19949232ba3d5b1d95f5b3d9ea310b88b81ee1a45gfan// Generated file (from: depth_to_space_float_3.mod.py). Do not edit
29949232ba3d5b1d95f5b3d9ea310b88b81ee1a45gfanvoid CreateModel(Model *model) {
39949232ba3d5b1d95f5b3d9ea310b88b81ee1a45gfan  OperandType type1(Type::INT32, {});
49949232ba3d5b1d95f5b3d9ea310b88b81ee1a45gfan  OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 8});
59949232ba3d5b1d95f5b3d9ea310b88b81ee1a45gfan  OperandType type2(Type::TENSOR_FLOAT32, {1, 4, 4, 2});
69949232ba3d5b1d95f5b3d9ea310b88b81ee1a45gfan  // Phase 1, operands
79949232ba3d5b1d95f5b3d9ea310b88b81ee1a45gfan  auto input = model->addOperand(&type0);
89949232ba3d5b1d95f5b3d9ea310b88b81ee1a45gfan  auto block_size = model->addOperand(&type1);
99949232ba3d5b1d95f5b3d9ea310b88b81ee1a45gfan  auto output = model->addOperand(&type2);
109949232ba3d5b1d95f5b3d9ea310b88b81ee1a45gfan  // Phase 2, operations
119949232ba3d5b1d95f5b3d9ea310b88b81ee1a45gfan  static int32_t block_size_init[] = {2};
129949232ba3d5b1d95f5b3d9ea310b88b81ee1a45gfan  model->setOperandValue(block_size, block_size_init, sizeof(int32_t) * 1);
139949232ba3d5b1d95f5b3d9ea310b88b81ee1a45gfan  model->addOperation(ANEURALNETWORKS_DEPTH_TO_SPACE, {input, block_size}, {output});
149949232ba3d5b1d95f5b3d9ea310b88b81ee1a45gfan  // Phase 3, inputs and outputs
159949232ba3d5b1d95f5b3d9ea310b88b81ee1a45gfan  model->identifyInputsAndOutputs(
169949232ba3d5b1d95f5b3d9ea310b88b81ee1a45gfan    {input},
179949232ba3d5b1d95f5b3d9ea310b88b81ee1a45gfan    {output});
189949232ba3d5b1d95f5b3d9ea310b88b81ee1a45gfan  assert(model->isValid());
199949232ba3d5b1d95f5b3d9ea310b88b81ee1a45gfan}
209949232ba3d5b1d95f5b3d9ea310b88b81ee1a45gfan
219949232ba3d5b1d95f5b3d9ea310b88b81ee1a45gfanbool is_ignored(int i) {
229949232ba3d5b1d95f5b3d9ea310b88b81ee1a45gfan  static std::set<int> ignore = {};
239949232ba3d5b1d95f5b3d9ea310b88b81ee1a45gfan  return ignore.find(i) != ignore.end();
249949232ba3d5b1d95f5b3d9ea310b88b81ee1a45gfan}
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