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      1 // clang-format off
      2 // Generated file (from: quantized_lstm.mod.py). Do not edit
      3 void CreateModel(Model *model) {
      4   OperandType type0(Type::TENSOR_QUANT8_ASYMM, {2, 2}, 0.0078125f, 128);
      5   OperandType type1(Type::TENSOR_QUANT8_ASYMM, {4, 2}, 0.00408021f, 100);
      6   OperandType type2(Type::TENSOR_QUANT8_ASYMM, {4, 4}, 0.00408021f, 100);
      7   OperandType type3(Type::TENSOR_INT32, {4}, 3.1876640625e-05f, 0);
      8   OperandType type4(Type::TENSOR_QUANT16_SYMM, {2, 4}, 0.00048828125f, 0);
      9   OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 4}, 0.0078125f, 128);
     10   // Phase 1, operands
     11   auto input = model->addOperand(&type0);
     12   auto inputToInputWeights = model->addOperand(&type1);
     13   auto inputToForgetWeights = model->addOperand(&type1);
     14   auto inputToCellWeights = model->addOperand(&type1);
     15   auto inputToOutputWeights = model->addOperand(&type1);
     16   auto recurrentToInputWeights = model->addOperand(&type2);
     17   auto recurrentToForgetWeights = model->addOperand(&type2);
     18   auto recurrentToCellWeights = model->addOperand(&type2);
     19   auto recurrentToOutputWeights = model->addOperand(&type2);
     20   auto inputGateBias = model->addOperand(&type3);
     21   auto forgetGateBias = model->addOperand(&type3);
     22   auto cellGateBias = model->addOperand(&type3);
     23   auto outputGateBias = model->addOperand(&type3);
     24   auto prevCellState = model->addOperand(&type4);
     25   auto prevOutput = model->addOperand(&type5);
     26   auto cellStateOut = model->addOperand(&type4);
     27   auto output = model->addOperand(&type5);
     28   // Phase 2, operations
     29   model->addOperation(ANEURALNETWORKS_QUANTIZED_16BIT_LSTM, {input, inputToInputWeights, inputToForgetWeights, inputToCellWeights, inputToOutputWeights, recurrentToInputWeights, recurrentToForgetWeights, recurrentToCellWeights, recurrentToOutputWeights, inputGateBias, forgetGateBias, cellGateBias, outputGateBias, prevCellState, prevOutput}, {cellStateOut, output});
     30   // Phase 3, inputs and outputs
     31   model->identifyInputsAndOutputs(
     32     {input, inputToInputWeights, inputToForgetWeights, inputToCellWeights, inputToOutputWeights, recurrentToInputWeights, recurrentToForgetWeights, recurrentToCellWeights, recurrentToOutputWeights, inputGateBias, forgetGateBias, cellGateBias, outputGateBias, prevCellState, prevOutput},
     33     {cellStateOut, output});
     34   assert(model->isValid());
     35 }
     36 
     37 inline bool is_ignored(int i) {
     38   static std::set<int> ignore = {};
     39   return ignore.find(i) != ignore.end();
     40 }
     41 
     42 void CreateModel_relaxed(Model *model) {
     43   OperandType type0(Type::TENSOR_QUANT8_ASYMM, {2, 2}, 0.0078125f, 128);
     44   OperandType type1(Type::TENSOR_QUANT8_ASYMM, {4, 2}, 0.00408021f, 100);
     45   OperandType type2(Type::TENSOR_QUANT8_ASYMM, {4, 4}, 0.00408021f, 100);
     46   OperandType type3(Type::TENSOR_INT32, {4}, 3.1876640625e-05f, 0);
     47   OperandType type4(Type::TENSOR_QUANT16_SYMM, {2, 4}, 0.00048828125f, 0);
     48   OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 4}, 0.0078125f, 128);
     49   // Phase 1, operands
     50   auto input = model->addOperand(&type0);
     51   auto inputToInputWeights = model->addOperand(&type1);
     52   auto inputToForgetWeights = model->addOperand(&type1);
     53   auto inputToCellWeights = model->addOperand(&type1);
     54   auto inputToOutputWeights = model->addOperand(&type1);
     55   auto recurrentToInputWeights = model->addOperand(&type2);
     56   auto recurrentToForgetWeights = model->addOperand(&type2);
     57   auto recurrentToCellWeights = model->addOperand(&type2);
     58   auto recurrentToOutputWeights = model->addOperand(&type2);
     59   auto inputGateBias = model->addOperand(&type3);
     60   auto forgetGateBias = model->addOperand(&type3);
     61   auto cellGateBias = model->addOperand(&type3);
     62   auto outputGateBias = model->addOperand(&type3);
     63   auto prevCellState = model->addOperand(&type4);
     64   auto prevOutput = model->addOperand(&type5);
     65   auto cellStateOut = model->addOperand(&type4);
     66   auto output = model->addOperand(&type5);
     67   // Phase 2, operations
     68   model->addOperation(ANEURALNETWORKS_QUANTIZED_16BIT_LSTM, {input, inputToInputWeights, inputToForgetWeights, inputToCellWeights, inputToOutputWeights, recurrentToInputWeights, recurrentToForgetWeights, recurrentToCellWeights, recurrentToOutputWeights, inputGateBias, forgetGateBias, cellGateBias, outputGateBias, prevCellState, prevOutput}, {cellStateOut, output});
     69   // Phase 3, inputs and outputs
     70   model->identifyInputsAndOutputs(
     71     {input, inputToInputWeights, inputToForgetWeights, inputToCellWeights, inputToOutputWeights, recurrentToInputWeights, recurrentToForgetWeights, recurrentToCellWeights, recurrentToOutputWeights, inputGateBias, forgetGateBias, cellGateBias, outputGateBias, prevCellState, prevOutput},
     72     {cellStateOut, output});
     73   // Phase 4: set relaxed execution
     74   model->relaxComputationFloat32toFloat16(true);
     75   assert(model->isValid());
     76 }
     77 
     78 inline bool is_ignored_relaxed(int i) {
     79   static std::set<int> ignore = {};
     80   return ignore.find(i) != ignore.end();
     81 }
     82 
     83 void CreateModel_dynamic_output_shape(Model *model) {
     84   OperandType type0(Type::TENSOR_QUANT8_ASYMM, {2, 2}, 0.0078125f, 128);
     85   OperandType type1(Type::TENSOR_QUANT8_ASYMM, {4, 2}, 0.00408021f, 100);
     86   OperandType type10(Type::TENSOR_QUANT8_ASYMM, {0, 0}, 0.0078125f, 128);
     87   OperandType type2(Type::TENSOR_QUANT8_ASYMM, {4, 4}, 0.00408021f, 100);
     88   OperandType type3(Type::TENSOR_INT32, {4}, 3.1876640625e-05f, 0);
     89   OperandType type4(Type::TENSOR_QUANT16_SYMM, {2, 4}, 0.00048828125f, 0);
     90   OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 4}, 0.0078125f, 128);
     91   OperandType type9(Type::TENSOR_QUANT16_SYMM, {0, 0}, 0.00048828125f, 0);
     92   // Phase 1, operands
     93   auto input = model->addOperand(&type0);
     94   auto inputToInputWeights = model->addOperand(&type1);
     95   auto inputToForgetWeights = model->addOperand(&type1);
     96   auto inputToCellWeights = model->addOperand(&type1);
     97   auto inputToOutputWeights = model->addOperand(&type1);
     98   auto recurrentToInputWeights = model->addOperand(&type2);
     99   auto recurrentToForgetWeights = model->addOperand(&type2);
    100   auto recurrentToCellWeights = model->addOperand(&type2);
    101   auto recurrentToOutputWeights = model->addOperand(&type2);
    102   auto inputGateBias = model->addOperand(&type3);
    103   auto forgetGateBias = model->addOperand(&type3);
    104   auto cellGateBias = model->addOperand(&type3);
    105   auto outputGateBias = model->addOperand(&type3);
    106   auto prevCellState = model->addOperand(&type4);
    107   auto prevOutput = model->addOperand(&type5);
    108   auto cellStateOut = model->addOperand(&type9);
    109   auto output = model->addOperand(&type10);
    110   // Phase 2, operations
    111   model->addOperation(ANEURALNETWORKS_QUANTIZED_16BIT_LSTM, {input, inputToInputWeights, inputToForgetWeights, inputToCellWeights, inputToOutputWeights, recurrentToInputWeights, recurrentToForgetWeights, recurrentToCellWeights, recurrentToOutputWeights, inputGateBias, forgetGateBias, cellGateBias, outputGateBias, prevCellState, prevOutput}, {cellStateOut, output});
    112   // Phase 3, inputs and outputs
    113   model->identifyInputsAndOutputs(
    114     {input, inputToInputWeights, inputToForgetWeights, inputToCellWeights, inputToOutputWeights, recurrentToInputWeights, recurrentToForgetWeights, recurrentToCellWeights, recurrentToOutputWeights, inputGateBias, forgetGateBias, cellGateBias, outputGateBias, prevCellState, prevOutput},
    115     {cellStateOut, output});
    116   assert(model->isValid());
    117 }
    118 
    119 inline bool is_ignored_dynamic_output_shape(int i) {
    120   static std::set<int> ignore = {};
    121   return ignore.find(i) != ignore.end();
    122 }
    123 
    124 void CreateModel_dynamic_output_shape_relaxed(Model *model) {
    125   OperandType type0(Type::TENSOR_QUANT8_ASYMM, {2, 2}, 0.0078125f, 128);
    126   OperandType type1(Type::TENSOR_QUANT8_ASYMM, {4, 2}, 0.00408021f, 100);
    127   OperandType type10(Type::TENSOR_QUANT8_ASYMM, {0, 0}, 0.0078125f, 128);
    128   OperandType type2(Type::TENSOR_QUANT8_ASYMM, {4, 4}, 0.00408021f, 100);
    129   OperandType type3(Type::TENSOR_INT32, {4}, 3.1876640625e-05f, 0);
    130   OperandType type4(Type::TENSOR_QUANT16_SYMM, {2, 4}, 0.00048828125f, 0);
    131   OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 4}, 0.0078125f, 128);
    132   OperandType type9(Type::TENSOR_QUANT16_SYMM, {0, 0}, 0.00048828125f, 0);
    133   // Phase 1, operands
    134   auto input = model->addOperand(&type0);
    135   auto inputToInputWeights = model->addOperand(&type1);
    136   auto inputToForgetWeights = model->addOperand(&type1);
    137   auto inputToCellWeights = model->addOperand(&type1);
    138   auto inputToOutputWeights = model->addOperand(&type1);
    139   auto recurrentToInputWeights = model->addOperand(&type2);
    140   auto recurrentToForgetWeights = model->addOperand(&type2);
    141   auto recurrentToCellWeights = model->addOperand(&type2);
    142   auto recurrentToOutputWeights = model->addOperand(&type2);
    143   auto inputGateBias = model->addOperand(&type3);
    144   auto forgetGateBias = model->addOperand(&type3);
    145   auto cellGateBias = model->addOperand(&type3);
    146   auto outputGateBias = model->addOperand(&type3);
    147   auto prevCellState = model->addOperand(&type4);
    148   auto prevOutput = model->addOperand(&type5);
    149   auto cellStateOut = model->addOperand(&type9);
    150   auto output = model->addOperand(&type10);
    151   // Phase 2, operations
    152   model->addOperation(ANEURALNETWORKS_QUANTIZED_16BIT_LSTM, {input, inputToInputWeights, inputToForgetWeights, inputToCellWeights, inputToOutputWeights, recurrentToInputWeights, recurrentToForgetWeights, recurrentToCellWeights, recurrentToOutputWeights, inputGateBias, forgetGateBias, cellGateBias, outputGateBias, prevCellState, prevOutput}, {cellStateOut, output});
    153   // Phase 3, inputs and outputs
    154   model->identifyInputsAndOutputs(
    155     {input, inputToInputWeights, inputToForgetWeights, inputToCellWeights, inputToOutputWeights, recurrentToInputWeights, recurrentToForgetWeights, recurrentToCellWeights, recurrentToOutputWeights, inputGateBias, forgetGateBias, cellGateBias, outputGateBias, prevCellState, prevOutput},
    156     {cellStateOut, output});
    157   // Phase 4: set relaxed execution
    158   model->relaxComputationFloat32toFloat16(true);
    159   assert(model->isValid());
    160 }
    161 
    162 inline bool is_ignored_dynamic_output_shape_relaxed(int i) {
    163   static std::set<int> ignore = {};
    164   return ignore.find(i) != ignore.end();
    165 }
    166 
    167 void CreateModel_2(Model *model) {
    168   OperandType type1(Type::TENSOR_QUANT8_ASYMM, {4, 2}, 0.00408021f, 100);
    169   OperandType type2(Type::TENSOR_QUANT8_ASYMM, {4, 4}, 0.00408021f, 100);
    170   OperandType type3(Type::TENSOR_INT32, {4}, 3.1876640625e-05f, 0);
    171   OperandType type6(Type::TENSOR_QUANT8_ASYMM, {1, 2}, 0.0078125f, 128);
    172   OperandType type7(Type::TENSOR_QUANT16_SYMM, {1, 4}, 0.00048828125f, 0);
    173   OperandType type8(Type::TENSOR_QUANT8_ASYMM, {1, 4}, 0.0078125f, 128);
    174   // Phase 1, operands
    175   auto input1 = model->addOperand(&type6);
    176   auto inputToInputWeights1 = model->addOperand(&type1);
    177   auto inputToForgetWeights1 = model->addOperand(&type1);
    178   auto inputToCellWeights1 = model->addOperand(&type1);
    179   auto inputToOutputWeights1 = model->addOperand(&type1);
    180   auto recurrentToInputWeights1 = model->addOperand(&type2);
    181   auto recurrentToForgetWeights1 = model->addOperand(&type2);
    182   auto recurrentToCellWeights1 = model->addOperand(&type2);
    183   auto recurrentToOutputWeights1 = model->addOperand(&type2);
    184   auto inputGateBias1 = model->addOperand(&type3);
    185   auto forgetGateBias1 = model->addOperand(&type3);
    186   auto cellGateBias1 = model->addOperand(&type3);
    187   auto outputGateBias1 = model->addOperand(&type3);
    188   auto prevCellState1 = model->addOperand(&type7);
    189   auto prevOutput1 = model->addOperand(&type8);
    190   auto cellStateOut1 = model->addOperand(&type7);
    191   auto output1 = model->addOperand(&type8);
    192   // Phase 2, operations
    193   static uint8_t inputToInputWeights1_init[] = {146, 250, 235, 171, 10, 218, 171, 108};
    194   model->setOperandValue(inputToInputWeights1, inputToInputWeights1_init, sizeof(uint8_t) * 8);
    195   static uint8_t inputToForgetWeights1_init[] = {24, 50, 132, 179, 158, 110, 3, 169};
    196   model->setOperandValue(inputToForgetWeights1, inputToForgetWeights1_init, sizeof(uint8_t) * 8);
    197   static uint8_t inputToCellWeights1_init[] = {133, 34, 29, 49, 206, 109, 54, 183};
    198   model->setOperandValue(inputToCellWeights1, inputToCellWeights1_init, sizeof(uint8_t) * 8);
    199   static uint8_t inputToOutputWeights1_init[] = {195, 187, 11, 99, 109, 10, 218, 48};
    200   model->setOperandValue(inputToOutputWeights1, inputToOutputWeights1_init, sizeof(uint8_t) * 8);
    201   static uint8_t recurrentToInputWeights1_init[] = {254, 206, 77, 168, 71, 20, 215, 6, 223, 7, 118, 225, 59, 130, 174, 26};
    202   model->setOperandValue(recurrentToInputWeights1, recurrentToInputWeights1_init, sizeof(uint8_t) * 16);
    203   static uint8_t recurrentToForgetWeights1_init[] = {137, 240, 103, 52, 68, 51, 237, 112, 0, 220, 89, 23, 69, 4, 207, 253};
    204   model->setOperandValue(recurrentToForgetWeights1, recurrentToForgetWeights1_init, sizeof(uint8_t) * 16);
    205   static uint8_t recurrentToCellWeights1_init[] = {172, 60, 205, 65, 14, 0, 140, 168, 240, 223, 133, 56, 142, 64, 246, 216};
    206   model->setOperandValue(recurrentToCellWeights1, recurrentToCellWeights1_init, sizeof(uint8_t) * 16);
    207   static uint8_t recurrentToOutputWeights1_init[] = {106, 214, 67, 23, 59, 158, 45, 3, 119, 132, 49, 205, 129, 218, 11, 98};
    208   model->setOperandValue(recurrentToOutputWeights1, recurrentToOutputWeights1_init, sizeof(uint8_t) * 16);
    209   static int32_t inputGateBias1_init[] = {-7876, 13488, -726, 32839};
    210   model->setOperandValue(inputGateBias1, inputGateBias1_init, sizeof(int32_t) * 4);
    211   static int32_t forgetGateBias1_init[] = {9206, -46884, -11693, -38724};
    212   model->setOperandValue(forgetGateBias1, forgetGateBias1_init, sizeof(int32_t) * 4);
    213   static int32_t cellGateBias1_init[] = {39481, 48624, 48976, -21419};
    214   model->setOperandValue(cellGateBias1, cellGateBias1_init, sizeof(int32_t) * 4);
    215   static int32_t outputGateBias1_init[] = {-58999, -17050, -41852, -40538};
    216   model->setOperandValue(outputGateBias1, outputGateBias1_init, sizeof(int32_t) * 4);
    217   model->addOperation(ANEURALNETWORKS_QUANTIZED_16BIT_LSTM, {input1, inputToInputWeights1, inputToForgetWeights1, inputToCellWeights1, inputToOutputWeights1, recurrentToInputWeights1, recurrentToForgetWeights1, recurrentToCellWeights1, recurrentToOutputWeights1, inputGateBias1, forgetGateBias1, cellGateBias1, outputGateBias1, prevCellState1, prevOutput1}, {cellStateOut1, output1});
    218   // Phase 3, inputs and outputs
    219   model->identifyInputsAndOutputs(
    220     {input1, prevCellState1, prevOutput1},
    221     {cellStateOut1, output1});
    222   assert(model->isValid());
    223 }
    224 
    225 inline bool is_ignored_2(int i) {
    226   static std::set<int> ignore = {};
    227   return ignore.find(i) != ignore.end();
    228 }
    229 
    230 void CreateModel_relaxed_2(Model *model) {
    231   OperandType type1(Type::TENSOR_QUANT8_ASYMM, {4, 2}, 0.00408021f, 100);
    232   OperandType type2(Type::TENSOR_QUANT8_ASYMM, {4, 4}, 0.00408021f, 100);
    233   OperandType type3(Type::TENSOR_INT32, {4}, 3.1876640625e-05f, 0);
    234   OperandType type6(Type::TENSOR_QUANT8_ASYMM, {1, 2}, 0.0078125f, 128);
    235   OperandType type7(Type::TENSOR_QUANT16_SYMM, {1, 4}, 0.00048828125f, 0);
    236   OperandType type8(Type::TENSOR_QUANT8_ASYMM, {1, 4}, 0.0078125f, 128);
    237   // Phase 1, operands
    238   auto input1 = model->addOperand(&type6);
    239   auto inputToInputWeights1 = model->addOperand(&type1);
    240   auto inputToForgetWeights1 = model->addOperand(&type1);
    241   auto inputToCellWeights1 = model->addOperand(&type1);
    242   auto inputToOutputWeights1 = model->addOperand(&type1);
    243   auto recurrentToInputWeights1 = model->addOperand(&type2);
    244   auto recurrentToForgetWeights1 = model->addOperand(&type2);
    245   auto recurrentToCellWeights1 = model->addOperand(&type2);
    246   auto recurrentToOutputWeights1 = model->addOperand(&type2);
    247   auto inputGateBias1 = model->addOperand(&type3);
    248   auto forgetGateBias1 = model->addOperand(&type3);
    249   auto cellGateBias1 = model->addOperand(&type3);
    250   auto outputGateBias1 = model->addOperand(&type3);
    251   auto prevCellState1 = model->addOperand(&type7);
    252   auto prevOutput1 = model->addOperand(&type8);
    253   auto cellStateOut1 = model->addOperand(&type7);
    254   auto output1 = model->addOperand(&type8);
    255   // Phase 2, operations
    256   static uint8_t inputToInputWeights1_init[] = {146, 250, 235, 171, 10, 218, 171, 108};
    257   model->setOperandValue(inputToInputWeights1, inputToInputWeights1_init, sizeof(uint8_t) * 8);
    258   static uint8_t inputToForgetWeights1_init[] = {24, 50, 132, 179, 158, 110, 3, 169};
    259   model->setOperandValue(inputToForgetWeights1, inputToForgetWeights1_init, sizeof(uint8_t) * 8);
    260   static uint8_t inputToCellWeights1_init[] = {133, 34, 29, 49, 206, 109, 54, 183};
    261   model->setOperandValue(inputToCellWeights1, inputToCellWeights1_init, sizeof(uint8_t) * 8);
    262   static uint8_t inputToOutputWeights1_init[] = {195, 187, 11, 99, 109, 10, 218, 48};
    263   model->setOperandValue(inputToOutputWeights1, inputToOutputWeights1_init, sizeof(uint8_t) * 8);
    264   static uint8_t recurrentToInputWeights1_init[] = {254, 206, 77, 168, 71, 20, 215, 6, 223, 7, 118, 225, 59, 130, 174, 26};
    265   model->setOperandValue(recurrentToInputWeights1, recurrentToInputWeights1_init, sizeof(uint8_t) * 16);
    266   static uint8_t recurrentToForgetWeights1_init[] = {137, 240, 103, 52, 68, 51, 237, 112, 0, 220, 89, 23, 69, 4, 207, 253};
    267   model->setOperandValue(recurrentToForgetWeights1, recurrentToForgetWeights1_init, sizeof(uint8_t) * 16);
    268   static uint8_t recurrentToCellWeights1_init[] = {172, 60, 205, 65, 14, 0, 140, 168, 240, 223, 133, 56, 142, 64, 246, 216};
    269   model->setOperandValue(recurrentToCellWeights1, recurrentToCellWeights1_init, sizeof(uint8_t) * 16);
    270   static uint8_t recurrentToOutputWeights1_init[] = {106, 214, 67, 23, 59, 158, 45, 3, 119, 132, 49, 205, 129, 218, 11, 98};
    271   model->setOperandValue(recurrentToOutputWeights1, recurrentToOutputWeights1_init, sizeof(uint8_t) * 16);
    272   static int32_t inputGateBias1_init[] = {-7876, 13488, -726, 32839};
    273   model->setOperandValue(inputGateBias1, inputGateBias1_init, sizeof(int32_t) * 4);
    274   static int32_t forgetGateBias1_init[] = {9206, -46884, -11693, -38724};
    275   model->setOperandValue(forgetGateBias1, forgetGateBias1_init, sizeof(int32_t) * 4);
    276   static int32_t cellGateBias1_init[] = {39481, 48624, 48976, -21419};
    277   model->setOperandValue(cellGateBias1, cellGateBias1_init, sizeof(int32_t) * 4);
    278   static int32_t outputGateBias1_init[] = {-58999, -17050, -41852, -40538};
    279   model->setOperandValue(outputGateBias1, outputGateBias1_init, sizeof(int32_t) * 4);
    280   model->addOperation(ANEURALNETWORKS_QUANTIZED_16BIT_LSTM, {input1, inputToInputWeights1, inputToForgetWeights1, inputToCellWeights1, inputToOutputWeights1, recurrentToInputWeights1, recurrentToForgetWeights1, recurrentToCellWeights1, recurrentToOutputWeights1, inputGateBias1, forgetGateBias1, cellGateBias1, outputGateBias1, prevCellState1, prevOutput1}, {cellStateOut1, output1});
    281   // Phase 3, inputs and outputs
    282   model->identifyInputsAndOutputs(
    283     {input1, prevCellState1, prevOutput1},
    284     {cellStateOut1, output1});
    285   // Phase 4: set relaxed execution
    286   model->relaxComputationFloat32toFloat16(true);
    287   assert(model->isValid());
    288 }
    289 
    290 inline bool is_ignored_relaxed_2(int i) {
    291   static std::set<int> ignore = {};
    292   return ignore.find(i) != ignore.end();
    293 }
    294 
    295 void CreateModel_dynamic_output_shape_2(Model *model) {
    296   OperandType type1(Type::TENSOR_QUANT8_ASYMM, {4, 2}, 0.00408021f, 100);
    297   OperandType type10(Type::TENSOR_QUANT8_ASYMM, {0, 0}, 0.0078125f, 128);
    298   OperandType type2(Type::TENSOR_QUANT8_ASYMM, {4, 4}, 0.00408021f, 100);
    299   OperandType type3(Type::TENSOR_INT32, {4}, 3.1876640625e-05f, 0);
    300   OperandType type6(Type::TENSOR_QUANT8_ASYMM, {1, 2}, 0.0078125f, 128);
    301   OperandType type7(Type::TENSOR_QUANT16_SYMM, {1, 4}, 0.00048828125f, 0);
    302   OperandType type8(Type::TENSOR_QUANT8_ASYMM, {1, 4}, 0.0078125f, 128);
    303   OperandType type9(Type::TENSOR_QUANT16_SYMM, {0, 0}, 0.00048828125f, 0);
    304   // Phase 1, operands
    305   auto input1 = model->addOperand(&type6);
    306   auto inputToInputWeights1 = model->addOperand(&type1);
    307   auto inputToForgetWeights1 = model->addOperand(&type1);
    308   auto inputToCellWeights1 = model->addOperand(&type1);
    309   auto inputToOutputWeights1 = model->addOperand(&type1);
    310   auto recurrentToInputWeights1 = model->addOperand(&type2);
    311   auto recurrentToForgetWeights1 = model->addOperand(&type2);
    312   auto recurrentToCellWeights1 = model->addOperand(&type2);
    313   auto recurrentToOutputWeights1 = model->addOperand(&type2);
    314   auto inputGateBias1 = model->addOperand(&type3);
    315   auto forgetGateBias1 = model->addOperand(&type3);
    316   auto cellGateBias1 = model->addOperand(&type3);
    317   auto outputGateBias1 = model->addOperand(&type3);
    318   auto prevCellState1 = model->addOperand(&type7);
    319   auto prevOutput1 = model->addOperand(&type8);
    320   auto cellStateOut1 = model->addOperand(&type9);
    321   auto output1 = model->addOperand(&type10);
    322   // Phase 2, operations
    323   static uint8_t inputToInputWeights1_init[] = {146, 250, 235, 171, 10, 218, 171, 108};
    324   model->setOperandValue(inputToInputWeights1, inputToInputWeights1_init, sizeof(uint8_t) * 8);
    325   static uint8_t inputToForgetWeights1_init[] = {24, 50, 132, 179, 158, 110, 3, 169};
    326   model->setOperandValue(inputToForgetWeights1, inputToForgetWeights1_init, sizeof(uint8_t) * 8);
    327   static uint8_t inputToCellWeights1_init[] = {133, 34, 29, 49, 206, 109, 54, 183};
    328   model->setOperandValue(inputToCellWeights1, inputToCellWeights1_init, sizeof(uint8_t) * 8);
    329   static uint8_t inputToOutputWeights1_init[] = {195, 187, 11, 99, 109, 10, 218, 48};
    330   model->setOperandValue(inputToOutputWeights1, inputToOutputWeights1_init, sizeof(uint8_t) * 8);
    331   static uint8_t recurrentToInputWeights1_init[] = {254, 206, 77, 168, 71, 20, 215, 6, 223, 7, 118, 225, 59, 130, 174, 26};
    332   model->setOperandValue(recurrentToInputWeights1, recurrentToInputWeights1_init, sizeof(uint8_t) * 16);
    333   static uint8_t recurrentToForgetWeights1_init[] = {137, 240, 103, 52, 68, 51, 237, 112, 0, 220, 89, 23, 69, 4, 207, 253};
    334   model->setOperandValue(recurrentToForgetWeights1, recurrentToForgetWeights1_init, sizeof(uint8_t) * 16);
    335   static uint8_t recurrentToCellWeights1_init[] = {172, 60, 205, 65, 14, 0, 140, 168, 240, 223, 133, 56, 142, 64, 246, 216};
    336   model->setOperandValue(recurrentToCellWeights1, recurrentToCellWeights1_init, sizeof(uint8_t) * 16);
    337   static uint8_t recurrentToOutputWeights1_init[] = {106, 214, 67, 23, 59, 158, 45, 3, 119, 132, 49, 205, 129, 218, 11, 98};
    338   model->setOperandValue(recurrentToOutputWeights1, recurrentToOutputWeights1_init, sizeof(uint8_t) * 16);
    339   static int32_t inputGateBias1_init[] = {-7876, 13488, -726, 32839};
    340   model->setOperandValue(inputGateBias1, inputGateBias1_init, sizeof(int32_t) * 4);
    341   static int32_t forgetGateBias1_init[] = {9206, -46884, -11693, -38724};
    342   model->setOperandValue(forgetGateBias1, forgetGateBias1_init, sizeof(int32_t) * 4);
    343   static int32_t cellGateBias1_init[] = {39481, 48624, 48976, -21419};
    344   model->setOperandValue(cellGateBias1, cellGateBias1_init, sizeof(int32_t) * 4);
    345   static int32_t outputGateBias1_init[] = {-58999, -17050, -41852, -40538};
    346   model->setOperandValue(outputGateBias1, outputGateBias1_init, sizeof(int32_t) * 4);
    347   model->addOperation(ANEURALNETWORKS_QUANTIZED_16BIT_LSTM, {input1, inputToInputWeights1, inputToForgetWeights1, inputToCellWeights1, inputToOutputWeights1, recurrentToInputWeights1, recurrentToForgetWeights1, recurrentToCellWeights1, recurrentToOutputWeights1, inputGateBias1, forgetGateBias1, cellGateBias1, outputGateBias1, prevCellState1, prevOutput1}, {cellStateOut1, output1});
    348   // Phase 3, inputs and outputs
    349   model->identifyInputsAndOutputs(
    350     {input1, prevCellState1, prevOutput1},
    351     {cellStateOut1, output1});
    352   assert(model->isValid());
    353 }
    354 
    355 inline bool is_ignored_dynamic_output_shape_2(int i) {
    356   static std::set<int> ignore = {};
    357   return ignore.find(i) != ignore.end();
    358 }
    359 
    360 void CreateModel_dynamic_output_shape_relaxed_2(Model *model) {
    361   OperandType type1(Type::TENSOR_QUANT8_ASYMM, {4, 2}, 0.00408021f, 100);
    362   OperandType type10(Type::TENSOR_QUANT8_ASYMM, {0, 0}, 0.0078125f, 128);
    363   OperandType type2(Type::TENSOR_QUANT8_ASYMM, {4, 4}, 0.00408021f, 100);
    364   OperandType type3(Type::TENSOR_INT32, {4}, 3.1876640625e-05f, 0);
    365   OperandType type6(Type::TENSOR_QUANT8_ASYMM, {1, 2}, 0.0078125f, 128);
    366   OperandType type7(Type::TENSOR_QUANT16_SYMM, {1, 4}, 0.00048828125f, 0);
    367   OperandType type8(Type::TENSOR_QUANT8_ASYMM, {1, 4}, 0.0078125f, 128);
    368   OperandType type9(Type::TENSOR_QUANT16_SYMM, {0, 0}, 0.00048828125f, 0);
    369   // Phase 1, operands
    370   auto input1 = model->addOperand(&type6);
    371   auto inputToInputWeights1 = model->addOperand(&type1);
    372   auto inputToForgetWeights1 = model->addOperand(&type1);
    373   auto inputToCellWeights1 = model->addOperand(&type1);
    374   auto inputToOutputWeights1 = model->addOperand(&type1);
    375   auto recurrentToInputWeights1 = model->addOperand(&type2);
    376   auto recurrentToForgetWeights1 = model->addOperand(&type2);
    377   auto recurrentToCellWeights1 = model->addOperand(&type2);
    378   auto recurrentToOutputWeights1 = model->addOperand(&type2);
    379   auto inputGateBias1 = model->addOperand(&type3);
    380   auto forgetGateBias1 = model->addOperand(&type3);
    381   auto cellGateBias1 = model->addOperand(&type3);
    382   auto outputGateBias1 = model->addOperand(&type3);
    383   auto prevCellState1 = model->addOperand(&type7);
    384   auto prevOutput1 = model->addOperand(&type8);
    385   auto cellStateOut1 = model->addOperand(&type9);
    386   auto output1 = model->addOperand(&type10);
    387   // Phase 2, operations
    388   static uint8_t inputToInputWeights1_init[] = {146, 250, 235, 171, 10, 218, 171, 108};
    389   model->setOperandValue(inputToInputWeights1, inputToInputWeights1_init, sizeof(uint8_t) * 8);
    390   static uint8_t inputToForgetWeights1_init[] = {24, 50, 132, 179, 158, 110, 3, 169};
    391   model->setOperandValue(inputToForgetWeights1, inputToForgetWeights1_init, sizeof(uint8_t) * 8);
    392   static uint8_t inputToCellWeights1_init[] = {133, 34, 29, 49, 206, 109, 54, 183};
    393   model->setOperandValue(inputToCellWeights1, inputToCellWeights1_init, sizeof(uint8_t) * 8);
    394   static uint8_t inputToOutputWeights1_init[] = {195, 187, 11, 99, 109, 10, 218, 48};
    395   model->setOperandValue(inputToOutputWeights1, inputToOutputWeights1_init, sizeof(uint8_t) * 8);
    396   static uint8_t recurrentToInputWeights1_init[] = {254, 206, 77, 168, 71, 20, 215, 6, 223, 7, 118, 225, 59, 130, 174, 26};
    397   model->setOperandValue(recurrentToInputWeights1, recurrentToInputWeights1_init, sizeof(uint8_t) * 16);
    398   static uint8_t recurrentToForgetWeights1_init[] = {137, 240, 103, 52, 68, 51, 237, 112, 0, 220, 89, 23, 69, 4, 207, 253};
    399   model->setOperandValue(recurrentToForgetWeights1, recurrentToForgetWeights1_init, sizeof(uint8_t) * 16);
    400   static uint8_t recurrentToCellWeights1_init[] = {172, 60, 205, 65, 14, 0, 140, 168, 240, 223, 133, 56, 142, 64, 246, 216};
    401   model->setOperandValue(recurrentToCellWeights1, recurrentToCellWeights1_init, sizeof(uint8_t) * 16);
    402   static uint8_t recurrentToOutputWeights1_init[] = {106, 214, 67, 23, 59, 158, 45, 3, 119, 132, 49, 205, 129, 218, 11, 98};
    403   model->setOperandValue(recurrentToOutputWeights1, recurrentToOutputWeights1_init, sizeof(uint8_t) * 16);
    404   static int32_t inputGateBias1_init[] = {-7876, 13488, -726, 32839};
    405   model->setOperandValue(inputGateBias1, inputGateBias1_init, sizeof(int32_t) * 4);
    406   static int32_t forgetGateBias1_init[] = {9206, -46884, -11693, -38724};
    407   model->setOperandValue(forgetGateBias1, forgetGateBias1_init, sizeof(int32_t) * 4);
    408   static int32_t cellGateBias1_init[] = {39481, 48624, 48976, -21419};
    409   model->setOperandValue(cellGateBias1, cellGateBias1_init, sizeof(int32_t) * 4);
    410   static int32_t outputGateBias1_init[] = {-58999, -17050, -41852, -40538};
    411   model->setOperandValue(outputGateBias1, outputGateBias1_init, sizeof(int32_t) * 4);
    412   model->addOperation(ANEURALNETWORKS_QUANTIZED_16BIT_LSTM, {input1, inputToInputWeights1, inputToForgetWeights1, inputToCellWeights1, inputToOutputWeights1, recurrentToInputWeights1, recurrentToForgetWeights1, recurrentToCellWeights1, recurrentToOutputWeights1, inputGateBias1, forgetGateBias1, cellGateBias1, outputGateBias1, prevCellState1, prevOutput1}, {cellStateOut1, output1});
    413   // Phase 3, inputs and outputs
    414   model->identifyInputsAndOutputs(
    415     {input1, prevCellState1, prevOutput1},
    416     {cellStateOut1, output1});
    417   // Phase 4: set relaxed execution
    418   model->relaxComputationFloat32toFloat16(true);
    419   assert(model->isValid());
    420 }
    421 
    422 inline bool is_ignored_dynamic_output_shape_relaxed_2(int i) {
    423   static std::set<int> ignore = {};
    424   return ignore.find(i) != ignore.end();
    425 }
    426 
    427