#include "stablehlo/dialect/StablehloOps.h"  // from @stablehlo
#include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_dialect.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"

namespace mlir {
namespace TFL {
namespace {
#define GEN_PASS_DEF_LEGALIZEJAXRANDOMPASS
#include "tensorflow/compiler/mlir/lite/transforms/passes.h.inc"

struct LegalizeJaxRandomPass

//	Pow(NaN, y) = NaN
//	Pow(x, NaN) = NaN
//	Pow(±0, y) = ±Inf for y an odd integer < 0
//	Pow(±0, -Inf) = +Inf
//	Pow(±0, +Inf) = +0
//	Pow(±0, y) = +Inf for finite y < 0 and not an odd integer
//	Pow(±0, y) = ±0 for y an odd integer > 0
//	Pow(±0, y) = +0 for finite y > 0 and not an odd integer
//	Pow(-1, ±Inf) = 1
//	Pow(x, +Inf) = +Inf for |x| > 1
//	Pow(x, -Inf) = +0 for |x| > 1
//	Pow(x, +Inf) = +0 for |x| < 1

  public static int log2(double x, RoundingMode mode) {
    checkArgument(x > 0.0 && isFinite(x), "x must be positive and finite");
    int exponent = getExponent(x);
    if (!isNormal(x)) {
      return log2(x * IMPLICIT_BIT, mode) - SIGNIFICAND_BITS;
      // Do the calculation on a normal value.
    }
    // x is positive, finite, and normal
    boolean increment;
    switch (mode) {
      case UNNECESSARY:

  public static int log2(double x, RoundingMode mode) {
    checkArgument(x > 0.0 && isFinite(x), "x must be positive and finite");
    int exponent = getExponent(x);
    if (!isNormal(x)) {
      return log2(x * IMPLICIT_BIT, mode) - SIGNIFICAND_BITS;
      // Do the calculation on a normal value.
    }
    // x is positive, finite, and normal
    boolean increment;
    switch (mode) {
      case UNNECESSARY:

Aber so hätten die Angreifer in wenigen Minuten oder Stunden mit der „Hilfe“ unserer Anwendung den richtigen Benutzernamen und das richtige Passwort erraten, indem sie die Zeitspanne zur Hilfe nehmen, die diese zur Beantwortung benötigt.

#### Das Problem beheben mittels `secrets.compare_digest()`

       *   `tflite::Interpreter::signature_keys`
       *   `tflite::Interpreter::signature_inputs`
       *   `tflite::Interpreter::signature_outputs`
       *   `tflite::Interpreter::input_tensor_by_signature`
       *   `tflite::Interpreter::output_tensor_by_signature`

    *  Similarly, the following signature runner functions in the TF Lite C API are no longer considered experimental:

       *    `TfLiteInterpreterGetSignatureCount`

    assertThat(TWO_VALUES_STATS.mean()).isWithin(ALLOWED_ERROR).of(TWO_VALUES_MEAN);
    // For datasets of many double values created from an array, we test many combinations of finite
    // and non-finite values:
    for (ManyValues values : ALL_MANY_VALUES) {
      double mean = Stats.of(values.asArray()).mean();
      if (values.hasAnyNaN()) {
        assertWithMessage("mean of " + values).that(mean).isNaN();

#include "tensorflow/compiler/mlir/lite/common/tfl_pass_config.h"
#include "tensorflow/compiler/mlir/lite/flatbuffer_export.h"
#include "tensorflow/compiler/mlir/lite/flatbuffer_import.h"
#include "tensorflow/compiler/mlir/lite/schema/schema_generated.h"
#include "tensorflow/compiler/mlir/lite/tf_tfl_passes.h"
#include "tensorflow/compiler/mlir/lite/transforms/passes.h"

    return -1;
  }

  // Set num of string
  //
  // NOTE: The string buffer is accessed here as if it's native endian (instead
  // of small endian, as documented in the header). This will protentially break
  // when TFLite is ported to big endian platforms.
  // TODO(b/165919229): This code will need changing if/when we port to a
  // big-endian platform.
  memcpy(*buffer, &num_strings, sizeof(int32_t));

  // Set offset of strings.

      .Case("TANH", tflite::ActivationFunctionType_TANH)
      .Case("SIGN_BIT", tflite::ActivationFunctionType_SIGN_BIT);
}

static tflite::TensorType ConvertDerivedTFLiteTypeAttrForOptionWriter(
    tflite::TensorType type, flatbuffers::FlatBufferBuilder* builder) {
  if (type == tflite::TensorType_INT64) {
    return tflite::TensorType_INT64;
  } else if (type == tflite::TensorType_INT32) {