// If set, use the value as the device type and mark the function graph for
  // XLA compilation.
  string xla_compile_device_type;
  // If true, enables moving ops to different devices or moving unsupported ops
  // out of a compilation cluster.
  bool enable_soft_placement = false;
  // If true, a function attribute, `tf._original_func_name`, will be set in
  // functions which contains the corresponding original TF function name.

	outputLen int             // output length for full security
	function  code            // KIMD/KLMD function code
	state     spongeDirection // whether the sponge is absorbing or squeezing
}

func newAsmState(function code) *asmState {
	var s asmState
	s.function = function
	switch function {
	case sha3_224:
		s.rate = 144
		s.outputLen = 28
	case sha3_256:
		s.rate = 136

#include "tensorflow/core/protobuf/meta_graph.pb.h"

namespace tensorflow::quantization {

// Declares pure virtual member functions for a python-side derived class to
// override. This allows calling python implementations from the C++ layer.
// Member functions should be pure not stateful; they should not access or rely
// on member fields.
class PyFunctionLibrary {
 public:
  virtual ~PyFunctionLibrary() = default;

	HasAES    bool // KM-AES{128,192,256} functions
	HasAESCBC bool // KMC-AES{128,192,256} functions
	HasAESCTR bool // KMCTR-AES{128,192,256} functions
	HasAESGCM bool // KMA-GCM-AES{128,192,256} functions
	HasGHASH  bool // KIMD-GHASH function
	HasSHA1   bool // K{I,L}MD-SHA-1 functions
	HasSHA256 bool // K{I,L}MD-SHA-256 functions
	HasSHA512 bool // K{I,L}MD-SHA-512 functions

}

// pstate records package-level coverage data state:
// - a table of functions (file/fname/literal)
// - a map recording the index/ID of each func encountered so far
// - a table storing execution count for the coverable units in each func
type pstate struct {
	// slice of unique functions
	funcs []fnfile
	// maps function to index in slice above (index acts as function ID)
	funcTable map[fnfile]uint32

        signature_def_map,
    const tensorflow::quantization::PyFunctionLibrary& py_function_library);

// Function used by the pywrap_quantization module to mirror
// `::stablehlo::quantization::PopulateDefaults`.
QuantizationConfig PywrapPopulateDefaults(
    const QuantizationConfig& user_provided_config);

// Function used by the pywrap_quantization module to mirror
// `::stablehlo::quantization::ExpandPresets`.

// are stored. This value will be fed to the "file_prefix" tensor to restore the
// variables.
// * `function_aliases` maps the actual function name to the function alias.
// This associates the quantized functions to the original functions' aliases.
// If there were no function aliases in the input model, this should be empty.
// * `asset_file_defs` include information about the assets, if any, that are

    cost *= kLookupTableFindStringKeyCostScale;

  return cost;
}

// The cost function for tf.GatherV2.
int64_t InferGatherV2Cost(const CostContext& context, mlir::TF::GatherV2Op op) {
  return InferTensorSize(
      context, mlir::cast<mlir::TensorType>(op.getOutput().getType()));
}

// The cost function for tf.SparseSegmentSumOp.
template <typename OpType>

// pointers visible to the GC.
#define LOCAL_RETVALID 40
#define LOCAL_REGARGS 48

// The frame size of the functions below is
// 32 (args of callReflect) + 8 (bool + padding) + 392 (abi.RegArgs) = 432.

// makeFuncStub is the code half of the function returned by MakeFunc.
// See the comment on the declaration of makeFuncStub in makefunc.go
// for more details.

- [SortedFunc](/pkg/slices#SortedFunc) is like `Sorted` but with a
  comparison function.
- [SortedStableFunc](/pkg/slices#SortedStableFunc) is like `SortFunc`
  but uses a stable sort algorithm.
- [Chunk](/pkg/slices#Chunk) returns an iterator over consecutive
  sub-slices of up to n elements of a slice.

The [maps] package adds several functions that work with iterators:
- [All](/pkg/maps#All) returns an iterator over key-value pairs from m.