func (addrs addrList) first(strategy func(Addr) bool) Addr {
	for _, addr := range addrs {
		if strategy(addr) {
			return addr
		}
	}
	return addrs[0]
}

// partition divides an address list into two categories, using a
// strategy function to assign a boolean label to each address.
// The first address, and any with a matching label, are returned as

// Trace time and clock.

package runtime

import (
	"internal/goarch"
	_ "unsafe"
)

// Timestamps in trace are produced through either nanotime or cputicks
// and divided by traceTimeDiv. nanotime is used everywhere except on
// platforms where osHasLowResClock is true, because the system clock
// isn't granular enough to get useful information out of a trace in
// many cases.
//

  %2 = stablehlo.divide %0, %1 : tensor<2xf32>
  return %2 : tensor<2xf32>
}

// CHECK-LABEL: foldDiv
// CHECK: stablehlo.constant dense<5.000000e-01> : tensor<2xf32>

// -----

func.func @foldDivLHSSplat() -> tensor<2xf32> {
  %0 = stablehlo.constant dense<2.0> : tensor<2xf32>
  %1 = stablehlo.constant dense<[4.0, 6.0]> : tensor<2xf32>
  %2 = stablehlo.divide %0, %1 : tensor<2xf32>

  mlir::Builder builder(ctx);

  // Collect devices with attached metadata.
  llvm::SmallVector<mlir::NamedAttribute, 8> devices;
  devices.reserve(device_set->devices().size());

  // For device that do not have any metadata, or if we failed to parse metadata
  // from the DeviceSet, we add a unit attribute to the `tf.devices` attribute.
  for (Device* device : device_set->devices()) {

  (*module_ref)->setAttr("tf.devices", device_dict);

  mlir::TF::RuntimeDevices devices;
  EXPECT_TRUE(mlir::succeeded(GetDevicesFromOp(*module_ref, &devices)));

  ASSERT_EQ(devices.NumDevices(), 1);
  ASSERT_EQ(devices.device_names().size(), 1);
  ASSERT_EQ(DeviceNameUtils::ParsedNameToString(devices.device_names()[0]),
            "/job:worker/replica:0/task:0/device:CPU:0");
}

			slices.SortFunc(s, func(a, b int) int {
				compares++
				return cmp.Compare(a, b)
			})
		}
		// This metric is per-operation, so divide by b.N and
		// report it as a "/op" unit.
		b.ReportMetric(float64(compares)/float64(b.N), "compares/op")
		// This metric is per-time, so divide by b.Elapsed and
		// report it as a "/ns" unit.
		b.ReportMetric(float64(compares)/float64(b.Elapsed().Nanoseconds()), "compares/ns")
	})
}

//    same type.
//  - GPU is preferred over CPU.
//  - If `allow_mixing_unknown_and_cpu` is true then unknown devices are
//    preferred over CPU.
//  - XLA devices count as "unrecognized devices".
//
// This set of rules above implicitly assume that XLA:GPU can compile all
// operations in the cluster that XLA:CPU can compile, and if

func New(devEntries []PodDevicesEntry, devices map[string][]string) DeviceManagerCheckpoint {
	return newV2(devEntries, devices)
}

func newV2(devEntries []PodDevicesEntry, devices map[string][]string) DeviceManagerCheckpoint {
	return &Data{
		Data: checkpointData{
			PodDeviceEntries:  devEntries,
			RegisteredDevices: devices,
		},
	}
}

// MarshalCheckpoint returns marshalled data

		prepared:       false,
	}
	for pluginName, devices := range info.CDIDevices {
		annotations, _ := cdi.GenerateAnnotations(info.ClaimUID, info.DriverName, devices)
		info.annotations[pluginName] = append(info.annotations[pluginName], annotations...)
	}
	return info
}

// setCDIDevices adds a set of CDI devices to the claim info.

    auto device_factory = DeviceFactory::GetFactory(device_type);
    SessionOptions options;
    std::vector<std::unique_ptr<Device>> devices;
    Status s = device_factory->CreateDevices(
        options, "/job:worker/replica:0/task:0", &devices);
    device_ = std::move(devices[0]);

    AllocatorAttributes host_alloc_attr;
    host_alloc_attr.set_on_host(true);
    host_allocator_ = device_->GetAllocator(host_alloc_attr);