func.return %0 : tensor<4x2x3x1x1xi32>
  }

  // CHECK-LABEL: mirror_pad
  func.func @mirror_pad(%arg0: tensor<2x3xcomplex<f64>>) -> tensor<4x7xcomplex<f64>> {
    %0 = mhlo.constant dense<[[1, 1], [2, 2]]> : tensor<2x2xi32>
    // CHECK-NOT: tf.MirrorPad
    %1 = "tf.MirrorPad"(%arg0, %0) {mode = "SYMMETRIC"} : (tensor<2x3xcomplex<f64>>, tensor<2x2xi32>) -> tensor<4x7xcomplex<f64>>

  REDUCE_ANY = 91,
  SQUARE = 92,
  ZEROS_LIKE = 93,
  FILL = 94,
  FLOOR_MOD = 95,
  RANGE = 96,
  RESIZE_NEAREST_NEIGHBOR = 97,
  LEAKY_RELU = 98,
  SQUARED_DIFFERENCE = 99,
  MIRROR_PAD = 100,
  ABS = 101,
  SPLIT_V = 102,
  UNIQUE = 103,
  CEIL = 104,
  REVERSE_V2 = 105,
  ADD_N = 106,
  GATHER_ND = 107,
  COS = 108,
  WHERE = 109,
  RANK = 110,
  ELU = 111,

  REDUCE_ANY = 91,
  SQUARE = 92,
  ZEROS_LIKE = 93,
  FILL = 94,
  FLOOR_MOD = 95,
  RANGE = 96,
  RESIZE_NEAREST_NEIGHBOR = 97,
  LEAKY_RELU = 98,
  SQUARED_DIFFERENCE = 99,
  MIRROR_PAD = 100,
  ABS = 101,
  SPLIT_V = 102,
  UNIQUE = 103,
  CEIL = 104,
  REVERSE_V2 = 105,
  ADD_N = 106,
  GATHER_ND = 107,
  COS = 108,
  WHERE = 109,
  RANK = 110,
  ELU = 111,

  REDUCE_ANY = 91,
  SQUARE = 92,
  ZEROS_LIKE = 93,
  FILL = 94,
  FLOOR_MOD = 95,
  RANGE = 96,
  RESIZE_NEAREST_NEIGHBOR = 97,
  LEAKY_RELU = 98,
  SQUARED_DIFFERENCE = 99,
  MIRROR_PAD = 100,
  ABS = 101,
  SPLIT_V = 102,
  UNIQUE = 103,
  CEIL = 104,
  REVERSE_V2 = 105,
  ADD_N = 106,
  GATHER_ND = 107,
  COS = 108,
  WHERE = 109,
  RANK = 110,
  ELU = 111,

	}
	return podToMirror, mirrorToPod
}

// IsMirrorPodOf returns true if pod and mirrorPod are associated with each other.
func IsMirrorPodOf(mirrorPod, pod *v1.Pod) bool {
	// Check name and namespace first.
	if pod.Name != mirrorPod.Name || pod.Namespace != mirrorPod.Namespace {
		return false
	}
	hash, ok := getHashFromMirrorPod(mirrorPod)
	if !ok {
		return false
	}
	return hash == getPodHash(pod)
}

  TensorShape y_shape({4, 7});
  auto y = MirrorPad(scope_, x, paddings, "REFLECT");
  RunTest(x, x_shape, y, y_shape);
}

TEST_F(ArrayGradTest, MirrorPadGrad_Symmetric) {
  TensorShape x_shape({2, 3});
  auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape));
  auto paddings = Const(scope_, {{1, 1}, {2, 2}});
  TensorShape y_shape({4, 7});
  auto y = MirrorPad(scope_, x, paddings, "SYMMETRIC");

				klog.InfoS("Got unexpected podIsFinished=false, while podIsFinished=true in status cache, programmer error.", "pod", klog.KObj(pod))
				podIsFinished = true
			}
		}
	} else if mirrorPod, ok := m.podManager.GetMirrorPodByPod(pod); ok {
		oldStatus = mirrorPod.Status
	} else {
		oldStatus = pod.Status
	}

	// Check for illegal state transition in containers

  grad_outputs->push_back(tensorflow::ops::internal::MirrorPadGrad(
      scope, grad_inputs[0], op.input(1), mode));
  grad_outputs->push_back(NoGradient());
  return scope.status();
}
REGISTER_GRADIENT_OP("MirrorPad", MirrorPadGrad);

// TODO(suharshs): b/34770860. This gradient was within 1e-3 but not 1e-4.
Status MirrorPadGradGrad(const Scope& scope, const Operation& op,
                         const std::vector<Output>& grad_inputs,

	RunningContainersKey               = "running_containers"
	DesiredPodCountKey                 = "desired_pods"
	ActivePodCountKey                  = "active_pods"
	MirrorPodCountKey                  = "mirror_pods"
	WorkingPodCountKey                 = "working_pods"
	OrphanedRuntimePodTotalKey         = "orphaned_runtime_pods_total"
	RestartedPodTotalKey               = "restarted_pods_total"