// CHECK-NEXT:     "tf.OpA"(%[[REDUCED]]) : (tensor<f32>) -> ()
  tf_device.replicate {n = 4 : i32} {
    "tf_device.cluster"() ({
      "tf.OpA"(%arg0) : (tensor<f32>) -> ()
      tf_device.return

//
// You should have received a copy of the GNU Affero General Public License
// along with this program.  If not, see <http://www.gnu.org/licenses/>.

package opa

import (
	"github.com/minio/minio/internal/config"
)

// Legacy OPA envs
const (
	EnvIamOpaURL       = "MINIO_IAM_OPA_URL"
	EnvIamOpaAuthToken = "MINIO_IAM_OPA_AUTHTOKEN"
)

    tf_executor.graph {
      // expected-error@+1 {{functions must be of a single Graph with single op Islands: tf_executor.island must perfectly wrap a single op}}
      %island = tf_executor.island {
        "tf.OpA"() : () -> ()
        "tf.OpB"() : () -> ()
        tf_executor.yield
      }
      tf_executor.fetch
    }
    func.return
  }
}

// -----

    during TPURewritePass.

    For example, the tf.OpA with the `mini_batch_splits` attribute will be
    moved after _TPUCompileMlir and the first input will use the
    _TPUCompileMlir program output:

    ```mlir
    "tf_device.launch"() ({
     %cst_0 = "tf.Const"() {value = dense<""> : tensor<1x!tf_type.string>} : () -> tensor<1x!tf_type.string>
     "tf.OpA"(%cst_0) { mini_batch_splits = ""} : (tensor<1x!tf_type.string>) -> ()

// that contains ops with the same attribute.
// CHECK-LABEL: func @control_input
func.func @control_input(%arg0 : tensor<i1>) -> tensor<i32> {
  %0:6 = tf_executor.graph {
    %1:2 = tf_executor.island wraps "tf.opA"(%arg0) {_xla_compile_device_type = "TPU", _replication_info = "cluster"} : (tensor<i1>) -> tensor<i32>

// guaranteed to dominate all live-in values. On the other hand, it is still
// possible that live-out values don't dominate the region. For example:
//
// ```
// %0 = "tf.OpA"()
// %1 = "tf.OpB"(%0)
// %2 = "tf.OpC"(%0)
// ```
//
// Assuming `tf.OpA` and `tf.OpC` are clustered together, the region will be
// inserted right after `tf.OpC`. The live-out `%0`, however, is used by

//
// tf_executor.island wraps "tf.OpA"
// tf_executor.island {
//  "tf_device.replicate" {n = 2} {
//    "tf.OpB"
//    "tf_device.parallel_execute"() ({
//      "tf.OpC"
//    }, {
//      "tf.OpD"
//    })
//  }
//
// The above IR will be flattened after `ReplicateToIslandPass` and
// `ParallelExecuteToIslandsPass` as follows:
//
// tf_executor.island wraps "tf.OpA"

    For example, the following:

    ```mlir
    func @single_op_launch() {
      %a = "tf.opA"() {device = "CPU:0"} : () -> tensor<i1>
      return %a
    }
    ```

    will be transformed into:

    ```mlir
    func @single_op_launch() {
      %1 = tf_device.launch() ( {
        %a = "tf.opA"() : () -> tensor<i1>
        tf_device.return %a
      }) {device = "CPU:0"} : () -> tensor<i1>

// CHECK: tf_executor.island
// CHECK-NOT: tf_executor.island
  %0 = tf_executor.graph {
    %1:2 = tf_executor.island {
      %a = "tf.opA"(%arg0) : (i32) -> i32
      %b = "tf.opB"(%a) : (i32) -> i32
      tf_executor.yield %b : i32
    }
    %2:2 = tf_executor.island {
      %a = "tf.opA"(%1#0) : (i32) -> i32
      tf_executor.yield %a : i32
    }
    tf_executor.fetch %1#0 : i32
  }
  func.return %0 : i32
}

// CHECK-LABEL: func @propagate_constraints
func.func @propagate_constraints(%arg0 : tensor<?x?xf32>)
    -> (tensor<?x?xf32> { tf.constraint = "value"  }) {
  // expected-remark@below {{operand #0 constrained to: rank}}
  %0 = "test.OpA"(%arg0) : (tensor<?x?xf32>) -> tensor<?x?xf32>
  // expected-remark@below {{operand #0 constrained to: shape}}
  %1 = "test.OpB"(%0) : (tensor<?x?xf32>) -> tensor<?x?xf32>
  // expected-remark@below {{operand #0 constrained to: value}}