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// Generated file (from: svdf.mod.py). Do not edit
void CreateModel(Model *model) {
OperandType type5(Type::INT32, {});
OperandType type0(Type::TENSOR_FLOAT32, {2, 3});
OperandType type4(Type::TENSOR_FLOAT32, {2, 40});
OperandType type6(Type::TENSOR_FLOAT32, {2, 4});
OperandType type2(Type::TENSOR_FLOAT32, {4, 10});
OperandType type1(Type::TENSOR_FLOAT32, {4, 3});
OperandType type3(Type::TENSOR_FLOAT32, {4});
// Phase 1, operands
auto input = model->addOperand(&type0);
auto weights_feature = model->addOperand(&type1);
auto weights_time = model->addOperand(&type2);
auto bias = model->addOperand(&type3);
auto state_in = model->addOperand(&type4);
auto rank_param = model->addOperand(&type5);
auto activation_param = model->addOperand(&type5);
auto state_out = model->addOperand(&type4);
auto output = model->addOperand(&type6);
// Phase 2, operations
static int32_t rank_param_init[] = {1};
model->setOperandValue(rank_param, rank_param_init, sizeof(int32_t) * 1);
static int32_t activation_param_init[] = {0};
model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_SVDF, {input, weights_feature, weights_time, bias, state_in, rank_param, activation_param}, {state_out, output});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{input, weights_feature, weights_time, bias, state_in},
{state_out, output});
assert(model->isValid());
}
bool is_ignored(int i) {
static std::set<int> ignore = {0};
return ignore.find(i) != ignore.end();
}
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