int_attr.getSplatValue<APInt>() == raw_value)
      return true;
  }

  return false;
}

// Returns true if `op` is placed on GPU device, and false if it's on other
// devices or the device is not specified.
bool IsOnGpuDevice(mlir::Operation *op);

// Wrappers for CopyDeviceAndUnderscoredAttributes
void CopyDeviceAndUnderscoredAttributesAdaptor(mlir::OpResult src,

	if err != nil {
		t.Errorf("fakeMounter.List() failed: %v", err)
	}
	if len(mntDevs) != 1 {
		t.Errorf("unexpected number of mounted devices. expected: %v, got %v", 1, len(mntDevs))
	} else {
		if mntDevs[0].Type != "nfs" {
			t.Errorf("unexpected type of mounted devices. expected: %v, got %v", "nfs", mntDevs[0].Type)
		}
		if mntDevs[0].Device != expectedDevice {

environments. TensorFlow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general purpose GPUs, and custom-designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility to the application developer, whereas in previous “parameter server” designs the management of...

  %5:2 = tf_device.replicate([%2#0, %3#0] as %r0: tensor<3x3x1x32xf32>, [%2#1, %3#1] as %r1: tensor<3x3x1x32xf32>)
      {n = 2 : i32, devices = {TPU_REPLICATED_CORE_0 = ["/device:TPU:0", "/device:TPU:1"]}} {
    // CHECK: "tf.TPUExecute"(%[[R0]], %[[R1]], %[[COMPILE]]#1)
    %execute = "tf_device.launch"() ({

					t.Fatalf("expected error message `%s` but got `%v`", tc.expectedErrMsg, err)
				}
				return
			}
			// otherwise validate the devices
			if err != nil {
				t.Fatal(err)
			}
			assert.Equal(t, tc.expectedDevices, blkVolumes, "devices of container %+v", tc.container)
		})
	}

namespace cc {

// An opaque representation of a tensor computed/managed by the Tensorflow
// runtime (tensorflow:cc::Runtime). Unlike a tensor, a Tensorhandle may refer
// to tensors placed in memory of different devices or remote address spaces.
// Note that tensorflow::cc::Runtime MUST outlive all TensorHandles created
// from it.
class TensorHandle {
 public:
  // Unwraps a Tensor from the given TensorHandle. If an error occurred,

// product of possible devices and inference datatypes. For example, we might
// raise a chunk of sequential operations from a block all having attributes
// `{ tac.device = "GPU", tac.inference_type = "FLOAT"}` to a function
// with the matching attributes. Assumed is that device type "CPU"
// is the only device that is allowed to call other devices. I.e. ancestors of a

			klog.ErrorS(err, "ListAndWatch ended unexpectedly for device plugin", "resource", c.resource)
			return
		}
		klog.V(2).InfoS("State pushed for device plugin", "resource", c.resource, "resourceCapacity", len(response.Devices))
		c.handler.PluginListAndWatchReceiver(c.resource, response)
	}
}

// Disconnect is for closing gRPC connection between device manager and device plugin.
func (c *client) Disconnect() error {
	c.mutex.Lock()

which will output something similar to

	export GOIOS_DEV_ID="iPhone Developer: ******@****.*** (XXXXXXXX)"
	export GOIOS_APP_ID=YYYYYYYY.some.bundle.id
	export GOIOS_TEAM_ID=ZZZZZZZZ

If you have multiple devices connected, specify the device UDID with the GOIOS_DEVICE_ID
variable. Use `idevice_id -l` to list all available UDIDs. Then, setting GOARCH to arm64
will select the device:

}

TEST(PickDeviceForXla, MoreSpecificDevice) {
  CheckPickDeviceResult(kCPU0, false, {kCPU0, kCPU0Partial});
  CheckPickDeviceResult(kGPU0, false, {kGPU0, kGPU0Partial});
  // Unknown devices do not support merging of full and partial specifications.
  CheckPickDeviceHasError(false, {kXPU1, kXPU0Partial});
}

TEST(PickDeviceForXla, DeviceOrder) {
  CheckPickDeviceResult(kGPU0, false, {kGPU0, kCPU0});