vectorsJSON, err := os.ReadFile("testdata/rfc9180-vectors.json")
	if err != nil {
		t.Fatal(err)
	}

	var vectors []struct {
		Name        string
		Setup       string
		Encryptions string
	}
	if err := json.Unmarshal(vectorsJSON, &vectors); err != nil {
		t.Fatal(err)
	}

	for _, vector := range vectors {
		t.Run(vector.Name, func(t *testing.T) {
			setup := parseVectorSetup(vector.Setup)

Roland Shoemaker <******@****.***> 1715710616 -0700

#include <cstdint>
#include <map>
#include <optional>
#include <string>
#include <utility>
#include <vector>

#include "flatbuffers/buffer.h"  // from @flatbuffers
#include "flatbuffers/flatbuffer_builder.h"  // from @flatbuffers
#include "flatbuffers/string.h"  // from @flatbuffers
#include "flatbuffers/vector.h"  // from @flatbuffers
#include "llvm/Support/Casting.h"
#include "mlir/Dialect/Arith/IR/Arith.h"  // from @llvm-project

	}
}

// TestPSSGolden tests all the test vectors in pss-vect.txt from
// ftp://ftp.rsasecurity.com/pub/pkcs/pkcs-1/pkcs-1v2-1-vec.zip
func TestPSSGolden(t *testing.T) {
	inFile, err := os.Open("testdata/pss-vect.txt.bz2")
	if err != nil {
		t.Fatalf("Failed to open input file: %s", err)
	}
	defer inFile.Close()

	// The pss-vect.txt file contains RSA keys and then a series of

    return std::vector<double>(vec.begin(), vec.end());
  }

  return std::vector<double>();
}

// Handles the case when the DenseElementsAttr doesn't exist, and when it
// doesn't returns a vector of length `default_size` all with the same value
// `default_value`.
template <typename T>
static inline std::vector<T> GetOptionalVector(

  // indices
  // in the idx map, the value is a pair of offset and size
  absl::flat_hash_map<std::pair<std::vector<int32_t>, std::vector<int32_t>>,
                      std::vector<uint8_t>>
      custom_op_data_map_;
  absl::flat_hash_map<std::pair<std::vector<int32_t>, std::vector<int32_t>>,
                      std::pair<uint64_t, uint64_t>>
      custom_op_idx_map_;

class CalibrationStatisticsSaverTest : public OpsTestBase {};

TEST_F(CalibrationStatisticsSaverTest, MissingOutputPath) {
  std::vector<std::string> ids{"1"};
  std::vector<int32_t> calibration_methods{
      CalibrationOptions::CALIBRATION_METHOD_AVERAGE_MIN_MAX};

  std::vector<NodeDefBuilder::NodeOut> inputs;
  inputs.emplace_back("min", 0, DT_FLOAT);
  inputs.emplace_back("max", 0, DT_FLOAT);

}

static mlir::Attribute BuildRankedTensorAttr(std::vector<int64_t> shape,
                                             std::vector<bool> value,
                                             mlir::Builder builder) {
  // The implementation of getBoolVectorAttr is flawed, so we bypass it here
  std::vector<llvm::APInt> extendVec;
  extendVec.resize(value.size());

Status PreparePjRtExecutableArguments(
    int num_missing_prefix_ctx_inputs, const std::vector<int>& input_mapping,
    const std::vector<const Tensor*>& inputs,
    const absl::flat_hash_map<int, const Tensor*>& variable_snapshots,
    xla::PjRtClient* pjrt_client, xla::PjRtDevice* pjrt_device,
    bool use_pjrt_tensor_buffer, std::vector<xla::PjRtBuffer*>* args,
    std::vector<std::unique_ptr<xla::PjRtBuffer>>* owned_args,

    const toco::ModelFlags& model_flags, toco::TocoFlags& toco_flags,
    mlir::quant::QuantizationSpecs* quant_specs,
    std::vector<string>* node_names, std::vector<string>* node_dtypes,
    std::vector<std::optional<std::vector<int>>>* node_shapes,
    std::vector<std::optional<double>>* node_mins,
    std::vector<std::optional<double>>* node_maxs);

// Convert imported MLIR file to TfLite flatbuffer.
// This will also run relevant passes as well.