}
    tf_executor.fetch
  }
  func.return
}
// CHECK: "tf.opA"
// device = "/TPU:0"
// CHECK: _parallel_execution_ids = "r0:0"
// CHECK: "tf.opA"
// device = "/TPU:0"
// CHECK: _parallel_execution_ids = "r0:1"
// CHECK: "tf.opA"
// device = "/TPU:1"
// CHECK: _parallel_execution_ids = "r1:0"
// CHECK: "tf.opA"
// device = "/TPU:1"

func.func @reorder_single_op_program_key() {
  // CHECK: %[[COMPILE_LAUNCH:[0-9]*]]:2 = "tf_device.launch"
  // CHECK: TPUCompileMlir
  // CHECK: "tf.OpA"(%[[COMPILE_LAUNCH]]#1
  "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>) -> ()
    tf_device.return

  // 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.OpA"(%0) : (tensor<f32>) -> ()
  }
  func.return
}

// -----

		*a = oa
		return nil
	}

	*a = oa
	return nil
}

// Opa - implements opa policy agent calls.
type Opa struct {
	args   Args
	client *http.Client
}

// Enabled returns if opa is enabled.
func Enabled(kvs config.KVS) bool {
	return kvs.Get(URL) != ""
}

// LookupConfig lookup Opa from config, override with any ENVs.

	Compression compress.Config `json:"compress"`

	// OpenID configuration
	OpenID openid.Config `json:"openid"`

	// External policy enforcements.
	Policy struct {
		// OPA configuration.
		OPA opa.Args `json:"opa"`

		// Add new external policy enforcements here.
	} `json:"policy"`

	LDAPServerConfig xldap.LegacyConfig `json:"ldapserverconfig"`

  // CHECK-NOT: "tf_device.launch"
  // CHECK: "tf.opA"
  "tf.opA"() : () -> tensor<i1>
  func.return
}

// Tests that single TensorFlow op with empty device attribute is not wrapped in `tf_device.launch`.
// CHECK-LABEL: func @empty_device_attribute
func.func @empty_device_attribute() {
  // CHECK-NOT: "tf_device.launch"
  // CHECK: "tf.opA"
  "tf.opA"() {device = ""} : () -> tensor<i1>
  func.return
}

  func.return
}

// CHECK: [[CLUSTER:%.*]] = "tf_device.cluster"() ({
// CHECK:    [[OPA:%.*]] = "tf.opA"() {is_stateless = true} : () -> tensor<i32>
// CHECK:    "tf.opB"() ({
// CHECK:      "tf.opC"([[OPA]])
// CHECK:    tf_device.return [[OPA]]
// CHECK:    "tf.opD"([[CLUSTER]])

// Preceding user is using resource updated by a nested op.

        "tf_device.launch"() ({
          "tf.opA"() : () -> ()
          tf_device.return
        }) {device = "CORE_0"} : () -> ()
        tf_device.return
      }
      tf_executor.yield
    }
    tf_executor.fetch
  }
  func.return
}

// CHECK: "tf_device.launch"
// CHECK: device = "CORE_0"
// CHECK: "tf.opA"
// CHECK: "tf_device.launch"
// CHECK: device = "CORE_0"

// CHECK-LABEL: does_basic_reordering
func.func @does_basic_reordering() -> () {
   // CHECK: _TPUCompileMlir
   // CHECK-SAME: X
   // CHECK: _TPUCompileMlir
   // CHECK-SAME: Y
   // CHECK: OpA
   // CHECK: OpB
   // CHECK: OpC
   "tf.OpA"() : () -> ()
   %status_x, %program_x = "tf._TPUCompileMlir"() { metadata = "X", mlir_module = "..." } : () -> (tensor<!tf_type.string>, tensor<!tf_type.string>)
   "tf.OpB"() : () -> ()

    %2 = "tf.opA"(%1, %ri) : (tensor<?xi32>, tensor<*xf32>) -> tensor<*xi32>
    tf_device.return %1, %2 : tensor<?xi32>, tensor<*xi32>
  }
  func.return
}

// CHECK: %[[SHAPE:[0-9]*]] = "tf.Shape"(%[[ARG_0]])
// CHECK: tf_device.replicate([%[[ARG_0]], %[[ARG_1]]] as %[[RI:[a-z0-9]*]]: tensor<*xf32>)
// CHECK:   %[[OP_A:[0-9]*]] = "tf.opA"(%[[SHAPE]], %[[RI]])