}

	putattr(ctxt, s.Info, DW_ABRV_FUNCTION, DW_FORM_string, DW_CLS_STRING, int64(len(name)), name)
	putattr(ctxt, s.Info, abbrev, DW_FORM_addr, DW_CLS_ADDRESS, 0, s.StartPC)
	putattr(ctxt, s.Info, abbrev, DW_FORM_addr, DW_CLS_ADDRESS, s.Size, s.StartPC)
	putattr(ctxt, s.Info, abbrev, DW_FORM_block1, DW_CLS_BLOCK, 1, []byte{DW_OP_call_frame_cfa})
	if isWrapper {

				funstr := exprToString(x.Fun)
				if funstr == "putattr" {
					form, _ := x.Args[3].(*ast.Ident)
					if form == nil {
						t.Fatalf("%s invalid use of putattr", fset.Position(x.Pos()))
					}
					cmt := findLineComment(cm, stmt)
					if cmt == nil {
						t.Fatalf("%s invalid use of putattr (no comment containing the attribute name)", fset.Position(x.Pos()))
					}

package fuzz

import (
	"encoding/binary"
	"fmt"
	"math"
	"unsafe"
)

type mutator struct {
	r       mutatorRand
	scratch []byte // scratch slice to avoid additional allocations
}

func newMutator() *mutator {
	return &mutator{r: newPcgRand()}
}

func (m *mutator) rand(n int) int {
	return m.r.intn(n)
}

func (m *mutator) randByteOrder() binary.ByteOrder {
	if m.r.bool() {
		return binary.LittleEndian
	}

                                          dq.getArg());
      dq.getResult().replaceAllUsesWith(dcast);
      if (auto extra_attr = op->getAttr(mlir::quant::kVolatileOpAttrName)) {
        dcast->setAttr(mlir::quant::kVolatileOpAttrName, extra_attr);
      }
      dq.erase();
    } else if (auto q = llvm::dyn_cast<quantfork::QuantizeCastOp>(op)) {
      auto out_type = q.getResult().getType();

    // Convert `_tpu_replicate`.
    if (op->hasAttr(TF::kTpuReplicateAttr)) {
      op->setAttr(tensorflow::kReplicationInfoAttr,
                  op->getAttr(TF::kTpuReplicateAttr));
      op->removeAttr(tensorflow::kTpuReplicateAttr);
      op->setAttr(tensorflow::kCompileDeviceTypeAttr,
                  builder.getStringAttr(tensorflow::kTpuDevice));
    }

    // Convert `_XlaMustCompile`.

  added_func_op->setAttr(kInterfaceNameAttr, interface_name);
  added_call_op->setAttr(kInterfaceNameAttr, interface_name);

  StringAttr device = mlir::cast<StringAttr>(
      added_func_op->getRegion(0).getBlocks().front().front().getAttr(kDevice));
  StringAttr inference_type = mlir::cast<StringAttr>(
      added_func_op->getRegion(0).getBlocks().front().front().getAttr(
          kInferenceType));

    if (op->getDialect() != tf_dialect) return WalkResult::advance();

    if (parallel_group_attr) {
      op->setAttr(TF::kParallelExecAnnotation, parallel_group_attr);
    }
    auto device_attr = op->getAttr(kDeviceAttr);
    if (!device_attr) {
      op->setAttr(kDeviceAttr, launch.getDeviceAttr());
      return WalkResult::advance();
    }

    if (failed(TF::HasValidCompilationAndReplicationAttributes(*op)))
      return WalkResult::interrupt();
    if (op->hasAttr(TF::kReplicationInfoAttr)) {
      op->setAttr(TF::kTpuReplicateAttr, op->getAttr(TF::kReplicationInfoAttr));
      op->removeAttr(TF::kReplicationInfoAttr);
      op->removeAttr(TF::kCompileDeviceTypeAttr);
    }
    return mlir::WalkResult::advance();
  });

    SmallVector<Type> result_types(op.getResultTypes().begin(),
                                   op.getResultTypes().end());
    if (!op->getAttr("layout")) {
      auto layout = mlir::GetTPUInfeedLayout(result_types, builder);
      if (failed(layout)) return;

      op->setAttr("layout", layout.value());
    }
  }
};

void InfeedsOpsXlaAdjustLayout::runOnOperation() {
  getOperation().walk(runOnInfeedOp);
}

  mlir::OwningOpRef<mlir::ModuleOp> module_ref =
      mlir::ModuleOp::create(mlir::UnknownLoc::get(&context));

  AddDevicesToOp(*module_ref, /*device_set=*/nullptr);
  EXPECT_EQ((*module_ref)->getAttr("tf.devices"), nullptr);
}

TEST(DeviceUtilTest, GetDevicesFromOpNoDevicesAttribute) {
  mlir::MLIRContext context;
  mlir::OwningOpRef<mlir::ModuleOp> module_ref =