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)

  // Given the required_consts, it will fill the 3 output vectors with
  // their respective data.
  // Expressions: Output XLA expressions as required by the compiled kernel.
  // Tensors: Vector of tensors that back the TensorValue inputs
  // Inputs: Vector of inputs that are backed by tensors.
  mlir::LogicalResult PrepareKernelInputs(
      const llvm::SmallDenseSet<int>& required_consts,
      std::vector<tensorflow::XlaExpression>& expressions,

#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 options;
}

Status ClientSession::Run(const std::vector<Output>& fetch_outputs,
                          std::vector<Tensor>* outputs) const {
  return Run(FeedType{}, fetch_outputs, {}, outputs);
}

Status ClientSession::Run(const FeedType& inputs,
                          const std::vector<Output>& fetch_outputs,
                          std::vector<Tensor>* outputs) const {
  return Run(inputs, fetch_outputs, {}, outputs);

    @SuppressWarnings("unused")
    @Test
    public void testIterable() throws Exception {
        final Vector<String> vector = new Vector<String>();
        vector.add("a");
        vector.add("b");
        int count = 0;
        for (final String s : iterable(vector.elements())) {
            ++count;
        }
        assertThat(count, is(2));
    }

    /**
     * Test method for

    bool use_tuple_args, XlaCompiler::CompilationResult* compilation_result,
    std::vector<std::unique_ptr<mlir::Pass>>& custom_legalization_passes,
    const std::vector<TensorShape>& arg_shapes,
    std::vector<tpu::ShardingAndIndex>* arg_core_mapping,
    std::vector<std::vector<xla::Shape>>* per_core_arg_shapes);

// Compiles a serialized MLIR module into XLA HLO, generates all accompanying

Output ToOutput(OutputTensor output_tensor) {
  return Output(const_cast<Node*>(output_tensor.node), output_tensor.index);
}

std::vector<Output> ToOutputVector(
    const std::vector<OutputTensor>& output_tensors) {
  const int n = output_tensors.size();
  std::vector<Output> result;
  result.reserve(n);
  for (int i = 0; i < n; ++i) result.push_back(ToOutput(output_tensors[i]));
  return result;
}

         const std::vector<string>& aliases);
  OpInfo(const OpDef& graph_op_def, const ApiDef& api_def);
  string GetOpAttrStruct() const;
  string GetConstructorDecl(StringPiece op_name_prefix,
                            bool include_attr) const;

  string op_name;
  std::vector<string> arg_types;
  std::vector<string> arg_names;
  std::vector<string> output_types;
  std::vector<string> output_names;

std::vector<OutputTensor> ToOutputTensors(const std::vector<Output>& outputs) {
  std::vector<OutputTensor> result(outputs.size());
  for (int i = 0; i < outputs.size(); ++i) {
    result[i] = ToOutputTensor(outputs[i]);
  }
  return result;
}

// Utility function for converting to internal C++ datatypes.
std::vector<Node*> ToNodes(const std::vector<Output>& outputs) {