];
}

def StripNoinlineAttributePass : Pass<"tf-strip-noinline-attribute", "ModuleOp"> {
  let summary = "Strip the tf._noinline attribute from top-level functions.";
  let constructor = "TF::CreateStripNoinlineAttributePass()";
}

def ExecutorConvertControlToDataOutputsPass : Pass<"tf-executor-convert-control-to-data-outputs", "ModuleOp"> {
  let summary = "Chain control outputs of while loop body";

  let description = [{

    bool disable_crash_analysis) {
  LoadImporterDialects(*context);
  mlir::OwningOpRef<mlir::ModuleOp> module =
      mlir::ModuleOp::create(mlir::UnknownLoc::get(context));
  NameUniquifier function_name_uniquifier(flib_def);

  // importer.PrepareConvert below will attemp to clone the original `graph`
  // via conversion to the graph def first. Convert graph to graph_def here

  void PropagateConstantToCallee(CallOpInterface call_op, FuncOp func,
                                 ModuleOp module);

  // Propagates any constant return value of the callee function to the call
  // op's corresponding result.
  void PropagateConstantFromCallee(CallOpInterface call_op, FuncOp func,
                                   ModuleOp module);

  // Tries to compute the result of folding the op. This doesn't actually

using absl::StatusOr;
using llvm::dyn_cast;
using llvm::formatv;
using llvm::isa;
using llvm::StringRef;
using mlir::Dialect;
using mlir::ElementsAttr;
using mlir::MLIRContext;
using mlir::ModuleOp;
using mlir::NoneType;
using mlir::Operation;
using mlir::Region;
using mlir::StringAttr;
using mlir::TensorType;
using mlir::Twine;
using mlir::Type;
using mlir::UnknownLoc;
using mlir::Value;

class ConvertCustomCallWithApproxTopK
    : public mlir::OpConversionPattern<mhlo::CustomCallOp> {
 public:
  explicit ConvertCustomCallWithApproxTopK(MLIRContext* context,
                                           mlir::ModuleOp* module_op)
      : OpConversionPattern<mhlo::CustomCallOp>(context, /*benefit=*/0),
        module_op_(module_op) {}

  mlir::LogicalResult matchAndRewrite(
      mhlo::CustomCallOp op, OpAdaptor adaptor,

             << "expects padding to be a matrix with minor dimension 2, got "
             << padding.getShapedType().getShape();
    }
  }

  auto module = op->getParentOfType<mlir::ModuleOp>();
  auto func = dyn_cast_or_null<mlir::func::FuncOp>(
      SymbolTable::lookupSymbolIn(module, op.getComputation()));
  if (!func) {
    return op.emitOpError() << "has no reduction function specified";
  }