absl::StrCat("found ", kDeviceTPUSystem, " devices with conflicting ",
                   attribute, "s '", a, "' and '", b, "'"));
}

// Find TPU_SYSTEM:0 devices in `devices`. If multiple TPU_SYSTEM devices are
// found, the first one lexicographically is returned. If no TPU_SYSTEM device
// is found or if there are multiple TPU_SYSTEM devices with different jobs or
// replicas, a failure will be returned.

LogicalResult GetTpuDeviceAssignment(
    ClusterOp cluster, ReplicateOp replicate, mlir::ModuleOp module,
    absl::StatusOr<TPUDeviceAssignment>& status_or_tpu_device_assignment) {
  mlir::TF::RuntimeDevices devices;
  if (failed(tensorflow::GetDevicesFromOp(module, &devices))) return failure();

  uint32_t num_replicas = replicate.getN();

  auto num_cores_per_replica_attr = cluster->getAttrOfType<mlir::IntegerAttr>(

// RUN: tf-opt %s -split-input-file -tf-xla-broadcast | FileCheck %s

// RUN: tf-tfrt-opt -split-input-file -tf-device-decompose-resource-ops -sink-variable-as-named-array %s | FileCheck %s

// -----
// Basic test: all variables tensors are for devices and sinked as named ifrt arrays
//
//
// CHECK-LABEL:  func.func @serving_default(%arg0: tensor<1x3xf32>) -> tensor<1x1xf32> {
// CHECK-NEXT:   [[HANDLE2:%.*]] = "tf.VarHandleOp"
// CHECK-NEXT:   [[KEY:%.*]], [[FUTURE:%.*]] = "tf.IfrtLoadVariable"([[HANDLE2]])

        ":__subpackages__",
        "//tensorflow/python:__subpackages__",
    ],
    deps = [
        "@llvm-project//mlir:AffineDialect",
        "@llvm-project//mlir:QuantOps",
        # Link jit lib to link JIT devices required to run
        # xla-legalize-tf-with-tf2xla pass.
        "//tensorflow/compiler/jit",
        "//tensorflow/compiler/mlir/lite:tensorflow_lite",
        "//tensorflow/compiler/mlir/lite:tensorflow_lite_legalize_tf",

    return
  fi

  local device="${devices[0]}"
  if [ "${#devices[@]}" -ne 1 ]; then
    seen_arrays=(/dev/md/*)
    device=${seen_arrays[0]}
    echo "Setting RAID array with local SSDs on device ${device}"
    if [ ! -e "$device" ]; then
      device="/dev/md/0"
      echo "y" | mdadm --create "${device}" --level=0 --raid-devices=${#devices[@]} "${devices[@]}"
    fi
  fi

// loop that iterates the dataset and calls the reduction function.
std::unique_ptr<OperationPass<func::FuncOp>> CreateDecomposeReduceDatasetPass();

// Create a pass that convert ops that copy tensors between devices, e.g.
// tf.Identity.
std::unique_ptr<OperationPass<mlir::func::FuncOp>>
CreateTensorDeviceCopyConversionPass();

// Returns a pass that folds tf.BroadcastTo nodes with subsequent nodes if they

type RunContainerOptions struct {
	// The environment variables list.
	Envs []EnvVar
	// The mounts for the containers.
	Mounts []Mount
	// The host devices mapped into the containers.
	Devices []DeviceInfo
	// The CDI devices for the container
	CDIDevices []CDIDevice
	// The annotations for the container
	// These annotations are generated by other components (i.e.,

			klog.V(4).InfoS("Device will be attached to container in the corresponding path on host", "containerName", container.Name, "path", symlinkPath)
			devices = append(devices, kubecontainer.DeviceInfo{PathOnHost: symlinkPath, PathInContainer: device.DevicePath, Permissions: permission})
		}
	}

	return devices, nil
}

// shouldMountHostsFile checks if the nodes /etc/hosts should be mounted
// Kubernetes only mounts on /etc/hosts if:

will cause the graph execution to fail to complete.

input: the input to the reduction
data: the value of the reduction across all `num_devices` devices.
reduction: the reduction operation to perform.
num_devices: The number of devices participating in this reduction.
shared_name: Identifier that shared between ops of the same reduction.
  }];

  let arguments = (ins