//    ["CPU", "GPU"]
//   }
//
//  subgraph_metadata: {
//    [
//      op1:
//       hardware: 0
//       op_costs: [10, -1],
//      op2:
//       hardware: 1
//       op_costs: [20, 5],
//      op3:
//       hardware: 1
//       op_costs: [30, 10],
//      op4:
//       hardware: 0
//       op_costs: [40, -1],
//    ]
//  }
// }
table HardwareMetadata {
  // List of different hardwares this model can use.

                expected_op_metadata->hardware());

      EXPECT_EQ(result_op_metadata->op_costs()->size(),
                expected_op_metadata->op_costs()->size());
      for (int i = 0; i < result_op_metadata->op_costs()->size(); ++i) {
        EXPECT_FLOAT_EQ(result_op_metadata->op_costs()->Get(i),
                        expected_op_metadata->op_costs()->Get(i));
      }
    }
  }
}

TEST(ExporterTest, Valid) {

void PrintOpStatsPass::CountOp(
    DenseMap<StringRef, llvm::StringMap<int64_t>> &op_count_map,
    StringRef op_name, StringRef dtype) {
  auto &op_counts = op_count_map[op_name];
  if (auto it = op_counts.find(dtype); it != op_counts.end()) {
    it->second++;
  } else {
    op_counts[dtype] = 1;
  }
}

void PrintOpStatsPass::PrintSummary() {
  *os_ << "Summary on the non-converted ops:\n";

    func::FuncOp func_op) {
  llvm::StringMap<FunctionMetadata> metadatas;
  WalkResult result = func_op.getBody().walk([&](Operation *op) {
    std::string op_host = GetHost(op);
    FunctionMetadata &func_metadata = metadatas[op_host];
    func_metadata.original_name = func_op.getName();
    func_metadata.insertion_point = ++Block::iterator(func_op);
    func_metadata.ops.push_back(op);

    const tflite::OperatorT& op, const std::vector<Value>& vals_map,
    const std::vector<mlir::TensorType>& intermediate_types,
    Value optional_arg_marker,
    const std::vector<std::unique_ptr<tflite::OperatorCodeT>>& op_codes,
    const std::vector<std::string>& func_names,
    const std::vector<std::unique_ptr<tflite::TensorT>>& tensors, Location loc,
    OpBuilder builder,
    const std::unique_ptr<tflite::FlatBufferModel>& model_ptr) {

	opConst4u    = 0x0C /* 1 op, 4 byte const */
	opConst4s    = 0x0D /*	" signed */
	opConst8u    = 0x0E /* 1 op, 8 byte const */
	opConst8s    = 0x0F /*	" signed */
	opConstu     = 0x10 /* 1 op, LEB128 const */
	opConsts     = 0x11 /*	" signed */
	opDup        = 0x12
	opDrop       = 0x13
	opOver       = 0x14
	opPick       = 0x15 /* 1 op, 1 byte stack index */
	opSwap       = 0x16
	opRot        = 0x17
	opXderef     = 0x18

  // The resulting model should be:
  // reshape->dequantize->custom->custom->quantize->squeeze.
  ASSERT_THAT(subgraph->operators, SizeIs(6));
  const std::vector<BuiltinOperator> op_codes = {
      BuiltinOperator_RESHAPE,  BuiltinOperator_DEQUANTIZE,
      BuiltinOperator_CUSTOM,   BuiltinOperator_CUSTOM,
      BuiltinOperator_QUANTIZE, BuiltinOperator_SQUEEZE};

	op_CDR     uint32 = 0x2900 // FORMAT_RR         COMPARE (long HFP)
	op_CDS     uint32 = 0xBB00 // FORMAT_RS1        COMPARE DOUBLE AND SWAP (32)
	op_CDSG    uint32 = 0xEB3E // FORMAT_RSY1       COMPARE DOUBLE AND SWAP (64)
	op_CDSTR   uint32 = 0xB3F3 // FORMAT_RRE        CONVERT FROM SIGNED PACKED (64 to long DFP)
	op_CDSY    uint32 = 0xEB31 // FORMAT_RSY1       COMPARE DOUBLE AND SWAP (32)
	op_CDTR    uint32 = 0xB3E4 // FORMAT_RRE        COMPARE (long DFP)

anged=true;},processPictureData_(){if(this.pictureOps_===undefined)return;let totalOpCost=0;this.opCosts_=this.pictureOps_.map(function(op){totalOpCost+=op.cmd_time;return op.cmd_time;});this.opCosts_.sort();const ninetyFifthPercentileCostIndex=Math.floor(this.opCosts_.length*0.95);this.ninetyFifthPercentileCost_=this.opCosts_[ninetyFifthPercentileCostIndex];this.maxCost_=this.opCosts_[this.opCosts_.length-1];this.totalOpCost_=totalOpCost;},extractBarIndex_(e){let index=undefined;if(this.picture...