// run the checks.
		if ops, found := fullops[srcFileLine]; found {
			for i := range ops {
				if !ops[i].found && ops[i].opcode.FindString(asm) != "" {
					ops[i].found = true
				}
			}
		}
	}
	functionMarkers = append(functionMarkers, len(lines))

	var failed []wantedAsmOpcode
	for _, ops := range fullops {
		for _, o := range ops {

namespace mlir {
namespace TFL {
namespace tac {

// Interface name here is the "hook" between the CallOp and FuncOps.
// Take the following example:
//
// call @func_1_CPU {tac.interface_name = "func_1"}
//
// "func_1" is the interface name where "func_1_cpu" is the real implementation
// we can have multiple FuncOps like "func_1_cpu" and "func_1_gpu" and they
// both implement "func_1".
//

#ifndef TENSORFLOW_COMPILER_MLIR_TFR_PASSES_PASSES_H_
#define TENSORFLOW_COMPILER_MLIR_TFR_PASSES_PASSES_H_

#include <memory>
#include <optional>

#include "mlir/Dialect/Func/IR/FuncOps.h"  // from @llvm-project
#include "mlir/IR/BuiltinOps.h"  // from @llvm-project
#include "mlir/Pass/Pass.h"  // from @llvm-project
#include "mlir/Support/LogicalResult.h"  // from @llvm-project

namespace mlir {

// `QuantizationResult`. Otherwise, it returns `std::nullopt`.
std::optional<QuantizationResult> GetQuantizationResult(func::CallOp call_op) {
  const StringRef callee_name = call_op.getCalleeAttr().getValue();
  if (!callee_name.starts_with(kQuantizedFuncPrefix)) {
    return std::nullopt;  // `call_op` is not a quantized function call.
  }

    const QuantizationConfig& user_provided_config);

// Returns a copy of `QuantizationConfig` where presets are expanded and
// transformed into other fields in `QuantizationConfig`.
//
// The expansion rules are as follows:
// * StaticRangePtqPreset
//   - The preset's `representative_datasets` field will be transferred to
//   `QuantizationConfig.calibration_options.representative_datasets`, unless

#ifndef TENSORFLOW_COMPILER_MLIR_TENSORFLOW_UTILS_VISITOR_H_
#define TENSORFLOW_COMPILER_MLIR_TENSORFLOW_UTILS_VISITOR_H_

#include "llvm/ADT/StringRef.h"
#include "mlir/Dialect/Func/IR/FuncOps.h"  // from @llvm-project
#include "mlir/IR/BuiltinOps.h"  // from @llvm-project
#include "mlir/IR/OwningOpRef.h"  // from @llvm-project
#include "mlir/IR/SymbolTable.h"  // from @llvm-project

}

std::optional<std::string> GetDeviceName(mlir::Operation* op) {
  if (IsConst(op)) return std::nullopt;

  // The model may contain quant stats op which is unrelevant to the
  // execution.
  if (llvm::isa<mlir::func::ReturnOp, mlir::quantfork::StatisticsOp>(op))
    return std::nullopt;

  if (!HasValidHardwareTarget(op)) return std::nullopt;

  auto device = op->getAttrOfType<mlir::StringAttr>(mlir::TFL::tac::kDevice);

                tensorflow::GetTypeFromTFTensorShape(
                    {}, splat_elements_attr.getType().getElementType()),
                splat_elements_attr.getSplatValue<Attribute>()));
    TFL::FillOp fill = op_builder->create<TFL::FillOp>(
        const_op->getLoc(), splat_elements_attr.getType(), fill_shape,
        fill_value);
    const_op->replaceAllUsesWith(fill);
    const_op->erase();
  }
};

}  // namespace

#define TENSORFLOW_COMPILER_MLIR_LITE_FLATBUFFER_EXPORT_H_

#include <cstddef>
#include <map>
#include <optional>
#include <string>
#include <unordered_set>

#include "mlir/Dialect/Func/IR/FuncOps.h"  // from @llvm-project
#include "mlir/IR/BuiltinOps.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/op_or_arg_name_mapper.h"
#include "tensorflow/lite/toco/toco_flags.pb.h"

namespace tflite {

#include "llvm/Support/SourceMgr.h"
#include "llvm/Support/ToolOutputFile.h"
#include "llvm/Support/raw_ostream.h"
#include "mlir/Dialect/Func/Extensions/AllExtensions.h"  // from @llvm-project
#include "mlir/Dialect/Func/IR/FuncOps.h"  // from @llvm-project
#include "mlir/IR/AsmState.h"  // from @llvm-project
#include "mlir/IR/BuiltinOps.h"  // from @llvm-project
#include "mlir/IR/Diagnostics.h"  // from @llvm-project