using ::tensorflow::quantization::OpSet;

constexpr absl::string_view kQuantizeCompositeFunctionsStepName =
    "_quantize_composite_functions";
constexpr StringRef kQuantizeFuncName = "quantize_i8";
constexpr StringRef kDequantizeFuncName = "dequantize_i8";
constexpr StringRef kAttrMapAttribute = "attr_map";
constexpr StringRef kQuantizedOpsAttribute = "tf_quant.quantized_ops";
constexpr StringRef kCompositeFuncPrefix = "composite_";

// LINT.IfChange(random_access_file_ops_version)
constexpr int TF_RANDOM_ACCESS_FILE_OPS_API = 0;
constexpr int TF_RANDOM_ACCESS_FILE_OPS_ABI = 0;
constexpr size_t TF_RANDOM_ACCESS_FILE_OPS_SIZE =
    sizeof(TF_RandomAccessFileOps);
// LINT.ThenChange()

// LINT.IfChange(writable_file_ops_version)
constexpr int TF_WRITABLE_FILE_OPS_API = 0;
constexpr int TF_WRITABLE_FILE_OPS_ABI = 0;

static constexpr char kEmbeddingPipelining[] = "_embedding_pipelining";
static constexpr char kEmbeddingPipeliningInlineAttr[] =
    "_embedding_pipelining_inline";
static constexpr char kEmbeddingForward[] = "forward";
static constexpr char kEmbeddingBackward[] = "backward";
static constexpr char kDevice[] = "device";
static constexpr char kLower[] = "_lower_using_switch_merge";

using mlir::Value;
using mlir::ValueRange;
using mlir::WalkResult;
using mlir::func::FuncOp;
using mlir::func::ReturnOp;

constexpr char kDeviceAttr[] = "device";
constexpr char kHostFunctionAttr[] = "host_func";
constexpr char kXlaMapOutsideCompilationAttr[] = "_xla_map_outside_compilation";
constexpr char kXlaOutsideCompilationAttr[] = "_xla_outside_compilation";

#define GEN_PASS_DEF_EXTRACTOUTSIDECOMPILATIONPASS

// log function. In particular, [5, 7] is the only choice where -1.5e-5 <=
// lgamma(2) <= 1.5e-5 for a particularly inaccurate log function.
static constexpr double kLanczosGamma = 7;  // aka g
static constexpr double kBaseLanczosCoeff = 0.99999999999980993227684700473478;
static constexpr std::array<double, 8> kLanczosCoefficients = {
    676.520368121885098567009190444019, -1259.13921672240287047156078755283,

#include "tensorflow/compiler/mlir/lite/stablehlo/transforms/passes.h.inc"

// These strings are used to identify the uniform_quantize / uniform_dequantize
// functions.
constexpr StringRef kUniformQuantizeFunctionNameSubstring = "uniform_quantize";
constexpr StringRef kUniformDequantizeFunctionNameSubstring =
    "uniform_dequantize";

class ComposeUniformQuantizedTypePass
    : public impl::ComposeUniformQuantizedTypePassBase<

  grad_outputs->push_back(sum.out);
  grad_outputs->push_back(NoGradient());
  return scope.status();
}
REGISTER_GRADIENT_OP("Cumsum", CumsumGrad);

bool IsFloatingPointDtype(DataType dtype) {
  static constexpr DataType valid_dtypes[] = {
      DT_FLOAT, DT_HALF, DT_DOUBLE, DT_BFLOAT16, DT_COMPLEX64, DT_COMPLEX128};
  return std::find(std::begin(valid_dtypes), std::end(valid_dtypes), dtype) !=
         std::end(valid_dtypes);
}

#include "tensorflow/compiler/mlir/tensorflow/utils/mangling_util.h"
#include "tensorflow/core/framework/tensor_shape.pb.h"

namespace mlir {

namespace {

constexpr char kDeviceAttr[] = "device";

#define GEN_PASS_DEF_RESOURCEOPLIFTINGPASS
#include "tensorflow/compiler/mlir/tensorflow/transforms/tf_device_passes.h.inc"

// Lift resource operations out of device computation.

using mlir::Value;
using mlir::func::FuncOp;
using tflite::OperatorT;
using tflite::TensorT;

namespace errors = tensorflow::errors;
namespace tfl = mlir::TFL;

namespace {

constexpr char kScatterRegionFuncName[] = "update_computation_func_name";

using ::mlir::tf_saved_model::kTfSavedModelExportedNamesAttr;
using ::mlir::tf_saved_model::kTfSavedModelIndexPathAttr;
using ::tflite::IsValidBufferOffset;

        TermQueryBuilder builder = regTermQ("boostExpr", boostExpr);
        if (opLambda != null) {
            opLambda.callback(builder);
        }
    }

    public void setBoostExpr_NotEqual(String boostExpr) {
        setBoostExpr_NotTerm(boostExpr, null);
    }

    public void setBoostExpr_NotTerm(String boostExpr) {
        setBoostExpr_NotTerm(boostExpr, null);