if (n->type_string() == "_XlaRecvAtHost" ||
        n->type_string() == "_XlaSendFromHost") {
      n->ClearAttr("device_ordinal");
      n->AddAttr("device_ordinal", device_ordinal_value);
    } else if (n->IsIfNode()) {
      for (const string& attr_name :
           std::vector<string>{"then_branch", "else_branch"}) {
        NameAttrList branch_func;
        TF_RETURN_IF_ERROR(GetNodeAttr(n->attrs(), attr_name, &branch_func));

      !IsCompilableWhile(node, lib_runtime, stack_trace, encapsulating_function,
                         uncompilable_nodes)) {
    LogNotCompilable(node, "unsupported while");
    return false;
  }

  if (node.IsIfNode() &&
      !IsCompilableIf(node, lib_runtime, stack_trace, encapsulating_function,
                      uncompilable_nodes)) {
    LogNotCompilable(node, "unsupported if");
    return false;
  }

    // enable interesting optimizations by themselves.
    int effective_cluster_size =
        (node->IsIdentity() || node->IsConstant()) ? 0 : 1;

    bool has_functional_control_flow = node->IsWhileNode() || node->IsIfNode();

    std::optional<DeadnessPredicate> deadness_predicate;
    if (deadness_analysis_) {
      TF_ASSIGN_OR_RETURN(
          deadness_predicate,

  // Map Case/If/While and StatelessCase/If/While op in TensorFlow to the common
  // Case/If/While op in MLIR and add the differentiating attribute.
  if (node.IsCaseNode()) composite_control_flow_op("Case");
  if (node.IsIfNode()) composite_control_flow_op("If");
  if (node.IsWhileNode()) {
    composite_control_flow_op("While");
    auto* output_shapes = node.attrs().Find("output_shapes");