// ops via their SaveSpec members
// https://github.com/tensorflow/tensorflow/blob/ddc1bbad3dfd4a089eb96014f26cc16664b1b2f8/tensorflow/python/training/saving/saveable_object.py#L21,
// which contain the "real" checkpoint keys into the TensorBundle SSTable.
// They also contain the logic needed to take the restored tensors from

// Test that RestoreV2 op is created with 1 resulting value.
// CHECK: %[[RESTORE:.*]] = "tf.RestoreV2"(%[[ARG_0]], %[[CST_1]], %[[CST_2]]) : (tensor<!tf_type.string>, tensor<1x!tf_type.string>, tensor<1x!tf_type.string>) -> tensor<2xf32>
// CHECK: "tf.AssignVariableOp"(%[[VAR_HANDLE]], %[[RESTORE]]) <{validate_shape = false}> : (tensor<!tf_type.resource<tensor<2xf32>>>, tensor<2xf32>) -> ()

		t.Fatalf("Expected 'non-existent chain' error from Restore, got %v", err)
	}

	// more Restore; empty out one chain and delete another, but also update its counters
	rules = dedent.Dedent(strings.Trim(`
		*nat
		:KUBE-RESTORED - [0:0]
		:KUBE-TEST - [99:9999]
		-X KUBE-RESTORED
		COMMIT
		`, "\n"))
	err = fake.Restore(iptables.TableNAT, []byte(rules), iptables.NoFlushTables, iptables.RestoreCounters)

type regPos struct {
	pos int

	saveOp    string
	restoreOp string
	reg       string

	// If this register requires special save and restore, these
	// give those operations with a %d placeholder for the stack
	// offset.
	save, restore string
}

func (l *layout) add(op, reg string, size int) {
	l.regs = append(l.regs, regPos{saveOp: op, restoreOp: op, reg: reg, pos: l.stack})
	l.stack += size
}

  are multiple output tensors from one node). Since Retval nodes are not used in
  SavedModel, this function removes them and restore the names to the actual
  output tensors.

  Args:
    graph_def: the converted GraphDef.

  Returns:
    The GraphDef with Retval nodes removed and output tensor names restored.
  """
  output_renaming_map = {}
  with session.Session(graph=ops.Graph()):
    importer.import_graph_def(graph_def, name='')

        0 * javaUtilLoggingSystem._

        when:
        loggingManager.stop()

        then:
        1 * slf4jLoggingSystem.restore(slf4jSnapshot)
        1 * javaUtilLoggingSystem.restore(javaUtilSnapshot)
        1 * loggingRouter.restore(routerSnapshot)
        0 * loggingRouter._
        0 * slf4jLoggingSystem._
        0 * javaUtilLoggingSystem._
    }

}

// Expiry returns expiry of restored object and true if restore-object has completed.
// Otherwise returns zero value of time.Time and false.
func (r restoreObjStatus) Expiry() (time.Time, bool) {
	if r.Ongoing() {
		return time.Time{}, false
	}
	return r.expiry, true
}

// Ongoing returns true if restore-object is ongoing.
func (r restoreObjStatus) Ongoing() bool {

func assertStateEqual(t *testing.T, restoredState, expectedState ClaimInfoStateList) {
	assert.Equal(t, expectedState, restoredState, "expected ClaimInfoState does not equal to restored one")
}

// TODO (https://github.com/kubernetes/kubernetes/issues/123552): reconsider what data gets stored in checkpoints and whether that is really necessary.
//

  let summary = "Restore variable tensors";
  let description = [{
    This is the MLRT version of tf.IfrtRestoreVariableOp.

    This Op is similar to a combination of RestoreV2 and AssignVariable Op, but
    this Op's execution is asynchronous.

    This Op is specific to MLRT runtime and is not a stable interface for
    serialization.

	if raceenabled {
		racereleasemerge(unsafe.Pointer(&racecgosync))
	}

	// Do not unwind m->g0->sched.sp.
	// Our caller, cgocallback, will do that.
	restore = false
}

func unwindm(restore *bool) {
	if *restore {
		// Restore sp saved by cgocallback during
		// unwind of g's stack (see comment at top of file).
		mp := acquirem()
		sched := &mp.g0.sched