for (const Node* node : graph.nodes()) {
      auto node_op = node->type_string();
      if (node_op == kStatefulPartitionedCallOp) {
        // Functions called by StatefulfulPartitionedCall ops with
        // _XlaMustCompile=true are compiled by XLA.
        auto attr = node->attrs().FindByString(std::string(kMustCompileAttr));
        if (attr != nullptr && attr->b() == true) {
          return true;
        }
      }
    }

    if (isa<TPUPartitionedCallOp>(call)) return false;
    if (isa<RemoteCallOp>(call)) return false;
    // Maintain inlining for  `tf.function`s with jit_compile option.
    if (callable->hasAttr("tf._XlaMustCompile")) return true;
    auto noinline_attr_name = absl::StrCat("tf.", tensorflow::kNoInlineAttr);
    if (auto noinline_attr =
            callable->getAttrOfType<BoolAttr>(noinline_attr_name))

  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