"IgnoreClientVersionOrder": "TODO: first pass, this should be fixed",
        "SupportedVersionSelection-TLS12": "TODO: first pass, this should be fixed",
        "MajorVersionTolerance": "TODO: first pass, this should be fixed",
        "DuplicateExtensionServer-TLS-TLS1": "TODO: first pass, this should be fixed",
        "DuplicateExtensionClient-TLS-TLS1": "TODO: first pass, this should be fixed",

				checkExampleName(pass, fn)
				checkExampleOutput(pass, fn, f.Comments)
			case strings.HasPrefix(fn.Name.Name, "Test"):
				checkTest(pass, fn, "Test")
			case strings.HasPrefix(fn.Name.Name, "Benchmark"):
				checkTest(pass, fn, "Benchmark")
			case strings.HasPrefix(fn.Name.Name, "Fuzz"):
				checkTest(pass, fn, "Fuzz")
				checkFuzz(pass, fn)
			}
		}
	}
	return nil, nil
}

		call := n.(*ast.CallExpr)
		fn, kind := printfNameAndKind(pass, call)
		switch kind {
		case KindPrintf, KindErrorf:
			checkPrintf(pass, kind, call, fn)
		case KindPrint:
			checkPrint(pass, call, fn)
		}
	})
}

func printfNameAndKind(pass *analysis.Pass, call *ast.CallExpr) (fn *types.Func, kind Kind) {
	fn, _ = typeutil.Callee(pass.TypesInfo, call).(*types.Func)
	if fn == nil {
		return nil, 0
	}

// Creates a pass which is responsible for legalizing TensorFlow variables to
// TensorFlow Lite variables.
std::unique_ptr<OperationPass<ModuleOp>> CreateLegalizeVariablesPass();

// Creates a pass which analyze the model whether it is safe to use
// native TFLite variables or not.
std::unique_ptr<OperationPass<ModuleOp>> CreateAnalyzeVariablesPass();

		case *ast.ReturnStmt:
			checkCopyLocksReturnStmt(pass, node)
		}
	})
	return nil, nil
}

// checkCopyLocksAssign checks whether an assignment
// copies a lock.
func checkCopyLocksAssign(pass *analysis.Pass, as *ast.AssignStmt) {
	for i, x := range as.Rhs {
		if path := lockPathRhs(pass, x); path != nil {

include "mlir/Pass/PassBase.td"


def AnalyzeVariablesPass : Pass<"tfl-analyze-variables-pass", "mlir::ModuleOp"> {
  let summary = "Analyze variables in the graph";
  let description = [{
      Pass which analyzes the variables in the graph and add an attribute whether
      variables should be legalized to TFLite native ones.
      This pass needs to run post TF->TFL legalization and before variable

// output model of this pass is expected to be ready for the TF quantizer.
std::unique_ptr<OperationPass<ModuleOp>> CreateConvertTpuModelToCpuPass();

// Creates a pass that casts BFloat16 operations to Float32 operations. This
// pass is a part of the ConvertTpuModelToCpu pass to support BF16 optimized TPU
// model quantization.
std::unique_ptr<OperationPass<ModuleOp>> CreateCastBf16OpsToF32Pass();

#include "mlir/IR/PatternMatch.h"  // from @llvm-project
#include "mlir/Pass/Pass.h"  // from @llvm-project
#include "mlir/Pass/PassOptions.h"  // from @llvm-project
#include "mlir/Support/LLVM.h"  // from @llvm-project
#include "mlir/Support/LogicalResult.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_device.h"

namespace mlir {

// Creates a pass that breaks up an island with multiple ops into multiple

include "mlir/Pass/PassBase.td"

def QuantizeWeightPass : Pass<"stablehlo-quantize-weight", "mlir::func::FuncOp"> {
  let summary = "Quantizes the weight component of StableHLO graph.";
  let dependentDialects = ["mlir::stablehlo::StablehloDialect"];
  let constructor = "mlir::quant::stablehlo::CreateQuantizeWeightPass()";
}

==============================================================================*/

include "mlir/Pass/PassBase.td"

// TF device dialect passes.

def ResourceOpLiftingPass : Pass<"tf-resource-op-lifting", "ModuleOp"> {
  let summary = "Lifting resource operations out of device computation";
  let description = [{
    This pass lifts resource variable operations outside of device computation.