%0 = "tf.Const"() {device = "/device:CPU:0", value = dense<0> : tensor<i64>} : () -> tensor<i64>
    %1 = "tf.Const"() {device = "/device:CPU:0", value = dense<5> : tensor<i64>} : () -> tensor<i64>
    %2 = "tf.Const"() {device = "/device:CPU:0", value = dense<1> : tensor<i64>} : () -> tensor<i64>

namespace mlir {
namespace TF {

void RuntimeDevices::AddDevice(const ParsedName& device) {
  device_names_.push_back(device);
}

void RuntimeDevices::AddGpuDevice(const ParsedName& device,
                                  const GpuDeviceMetadata& metadata) {
  device_names_.push_back(device);
  gpu_metadata_.insert({DeviceNameUtils::ParsedNameToString(device), metadata});
}

// CHECK: return %[[identity]] : tensor<1x3x2x2xf32>

func.func @cast_bf16_matmul_to_fp32(%arg0: tensor<1x10xf32>) -> (tensor<1x2xf32>) {
  %cst = "tf.Const"() {device = "", value = dense<1.000000e+01> : tensor<10x2xbf16>} : () -> tensor<10x2xbf16>
  %0 = "tf.Cast"(%arg0) {Truncate = false, device = ""} : (tensor<1x10xf32>) -> tensor<1x10xbf16>

  %value0 = "tf.LookupTableFindV2"(%handle, %key0, %default) {device = "/job:localhost/replica:0/task:0/device:CPU:0"} : (tensor<!tf_type.resource>, tensor<!tf_type.string>, tensor<i64>) -> tensor<i64>
  %cond = "tf.Equal"(%value0, %default) {device = "/job:localhost/replica:0/task:0/device:CPU:0", incompatible_shape_error = true} : (tensor<i64>, tensor<i64>) -> tensor<i1>

// `tf_device.launch`.
func.func @conflicting_device() {
  tf_executor.graph {
    %0 = tf_executor.island {
      // expected-error@+1 {{'tf_device.launch' op inner op has conflicting 'device' attribute, got 'GPU:0' but expected 'CPU:0'}}
      "tf_device.launch"() ({
        "tf.opA"() {device = "GPU:0"} : () -> ()
        tf_device.return
      }) {device = "CPU:0"} : () -> ()

  tf_device.replicate() {n = 2 : i32, devices = {TPU_REPLICATED_CORE_0 = ["/DEVICE:0", "/DEVICE:1"], TPU_REPLICATED_CORE_1 = ["/DEVICE:2", "/DEVICE:3"]}} {
    tf_device.return
  }
  func.return

// CHECK:      tf_device.replicate
// CHECK-SAME: devices = {TPU_REPLICATED_CORE_0 = ["/DEVICE:0", "/DEVICE:1"], TPU_REPLICATED_CORE_1 = ["/DEVICE:2", "/DEVICE:3"]}
// CHECK-SAME: n = 2
// CHECK-NEXT:   tf_device.return
}

apiVersion: apps/v1
kind: DaemonSet
metadata:
  name: nvidia-gpu-device-plugin
  namespace: kube-system
  labels:
    k8s-app: nvidia-gpu-device-plugin
    addonmanager.kubernetes.io/mode: EnsureExists
spec:
  selector:
    matchLabels:
      k8s-app: nvidia-gpu-device-plugin
  template:
    metadata:
      labels:
        k8s-app: nvidia-gpu-device-plugin
    spec:
      priorityClassName: system-node-critical
      affinity:

// CHECK-SAME: device = "CPU:0"
// CHECK:      %[[C:.*]], {{.*}} = tf_executor.island wraps "tf.opC"(%[[B]])
// CHECK-SAME: device = "CPU:0"
// CHECK:      %[[D:.*]], {{.*}} = tf_executor.island wraps "tf.opD"
// CHECK-NOT:  "tf_device.launch"
// CHECK-NOT:      tf_executor.yield %[[A]], %[[C]], %[[B]], %[[D]]


// Tests empty device string attributes are overwritten.

	if err != nil {
		if _, err := os.Stat(device); os.IsNotExist(err) {
			return nil
		}
		klog.V(2).Infof("Failed to remove loopback device: %s: %v %s", device, err, out)
		return fmt.Errorf("losetup -d %s failed: %v", device, err)
	}
	return nil
}

// getLoopDeviceFromSysfs finds the backing file for a loop
// device from sysfs via "/sys/block/loop*/loop/backing_file".

  %0 = "tf.HashTableV2"() {container = "", device = "", key_dtype = !tf_type.string, shared_name = "hash_table_1dd4fef4-646d-491f-a3a8-bf5334f45813", use_node_name_sharing = false, value_dtype = i64} : () -> tensor<!tf_type.resource>
  "tf.LookupTableImportV2"(%0, %cst, %cst_1) {device = ""} : (tensor<!tf_type.resource>, tensor<3x!tf_type.string>, tensor<3xi64>) -> ()