// CHECK:      tf_device.replicate
  // CHECK-SAME: (%[[ARG0]] as %[[RI_0:[a-z0-9]*]]: tensor<*x!tf_type.resource<tensor<4xf32>>>)
  tf_device.replicate(%arg0 as %arg1: tensor<*x!tf_type.resource<tensor<4xf32>>>) {
    _mirrored_variable_indices = [0],
    devices = {TPU_REPLICATED_CORE_0 = ["/job:worker/replica:0/task:0/device:TPU:0", "/job:worker/replica:0/task:0/device:TPU:1"]},
    n = 2 : i32} {

	--replicate "existing-objects,delete,delete-marker,replica-metadata-sync"
sleep 1

echo "adding replication rule for olockbucket a -> c : ${remote_arn}"
./mc replicate add sitea/olockbucket/ \
	--remote-bucket http://minio:minio123@127.0.0.1:9006/olockbucket \
	--replicate "existing-objects,delete,delete-marker,replica-metadata-sync" --priority 2
sleep 1

echo "adding replication rule for olockbucket c -> a : ${remote_arn}"

		// return the current replicas if the change of metrics length would cause a change in scale direction
		return currentReplicas, usage, nil
	}

	// return the result, where the number of replicas considered is
	// however many replicas factored into our calculation
	return newReplicas, usage, nil
}

// GetObjectMetricReplicas calculates the desired replica count based on a target metric usage (as a milli-value)

The replication configuration in use on a bucket can be viewed using the `mc replicate export alias/bucket` command.

To disable replica metadata modification syncing, use `mc replicate edit` with the --replicate flag.

```
mc replicate edit alias/bucket --id xyz.id --replicate "delete,delete-marker"
```

		replicas:          3,
		readyReplicas:     3,
		currentReplicas:   2,
		availableReplicas: int32(-1),
		expectedErr:       true,
	}, {
		name:              "invalid: available replicas greater than replicas",
		replicas:          3,
		readyReplicas:     3,
		currentReplicas:   2,
		availableReplicas: int32(4),
		expectedErr:       true,
	}, {

// CHECK-LABEL: func @hoist_cpu
func.func @hoist_cpu(%arg0: tensor<*x!tf_type.resource<tensor<f32>>> {tf.device = "/job:tpu_host_worker/replica:0/task:0/device:CPU:0"}) -> () {
  // CHECK:      %[[READ:.*]] = "tf.ReadVariableOp"
  // CHECK-NEXT: tf_device.replicate
  // CHECK-NEXT:   "tf.OpA"(%[[READ]])
  tf_device.replicate {n = 2 : i32} {
    %0 = "tf.ReadVariableOp"(%arg0) : (tensor<*x!tf_type.resource<tensor<f32>>>) -> tensor<f32>

      tf_device.return
    }) {device = "/job:localhost/replica:0/task:0/device:CPU:0"} : () -> ()
    tf_device.return
  }) {n = 2: i32, operandSegmentSizes = array<i32: 0, 0>} : () -> ()
  return
}

// -----

func.func @duplicate_compile() {
  "tf_device.replicate"() ({
    // Result of the launch not propagated out of the replicate
    %0:2 = "tf_device.launch"() ({

replicated_inputs: each group of `n` inputs corresponds to an input for a single
individual replica and is mapped to a single region argument. Inside one group
the operands are matching in order the `devices` attribute. Each replicated
input must have compatible shapes and types.
packed_inputs: each input corresponds to an input broadcasted across all
replicas and is mapped to a single region argument.

// Different TPU_REPLICATED_HOST_*s can map to the same physical host within the
// same replica. Also, TPU_REPLICATE_HOST_{core} in different replicas can map
// to the same physical host. For example, if there are 2 hosts, num_replicas=8,
// and num_cores_per_replica=2, then all cores in the first 4 replicas will map
// to the first host and all cores in the second 4 replicas will map to the
// second host.

	}
	if err := s.AddGeneratedConversionFunc((*v1beta2.ReplicaSet)(nil), (*apps.ReplicaSet)(nil), func(a, b interface{}, scope conversion.Scope) error {
		return Convert_v1beta2_ReplicaSet_To_apps_ReplicaSet(a.(*v1beta2.ReplicaSet), b.(*apps.ReplicaSet), scope)
	}); err != nil {
		return err
	}
	if err := s.AddGeneratedConversionFunc((*apps.ReplicaSet)(nil), (*v1beta2.ReplicaSet)(nil), func(a, b interface{}, scope conversion.Scope) error {