});
}

// Copies attributes that are either `device` or whose name begins with an _
// from `from` to `to`.
// TODO(b/158769932): This should be a general feature instead post some policy
// discussion.
inline void CopyDeviceAndUnderscoredAttributes(Operation *from, Operation *to) {
  auto device = mlir::StringAttr::get(from->getContext(), "device");
  CopyAttributes(from, to, [&device](const NamedAttribute &attr) {

    %0 = "tf.HashTableV2"() {container = "", device = "", key_dtype = i64, shared_name = "hash_table_dba2ccaa-f1b1-46d6-b276-98008f69da71", use_node_name_sharing = false, value_dtype = !tf_type.string} : () -> tensor<!tf_type.resource>
    "tf.LookupTableImportV2"(%0, %cst, %cst_0) {device = ""} : (tensor<!tf_type.resource>, tensor<4xi64>, tensor<4x!tf_type.string>) -> ()
    func.return

// CHECK-NEXT: [[o_chain:%.*]], [[o:%.*]] = tfrt_fallback_async.executeop.seq([[in_chain]]) key(0) cost({{.*}}) device("/job:localhost/replica:0/task:0/device:CPU:0") "tf.VarHandleOp"()
// CHECK-NEXT: [[o_chain_0:%.*]], [[o1:%.*]] = tfrt_fallback_async.executeop.seq([[in_chain]]) key(1) cost({{.*}}) device("/job:localhost/replica:0/task:0/device:CPU:0") "tf.ReadVariableOp"([[o]]) {dtype = f32} : 1

  %cst = "tf.Const"() <{value = dense<1> : tensor<i32>}> {device = ""} : () -> tensor<i32>
  %0 = "tf.MultiDeviceIteratorFromStringHandle"(%arg0) <{output_shapes = [#tf_type.shape<>], output_types = [!tf_type.string]}> {device = ""} : (tensor<!tf_type.string>) -> tensor<!tf_type.resource>
  %1 = "tf.MultiDeviceIteratorGetNextFromShard"(%0, %cst, %arg1) {device = ""} : (tensor<!tf_type.resource>, tensor<i32>, tensor<i64>) -> tensor<!tf_type.string>

  }];
}

// This corresponds to tensorflow::Device* .
def TFDeviceType : DialectType<TensorflowMlrt_Dialect,
    CPred<"$_self.isa<::tensorflow::tf_mlrt::TFDeviceType>()">, "!tf_mlrt.device type">,
    BuildableType<"$_builder.getType<::tensorflow::tf_mlrt::TFDeviceType>()"> {
  let description = [{
    `!tf_mlrt.device type` represents a tensorflow::device.
  }];
}

    %cst = "tf.Const"() {device = "", value = dense<[7.11401462, 7.05456924]> : tensor<2xf32>} : () -> tensor<2xf32>
    // CHECK: %[[q_weight_per_channel:.*]] = "quantfork.qcast"
    // CHECK-SAME: -> tensor<2x3x3x2x!quant.uniform<i8<-127:127>:f32:3, {0.075123051020104109,0.072960192762960605}>>
    // CHECK: %[[dq_weight:.*]] = "quantfork.dcast"(%[[q_weight_per_channel]])

namespace tensorflow {
namespace {

// A fake device used to populate a DeviceSet.
class FakeDevice : public Device {
 public:
  explicit FakeDevice(const DeviceAttributes& device_attributes)
      : Device(nullptr, device_attributes) {}

  Status Sync() override { return errors::Unimplemented("FakeDevice::Sync()"); }

  static std::unique_ptr<Device> Make(const string& name,

type DeviceManagerCheckpoint interface {
	checkpointmanager.Checkpoint
	GetData() ([]PodDevicesEntry, map[string][]string)
}

// DevicesPerNUMA represents device ids obtained from device plugin per NUMA node id
type DevicesPerNUMA map[int64][]string

// PodDevicesEntry connects pod information to devices
type PodDevicesEntry struct {
	PodUID        string
	ContainerName string

        return launch.emitOpError()
               << "inner op has conflicting 'device' attribute, "
                  "got '"
               << device_str_attr.getValue() << "' but expected '"
               << launch.getDevice() << "'";
      }
    } else {
      return launch.emitOpError()
             << "inner op has bad 'device' attribute, got " << device_attr;
    }

    return WalkResult::advance();
  });

                                                      kFuncDeviceAttr)) {
      std::string device = attr.getValue().str();
      tensorflow::DeviceNameUtils::ParsedName parsed_name;
      if (!tensorflow::DeviceNameUtils::ParseFullName(device, &parsed_name)) {
        return read->emitOpError() << "invalid device '" << device << "'";
      }
      is_cpu_read = parsed_name.type == kCpuDeviceType;
      return success();
    }