i := s.sparse[x]
	if i < int32(len(s.dense)) && s.dense[i] == x {
		return
	}
	s.dense = append(s.dense, x)
	s.sparse[x] = int32(len(s.dense)) - 1
}

func (s *sparseSet) clear() {
	s.dense = s.dense[:0]
}

// Variant to use for small functions.
type simplePhiState struct {
	s         *state                   // SSA state
	f         *ssa.Func                // function to work on

// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.

//go:build ignore

/*
Input to cgo -cdefs

GOARCH=amd64 cgo -cdefs defs.go defs1.go >amd64/defs.h
*/

package runtime

/*
#include <ucontext.h>
#include <fcntl.h>
#include <asm/signal.h>
*/
import "C"

const (
	O_RDONLY    = C.O_RDONLY
	O_NONBLOCK  = C.O_NONBLOCK

limitations under the License.
==============================================================================*/

// This transformation pass decomposes dense operations that assume
// support for hybrid quantization. These cases cover when a dense operation
// (e.g. matmul) has both quantized and unquantized inputs by dequantizing
// the quantized inputs, performing the operation in the expressed type, then

limitations under the License.
==============================================================================*/

#ifndef TENSORFLOW_COMPILER_MLIR_TF2XLA_TRANSFORMS_UTILS_H_
#define TENSORFLOW_COMPILER_MLIR_TF2XLA_TRANSFORMS_UTILS_H_

#include "llvm/ADT/ArrayRef.h"
#include "mlir/IR/Builders.h"  // from @llvm-project
#include "mlir/IR/BuiltinAttributes.h"  // from @llvm-project

// ModuleOp.
using ConvertMlirModuleToExportedModelTest =
    ::mlir::quant::QuantizationTestBase;

TEST_F(ConvertMlirModuleToExportedModelTest, SimpleGraphDefSet) {
  // Define a module a no-op main function.
  mlir::OwningOpRef<mlir::ModuleOp> module_op = ParseModuleOpString(R"mlir(
    module attributes {tf_saved_model.semantics} {

limitations under the License.
==============================================================================*/

#ifndef TENSORFLOW_COMPILER_MLIR_LITE_STABLEHLO_TRANSFORMS_COMPOSITE_AVG_POOL_H_
#define TENSORFLOW_COMPILER_MLIR_LITE_STABLEHLO_TRANSFORMS_COMPOSITE_AVG_POOL_H_

#include "mlir/IR/Attributes.h"  // from @llvm-project
#include "mlir/IR/Builders.h"  // from @llvm-project

      override {
    for (const std::string& output_name : output_names) {
      Tensor output;
      if (output_name == "dense/bias") {
        Tensor t = Tensor(tensorflow::DT_FLOAT, tensorflow::TensorShape({50}));
        t.flat<float>().setZero();
        outputs->push_back(t);
      } else if (output_name == "dense/kernel") {
        Tensor t =
            Tensor(tensorflow::DT_FLOAT, tensorflow::TensorShape({100, 50}));

  emitSourceFileHeader("Generated Ops Quant Spec Getters", os);

  // Retrieve all the definitions derived from Op definition and sort by record
  // name.
  std::vector<Record *> defs = records.getAllDerivedDefinitions("Op");
  llvm::sort(defs, LessRecord());

  OUT(0) << "static std::unique_ptr<quant::OpQuantSpec> "
            "GetOpQuantSpec(mlir::Operation *op, bool "
            "disable_per_channel_for_dense_layers = false) {\n";

```kotlin
    dependencies {
       // define a BOM and its version
       implementation(platform("com.squareup.okhttp3:okhttp-bom:4.12.0"))

       // define any required OkHttp artifacts without version
       implementation("com.squareup.okhttp3:okhttp")
       implementation("com.squareup.okhttp3:logging-interceptor")

#  define GTEST_OS_WINDOWS_MINGW 1
# else
#  define GTEST_OS_WINDOWS_DESKTOP 1
# endif  // _WIN32_WCE
#elif defined __APPLE__
# define GTEST_OS_MAC 1
# if TARGET_OS_IPHONE
#  define GTEST_OS_IOS 1
#  if TARGET_IPHONE_SIMULATOR
#   define GTEST_OS_IOS_SIMULATOR 1
#  endif
# endif
#elif defined __linux__
# define GTEST_OS_LINUX 1
# if defined __ANDROID__
#  define GTEST_OS_LINUX_ANDROID 1