return %x : tensor<i32>
}

// -----

// Test for XlaLaunch

func.func private @xla_func_0(%arg0: tensor<1x3xf32>, %arg1: tensor<1x3xf32>) -> tensor<1x3xf32> attributes {tf._XlaMustCompile = true, tf._noinline = true, tf._original_func_name = "should_not_be_used"} {
  %1 = "tf.AddV2"(%arg0, %arg1) {__op_key = 0: i32} : (tensor<1x3xf32>, tensor<1x3xf32>) -> tensor<1x3xf32>
  func.return %1 : tensor<1x3xf32>
}

      // tf.StatefulPartitionedCall ops with and without
      // _tpu_replicate attributes may exist in the same graph. Ops without
      // the attribute but with _XlaMustCompile=true would have
      // _xla_compile_device_type="" after
      // CanonicalizeCompileAndReplicateAttributesPass. Skip empty value here.
      if (!device_type_attr.getValue().empty()) {

    ```

    `_XlaMustCompile=true` in the following code

    ```mlir

```

`_XlaMustCompile=true` in the following code

```mlir

// expected-remark@below {{ID: 2}}
func.func private @__inference_callable_flat_tf_150(%arg0: tensor<i32> {tf._user_specified_name = "args_tf_flat_0"}, %arg1: tensor<i32> {tf._user_specified_name = "args_tf_flat_1"}) attributes {tf._XlaMustCompile = false, tf._construction_context = "kEagerRuntime", tf._input_shapes = [#tf_type.shape<>, #tf_type.shape<>], tf._original_func_name = "__inference_callable_flat_tf_15", tf.signature.is_stateful} {