()->size();
    m_InputBindings.clear();
    m_InputBindings.reserve(numInputs);

    for (unsigned int i = 0; i < numInputs; i++)
    {
        unsigned int inputLayerIndex = 0xFFFFFFFF;
        if (GetFeatureVersions(graph).m_BindingIdScheme == 0)
        {
            const unsigned int inputId = armnn::numeric_cast<unsigned int>(graph->inputIds()->Get(i));
            inputLayerIndex = GetLayerIndexInVector(graph, inputId);
        }
        else
        {
            const int inputId = graph->inputIds()->Get(i);
            inputLayerIndex = GetInputLayerInVector(graph, inputId);
        }

        LayerBaseRawPtr baseLayer = GetBaseLayer(graph, inputLayerIndex);

        // GetBindingLayerInfo expect the index to be index in the vector not index property on each layer base
        LayerBindingId bindingId = GetBindingLayerInfo(graph, inputLayerIndex);
        ARMNN_ASSERT_MSG(baseLayer->layerName()->c_str(), "Input has no name.");

        IConnectableLayer* inputLayer =
            m_Network->AddInputLayer(bindingId, baseLayer->layerName()->c_str());

        const armnn::TensorInfo& tensorInfo = ToTensorInfo(baseLayer->outputSlots()->Get(0)->tensorInfo());
        inputLayer->GetOutputSlot(0).SetTensorInfo(tensorInfo);
        RegisterOutputSlots(graph, inputLayerIndex, inputLayer);

        BindingPointInfo bindingInfo = {bindingId, tensorInfo};
        m_InputBindings.push_back(std::make_pair(baseLayer->layerName()->c_str(), bindingInfo));
    }
}

void IDeserializer::DeserializerImpl::SetupOutputLayers(GraphPtr graph)
{
    CHECK_GRAPH(graph, 0);
    const unsigned int numOutputs = graph->outputIds()->size();
    m_OutputBindings.clear();
    m_OutputBindings.reserve(numOutputs);

    for (unsigned int i = 0; i < numOutputs; i++)
    {
        unsigned int outputLayerIndex = 0xFFFFFFFF;
        if (GetFeatureVersions(graph).m_BindingIdScheme == 0)
        {
            const unsigned int outputId = armnn::numeric_cast<unsigned int>(graph->outputIds()->Get(i));
            outputLayerIndex = GetLayerIndexInVector(graph, outputId);
        }
        else
        {
            const int outputId = graph->outputIds()->Get(i);
            outputLayerIndex = GetOutputLayerInVector(graph, outputId);
        }

        LayerBaseRawPtr baseLayer = GetBaseLayer(graph, outputLayerIndex);

        // GetBindingLayerInfo expect the index to be index in the vector not index property on each layer base
        LayerBindingId bindingId = GetBindingLayerInfo(graph, outputLayerIndex);
        ARMNN_ASSERT_MSG(baseLayer->layerName()->c_str(), "Output has no name.");

        IConnectableLayer* outputLayer =
            m_Network->AddOutputLayer(bindingId, baseLayer->layerName()->c_str());

        RegisterInputSlots(graph, outputLayerIndex, outputLayer);
        unsigned int sourceLayerIndex =
            GetLayerIndexInVector(graph, baseLayer->inputSlots()->Get(0)->connection()->sourceLayerIndex());
        unsigned int outputSlotIndex =
            GetLayerIndexInVector(graph, baseLayer->inputSlots()->Get(0)->connection()->outputSlotIndex());
        LayerBaseRawPtr sourceBaseLayer = GetBaseLayer(graph, sourceLayerIndex);
        const armnn::TensorInfo& tensorInfo = ToTensorInfo(
            sourceBaseLayer->outputSlots()->Get(outputSlotIndex)->tensorInfo());
        BindingPointInfo bindingInfo = {bindingId, tensorInfo};
        m_OutputBindings.push_back(std::make_pair(baseLayer->layerName()->c_str(), bindingInfo));
    }
}

void IDeserializer::DeserializerImpl::RegisterOutputSlots(GraphPtr graph,
                                       uint32_t layerIndex,
                                       IConnectableLayer* layer)
{
    CHECK_LAYERS(graph, 0, layerIndex);
    ARMNN_ASSERT(layer != nullptr);
    LayerBaseRawPtr baseLayer = GetBaseLayer(graph, layerIndex);
    if (baseLayer->outputSlots()->size() != layer->GetNumOutputSlots())
    {
        throw ParseException(fmt::format("The number of outputslots ({0}) does not match the number expected ({1})"
                                         " for layer index: {2} {3}",
                                         baseLayer->outputSlots()->size(),
                                         layer->GetNumOutputSlots(),
                                         layerIndex,
                                         CHECK_LOCATION().AsString()));
    }

    for (unsigned int i = 0; i < layer->GetNumOutputSlots(); ++i)
    {
        const unsigned int slotIndex = baseLayer->outputSlots()->Get(i)->index();
        armnn::IOutputSlot* outputSlot = &(layer->GetOutputSlot(slotIndex));
        // layerIndex is not necessarily the same as baseLayer->index(). The latter is needed here
        RegisterOutputSlotOfConnection(baseLayer->index(), slotIndex, outputSlot);
    }
}

void IDeserializer::DeserializerImpl::RegisterInputSlots(GraphPtr graph,
                                                         uint32_t layerIndex,
                                                         armnn::IConnectableLayer* layer,
                                                         std::vector<unsigned int> ignoreSlots)
{
    CHECK_LAYERS(graph, 0, layerIndex);
    ARMNN_ASSERT(layer != nullptr);
    LayerBaseRawPtr baseLayer = GetBaseLayer(graph, layerIndex);

    if (baseLayer->inputSlots()->size() != (layer->GetNumInputSlots() - ignoreSlots.size()))
    {
        throw ParseException(fmt::format("The number of inputslots ({0}) does not match the number expected ({1})"
                                         " for layer index:{2} {3}",
                                         baseLayer->inputSlots()->size(),
                                         layer->GetNumInputSlots(),
                                         layerIndex,
                                         CHECK_LOCATION().AsString()));
    }

    for (unsigned int i = 0; i < layer->GetNumInputSlots(); ++i)
    {
        // Check if slot should be ignored.
        if (std::find(ignoreSlots.begin(), ignoreSlots.end(), i) == ignoreSlots.end())
        {
            auto fbInputSlot = baseLayer->inputSlots()->Get(i);
            auto fbConnection = fbInputSlot->connection();
            armnn::IInputSlot* inputSlot = &(layer->GetInputSlot(fbInputSlot->index()));
            RegisterInputSlotOfConnection(fbConnection->sourceLayerIndex(), fbConnection->outputSlotIndex(), inputSlot);
        }
    }
}

void IDeserializer::DeserializerImpl::RegisterInputSlotOfConnection(uint32_t sourceLayerIndex,
                                                 uint32_t outputSlotIndex,
                                                 armnn::IInputSlot* inputSlot)
{
    if (m_GraphConnections.find(sourceLayerIndex) == m_GraphConnections.end())
    {
        m_GraphConnections[sourceLayerIndex] = Connections();
    }

    Connections& connections = m_GraphConnections[sourceLayerIndex];
    if (connections.inputSlots.find(outputSlotIndex) == connections.inputSlots.end())
    {
        connections.inputSlots[outputSlotIndex] = {inputSlot};
    }
    else
    {
        connections.inputSlots[outputSlotIndex].push_back(inputSlot);
    }
}

void IDeserializer::DeserializerImpl::RegisterOutputSlotOfConnection(uint32_t sourceLayerIndex,
                                                  uint32_t outputSlotIndex,
                                                  armnn::IOutputSlot* outputSlot)
{
    if (m_GraphConnections.find(sourceLayerIndex) == m_GraphConnections.end())
    {
        m_GraphConnections[sourceLayerIndex] = Connections();
    }

    Connections& connections = m_GraphConnections[sourceLayerIndex];
    if (connections.outputSlots.find(outputSlotIndex) != connections.outputSlots.end())
    {
        throw ParseException("Same output slot index processed twice");
    }

    connections.outputSlots[outputSlotIndex] = outputSlot;
}

void IDeserializer::DeserializerImpl::ParseAbs(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);
    auto inputs = GetInputs(graph, layerIndex);
    CHECK_LOCATION();
    CHECK_VALID_SIZE(inputs.size(), 1);

    auto outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);

    auto layerName = GetLayerName(graph, layerIndex);

    armnn::ElementwiseUnaryDescriptor descriptor(armnn::UnaryOperation::Abs);
    IConnectableLayer* layer = m_Network->AddElementwiseUnaryLayer(descriptor, layerName.c_str());
    armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseActivation(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);
    auto inputs = GetInputs(graph, layerIndex);
    CHECK_LOCATION();
    CHECK_VALID_SIZE(inputs.size(), 1);

    auto outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);

    auto serializerLayer = graph->layers()->Get(layerIndex)->layer_as_ActivationLayer();
    auto layerName = GetLayerName(graph, layerIndex);
    auto serializerDescriptor = serializerLayer->descriptor();

    armnn::ActivationDescriptor descriptor;
    descriptor.m_Function = ToActivationFunction(serializerDescriptor->activationFunction());
    descriptor.m_A = serializerDescriptor->a();
    descriptor.m_B = serializerDescriptor->b();

    IConnectableLayer* layer = m_Network->AddActivationLayer(descriptor,
                                                             layerName.c_str());
    armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseAdd(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);
    auto inputs = GetInputs(graph, layerIndex);
    CHECK_LOCATION();
    CHECK_VALID_SIZE(inputs.size(), 2);

    auto outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);

    auto layerName = GetLayerName(graph, layerIndex);
    IConnectableLayer* layer = m_Network->AddAdditionLayer(layerName.c_str());

    armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseArgMinMax(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);
    auto inputs = GetInputs(graph, layerIndex);
    CHECK_LOCATION();
    CHECK_VALID_SIZE(inputs.size(), 1);

    auto outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);

    auto serializerLayer = graph->layers()->Get(layerIndex)->layer_as_ArgMinMaxLayer();
    auto serializerDescriptor = serializerLayer->descriptor();

    armnn::ArgMinMaxDescriptor descriptor;
    descriptor.m_Function = ToArgMinMaxFunction(serializerDescriptor->argMinMaxFunction());
    descriptor.m_Axis = serializerDescriptor->axis();
    auto layerName = GetLayerName(graph, layerIndex);
    IConnectableLayer* layer = m_Network->AddArgMinMaxLayer(descriptor, layerName.c_str());

    armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseBatchMatMul(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);

    auto inputs = GetInputs(graph, layerIndex);
    CHECK_LOCATION();
    CHECK_VALID_SIZE(inputs.size(), 2);

    auto outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);

    auto serializerLayer = graph->layers()->Get(layerIndex)->layer_as_BatchMatMulLayer();
    auto serializerDescriptor = serializerLayer->descriptor();

    armnn::BatchMatMulDescriptor descriptor(serializerDescriptor->transposeX(),
                                            serializerDescriptor->transposeY(),
                                            serializerDescriptor->adjointX(),
                                            serializerDescriptor->adjointY(),
                                            ToDataLayout(serializerDescriptor->dataLayoutX()),
                                            ToDataLayout(serializerDescriptor->dataLayoutY()));

    auto layerName = GetLayerName(graph, layerIndex);
    IConnectableLayer* layer = m_Network->AddBatchMatMulLayer(descriptor, layerName.c_str());

    armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseBatchToSpaceNd(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);

    TensorRawPtrVector inputs = GetInputs(graph, layerIndex);
    CHECK_VALID_SIZE(inputs.size(), 1);

    TensorRawPtrVector outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);

    auto flatBufferDescriptor = graph->layers()->Get(layerIndex)->layer_as_BatchToSpaceNdLayer()->descriptor();
    auto flatBufferCrops = flatBufferDescriptor->crops();
    auto flatBufferBlockShape = flatBufferDescriptor->blockShape();

    if (flatBufferCrops->size() % 2 != 0)
    {
        throw ParseException(fmt::format("The size of crops must be divisible by 2 {}", CHECK_LOCATION().AsString()));
    }

    std::vector<std::pair<unsigned int, unsigned int>> crops;
    crops.reserve(flatBufferCrops->size() / 2);
    for (unsigned int i = 0; i < flatBufferCrops->size() - 1; i += 2)
    {
        crops.emplace_back(flatBufferCrops->Get(i), flatBufferCrops->Get(i+1));
    }

    armnn::BatchToSpaceNdDescriptor descriptor;
    descriptor.m_DataLayout = ToDataLayout(flatBufferDescriptor->dataLayout());
    descriptor.m_BlockShape =
        std::vector<unsigned int>(flatBufferBlockShape->begin(), flatBufferBlockShape->end());
    descriptor.m_Crops = crops;

    auto layerName = GetLayerName(graph, layerIndex);
    IConnectableLayer* layer = m_Network->AddBatchToSpaceNdLayer(descriptor, layerName.c_str());

    armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseBatchNormalization(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);

    auto inputs = GetInputs(graph, layerIndex);
    CHECK_VALID_SIZE(inputs.size(), 1);

    auto outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);
    auto outputInfo = ToTensorInfo(outputs[0]);

    auto layerName = GetLayerName(graph, layerIndex);

    auto serializerLayer = graph->layers()->Get(layerIndex)->layer_as_BatchNormalizationLayer();
    auto serializerDescriptor = serializerLayer->descriptor();

    armnn::BatchNormalizationDescriptor descriptor;
    descriptor.m_Eps = serializerDescriptor->eps();
    descriptor.m_DataLayout = ToDataLayout(serializerDescriptor->dataLayout());

    armnn::ConstTensor mean     = ToConstTensor(serializerLayer->mean());
    armnn::ConstTensor variance = ToConstTensor(serializerLayer->variance());
    armnn::ConstTensor beta     = ToConstTensor(serializerLayer->beta());
    armnn::ConstTensor gamma    = ToConstTensor(serializerLayer->gamma());

    IConnectableLayer* layer = m_Network->AddBatchNormalizationLayer(descriptor,
                                                                     mean,
                                                                     variance,
                                                                     beta,
                                                                     gamma,
                                                                     layerName.c_str());
    layer->GetOutputSlot(0).SetTensorInfo(outputInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseCast(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);
    TensorRawPtrVector inputs = GetInputs(graph, layerIndex);
    CHECK_LOCATION();
    CHECK_VALID_SIZE(inputs.size(), 1);

    TensorRawPtrVector outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);

    auto layerName = GetLayerName(graph, layerIndex);

    IConnectableLayer* layer = m_Network->AddCastLayer(layerName.c_str());

    armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseConstant(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);
    CHECK_LOCATION();

    auto outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);

    auto layerName = GetLayerName(graph, layerIndex);

    auto serializerLayer = graph->layers()->Get(layerIndex)->layer_as_ConstantLayer();
    auto serializerInput = serializerLayer->input();

    armnn::ConstTensor input = ToConstTensor(serializerInput);
    IConnectableLayer* layer;

    // Required for when Constant Layer is used as an inputs to DepthwiseConvolution2d Layer.
    // Running a model that was created before weights layout scheme version was added to our flatbuffers
    // file ensuring older models can still be read and executed. featureVersion weights layout scheme 1
    // indicates a change in the depthwise weights layout within ArmNN from [M,I,H,W] --> [1,H,W,I*M]
    if (this->GetFeatureVersions(graph).m_WeightsLayoutScheme <= 0)
    {
        // Permute weights  [ H, W, M, I ] --> [ 1, H, W, I*M ]
        // Step1: [ M, I, H, W ] --> [ H, W, I, M]
        PermutationVector permutationVector = { 3, 2, 0, 1 };
        armnn::TensorInfo weightsInfo = input.GetInfo();
        std::unique_ptr<unsigned char[]> permuteBuffer(new unsigned char[weightsInfo.GetNumBytes()]);
        weightsInfo = armnnUtils::Permuted(weightsInfo, permutationVector);
        armnnUtils::Permute(weightsInfo.GetShape(), permutationVector,
                            input.GetMemoryArea(), permuteBuffer.get(),
                            GetDataTypeSize(weightsInfo.GetDataType()));

        // Step2: Reshape [ H, W, I, M] --> [ 1, H, W, I*M ]
        auto weightsShape = weightsInfo.GetShape();
        weightsInfo.SetShape({1,
                              weightsShape[0],
                              weightsShape[1],
                              weightsShape[2]*weightsShape[3]});
        weightsInfo.SetConstant(true);

        armnn::ConstTensor weightsPermuted(weightsInfo, permuteBuffer.get());

        layer = m_Network->AddConstantLayer(weightsPermuted, layerName.c_str());

        layer->GetOutputSlot(0).SetTensorInfo(weightsPermuted.GetInfo());

        RegisterOutputSlots(graph, layerIndex, layer);

        return;
    }
    else
    {
        layer = m_Network->AddConstantLayer(input, layerName.c_str());

        armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
        outputTensorInfo.SetConstant(true);
        layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
    }

    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseConvolution2d(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);
    auto inputs = GetInputs(graph, layerIndex);
    CHECK_LOCATION();

    auto outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);

    auto flatBufferLayer = graph->layers()->Get(layerIndex)->layer_as_Convolution2dLayer();

    auto layerName = GetLayerName(graph, layerIndex);
    auto flatbufferDescriptor = flatBufferLayer->descriptor();

    armnn::Convolution2dDescriptor descriptor;
    descriptor.m_PadLeft = flatbufferDescriptor->padLeft();
    descriptor.m_PadRight = flatbufferDescriptor->padRight();
    descriptor.m_PadTop = flatbufferDescriptor->padTop();
    descriptor.m_PadBottom = flatbufferDescriptor->padBottom();
    descriptor.m_StrideX = flatbufferDescriptor->strideX();
    descriptor.m_StrideY = flatbufferDescriptor->strideY();;
    descriptor.m_DilationX = flatbufferDescriptor->dilationX();
    descriptor.m_DilationY = flatbufferDescriptor->dilationY();;
    descriptor.m_BiasEnabled = flatbufferDescriptor->biasEnabled();;
    descriptor.m_DataLayout = ToDataLayout(flatbufferDescriptor->dataLayout());

    armnn::IConnectableLayer* layer;
    std::vector<unsigned int> ignoreSlots {};

    armnn::ConstTensor biasTensor;
    // Weights and biases used to be always constant and were stored as members of the layer. This has changed and
    // they are now passed as inputs. If they are constant then they will be stored in a ConstantLayer.
    if (this->GetFeatureVersions(graph).m_ConstTensorsAsInputs <= 0)
    {
        // If the model stores weights and biases as members of the layer we have to read them from there
        // but add them to their own ConstantLayer for compatibility
        CHECK_VALID_SIZE(inputs.size(), 1);

        layer = m_Network->AddConvolution2dLayer(descriptor,
                                                 layerName.c_str());

        armnn::ConstTensor weightsTensor = ToConstTensor(flatBufferLayer->weights());
        auto weightsLayer = m_Network->AddConstantLayer(weightsTensor);
        weightsLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1u));
        weightsLayer->GetOutputSlot(0).SetTensorInfo(weightsTensor.GetInfo());
        ignoreSlots.emplace_back(1u);

        if (descriptor.m_BiasEnabled)
        {
            biasTensor = ToConstTensor(flatBufferLayer->biases());
            auto biasLayer = m_Network->AddConstantLayer(biasTensor);
            biasLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(2u));
            biasLayer->GetOutputSlot(0).SetTensorInfo(biasTensor.GetInfo());
            ignoreSlots.emplace_back(2u);
        }
    }
    else
    {
        layer = m_Network->AddConvolution2dLayer(descriptor,
                                                 layerName.c_str());
        uint32_t numInputs = descriptor.GetNumInputs();
        CHECK_VALID_SIZE(inputs.size(), numInputs);
    }

    armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);

    RegisterInputSlots(graph, layerIndex, layer, ignoreSlots);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseConvolution3d(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);
    auto inputs = GetInputs(graph, layerIndex);
    CHECK_LOCATION();

    auto outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);

    auto serializerLayer = graph->layers()->Get(layerIndex)->layer_as_Convolution3dLayer();
    auto layerName = GetLayerName(graph, layerIndex);
    auto serializerDescriptor = serializerLayer->descriptor();

    armnn::Convolution3dDescriptor descriptor;
    descriptor.m_PadLeft = serializerDescriptor->padLeft();
    descriptor.m_PadRight = serializerDescriptor->padRight();
    descriptor.m_PadTop = serializerDescriptor->padTop();
    descriptor.m_PadBottom = serializerDescriptor->padBottom();
    descriptor.m_PadFront = serializerDescriptor->padFront();
    descriptor.m_PadBack = serializerDescriptor->padBack();
    descriptor.m_StrideX = serializerDescriptor->strideX();
    descriptor.m_StrideY = serializerDescriptor->strideY();
    descriptor.m_StrideZ = serializerDescriptor->strideZ();
    descriptor.m_DilationX = serializerDescriptor->dilationX();
    descriptor.m_DilationY = serializerDescriptor->dilationY();
    descriptor.m_DilationZ = serializerDescriptor->dilationZ();
    descriptor.m_BiasEnabled = serializerDescriptor->biasEnabled();
    descriptor.m_DataLayout = ToDataLayout(serializerDescriptor->dataLayout());

    uint32_t numInputs = descriptor.GetNumInputs();
    CHECK_VALID_SIZE(inputs.size(), numInputs);

    IConnectableLayer* layer = m_Network->AddConvolution3dLayer(descriptor, layerName.c_str());

    armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseDepthToSpace(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);

    auto inputs = GetInputs(graph, layerIndex);
    CHECK_VALID_SIZE(inputs.size(), 1);

    auto outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);

    auto fbDescriptor = graph->layers()->Get(layerIndex)->layer_as_DepthToSpaceLayer()->descriptor();

    armnn::DepthToSpaceDescriptor descriptor;
    descriptor.m_BlockSize  = fbDescriptor->blockSize();
    descriptor.m_DataLayout = ToDataLayout(fbDescriptor->dataLayout());

    auto layerName = GetLayerName(graph, layerIndex);
    IConnectableLayer* layer = m_Network->AddDepthToSpaceLayer(descriptor, layerName.c_str());

    armnn::TensorInfo outputInfo = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(outputInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseDepthwiseConvolution2d(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);
    auto inputs = GetInputs(graph, layerIndex);
    CHECK_LOCATION();

    auto outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);

    auto serializerLayer = graph->layers()->Get(layerIndex)->layer_as_DepthwiseConvolution2dLayer();
    auto layerName = GetLayerName(graph, layerIndex);
    auto serializerDescriptor = serializerLayer->descriptor();

    armnn::DepthwiseConvolution2dDescriptor descriptor;
    descriptor.m_PadLeft = serializerDescriptor->padLeft();
    descriptor.m_PadRight = serializerDescriptor->padRight();
    descriptor.m_PadTop = serializerDescriptor->padTop();
    descriptor.m_PadBottom = serializerDescriptor->padBottom();
    descriptor.m_StrideX = serializerDescriptor->strideX();
    descriptor.m_StrideY = serializerDescriptor->strideY();
    descriptor.m_DilationX = serializerDescriptor->dilationX();
    descriptor.m_DilationY = serializerDescriptor->dilationY();
    descriptor.m_BiasEnabled = serializerDescriptor->biasEnabled();
    descriptor.m_DataLayout = ToDataLayout(serializerDescriptor->dataLayout());

    IConnectableLayer* layer;
    std::vector<unsigned int> ignoreSlots {};

    // Weights and biases used to be always constant and were stored as members of the layer. This has changed and
    // they are now passed as inputs. If they are constant then they will be stored in a ConstantLayer.
    if (this->GetFeatureVersions(graph).m_ConstTensorsAsInputs <= 0)
    {
        CHECK_VALID_SIZE(inputs.size(), 1);

        // If the model stores weights and biases as members of the layer we have to read them from there
        // but add them to their own ConstantLayer for compatibility
        armnn::ConstTensor weights = ToConstTensor(serializerLayer->weights());
        ignoreSlots.emplace_back(1u);

        layer = m_Network->AddDepthwiseConvolution2dLayer(descriptor,
                                                          layerName.c_str());

        armnn::Optional<armnn::ConstTensor> optionalBiases = armnn::EmptyOptional();
        if (descriptor.m_BiasEnabled)
        {
            armnn::ConstTensor biases = ToConstTensor(serializerLayer->biases());
            ignoreSlots.emplace_back(2u);

            auto biasLayer = m_Network->AddConstantLayer(biases);
            biasLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(2u));
            biasLayer->GetOutputSlot(0).SetTensorInfo(biases.GetInfo());
        }

        if (this->GetFeatureVersions(graph).m_WeightsLayoutScheme <= 0)
        {
            // Permute weights  [ H, W, M, I ] --> [ 1, H, W, I*M ]
            // Step1: [ M, I, H, W ] --> [ H, W, I, M]
            PermutationVector permutationVector = { 3, 2, 0, 1 };
            armnn::TensorInfo weightsInfo = weights.GetInfo();
            std::unique_ptr<unsigned char[]> permuteBuffer(new unsigned char[weightsInfo.GetNumBytes()]);
            weightsInfo = armnnUtils::Permuted(weightsInfo, permutationVector);
            armnnUtils::Permute(weightsInfo.GetShape(), permutationVector,
                                weights.GetMemoryArea(), permuteBuffer.get(),
                                GetDataTypeSize(weightsInfo.GetDataType()));

            // Step2: Reshape [ H, W, I, M] --> [ 1, H, W, I*M ]
            auto weightsShape = weightsInfo.GetShape();
            weightsInfo.SetShape({1,
                                  weightsShape[0],
                                  weightsShape[1],
                                  weightsShape[2]*weightsShape[3]});

            armnn::ConstTensor weightsPermuted(weightsInfo, permuteBuffer.get());

            auto weightsLayer = m_Network->AddConstantLayer(weightsPermuted);
            weightsLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1u));
            weightsLayer->GetOutputSlot(0).SetTensorInfo(weightsPermuted.GetInfo());
        }
        else
        {
            auto weightsLayer = m_Network->AddConstantLayer(weights);
            weightsLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1u));
            weightsLayer->GetOutputSlot(0).SetTensorInfo(weights.GetInfo());
        }
    }
    else
    {
        layer = m_Network->AddDepthwiseConvolution2dLayer(descriptor,
                                                          layerName.c_str());
        uint32_t numInputs = descriptor.GetNumInputs();
        CHECK_VALID_SIZE(inputs.size(), numInputs);
    }

    armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);

    RegisterInputSlots(graph, layerIndex, layer, ignoreSlots);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseDetectionPostProcess(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);
    auto inputs = GetInputs(graph, layerIndex);
    CHECK_LOCATION();
    CHECK_VALID_SIZE(inputs.size(), 2);

    auto outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 4);

    auto flatBufferLayer = graph->layers()->Get(layerIndex)->layer_as_DetectionPostProcessLayer();
    auto layerName = GetLayerName(graph, layerIndex);
    auto flatBufferDescriptor = flatBufferLayer->descriptor();

    armnn::DetectionPostProcessDescriptor descriptor;
    descriptor.m_MaxDetections = flatBufferDescriptor->maxDetections();
    descriptor.m_MaxClassesPerDetection = flatBufferDescriptor->maxClassesPerDetection();
    descriptor.m_DetectionsPerClass = flatBufferDescriptor->detectionsPerClass();
    descriptor.m_NmsScoreThreshold = flatBufferDescriptor->nmsScoreThreshold();
    descriptor.m_NmsIouThreshold = flatBufferDescriptor->nmsIouThreshold();
    descriptor.m_NumClasses = flatBufferDescriptor->numClasses();
    descriptor.m_UseRegularNms = flatBufferDescriptor->useRegularNms();
    descriptor.m_ScaleX = flatBufferDescriptor->scaleX();
    descriptor.m_ScaleY = flatBufferDescriptor->scaleY();
    descriptor.m_ScaleW = flatBufferDescriptor->scaleW();
    descriptor.m_ScaleH = flatBufferDescriptor->scaleH();

    armnn::ConstTensor anchors = ToConstTensor(flatBufferLayer->anchors());

    IConnectableLayer* layer = m_Network->AddDetectionPostProcessLayer(descriptor,
                                                                       anchors,
                                                                       layerName.c_str());

    for (unsigned int i = 0; i < 4; i++)
    {
        layer->GetOutputSlot(i).SetTensorInfo(ToTensorInfo(outputs[i]));
    }

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseDivision(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);
    auto inputs = GetInputs(graph, layerIndex);
    CHECK_LOCATION();
    CHECK_VALID_SIZE(inputs.size(), 2);

    auto outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);

    auto layerName = GetLayerName(graph, layerIndex);
    IConnectableLayer* layer = m_Network->AddDivisionLayer(layerName.c_str());

    armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseEqual(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);
    auto inputs = GetInputs(graph, layerIndex);
    CHECK_LOCATION();
    CHECK_VALID_SIZE(inputs.size(), 2);

    auto outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);

    auto layerName = GetLayerName(graph, layerIndex);
    armnn::ComparisonDescriptor descriptor(armnn::ComparisonOperation::Equal);
    IConnectableLayer* layer = m_Network->AddComparisonLayer(descriptor, layerName.c_str());

    armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseFill(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);
    auto inputs = GetInputs(graph, layerIndex);
    CHECK_LOCATION();
    CHECK_VALID_SIZE(inputs.size(), 1);

    auto outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);

    auto layerName = GetLayerName(graph, layerIndex);
    armnn::FillDescriptor descriptor;
    descriptor.m_Value = graph->layers()->Get(layerIndex)->layer_as_FillLayer()->descriptor()->value();
    IConnectableLayer* layer = m_Network->AddFillLayer(descriptor, layerName.c_str());

    armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseGreater(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);
    auto inputs = GetInputs(graph, layerIndex);
    CHECK_LOCATION();
    CHECK_VALID_SIZE(inputs.size(), 2);

    auto outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);

    auto layerName = GetLayerName(graph, layerIndex);
    armnn::ComparisonDescriptor descriptor(armnn::ComparisonOperation::Greater);
    IConnectableLayer* layer = m_Network->AddComparisonLayer(descriptor, layerName.c_str());

    armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseInstanceNormalization(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);

    auto inputs = GetInputs(graph, layerIndex);
    CHECK_VALID_SIZE(inputs.size(), 1);

    auto outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);

    auto fbLayer      = graph->layers()->Get(layerIndex)->layer_as_InstanceNormalizationLayer();
    auto fbDescriptor = fbLayer->descriptor();

    armnn::InstanceNormalizationDescriptor descriptor;
    descriptor.m_Gamma      = fbDescriptor->gamma();
    descriptor.m_Beta       = fbDescriptor->beta();
    descriptor.m_Eps        = fbDescriptor->eps();
    descriptor.m_DataLayout = ToDataLayout(fbDescriptor->dataLayout());

    const std::string layerName        = GetLayerName(graph, layerIndex);
    const armnn::TensorInfo outputInfo = ToTensorInfo(outputs[0]);

    IConnectableLayer* layer = m_Network->AddInstanceNormalizationLayer(descriptor, layerName.c_str());
    layer->GetOutputSlot(0).SetTensorInfo(outputInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseL2Normalization(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);

    auto inputs = GetInputs(graph, layerIndex);
    CHECK_VALID_SIZE(inputs.size(), 1);

    auto outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);
    auto outputInfo = ToTensorInfo(outputs[0]);

    auto flatBufferLayer = graph->layers()->Get(layerIndex)->layer_as_L2NormalizationLayer();
    auto flatBufferDescriptor = flatBufferLayer->descriptor();

    auto layerName = GetLayerName(graph, layerIndex);
    armnn::L2NormalizationDescriptor descriptor;
    descriptor.m_DataLayout = ToDataLayout(flatBufferDescriptor->dataLayout());
    descriptor.m_Eps = flatBufferDescriptor->eps();

    IConnectableLayer* layer = m_Network->AddL2NormalizationLayer(descriptor, layerName.c_str());
    layer->GetOutputSlot(0).SetTensorInfo(outputInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseLogicalBinary(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);
    CHECK_LOCATION();

    auto inputs = GetInputs(graph, layerIndex);
    CHECK_VALID_SIZE(inputs.size(), 2);

    auto outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);

    auto fbLayer      = graph->layers()->Get(layerIndex)->layer_as_LogicalBinaryLayer();
    auto fbDescriptor = fbLayer->descriptor();

    armnn::LogicalBinaryDescriptor descriptor;
    descriptor.m_Operation = ToLogicalBinaryOperation(fbDescriptor->operation());

    const std::string& layerName = GetLayerName(graph, layerIndex);
    IConnectableLayer* layer     = m_Network->AddLogicalBinaryLayer(descriptor, layerName.c_str());

    armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseLogSoftmax(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);

    TensorRawPtrVector inputs = GetInputs(graph, layerIndex);
    CHECK_VALID_SIZE(inputs.size(), 1);

    TensorRawPtrVector outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);

    armnn::LogSoftmaxDescriptor descriptor;
    descriptor.m_Beta = graph->layers()->Get(layerIndex)->layer_as_LogSoftmaxLayer()->descriptor()->beta();
    descriptor.m_Axis = graph->layers()->Get(layerIndex)->layer_as_LogSoftmaxLayer()->descriptor()->axis();
    auto layerName = GetLayerName(graph, layerIndex);

    IConnectableLayer* layer = m_Network->AddLogSoftmaxLayer(descriptor, layerName.c_str());

    armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseMinimum(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);
    auto inputs = GetInputs(graph, layerIndex);
    CHECK_LOCATION();
    CHECK_VALID_SIZE(inputs.size(), 2);

    auto outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);

    auto layerName = GetLayerName(graph, layerIndex);
    IConnectableLayer* layer = m_Network->AddMinimumLayer(layerName.c_str());

    armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseMaximum(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);
    auto inputs = GetInputs(graph, layerIndex);
    CHECK_LOCATION();
    CHECK_VALID_SIZE(inputs.size(), 2);

    auto outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);

    auto layerName = GetLayerName(graph, layerIndex);
    IConnectableLayer* layer = m_Network->AddMaximumLayer(layerName.c_str());

    armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

const armnnSerializer::OriginsDescriptor* GetOriginsDescriptor(const armnnSerializer::SerializedGraph* graph,
                                                               unsigned int layerIndex)
{
    auto layerType = graph->layers()->Get(layerIndex)->layer_type();

    switch (layerType)
    {
        case Layer::Layer_ConcatLayer:
            return graph->layers()->Get(layerIndex)->layer_as_ConcatLayer()->descriptor();
        case Layer::Layer_MergerLayer:
            return graph->layers()->Get(layerIndex)->layer_as_MergerLayer()->descriptor();
        default:
            throw armnn::Exception("unknown layer type, should be concat or merger");
    }
}
void IDeserializer::DeserializerImpl::ParseChannelShuffle(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);

    TensorRawPtrVector inputs = GetInputs(graph, layerIndex);
    CHECK_VALID_SIZE(inputs.size(), 1);

    TensorRawPtrVector outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);

    armnn::ChannelShuffleDescriptor descriptor;
    descriptor.m_Axis = graph->layers()->Get(layerIndex)->layer_as_ChannelShuffleLayer()->descriptor()->axis();
    descriptor.m_NumGroups =
                   graph->layers()->Get(layerIndex)->layer_as_ChannelShuffleLayer()->descriptor()->numGroups();

    auto layerName = GetLayerName(graph, layerIndex);
    IConnectableLayer* layer = m_Network->AddChannelShuffleLayer(descriptor, layerName.c_str());

    armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}
void IDeserializer::DeserializerImpl::ParseComparison(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);
    CHECK_LOCATION();

    auto inputs = GetInputs(graph, layerIndex);
    CHECK_VALID_SIZE(inputs.size(), 2);

    auto outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);

    auto fbLayer      = graph->layers()->Get(layerIndex)->layer_as_ComparisonLayer();
    auto fbDescriptor = fbLayer->descriptor();

    armnn::ComparisonDescriptor descriptor;
    descriptor.m_Operation = ToComparisonOperation(fbDescriptor->operation());

    const std::string& layerName = GetLayerName(graph, layerIndex);
    IConnectableLayer* layer     = m_Network->AddComparisonLayer(descriptor, layerName.c_str());

    armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseElementwiseUnary(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);
    CHECK_LOCATION();

    auto inputs = GetInputs(graph, layerIndex);
    CHECK_VALID_SIZE(inputs.size(), 1);

    auto outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);

    auto fbLayer      = graph->layers()->Get(layerIndex)->layer_as_ElementwiseUnaryLayer();
    auto fbDescriptor = fbLayer->descriptor();

    armnn::ElementwiseUnaryDescriptor descriptor;
    descriptor.m_Operation = ToUnaryOperation(fbDescriptor->operation());

    const std::string& layerName = GetLayerName(graph, layerIndex);
    IConnectableLayer* layer     = m_Network->AddElementwiseUnaryLayer(descriptor, layerName.c_str());

    armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseConcat(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);
    CHECK_LOCATION();

    auto outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);

    auto layerName = GetLayerName(graph, layerIndex);
    auto originsDescriptor = GetOriginsDescriptor(graph, layerIndex);
    unsigned int numViews = originsDescriptor->numViews();
    unsigned int numDimensions = originsDescriptor->numDimensions();

    // can now check the number of inputs == number of views
    auto inputs = GetInputs(graph, layerIndex);
    CHECK_VALID_SIZE(inputs.size(), numViews);

    armnn::OriginsDescriptor descriptor(numViews, numDimensions);
    auto originsPtr = originsDescriptor->viewOrigins();
    for (unsigned int v = 0; v < numViews; ++v)
    {
        auto originPtr = originsPtr->Get(v);
        for (unsigned int d = 0; d < numDimensions; ++d)
        {
            uint32_t value = originPtr->data()->Get(d);
            descriptor.SetViewOriginCoord(v, d, value);
        }
    }
    descriptor.SetConcatAxis(originsDescriptor->concatAxis());

    IConnectableLayer* layer = m_Network->AddConcatLayer(descriptor, layerName.c_str());
    armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseMultiplication(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);
    auto inputs = GetInputs(graph, layerIndex);
    CHECK_LOCATION();
    CHECK_VALID_SIZE(inputs.size(), 2);

    auto outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);

    auto layerName = GetLayerName(graph, layerIndex);
    IConnectableLayer* layer = m_Network->AddMultiplicationLayer(layerName.c_str());

    armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseFloor(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);
    CHECK_LOCATION();

    auto inputs = GetInputs(graph, layerIndex);
    CHECK_VALID_SIZE(inputs.size(), 1);

    auto outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);

    auto layerName = GetLayerName(graph, layerIndex);

    armnn::IConnectableLayer* layer;

    layer = m_Network->AddFloorLayer(layerName.c_str());

    armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseFullyConnected(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);
    auto inputs = GetInputs(graph, layerIndex);
    CHECK_LOCATION();

    auto outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);

    auto flatBufferLayer = graph->layers()->Get(layerIndex)->layer_as_FullyConnectedLayer();
    auto layerName = GetLayerName(graph, layerIndex);
    auto flatBufferDescriptor = flatBufferLayer->descriptor();

    armnn::FullyConnectedDescriptor fullyConnectedDescriptor;
    fullyConnectedDescriptor.m_BiasEnabled = flatBufferDescriptor->biasEnabled();
    fullyConnectedDescriptor.m_TransposeWeightMatrix = flatBufferDescriptor->transposeWeightsMatrix();
    fullyConnectedDescriptor.m_ConstantWeights = flatBufferDescriptor->constantWeights();

    armnn::IConnectableLayer* layer;
    std::vector<unsigned int> ignoreSlots {};

    // Weights and biases used to be always constant and were stored as members of the layer. This has changed and
    // they are now passed as inputs. If they are constant then they will be stored in a ConstantLayer.
    if (this->GetFeatureVersions(graph).m_ConstTensorsAsInputs <= 0)
    {
        // If the model stores weights and biases as members of the layer we have to read them from there
        // but add them to their own ConstantLayer for compatibility
        CHECK_VALID_SIZE(inputs.size(), 1);
        layer = m_Network->AddFullyConnectedLayer(fullyConnectedDescriptor,
                                                  layerName.c_str());

        armnn::ConstTensor weightsTensor = ToConstTensor(flatBufferLayer->weights());
        auto weightsLayer = m_Network->AddConstantLayer(weightsTensor);
        weightsLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1u));
        weightsLayer->GetOutputSlot(0).SetTensorInfo(weightsTensor.GetInfo());
        ignoreSlots.emplace_back(1u);

        if (fullyConnectedDescriptor.m_BiasEnabled)
        {
            armnn::ConstTensor biasTensor = ToConstTensor(flatBufferLayer->biases());
            auto biasLayer = m_Network->AddConstantLayer(biasTensor);
            biasLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(2u));
            biasLayer->GetOutputSlot(0).SetTensorInfo(biasTensor.GetInfo());
            ignoreSlots.emplace_back(2u);
        }
    }
    else
    {
        layer = m_Network->AddFullyConnectedLayer(fullyConnectedDescriptor,
                                                  layerName.c_str());
        uint32_t numInputs = fullyConnectedDescriptor.GetNumInputs();
        CHECK_VALID_SIZE(inputs.size(), numInputs);
    }

    armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);

    RegisterInputSlots(graph, layerIndex, layer, ignoreSlots);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParsePad(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);

    TensorRawPtrVector inputs = GetInputs(graph, layerIndex);
    CHECK_VALID_SIZE(inputs.size(), 1);

    TensorRawPtrVector outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);

    auto flatBufferDescriptor = graph->layers()->Get(layerIndex)->layer_as_PadLayer()->descriptor();
    auto flatBufferPadList = flatBufferDescriptor->padList();
    auto paddingMode = flatBufferDescriptor->paddingMode();
    float padValue = flatBufferDescriptor->padValue();

    if (flatBufferPadList->size() % 2 != 0)
    {
        throw ParseException(fmt::format("The size of the pad list must be divisible by 2 {}",
                                         CHECK_LOCATION().AsString()));
    }

    std::vector<std::pair<unsigned int, unsigned int>> padList;
    padList.reserve(flatBufferPadList->size() / 2);
    for (unsigned int i = 0; i < flatBufferPadList->size() - 1; i += 2)
    {
        padList.emplace_back(flatBufferPadList->Get(i), flatBufferPadList->Get(i+1));
    }

    armnn::PadDescriptor descriptor(padList, padValue, ToPaddingMode(paddingMode));

    auto layerName = GetLayerName(graph, layerIndex);
    IConnectableLayer* layer = m_Network->AddPadLayer(descriptor, layerName.c_str());

    armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParsePermute(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);

    auto dimsMapping =
    graph->layers()->Get(layerIndex)->layer_as_PermuteLayer()->descriptor()->dimMappings();

    auto inputs = GetInputs(graph, layerIndex);
    CHECK_VALID_SIZE(inputs.size(), 1);

    auto outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);
    auto outputInfo = ToTensorInfo(outputs[0]);

    auto layerName = GetLayerName(graph, layerIndex);
    const armnn::PermuteDescriptor descriptor(armnn::PermutationVector(dimsMapping->data(), dimsMapping->size()));

    IConnectableLayer* layer = m_Network->AddPermuteLayer(descriptor, layerName.c_str());
    layer->GetOutputSlot(0).SetTensorInfo(outputInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

armnn::Pooling2dDescriptor IDeserializer::DeserializerImpl::GetPooling2dDescriptor(Pooling2dDescriptor pooling2dDesc,
                                                              unsigned int layerIndex)
{
    IgnoreUnused(layerIndex);
    armnn::Pooling2dDescriptor desc;

    switch (pooling2dDesc->poolType())
    {
        case PoolingAlgorithm_Average:
        {
            desc.m_PoolType = armnn::PoolingAlgorithm::Average;
            break;
        }
        case PoolingAlgorithm_Max:
        {
            desc.m_PoolType = armnn::PoolingAlgorithm::Max;
            break;
        }
        case PoolingAlgorithm_L2:
        {
            desc.m_PoolType = armnn::PoolingAlgorithm::L2;
            break;
        }
        default:
        {
            ARMNN_ASSERT_MSG(false, "Unsupported pooling algorithm");
        }
    }

    switch (pooling2dDesc->outputShapeRounding())
    {
        case OutputShapeRounding_Floor:
        {
            desc.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor;
            break;
        }
        case OutputShapeRounding_Ceiling:
        {
            desc.m_OutputShapeRounding = armnn::OutputShapeRounding::Ceiling;
            break;
        }
        default:
        {
            ARMNN_ASSERT_MSG(false, "Unsupported output shape rounding");
        }
    }

    switch (pooling2dDesc->paddingMethod())
    {
        case PaddingMethod_Exclude:
        {
            desc.m_PaddingMethod = armnn::PaddingMethod::Exclude;
            break;
        }
        case PaddingMethod_IgnoreValue:
        {
            desc.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue;
            break;
        }
        default:
        {
            ARMNN_ASSERT_MSG(false, "Unsupported padding method");
        }
    }

    switch (pooling2dDesc->dataLayout())
    {
        case DataLayout_NCHW:
        {
            desc.m_DataLayout = armnn::DataLayout::NCHW;
            break;
        }
        case DataLayout_NHWC:
        {
            desc.m_DataLayout = armnn::DataLayout::NHWC;
            break;
        }
        default:
        {
            ARMNN_ASSERT_MSG(false, "Unsupported data layout");
        }
    }

    desc.m_PadRight   = pooling2dDesc->padRight();
    desc.m_PadLeft    = pooling2dDesc->padLeft();
    desc.m_PadBottom  = pooling2dDesc->padBottom();
    desc.m_PadTop     = pooling2dDesc->padTop();
    desc.m_StrideX    = pooling2dDesc->strideX();
    desc.m_StrideY    = pooling2dDesc->strideY();
    desc.m_PoolWidth  = pooling2dDesc->poolWidth();
    desc.m_PoolHeight = pooling2dDesc->poolHeight();

    return desc;
}

armnn::Pooling3dDescriptor IDeserializer::DeserializerImpl::GetPooling3dDescriptor(Pooling3dDescriptor pooling3dDesc,
                                                              unsigned int layerIndex)
{
    IgnoreUnused(layerIndex);
    armnn::Pooling3dDescriptor desc;

    switch (pooling3dDesc->poolType())
    {
        case PoolingAlgorithm_Average:
        {
            desc.m_PoolType = armnn::PoolingAlgorithm::Average;
            break;
        }
        case PoolingAlgorithm_Max:
        {
            desc.m_PoolType = armnn::PoolingAlgorithm::Max;
            break;
        }
        case PoolingAlgorithm_L2:
        {
            desc.m_PoolType = armnn::PoolingAlgorithm::L2;
            break;
        }
        default:
        {
            ARMNN_ASSERT_MSG(false, "Unsupported pooling algorithm");
        }
    }

    switch (pooling3dDesc->outputShapeRounding())
    {
        case OutputShapeRounding_Floor:
        {
            desc.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor;
            break;
        }
        case OutputShapeRounding_Ceiling:
        {
            desc.m_OutputShapeRounding = armnn::OutputShapeRounding::Ceiling;
            break;
        }
        default:
        {
            ARMNN_ASSERT_MSG(false, "Unsupported output shape rounding");
        }
    }

    switch (pooling3dDesc->paddingMethod())
    {
        case PaddingMethod_Exclude:
        {
            desc.m_PaddingMethod = armnn::PaddingMethod::Exclude;
            break;
        }
        case PaddingMethod_IgnoreValue:
        {
            desc.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue;
            break;
        }
        default:
        {
            ARMNN_ASSERT_MSG(false, "Unsupported padding method");
        }
    }

    switch (pooling3dDesc->dataLayout())
    {
        case DataLayout_NCDHW:
        {
            desc.m_DataLayout = armnn::DataLayout::NCDHW;
            break;
        }
        case DataLayout_NDHWC:
        {
            desc.m_DataLayout = armnn::DataLayout::NDHWC;
            break;
        }
        default:
        {
            ARMNN_ASSERT_MSG(false, "Unsupported data layout");
        }
    }

    desc.m_PadRight   = pooling3dDesc->padRight();
    desc.m_PadLeft    = pooling3dDesc->padLeft();
    desc.m_PadBottom  = pooling3dDesc->padBottom();
    desc.m_PadTop     = pooling3dDesc->padTop();
    desc.m_PadFront   = pooling3dDesc->padFront();
    desc.m_PadBack    = pooling3dDesc->padBack();
    desc.m_StrideX    = pooling3dDesc->strideX();
    desc.m_StrideY    = pooling3dDesc->strideY();
    desc.m_StrideZ    = pooling3dDesc->strideZ();
    desc.m_PoolWidth  = pooling3dDesc->poolWidth();
    desc.m_PoolHeight = pooling3dDesc->poolHeight();
    desc.m_PoolDepth  = pooling3dDesc->poolDepth();

    return desc;
}

void IDeserializer::DeserializerImpl::ParsePooling2d(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);

    auto pooling2dDes = graph->layers()->Get(layerIndex)->layer_as_Pooling2dLayer()->descriptor();
    auto inputs = GetInputs(graph, layerIndex);
    CHECK_VALID_SIZE(inputs.size(), 1);

    auto outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);
    auto outputInfo = ToTensorInfo(outputs[0]);

    auto pooling2dDescriptor = GetPooling2dDescriptor(pooling2dDes, layerIndex);
    auto layerName = GetLayerName(graph, layerIndex);
    IConnectableLayer* layer = m_Network->AddPooling2dLayer(pooling2dDescriptor, layerName.c_str());
    layer->GetOutputSlot(0).SetTensorInfo(outputInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParsePooling3d(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);

    auto pooling3dDes = graph->layers()->Get(layerIndex)->layer_as_Pooling3dLayer()->descriptor();
    auto inputs = GetInputs(graph, layerIndex);
    CHECK_VALID_SIZE(inputs.size(), 1);

    auto outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);
    auto outputInfo = ToTensorInfo(outputs[0]);

    auto pooling3dDescriptor = GetPooling3dDescriptor(pooling3dDes, layerIndex);
    auto layerName = GetLayerName(graph, layerIndex);
    IConnectableLayer* layer = m_Network->AddPooling3dLayer(pooling3dDescriptor, layerName.c_str());
    layer->GetOutputSlot(0).SetTensorInfo(outputInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseQuantize(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);

    auto inputs = GetInputs(graph, layerIndex);
    CHECK_VALID_SIZE(inputs.size(), 1);

    auto outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);
    auto outputInfo = ToTensorInfo(outputs[0]);

    auto layerName = GetLayerName(graph, layerIndex);
    IConnectableLayer* layer = m_Network->AddQuantizeLayer(layerName.c_str());
    layer->GetOutputSlot(0).SetTensorInfo(outputInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

armnn::TensorInfo IDeserializer::DeserializerImpl::OutputShapeOfReshape(const armnn::TensorInfo& inputTensorInfo,
                                                          const std::vector<uint32_t>& targetDimsIn)
{
    std::vector<unsigned int> outputDims(targetDimsIn.begin(), targetDimsIn.end());
    const auto stretchDim = std::find(targetDimsIn.begin(), targetDimsIn.end(), -1);

    if (stretchDim != targetDimsIn.end())
    {
        if (std::find(std::next(stretchDim), targetDimsIn.end(), -1) != targetDimsIn.end())
        {
            throw ParseException(fmt::format("At most one component of shape can be -1 {}",
                                             CHECK_LOCATION().AsString()));
        }

        auto targetNumElements =
           armnn::numeric_cast<unsigned int>(
               std::accumulate(targetDimsIn.begin(), targetDimsIn.end(), -1, std::multiplies<int32_t>()));

        auto stretchIndex = static_cast<size_t>(std::distance(targetDimsIn.begin(), stretchDim));
        outputDims[stretchIndex] = inputTensorInfo.GetNumElements() / targetNumElements;
    }

    TensorShape outputShape = TensorShape(static_cast<unsigned int>(outputDims.size()), outputDims.data());

    armnn::TensorInfo reshapeInfo = inputTensorInfo;
    reshapeInfo.SetShape(outputShape);

    return reshapeInfo;
}

void IDeserializer::DeserializerImpl::ParseRank(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);

    TensorRawPtrVector inputs = GetInputs(graph, layerIndex);
    CHECK_VALID_SIZE(inputs.size(), 1);

    TensorRawPtrVector outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);

    auto layerName = GetLayerName(graph, layerIndex);
    IConnectableLayer* layer = m_Network->AddRankLayer( layerName.c_str());

    armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseReduce(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);
    CHECK_LOCATION();

    auto inputs = GetInputs(graph, layerIndex);
    CHECK_VALID_SIZE(inputs.size(), 1);

    auto outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);

    auto fbLayer      = graph->layers()->Get(layerIndex)->layer_as_ReduceLayer();
    auto fbDescriptor = fbLayer->descriptor();
    auto flatBufferAxis = fbDescriptor->axis();

    armnn::ReduceDescriptor descriptor;
    descriptor.m_KeepDims     = fbDescriptor->keepDims();
    descriptor.m_vAxis = std::vector<unsigned int>(flatBufferAxis->begin(), flatBufferAxis->end());
    descriptor.m_ReduceOperation = ToReduceOperation(fbDescriptor->reduceOperation());

    const std::string& layerName = GetLayerName(graph, layerIndex);
    IConnectableLayer* layer     = m_Network->AddReduceLayer(descriptor, layerName.c_str());

    armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseReshape(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);
    auto inputs = GetInputs(graph, layerIndex);

    auto outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);

    armnn::TensorInfo inputTensorInfo = ToTensorInfo(inputs[0]);
    armnn::TensorInfo actualOutputTensorInfo = ToTensorInfo(outputs[0]);

    const auto targetDims = graph->layers()->Get(layerIndex)->layer_as_ReshapeLayer()->descriptor()->targetShape();
    std::vector<uint32_t> outputDims(targetDims->begin(), targetDims->begin() + targetDims->size());

    armnn::TensorInfo reshapeOutputTensorInfo = DeserializerImpl::OutputShapeOfReshape(inputTensorInfo, outputDims);
    const armnn::TensorShape& reshapeOutputTensorShape = reshapeOutputTensorInfo.GetShape();

    const std::vector<uint32_t> expectedDims(outputs[0]->dimensions()->begin(),
                                             outputs[0]->dimensions()->begin() + outputs[0]->dimensions()->size());

    if (inputs.size() > 1 && !CheckShape(reshapeOutputTensorShape, expectedDims))
    {
        std::stringstream ss;
        ss << "New shape defined in reshape parameters "
           << reshapeOutputTensorShape
           << " does not equal output shape "
           << actualOutputTensorInfo.GetShape()
           << ": "
           << CHECK_LOCATION().AsString();
        throw ParseException(ss.str());
    }

    armnn::ReshapeDescriptor reshapeDesc;
    reshapeDesc.m_TargetShape = reshapeOutputTensorShape;

    auto layerName = GetLayerName(graph, layerIndex);
    IConnectableLayer* layer = m_Network->AddReshapeLayer(reshapeDesc, layerName.c_str());
    layer->GetOutputSlot(0).SetTensorInfo(reshapeOutputTensorInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseResize(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);

    TensorRawPtrVector inputs = GetInputs(graph, layerIndex);
    CHECK_VALID_SIZE(inputs.size(), 1);

    TensorRawPtrVector outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);

    auto flatBufferDescriptor = graph->layers()->Get(layerIndex)->layer_as_ResizeLayer()->descriptor();

    armnn::ResizeDescriptor descriptor;
    descriptor.m_TargetWidth = flatBufferDescriptor->targetWidth();
    descriptor.m_TargetHeight = flatBufferDescriptor->targetHeight();
    descriptor.m_Method = ToResizeMethod(flatBufferDescriptor->method());
    descriptor.m_DataLayout = ToDataLayout(flatBufferDescriptor->dataLayout());
    descriptor.m_AlignCorners = flatBufferDescriptor->alignCorners();
    descriptor.m_HalfPixelCenters = flatBufferDescriptor->halfPixelCenters();

    auto layerName = GetLayerName(graph, layerIndex);
    IConnectableLayer* layer = m_Network->AddResizeLayer(descriptor, layerName.c_str());

    armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}


/// @Note The ResizeBiliniar operation was deprecated and removed in favor of the Resize operation.
///       This function is kept for backwards compatibility.
void IDeserializer::DeserializerImpl::ParseResizeBilinear(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);

    TensorRawPtrVector inputs = GetInputs(graph, layerIndex);
    CHECK_VALID_SIZE(inputs.size(), 1);

    TensorRawPtrVector outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);

    auto flatBufferDescriptor = graph->layers()->Get(layerIndex)->layer_as_ResizeBilinearLayer()->descriptor();

    armnn::ResizeDescriptor descriptor;
    descriptor.m_TargetWidth  = flatBufferDescriptor->targetWidth();
    descriptor.m_TargetHeight = flatBufferDescriptor->targetHeight();
    descriptor.m_Method       = armnn::ResizeMethod::Bilinear;
    descriptor.m_DataLayout   = ToDataLayout(flatBufferDescriptor->dataLayout());
    descriptor.m_AlignCorners = flatBufferDescriptor->alignCorners();
    descriptor.m_HalfPixelCenters = flatBufferDescriptor->halfPixelCenters();

    auto layerName = GetLayerName(graph, layerIndex);
    IConnectableLayer* layer = m_Network->AddResizeLayer(descriptor, layerName.c_str());

    armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseShape(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);

    TensorRawPtrVector inputs = GetInputs(graph, layerIndex);
    CHECK_VALID_SIZE(inputs.size(), 1);

    TensorRawPtrVector outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);

    auto layerName = GetLayerName(graph, layerIndex);
    IConnectableLayer* layer = m_Network->AddShapeLayer( layerName.c_str());

    armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseSoftmax(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);

    TensorRawPtrVector inputs = GetInputs(graph, layerIndex);
    CHECK_VALID_SIZE(inputs.size(), 1);

    TensorRawPtrVector outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);

    armnn::SoftmaxDescriptor descriptor;
    descriptor.m_Beta = graph->layers()->Get(layerIndex)->layer_as_SoftmaxLayer()->descriptor()->beta();
    descriptor.m_Axis = graph->layers()->Get(layerIndex)->layer_as_SoftmaxLayer()->descriptor()->axis();
    auto layerName = GetLayerName(graph, layerIndex);

    IConnectableLayer* layer = m_Network->AddSoftmaxLayer(descriptor, layerName.c_str());

    armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseSpaceToBatchNd(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);

    TensorRawPtrVector inputs = GetInputs(graph, layerIndex);
    CHECK_VALID_SIZE(inputs.size(), 1);

    TensorRawPtrVector outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);

    auto flatBufferDescriptor = graph->layers()->Get(layerIndex)->layer_as_SpaceToBatchNdLayer()->descriptor();
    auto flatBufferPadList = flatBufferDescriptor->padList();
    auto flatBufferBlockShape = flatBufferDescriptor->blockShape();

    if (flatBufferPadList->size() % 2 != 0)
    {
        throw ParseException(fmt::format("The size of the pad list must be divisible by 2 {}",
                                         CHECK_LOCATION().AsString()));
    }

    std::vector<std::pair<unsigned int, unsigned int>> padList;
    padList.reserve(flatBufferPadList->size() / 2);
    for (unsigned int i = 0; i < flatBufferPadList->size() - 1; i += 2)
    {
        padList.emplace_back(flatBufferPadList->Get(i), flatBufferPadList->Get(i+1));
    }

    armnn::SpaceToBatchNdDescriptor descriptor;
    descriptor.m_DataLayout = ToDataLayout(flatBufferDescriptor->dataLayout());
    descriptor.m_BlockShape =
        std::vector<unsigned int>(flatBufferBlockShape->begin(), flatBufferBlockShape->end());
    descriptor.m_PadList = padList;

    auto layerName = GetLayerName(graph, layerIndex);
    IConnectableLayer* layer = m_Network->AddSpaceToBatchNdLayer(descriptor, layerName.c_str());

    armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseSpaceToDepth(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);

    TensorRawPtrVector inputs = GetInputs(graph, layerIndex);
    CHECK_VALID_SIZE(inputs.size(), 1);

    TensorRawPtrVector outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);

    auto flatBufferDescriptor = graph->layers()->Get(layerIndex)->layer_as_SpaceToDepthLayer()->descriptor();

    armnn::SpaceToDepthDescriptor descriptor;
    descriptor.m_BlockSize  = flatBufferDescriptor->blockSize();
    descriptor.m_DataLayout = ToDataLayout(flatBufferDescriptor->dataLayout());

    auto layerName = GetLayerName(graph, layerIndex);
    IConnectableLayer* layer = m_Network->AddSpaceToDepthLayer(descriptor, layerName.c_str());

    armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

armnn::NormalizationDescriptor IDeserializer::DeserializerImpl::GetNormalizationDescriptor(
    NormalizationDescriptorPtr normalizationDescriptor,
    unsigned int layerIndex)
{
    IgnoreUnused(layerIndex);
    armnn::NormalizationDescriptor desc;

    switch (normalizationDescriptor->normChannelType())
    {
        case NormalizationAlgorithmChannel_Across:
        {
            desc.m_NormChannelType = armnn::NormalizationAlgorithmChannel::Across;
            break;
        }
        case NormalizationAlgorithmChannel_Within:
        {
            desc.m_NormChannelType = armnn::NormalizationAlgorithmChannel::Within;
            break;
        }
        default:
        {
            ARMNN_ASSERT_MSG(false, "Unsupported normalization channel type");
        }
    }

    switch (normalizationDescriptor->normMethodType())
    {
        case NormalizationAlgorithmMethod_LocalBrightness:
        {
            desc.m_NormMethodType = armnn::NormalizationAlgorithmMethod::LocalBrightness;
            break;
        }
        case NormalizationAlgorithmMethod_LocalContrast:
        {
            desc.m_NormMethodType = armnn::NormalizationAlgorithmMethod::LocalContrast;
            break;
        }
        default:
        {
            ARMNN_ASSERT_MSG(false, "Unsupported normalization method type");
        }
    }

    switch (normalizationDescriptor->dataLayout())
    {
        case DataLayout_NCHW:
        {
            desc.m_DataLayout = armnn::DataLayout::NCHW;
            break;
        }
        case DataLayout_NHWC:
        {
            desc.m_DataLayout = armnn::DataLayout::NHWC;
            break;
        }
        default:
        {
            ARMNN_ASSERT_MSG(false, "Unsupported data layout");
        }
    }

    desc.m_Alpha    = normalizationDescriptor->alpha();
    desc.m_Beta     = normalizationDescriptor->beta();
    desc.m_K        = normalizationDescriptor->k();
    desc.m_NormSize = normalizationDescriptor->normSize();

    return desc;
}

void IDeserializer::DeserializerImpl::ParseNormalization(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);

    auto normalizationDes = graph->layers()->Get(layerIndex)->layer_as_NormalizationLayer()->descriptor();

    TensorRawPtrVector inputs = GetInputs(graph, layerIndex);
    CHECK_VALID_SIZE(inputs.size(), 1);

    TensorRawPtrVector outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);

    auto outputInfo = ToTensorInfo(outputs[0]);

    auto normalizationDescriptor = GetNormalizationDescriptor(normalizationDes, layerIndex);
    auto layerName = GetLayerName(graph, layerIndex);

    IConnectableLayer* layer = m_Network->AddNormalizationLayer(normalizationDescriptor, layerName.c_str());
    layer->GetOutputSlot(0).SetTensorInfo(outputInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseRsqrt(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);
    auto inputs = GetInputs(graph, layerIndex);
    CHECK_LOCATION();
    CHECK_VALID_SIZE(inputs.size(), 1);

    auto outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);

    auto layerName = GetLayerName(graph, layerIndex);

    armnn::ElementwiseUnaryDescriptor descriptor(armnn::UnaryOperation::Rsqrt);
    IConnectableLayer* layer = m_Network->AddElementwiseUnaryLayer(descriptor, layerName.c_str());
    armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseSlice(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);

    auto inputs = GetInputs(graph, layerIndex);
    CHECK_VALID_SIZE(inputs.size(), 1);

    auto outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);

    auto fbDescriptor = graph->layers()->Get(layerIndex)->layer_as_SliceLayer()->descriptor();

    auto fbBegin = fbDescriptor->begin();
    auto fbSize  = fbDescriptor->size();

    if (fbBegin->size() != fbSize->size())
    {
        throw ParseException(fmt::format("Begin and size descriptors must have the same length {}",
                                         CHECK_LOCATION().AsString()));
    }

    armnn::SliceDescriptor descriptor;
    descriptor.m_Begin.insert(descriptor.m_Begin.end(), fbBegin->begin(), fbBegin->end());
    descriptor.m_Size.insert(descriptor.m_Size.end(), fbSize->begin(), fbSize->end());

    auto layerName = GetLayerName(graph, layerIndex);
    IConnectableLayer* layer = m_Network->AddSliceLayer(descriptor, layerName.c_str());

    armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseStridedSlice(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);

    TensorRawPtrVector inputs = GetInputs(graph, layerIndex);
    CHECK_VALID_SIZE(inputs.size(), 1);

    TensorRawPtrVector outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);

    auto flatBufferDescriptor = graph->layers()->Get(layerIndex)->layer_as_StridedSliceLayer()->descriptor();

    auto flatBufferBegin = flatBufferDescriptor->begin();
    auto flatBufferEnd = flatBufferDescriptor->end();
    auto flatBufferStride = flatBufferDescriptor->stride();

    if (!(flatBufferBegin->size() == flatBufferEnd->size() &&
          flatBufferBegin->size() == flatBufferStride->size()))
    {
        throw ParseException(fmt::format("The size of the begin, end, and stride must be equal {}",
                                         CHECK_LOCATION().AsString()));
    }

    std::vector<int> begin(flatBufferBegin->begin(), flatBufferBegin->end());
    std::vector<int> end(flatBufferEnd->begin(), flatBufferEnd->end());
    std::vector<int> stride(flatBufferStride->begin(), flatBufferStride->end());

    armnn::StridedSliceDescriptor descriptor(begin, end, stride);
    descriptor.m_BeginMask = flatBufferDescriptor->beginMask();
    descriptor.m_EndMask = flatBufferDescriptor->endMask();
    descriptor.m_ShrinkAxisMask = flatBufferDescriptor->shrinkAxisMask();
    descriptor.m_EllipsisMask = flatBufferDescriptor->ellipsisMask();
    descriptor.m_NewAxisMask = flatBufferDescriptor->newAxisMask();
    descriptor.m_DataLayout = ToDataLayout(flatBufferDescriptor->dataLayout());

    auto layerName = GetLayerName(graph, layerIndex);
    IConnectableLayer* layer = m_Network->AddStridedSliceLayer(descriptor, layerName.c_str());

    armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseSubtraction(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);
    auto inputs = GetInputs(graph, layerIndex);
    CHECK_LOCATION();
    CHECK_VALID_SIZE(inputs.size(), 2);

    auto outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);

    auto layerName = GetLayerName(graph, layerIndex);
    IConnectableLayer* layer = m_Network->AddSubtractionLayer(layerName.c_str());

    armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseGather(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);

    TensorRawPtrVector inputs = GetInputs(graph, layerIndex);
    CHECK_VALID_SIZE(inputs.size(), 2);

    TensorRawPtrVector outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);

    armnn::GatherDescriptor descriptor;
    descriptor.m_Axis = graph->layers()->Get(layerIndex)->layer_as_GatherLayer()->descriptor()->axis();

    auto layerName = GetLayerName(graph, layerIndex);
    IConnectableLayer* layer = m_Network->AddGatherLayer(descriptor, layerName.c_str());

    armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseGatherNd(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);

    TensorRawPtrVector inputs = GetInputs(graph, layerIndex);
    CHECK_VALID_SIZE(inputs.size(), 2);

    TensorRawPtrVector outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);

    auto layerName = GetLayerName(graph, layerIndex);
    IConnectableLayer* layer = m_Network->AddGatherNdLayer(layerName.c_str());

    armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseMean(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);

    TensorRawPtrVector inputs = GetInputs(graph, layerIndex);
    CHECK_VALID_SIZE(inputs.size(), 1);

    TensorRawPtrVector outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);

    auto flatBufferDescriptor = graph->layers()->Get(layerIndex)->layer_as_MeanLayer()->descriptor();
    auto flatBufferAxis = flatBufferDescriptor->axis();
    auto flatBufferKeepDims = flatBufferDescriptor->keepDims();

    armnn::MeanDescriptor descriptor;
    descriptor.m_Axis = std::vector<unsigned int>(flatBufferAxis->begin(), flatBufferAxis->end());
    descriptor.m_KeepDims = flatBufferKeepDims;

    auto layerName = GetLayerName(graph, layerIndex);
    IConnectableLayer* layer = m_Network->AddMeanLayer(descriptor, layerName.c_str());

    armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseSplitter(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);

    TensorRawPtrVector inputs = GetInputs(graph, layerIndex);
    CHECK_VALID_SIZE(inputs.size(), 1);

    TensorRawPtrVector outputs = GetOutputs(graph, layerIndex);

    auto flatBufferViewsDescriptor = graph->layers()->Get(layerIndex)->layer_as_SplitterLayer()->descriptor();
    auto flatBufferViewSizes = flatBufferViewsDescriptor->viewSizes();
    auto flatBufferOriginsDescriptor = flatBufferViewsDescriptor->origins();
    auto flatBufferViewOrigins = flatBufferOriginsDescriptor->viewOrigins();
    uint32_t numViews = flatBufferOriginsDescriptor->numViews();
    uint32_t numDimensions = flatBufferOriginsDescriptor->numDimensions();

    // Check numViews and numDimensions corresponds to the ones already serialized ...
    // numViews ==  flatBufferViewSizes.size();
    // foreach: numDimensions == flatBufferViewSizes[x].size();

    armnn::ViewsDescriptor viewsDescriptor(numViews, numDimensions);
    for(unsigned int vIdx = 0; vIdx < numViews; ++vIdx)
    {
        for (unsigned int dIdx = 0; dIdx < numDimensions; ++dIdx)
        {
            viewsDescriptor.SetViewSize(vIdx, dIdx, flatBufferViewSizes->Get(vIdx)->data()->Get(dIdx));
            viewsDescriptor.SetViewOriginCoord(vIdx, dIdx, flatBufferViewOrigins->Get(vIdx)->data()->Get(dIdx));
        }
    }

    auto layerName = GetLayerName(graph, layerIndex);
    IConnectableLayer* layer = m_Network->AddSplitterLayer(viewsDescriptor, layerName.c_str());

    // I could have as many outputs as views ...
    for(unsigned int vIdx = 0; vIdx < numViews; ++vIdx)
    {
        armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[vIdx]);
        layer->GetOutputSlot(vIdx).SetTensorInfo(outputTensorInfo);
    }

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

armnn::LstmDescriptor IDeserializer::DeserializerImpl::GetLstmDescriptor(LstmDescriptorPtr lstmDescriptor)
{
    armnn::LstmDescriptor desc;

    desc.m_ActivationFunc = lstmDescriptor->activationFunc();
    desc.m_ClippingThresCell = lstmDescriptor->clippingThresCell();
    desc.m_ClippingThresProj = lstmDescriptor->clippingThresProj();
    desc.m_CifgEnabled = lstmDescriptor->cifgEnabled();
    desc.m_PeepholeEnabled = lstmDescriptor->peepholeEnabled();
    desc.m_ProjectionEnabled = lstmDescriptor->projectionEnabled();
    desc.m_LayerNormEnabled = lstmDescriptor->layerNormEnabled();

    return desc;
}

void IDeserializer::DeserializerImpl::ParseLstm(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);

    auto inputs = GetInputs(graph, layerIndex);
    CHECK_VALID_SIZE(inputs.size(), 3);

    auto outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 4);

    auto flatBufferLayer = graph->layers()->Get(layerIndex)->layer_as_LstmLayer();
    auto layerName = GetLayerName(graph, layerIndex);
    auto flatBufferDescriptor = flatBufferLayer->descriptor();
    auto flatBufferInputParams = flatBufferLayer->inputParams();

    auto lstmDescriptor = GetLstmDescriptor(flatBufferDescriptor);

    armnn::LstmInputParams lstmInputParams;

    armnn::ConstTensor inputToForgetWeights = ToConstTensor(flatBufferInputParams->inputToForgetWeights());
    armnn::ConstTensor inputToCellWeights = ToConstTensor(flatBufferInputParams->inputToCellWeights());
    armnn::ConstTensor inputToOutputWeights = ToConstTensor(flatBufferInputParams->inputToOutputWeights());
    armnn::ConstTensor recurrentToForgetWeights = ToConstTensor(flatBufferInputParams->recurrentToForgetWeights());
    armnn::ConstTensor recurrentToCellWeights = ToConstTensor(flatBufferInputParams->recurrentToCellWeights());
    armnn::ConstTensor recurrentToOutputWeights = ToConstTensor(flatBufferInputParams->recurrentToOutputWeights());
    armnn::ConstTensor forgetGateBias = ToConstTensor(flatBufferInputParams->forgetGateBias());
    armnn::ConstTensor cellBias = ToConstTensor(flatBufferInputParams->cellBias());
    armnn::ConstTensor outputGateBias = ToConstTensor(flatBufferInputParams->outputGateBias());

    lstmInputParams.m_InputToForgetWeights = &inputToForgetWeights;
    lstmInputParams.m_InputToCellWeights = &inputToCellWeights;
    lstmInputParams.m_InputToOutputWeights = &inputToOutputWeights;
    lstmInputParams.m_RecurrentToForgetWeights = &recurrentToForgetWeights;
    lstmInputParams.m_RecurrentToCellWeights = &recurrentToCellWeights;
    lstmInputParams.m_RecurrentToOutputWeights = &recurrentToOutputWeights;
    lstmInputParams.m_ForgetGateBias = &forgetGateBias;
    lstmInputParams.m_CellBias = &cellBias;
    lstmInputParams.m_OutputGateBias = &outputGateBias;

    armnn::ConstTensor inputToInputWeights;
    armnn::ConstTensor recurrentToInputWeights;
    armnn::ConstTensor cellToInputWeights;
    armnn::ConstTensor inputGateBias;
    if (!lstmDescriptor.m_CifgEnabled)
    {
        inputToInputWeights = ToConstTensor(flatBufferInputParams->inputToInputWeights());
        recurrentToInputWeights = ToConstTensor(flatBufferInputParams->recurrentToInputWeights());
        cellToInputWeights = ToConstTensor(flatBufferInputParams->cellToInputWeights());
        inputGateBias = ToConstTensor(flatBufferInputParams->inputGateBias());

        lstmInputParams.m_InputToInputWeights = &inputToInputWeights;
        lstmInputParams.m_RecurrentToInputWeights = &recurrentToInputWeights;
        lstmInputParams.m_CellToInputWeights = &cellToInputWeights;
        lstmInputParams.m_InputGateBias = &inputGateBias;
    }

    armnn::ConstTensor projectionWeights;
    armnn::ConstTensor projectionBias;
    if (lstmDescriptor.m_ProjectionEnabled)
    {
        projectionWeights = ToConstTensor(flatBufferInputParams->projectionWeights());
        projectionBias = ToConstTensor(flatBufferInputParams->projectionBias());

        lstmInputParams.m_ProjectionWeights = &projectionWeights;
        lstmInputParams.m_ProjectionBias = &projectionBias;
    }

    armnn::ConstTensor cellToForgetWeights;
    armnn::ConstTensor cellToOutputWeights;
    if (lstmDescriptor.m_PeepholeEnabled)
    {
        cellToForgetWeights = ToConstTensor(flatBufferInputParams->cellToForgetWeights());
        cellToOutputWeights = ToConstTensor(flatBufferInputParams->cellToOutputWeights());

        lstmInputParams.m_CellToForgetWeights = &cellToForgetWeights;
        lstmInputParams.m_CellToOutputWeights = &cellToOutputWeights;
    }

    armnn::ConstTensor inputLayerNormWeights;
    armnn::ConstTensor forgetLayerNormWeights;
    armnn::ConstTensor cellLayerNormWeights;
    armnn::ConstTensor outputLayerNormWeights;
    if (lstmDescriptor.m_LayerNormEnabled)
    {
        if (!lstmDescriptor.m_CifgEnabled)
        {
            inputLayerNormWeights = ToConstTensor(flatBufferInputParams->inputLayerNormWeights());
            lstmInputParams.m_InputLayerNormWeights = &inputLayerNormWeights;
        }
        forgetLayerNormWeights = ToConstTensor(flatBufferInputParams->forgetLayerNormWeights());
        cellLayerNormWeights = ToConstTensor(flatBufferInputParams->cellLayerNormWeights());
        outputLayerNormWeights = ToConstTensor(flatBufferInputParams->outputLayerNormWeights());

        lstmInputParams.m_ForgetLayerNormWeights = &forgetLayerNormWeights;
        lstmInputParams.m_CellLayerNormWeights = &cellLayerNormWeights;
        lstmInputParams.m_OutputLayerNormWeights = &outputLayerNormWeights;
    }

    IConnectableLayer* layer = m_Network->AddLstmLayer(lstmDescriptor, lstmInputParams, layerName.c_str());

    armnn::TensorInfo outputTensorInfo1 = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo1);

    armnn::TensorInfo outputTensorInfo2 = ToTensorInfo(outputs[1]);
    layer->GetOutputSlot(1).SetTensorInfo(outputTensorInfo2);

    armnn::TensorInfo outputTensorInfo3 = ToTensorInfo(outputs[2]);
    layer->GetOutputSlot(2).SetTensorInfo(outputTensorInfo3);

    armnn::TensorInfo outputTensorInfo4 = ToTensorInfo(outputs[3]);
    layer->GetOutputSlot(3).SetTensorInfo(outputTensorInfo4);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

armnn::QLstmDescriptor IDeserializer::DeserializerImpl::GetQLstmDescriptor(QLstmDescriptorPtr qLstmDescriptor)
{
    armnn::QLstmDescriptor desc;

    desc.m_CifgEnabled       = qLstmDescriptor->cifgEnabled();
    desc.m_PeepholeEnabled   = qLstmDescriptor->peepholeEnabled();
    desc.m_ProjectionEnabled = qLstmDescriptor->projectionEnabled();
    desc.m_LayerNormEnabled  = qLstmDescriptor->layerNormEnabled();

    desc.m_CellClip       = qLstmDescriptor->cellClip();
    desc.m_ProjectionClip = qLstmDescriptor->projectionClip();

    desc.m_InputIntermediateScale  = qLstmDescriptor->inputIntermediateScale();
    desc.m_ForgetIntermediateScale = qLstmDescriptor->forgetIntermediateScale();
    desc.m_CellIntermediateScale   = qLstmDescriptor->cellIntermediateScale();
    desc.m_OutputIntermediateScale = qLstmDescriptor->outputIntermediateScale();

    desc.m_HiddenStateScale     = qLstmDescriptor->hiddenStateScale();
    desc.m_HiddenStateZeroPoint = qLstmDescriptor->hiddenStateZeroPoint();

    return desc;
}

void IDeserializer::DeserializerImpl::ParseQLstm(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);

    auto inputs = GetInputs(graph, layerIndex);
    CHECK_VALID_SIZE(inputs.size(), 3);

    auto outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 3);

    auto flatBufferLayer       = graph->layers()->Get(layerIndex)->layer_as_QLstmLayer();
    auto layerName             = GetLayerName(graph, layerIndex);
    auto flatBufferDescriptor  = flatBufferLayer->descriptor();
    auto flatBufferInputParams = flatBufferLayer->inputParams();

    auto qLstmDescriptor = GetQLstmDescriptor(flatBufferDescriptor);
    armnn::LstmInputParams qLstmInputParams;

    // Mandatory params
    armnn::ConstTensor inputToForgetWeights     = ToConstTensor(flatBufferInputParams->inputToForgetWeights());
    armnn::ConstTensor inputToCellWeights       = ToConstTensor(flatBufferInputParams->inputToCellWeights());
    armnn::ConstTensor inputToOutputWeights     = ToConstTensor(flatBufferInputParams->inputToOutputWeights());
    armnn::ConstTensor recurrentToForgetWeights = ToConstTensor(flatBufferInputParams->recurrentToForgetWeights());
    armnn::ConstTensor recurrentToCellWeights   = ToConstTensor(flatBufferInputParams->recurrentToCellWeights());
    armnn::ConstTensor recurrentToOutputWeights = ToConstTensor(flatBufferInputParams->recurrentToOutputWeights());
    armnn::ConstTensor forgetGateBias           = ToConstTensor(flatBufferInputParams->forgetGateBias());
    armnn::ConstTensor cellBias                 = ToConstTensor(flatBufferInputParams->cellBias());
    armnn::ConstTensor outputGateBias           = ToConstTensor(flatBufferInputParams->outputGateBias());

    qLstmInputParams.m_InputToForgetWeights = &inputToForgetWeights;
    qLstmInputParams.m_InputToCellWeights = &inputToCellWeights;
    qLstmInputParams.m_InputToOutputWeights = &inputToOutputWeights;
    qLstmInputParams.m_RecurrentToForgetWeights = &recurrentToForgetWeights;
    qLstmInputParams.m_RecurrentToCellWeights = &recurrentToCellWeights;
    qLstmInputParams.m_RecurrentToOutputWeights = &recurrentToOutputWeights;
    qLstmInputParams.m_ForgetGateBias = &forgetGateBias;
    qLstmInputParams.m_CellBias = &cellBias;
    qLstmInputParams.m_OutputGateBias = &outputGateBias;

    // Optional CIFG params
    armnn::ConstTensor inputToInputWeights;
    armnn::ConstTensor recurrentToInputWeights;
    armnn::ConstTensor inputGateBias;

    if (!qLstmDescriptor.m_CifgEnabled)
    {
        inputToInputWeights     = ToConstTensor(flatBufferInputParams->inputToInputWeights());
        recurrentToInputWeights = ToConstTensor(flatBufferInputParams->recurrentToInputWeights());
        inputGateBias           = ToConstTensor(flatBufferInputParams->inputGateBias());

        qLstmInputParams.m_InputToInputWeights     = &inputToInputWeights;
        qLstmInputParams.m_RecurrentToInputWeights = &recurrentToInputWeights;
        qLstmInputParams.m_InputGateBias           = &inputGateBias;
    }

    // Optional projection params
    armnn::ConstTensor projectionWeights;
    armnn::ConstTensor projectionBias;

    if (qLstmDescriptor.m_ProjectionEnabled)
    {
        projectionWeights = ToConstTensor(flatBufferInputParams->projectionWeights());
        projectionBias = ToConstTensor(flatBufferInputParams->projectionBias());

        qLstmInputParams.m_ProjectionWeights = &projectionWeights;
        qLstmInputParams.m_ProjectionBias = &projectionBias;
    }

    // Optional peephole params
    armnn::ConstTensor cellToInputWeights;
    armnn::ConstTensor cellToForgetWeights;
    armnn::ConstTensor cellToOutputWeights;

    if (qLstmDescriptor.m_PeepholeEnabled)
    {
        if (!qLstmDescriptor.m_CifgEnabled)
        {
            cellToInputWeights = ToConstTensor(flatBufferInputParams->cellToInputWeights());
            qLstmInputParams.m_CellToInputWeights = &cellToInputWeights;
        }

        cellToForgetWeights = ToConstTensor(flatBufferInputParams->cellToForgetWeights());
        cellToOutputWeights = ToConstTensor(flatBufferInputParams->cellToOutputWeights());

        qLstmInputParams.m_CellToForgetWeights = &cellToForgetWeights;
        qLstmInputParams.m_CellToOutputWeights = &cellToOutputWeights;
    }

    // Optional layer norm params
    armnn::ConstTensor inputLayerNormWeights;
    armnn::ConstTensor forgetLayerNormWeights;
    armnn::ConstTensor cellLayerNormWeights;
    armnn::ConstTensor outputLayerNormWeights;

    if (qLstmDescriptor.m_LayerNormEnabled)
    {
        if (!qLstmDescriptor.m_CifgEnabled)
        {
            inputLayerNormWeights = ToConstTensor(flatBufferInputParams->inputLayerNormWeights());
            qLstmInputParams.m_InputLayerNormWeights = &inputLayerNormWeights;
        }

        forgetLayerNormWeights = ToConstTensor(flatBufferInputParams->forgetLayerNormWeights());
        cellLayerNormWeights   = ToConstTensor(flatBufferInputParams->cellLayerNormWeights());
        outputLayerNormWeights = ToConstTensor(flatBufferInputParams->outputLayerNormWeights());

        qLstmInputParams.m_ForgetLayerNormWeights = &forgetLayerNormWeights;
        qLstmInputParams.m_CellLayerNormWeights   = &cellLayerNormWeights;
        qLstmInputParams.m_OutputLayerNormWeights = &outputLayerNormWeights;
    }

    IConnectableLayer* layer = m_Network->AddQLstmLayer(qLstmDescriptor, qLstmInputParams, layerName.c_str());

    armnn::TensorInfo outputStateOutInfo = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(outputStateOutInfo);

    armnn::TensorInfo cellStateOutInfo = ToTensorInfo(outputs[1]);
    layer->GetOutputSlot(1).SetTensorInfo(cellStateOutInfo);

    armnn::TensorInfo outputInfo = ToTensorInfo(outputs[2]);
    layer->GetOutputSlot(2).SetTensorInfo(outputInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseQuantizedLstm(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);

    auto inputs = GetInputs(graph, layerIndex);
    CHECK_VALID_SIZE(inputs.size(), 3);

    auto outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 2);

    auto flatBufferLayer = graph->layers()->Get(layerIndex)->layer_as_QuantizedLstmLayer();
    auto layerName = GetLayerName(graph, layerIndex);
    auto flatBufferInputParams = flatBufferLayer->inputParams();

    armnn::QuantizedLstmInputParams lstmInputParams;

    armnn::ConstTensor inputToInputWeights = ToConstTensor(flatBufferInputParams->inputToInputWeights());
    armnn::ConstTensor inputToForgetWeights = ToConstTensor(flatBufferInputParams->inputToForgetWeights());
    armnn::ConstTensor inputToCellWeights = ToConstTensor(flatBufferInputParams->inputToCellWeights());
    armnn::ConstTensor inputToOutputWeights = ToConstTensor(flatBufferInputParams->inputToOutputWeights());
    armnn::ConstTensor recurrentToInputWeights = ToConstTensor(flatBufferInputParams->recurrentToInputWeights());
    armnn::ConstTensor recurrentToForgetWeights = ToConstTensor(flatBufferInputParams->recurrentToForgetWeights());
    armnn::ConstTensor recurrentToCellWeights = ToConstTensor(flatBufferInputParams->recurrentToCellWeights());
    armnn::ConstTensor recurrentToOutputWeights = ToConstTensor(flatBufferInputParams->recurrentToOutputWeights());
    armnn::ConstTensor inputGateBias = ToConstTensor(flatBufferInputParams->inputGateBias());
    armnn::ConstTensor forgetGateBias = ToConstTensor(flatBufferInputParams->forgetGateBias());
    armnn::ConstTensor cellBias = ToConstTensor(flatBufferInputParams->cellBias());
    armnn::ConstTensor outputGateBias = ToConstTensor(flatBufferInputParams->outputGateBias());

    lstmInputParams.m_InputToInputWeights = &inputToInputWeights;
    lstmInputParams.m_InputToForgetWeights = &inputToForgetWeights;
    lstmInputParams.m_InputToCellWeights = &inputToCellWeights;
    lstmInputParams.m_InputToOutputWeights = &inputToOutputWeights;
    lstmInputParams.m_RecurrentToInputWeights = &recurrentToInputWeights;
    lstmInputParams.m_RecurrentToForgetWeights = &recurrentToForgetWeights;
    lstmInputParams.m_RecurrentToCellWeights = &recurrentToCellWeights;
    lstmInputParams.m_RecurrentToOutputWeights = &recurrentToOutputWeights;
    lstmInputParams.m_InputGateBias = &inputGateBias;
    lstmInputParams.m_ForgetGateBias = &forgetGateBias;
    lstmInputParams.m_CellBias = &cellBias;
    lstmInputParams.m_OutputGateBias = &outputGateBias;

    IConnectableLayer* layer = m_Network->AddQuantizedLstmLayer(lstmInputParams, layerName.c_str());

    armnn::TensorInfo outputTensorInfo1 = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo1);

    armnn::TensorInfo outputTensorInfo2 = ToTensorInfo(outputs[1]);
    layer->GetOutputSlot(1).SetTensorInfo(outputTensorInfo2);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseDequantize(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);

    TensorRawPtrVector inputs = GetInputs(graph, layerIndex);
    CHECK_VALID_SIZE(inputs.size(), 1);

    TensorRawPtrVector outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);

    const std::string layerName = GetLayerName(graph, layerIndex);
    IConnectableLayer* layer = m_Network->AddDequantizeLayer(layerName.c_str());

    armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseMerge(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);

    TensorRawPtrVector inputs = GetInputs(graph, layerIndex);
    CHECK_VALID_SIZE(inputs.size(), 2);

    TensorRawPtrVector outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);

    const std::string layerName = GetLayerName(graph, layerIndex);
    IConnectableLayer* layer = m_Network->AddMergeLayer(layerName.c_str());

    armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseSwitch(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);
    auto inputs = GetInputs(graph, layerIndex);
    CHECK_LOCATION();
    CHECK_VALID_SIZE(inputs.size(), 2);

    auto outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 2);

    auto layerName = GetLayerName(graph, layerIndex);
    IConnectableLayer* layer = m_Network->AddSwitchLayer(layerName.c_str());

    armnn::TensorInfo output0TensorInfo = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(output0TensorInfo);

    armnn::TensorInfo output1TensorInfo = ToTensorInfo(outputs[1]);
    layer->GetOutputSlot(1).SetTensorInfo(output1TensorInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParsePrelu(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);
    auto inputs = GetInputs(graph, layerIndex);
    CHECK_LOCATION();
    CHECK_VALID_SIZE(inputs.size(), 2);

    auto outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);

    auto layerName = GetLayerName(graph, layerIndex);
    IConnectableLayer* layer = m_Network->AddPreluLayer(layerName.c_str());

    armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseTranspose(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);

    auto dimsMapping = graph->layers()->Get(layerIndex)->layer_as_TransposeLayer()->descriptor()->dimMappings();

    auto inputs = GetInputs(graph, layerIndex);
    CHECK_VALID_SIZE(inputs.size(), 1);

    auto outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);
    auto outputInfo = ToTensorInfo(outputs[0]);

    auto layerName = GetLayerName(graph, layerIndex);
    const armnn::TransposeDescriptor descriptor(armnn::PermutationVector(dimsMapping->data(), dimsMapping->size()));

    IConnectableLayer* layer = m_Network->AddTransposeLayer(descriptor, layerName.c_str());
    layer->GetOutputSlot(0).SetTensorInfo(outputInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseTransposeConvolution2d(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);

    auto inputs = GetInputs(graph, layerIndex);
    CHECK_VALID_SIZE(inputs.size(), 1);

    auto outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);

    auto serializerLayer = graph->layers()->Get(layerIndex)->layer_as_TransposeConvolution2dLayer();
    auto layerName = GetLayerName(graph, layerIndex);
    auto serializerDescriptor = serializerLayer->descriptor();

    armnn::TransposeConvolution2dDescriptor descriptor;
    descriptor.m_PadLeft     = serializerDescriptor->padLeft();
    descriptor.m_PadRight    = serializerDescriptor->padRight();
    descriptor.m_PadTop      = serializerDescriptor->padTop();
    descriptor.m_PadBottom   = serializerDescriptor->padBottom();
    descriptor.m_StrideX     = serializerDescriptor->strideX();
    descriptor.m_StrideY     = serializerDescriptor->strideY();;
    descriptor.m_BiasEnabled = serializerDescriptor->biasEnabled();;
    descriptor.m_DataLayout  = ToDataLayout(serializerDescriptor->dataLayout());

    // weights & biases
    armnn::ConstTensor weights = ToConstTensor(serializerLayer->weights());
    armnn::Optional<armnn::ConstTensor> optionalBiases;
    if (descriptor.m_BiasEnabled)
    {
        armnn::ConstTensor biases = ToConstTensor(serializerLayer->biases());
        optionalBiases = armnn::MakeOptional<armnn::ConstTensor>(biases);
    }

    IConnectableLayer* layer = m_Network->AddTransposeConvolution2dLayer(descriptor,
                                                                         weights,
                                                                         optionalBiases,
                                                                         layerName.c_str());

    armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseStack(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);
    auto inputs = GetInputs(graph, layerIndex);

    auto outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 1);

    auto flatBufferDescriptor = graph->layers()->Get(layerIndex)->layer_as_StackLayer()->descriptor();
    unsigned int axis = flatBufferDescriptor->axis();
    unsigned int numInputs = flatBufferDescriptor->numInputs();
    CHECK_VALID_SIZE(inputs.size(), numInputs);

    auto flatBufferInputShape = flatBufferDescriptor->inputShape();
    std::vector<uint32_t> vectorInputShape(flatBufferInputShape->begin(),
                                           flatBufferInputShape->begin() + flatBufferInputShape->size());

    TensorShape inputShape(static_cast<unsigned int>(vectorInputShape.size()), vectorInputShape.data());
    armnn::StackDescriptor descriptor(axis, numInputs, inputShape);

    for (unsigned int i=0; i<inputs.size(); ++i)
    {
        armnn::TensorShape inputShape = ToTensorInfo(inputs[i]).GetShape();
        if (descriptor.m_InputShape != inputShape)
        {
            std::stringstream ss;
            ss << "Shape of input  "
               << i
               << " "
               << inputShape
               << " does not equal defined input shape "
               << descriptor.m_InputShape
               << ": "
               << CHECK_LOCATION().AsString();
            throw ParseException(ss.str());
        }
    }

    auto layerName = GetLayerName(graph, layerIndex);
    IConnectableLayer* layer = m_Network->AddStackLayer(descriptor, layerName.c_str());

    armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

void IDeserializer::DeserializerImpl::ParseStandIn(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);

    auto inputs  = GetInputs(graph, layerIndex);
    auto outputs = GetOutputs(graph, layerIndex);

    auto fbLayer      = graph->layers()->Get(layerIndex)->layer_as_StandInLayer();
    auto fbDescriptor = fbLayer->descriptor();

    armnn::StandInDescriptor descriptor;
    descriptor.m_NumInputs  = fbDescriptor->numInputs();
    descriptor.m_NumOutputs = fbDescriptor->numOutputs();

    CHECK_VALID_SIZE(inputs.size(),  descriptor.m_NumInputs);
    CHECK_VALID_SIZE(outputs.size(), descriptor.m_NumOutputs);

    const std::string layerName     = GetLayerName(graph, layerIndex);
    armnn::IConnectableLayer* layer = m_Network->AddStandInLayer(descriptor, layerName.c_str());

    for (unsigned int i = 0u; i < descriptor.m_NumOutputs; ++i)
    {
        armnn::TensorInfo outputInfo = ToTensorInfo(outputs[i]);
        layer->GetOutputSlot(i).SetTensorInfo(outputInfo);
    }

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

armnn::UnidirectionalSequenceLstmDescriptor IDeserializer::DeserializerImpl::GetUnidirectionalSequenceLstmDescriptor(
    UnidirectionalSequenceLstmDescriptorPtr descriptor)
{
    armnn::UnidirectionalSequenceLstmDescriptor desc;

    desc.m_ActivationFunc = descriptor->activationFunc();
    desc.m_ClippingThresCell = descriptor->clippingThresCell();
    desc.m_ClippingThresProj = descriptor->clippingThresProj();
    desc.m_CifgEnabled = descriptor->cifgEnabled();
    desc.m_PeepholeEnabled = descriptor->peepholeEnabled();
    desc.m_ProjectionEnabled = descriptor->projectionEnabled();
    desc.m_LayerNormEnabled = descriptor->layerNormEnabled();
    desc.m_TimeMajor = descriptor->timeMajor();

    return desc;
}

void IDeserializer::DeserializerImpl::ParseUnidirectionalSequenceLstm(GraphPtr graph, unsigned int layerIndex)
{
    CHECK_LAYERS(graph, 0, layerIndex);

    auto inputs = GetInputs(graph, layerIndex);
    CHECK_VALID_SIZE(inputs.size(), 3);

    auto outputs = GetOutputs(graph, layerIndex);
    CHECK_VALID_SIZE(outputs.size(), 3);

    auto flatBufferLayer = graph->layers()->Get(layerIndex)->layer_as_UnidirectionalSequenceLstmLayer();
    auto layerName = GetLayerName(graph, layerIndex);
    auto flatBufferDescriptor = flatBufferLayer->descriptor();
    auto flatBufferInputParams = flatBufferLayer->inputParams();

    auto descriptor = GetUnidirectionalSequenceLstmDescriptor(flatBufferDescriptor);

    armnn::LstmInputParams lstmInputParams;

    armnn::ConstTensor inputToForgetWeights = ToConstTensor(flatBufferInputParams->inputToForgetWeights());
    armnn::ConstTensor inputToCellWeights = ToConstTensor(flatBufferInputParams->inputToCellWeights());
    armnn::ConstTensor inputToOutputWeights = ToConstTensor(flatBufferInputParams->inputToOutputWeights());
    armnn::ConstTensor recurrentToForgetWeights = ToConstTensor(flatBufferInputParams->recurrentToForgetWeights());
    armnn::ConstTensor recurrentToCellWeights = ToConstTensor(flatBufferInputParams->recurrentToCellWeights());
    armnn::ConstTensor recurrentToOutputWeights = ToConstTensor(flatBufferInputParams->recurrentToOutputWeights());
    armnn::ConstTensor forgetGateBias = ToConstTensor(flatBufferInputParams->forgetGateBias());
    armnn::ConstTensor cellBias = ToConstTensor(flatBufferInputParams->cellBias());
    armnn::ConstTensor outputGateBias = ToConstTensor(flatBufferInputParams->outputGateBias());

    lstmInputParams.m_InputToForgetWeights = &inputToForgetWeights;
    lstmInputParams.m_InputToCellWeights = &inputToCellWeights;
    lstmInputParams.m_InputToOutputWeights = &inputToOutputWeights;
    lstmInputParams.m_RecurrentToForgetWeights = &recurrentToForgetWeights;
    lstmInputParams.m_RecurrentToCellWeights = &recurrentToCellWeights;
    lstmInputParams.m_RecurrentToOutputWeights = &recurrentToOutputWeights;
    lstmInputParams.m_ForgetGateBias = &forgetGateBias;
    lstmInputParams.m_CellBias = &cellBias;
    lstmInputParams.m_OutputGateBias = &outputGateBias;

    armnn::ConstTensor inputToInputWeights;
    armnn::ConstTensor recurrentToInputWeights;
    armnn::ConstTensor cellToInputWeights;
    armnn::ConstTensor inputGateBias;
    if (!descriptor.m_CifgEnabled)
    {
        inputToInputWeights = ToConstTensor(flatBufferInputParams->inputToInputWeights());
        recurrentToInputWeights = ToConstTensor(flatBufferInputParams->recurrentToInputWeights());
        inputGateBias = ToConstTensor(flatBufferInputParams->inputGateBias());

        lstmInputParams.m_InputToInputWeights = &inputToInputWeights;
        lstmInputParams.m_RecurrentToInputWeights = &recurrentToInputWeights;
        lstmInputParams.m_InputGateBias = &inputGateBias;

        if (descriptor.m_PeepholeEnabled)
        {
            cellToInputWeights = ToConstTensor(flatBufferInputParams->cellToInputWeights());
            lstmInputParams.m_CellToInputWeights = &cellToInputWeights;
        }
    }

    armnn::ConstTensor projectionWeights;
    armnn::ConstTensor projectionBias;
    if (descriptor.m_ProjectionEnabled)
    {
        projectionWeights = ToConstTensor(flatBufferInputParams->projectionWeights());
        projectionBias = ToConstTensor(flatBufferInputParams->projectionBias());

        lstmInputParams.m_ProjectionWeights = &projectionWeights;
        lstmInputParams.m_ProjectionBias = &projectionBias;
    }

    armnn::ConstTensor cellToForgetWeights;
    armnn::ConstTensor cellToOutputWeights;
    if (descriptor.m_PeepholeEnabled)
    {
        cellToForgetWeights = ToConstTensor(flatBufferInputParams->cellToForgetWeights());
        cellToOutputWeights = ToConstTensor(flatBufferInputParams->cellToOutputWeights());

        lstmInputParams.m_CellToForgetWeights = &cellToForgetWeights;
        lstmInputParams.m_CellToOutputWeights = &cellToOutputWeights;
    }

    armnn::ConstTensor inputLayerNormWeights;
    armnn::ConstTensor forgetLayerNormWeights;
    armnn::ConstTensor cellLayerNormWeights;
    armnn::ConstTensor outputLayerNormWeights;
    if (descriptor.m_LayerNormEnabled)
    {
        if (!descriptor.m_CifgEnabled)
        {
            inputLayerNormWeights = ToConstTensor(flatBufferInputParams->inputLayerNormWeights());
            lstmInputParams.m_InputLayerNormWeights = &inputLayerNormWeights;
        }
        forgetLayerNormWeights = ToConstTensor(flatBufferInputParams->forgetLayerNormWeights());
        cellLayerNormWeights = ToConstTensor(flatBufferInputParams->cellLayerNormWeights());
        outputLayerNormWeights = ToConstTensor(flatBufferInputParams->outputLayerNormWeights());

        lstmInputParams.m_ForgetLayerNormWeights = &forgetLayerNormWeights;
        lstmInputParams.m_CellLayerNormWeights = &cellLayerNormWeights;
        lstmInputParams.m_OutputLayerNormWeights = &outputLayerNormWeights;
    }

    IConnectableLayer* layer = m_Network->AddUnidirectionalSequenceLstmLayer(descriptor,
                                                                             lstmInputParams,
                                                                             layerName.c_str());

    armnn::TensorInfo outputTensorInfo0 = ToTensorInfo(outputs[0]);
    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo0);

    armnn::TensorInfo outputTensorInfo1 = ToTensorInfo(outputs[1]);
    layer->GetOutputSlot(1).SetTensorInfo(outputTensorInfo1);

    armnn::TensorInfo outputTensorInfo2 = ToTensorInfo(outputs[2]);
    layer->GetOutputSlot(2).SetTensorInfo(outputTensorInfo2);

    RegisterInputSlots(graph, layerIndex, layer);
    RegisterOutputSlots(graph, layerIndex, layer);
}

} // namespace armnnDeserializer