#include "tensorflow/compiler/mlir/lite/metrics/error_collector.h"
#include "tensorflow/compiler/mlir/lite/metrics/types_util.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_dialect.h"
#include "tensorflow/core/platform/errors.h"
#include "tensorflow/core/platform/resource_loader.h"
#include "tensorflow/core/platform/test.h"
#include "tensorflow/lite/python/metrics/converter_error_data.pb.h"

#include "tensorflow/compiler/mlir/lite/experimental/tac/py_wrapper/tac_wrapper.h"

// Warning: The API is experimental and subject to change.
PYBIND11_MODULE(_pywrap_tac_wrapper, m) {
  m.def(
      "run_tac",
      [](const std::string& model_file_path,
         const std::vector<std::string>& device_specs,
         const std::string& model_output_path) {
        return ::tflite::run_tac(model_file_path, device_specs,

---
name: TensorFlow Lite Op Request
about: Use this template for reporting Lite ops you are using or missing
labels: 'comp:lite'

---

**System information**
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
- TensorFlow installed from (source or binary):
- TensorFlow version (or github SHA if from source):


**Provide the text output from tflite_convert**

```
# Copy and paste here
```

#include "mlir/Pass/Pass.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h"
#include "tensorflow/compiler/mlir/lite/transforms/passes.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"

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

#include <memory>

#include "tensorflow/compiler/mlir/lite/experimental/tac/common/targets.h"
#include "tensorflow/compiler/mlir/lite/experimental/tac/common/utils.h"
#include "tensorflow/compiler/mlir/lite/experimental/tac/hardwares/target_hardware.h"
#include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h"
#include "tensorflow/compiler/mlir/lite/utils/arithmetic_count_util.h"

namespace mlir {
namespace TFL {

---
name: TensorFlow Lite in Play Services issue
about: Use this template for issues with TensorFlow Lite in Google Play Services
labels: 'comp:lite-in-play-services'

---

**System information**
- Android Device information (use `adb shell getprop ro.build.fingerprint`
  if possible):
- TensorFlow Lite in Play Services SDK version (found in `build.gradle`):
- Google Play Services version

	z.mode = RoundingMode((b >> 5) & 7)
	z.acc = Accuracy((b>>3)&3) - 1
	z.form = form((b >> 1) & 3)
	z.neg = b&1 != 0
	z.prec = byteorder.BeUint32(buf[2:])

	if z.form == finite {
		if len(buf) < 10 {
			return errors.New("Float.GobDecode: buffer too small for finite form float")
		}
		z.exp = int32(byteorder.BeUint32(buf[6:]))
		z.mant = z.mant.setBytes(buf[10:])
	}

	if oldPrec != 0 {
		z.mode = oldMode

    case tflite::TensorType_FLOAT16:
      return builder.getF16Type();
    case tflite::TensorType_BFLOAT16:
      return builder.getBF16Type();
    case tflite::TensorType_FLOAT32:
      return builder.getF32Type();
    case tflite::TensorType_FLOAT64:
      return builder.getF64Type();
    case tflite::TensorType_INT32:
      return builder.getIntegerType(32);
    case tflite::TensorType_UINT16:

#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