}
		if len(errors) != 2 {
			t.Errorf("expected 2 errors, got %d: %s", len(errors), errors)
		}
		// the inaccessible subtrees were marked manually
		checkMarks(t, true)
		errors = errors[0:0]

		// 4) capture errors, stop after first error.
		// mark respective subtrees manually
		markTree(tree.entries[1])
		markTree(tree.entries[3])
		// correct double-marking of directory itself

	//   [    4] section_0001      PROGBITS        00000000 000034 000004 00  WA  0   0  4
	//   [    5] section_0002      PROGBITS        00000000 000038 000004 00  WA  0   0  4
	//   [ section_0003 ~ section_ff06 truncated ]
	//   [65290] section_ff07      PROGBITS        00000000 03fc4c 000004 00  WA  0   0  4
	//   [65291] section_ff08      PROGBITS        00000000 03fc50 000004 00  WA  0   0  4

        %4 = "tf.Const"() {device = "", value = dense<0> : tensor<i32>} : () -> tensor<i32>
        %5 = "tf.Const"() {device = "", value = dense<[[1.000000e+00, 2.000000e+00], [3.000000e+00, 4.000000e+00]]> : tensor<2x2xf32>} : () -> tensor<2x2xf32>
        // CHECK: tf.TensorListSetItem{{.*}}: (tensor<!tf_type.variant<tensor<2x2xf32>>>, tensor<i32>, tensor<2x2xf32>) -> tensor<!tf_type.variant<tensor<2x2xf32>>>

			autoscalingv2.ResourceMetricSourceType: monitor.ErrorLabelNone,
		},
	}
	tc1.runTest(t)

	target = math.Abs(1/(requestedToUsed*(1-defaultTestingTolerance))) + .004
	finalCPUPercentTarget = int32(target * 100)
	tc2 := testCase{
		minReplicas:             0,
		maxReplicas:             1000,
		specReplicas:            startPods,
		statusReplicas:          startPods,

		// accumulating at approximately the same time from the set of nodes due to priority and
		// fairness effect.
		go wait.JitterUntil(kl.syncNodeStatus, kl.nodeStatusUpdateFrequency, 0.04, true, wait.NeverStop)
		go kl.fastStatusUpdateOnce()

		// start syncing lease
		go kl.nodeLeaseController.Run(context.Background())
	}
	go wait.Until(kl.updateRuntimeUp, 5*time.Second, wait.NeverStop)

// CHECK: }
func.func @convert_floor_div_broadcast_cst(%arg0: tensor<10x8xf32>) -> tensor<10x8xf32> {
  %0 = mhlo.constant dense<1.000000e+00> : tensor<10x8xf32>
  %1 = mhlo.constant dense<[1.000000e+00, 2.000000e+00, 4.000000e+00, 8.000000e+00, 1.600000e+01, 3.200000e+01, 6.400000e+01, 1.280000e+02]> : tensor<8xf32>
  %2 = mhlo.constant dense<0.000000e+00> : tensor<10x8xf32>
  %3 = mhlo.constant dense<-1.000000e+00> : tensor<10x8xf32>

// CHECK: [[DYNSLICE:%.*]] = "mhlo.dynamic_slice
// CHECK: [[RESHAPE:%.*]] = mhlo.reshape [[DYNSLICE]]
// CHECK: return [[RESHAPE]]
  %0 = "tf.Const"() {value = dense<[[0.000000e+00, 1.000000e+00, 2.000000e+00, 3.000000e+00], [4.000000e+00, 5.000000e+00, 6.000000e+00, 7.000000e+00]]> : tensor<2x4xf32>} : () -> tensor<2x4xf32>
  %1 = "tf.Const"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>

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