// may cause spurious cache misses.
  TF_ASSIGN_OR_RETURN(uint64 fingerprint, FingerprintGraph(*graph));
  VLOG(1) << "Subgraph fingerprint:" << fingerprint;
  call_def->set_op(absl::StrCat(call_def->op(), "_", fingerprint));
  return absl::OkStatus();
}

}  // namespace

/*static*/ Status EncapsulateXlaComputationsPass::Encapsulate(

                             const string& node_name, int num_constant_args,
                             int num_resource_args, Node** result) {
  NodeDef call_node;
  call_node.set_name(node_name);
  call_node.set_op(callee_name);
  AddNodeAttr(kXlaCompiledKernelAttr, true, &call_node);
  AddNodeAttr(kXlaNumConstantArgsAttr, num_constant_args, &call_node);
  AddNodeAttr(kXlaNumResourceArgsAttr, num_resource_args, &call_node);

                                 NodeDef* node_def) {
  const NameAttrList* name_attr;
  TF_RETURN_IF_ERROR(GetNodeAttr(node.attrs(), attr_name, &name_attr));
  node_def->set_op(name_attr->name());
  *(node_def->mutable_attr()) = name_attr->attr();
  return absl::OkStatus();
}

absl::StatusOr<std::vector<NodeDef>> MakeCallNodesFromAttribute(
    const Node& node, absl::string_view attr_name,

        TensorShapeProto shape_proto;
        context->ShapeHandleToProto(handle, &shape_proto);
        if (!shape_proto.unknown_rank()) {
          NodeDef const_def;
          const_def.set_op("Const");
          Node* var_node;
          TF_RETURN_IF_ERROR(n->input_node(0, &var_node));
          const_def.set_name(
              graph->NewName(absl::StrCat("var_shape_", var_node->name())));

      attr->contains("DstT") &&
      attr->at("SrcT").type() == attr->at("DstT").type() &&
      attr->contains("Truncate") && !attr->at("Truncate").b()) {
    node_def->set_op("Identity");
    attr->insert({{"T", attr->at("SrcT")}});
    attr->erase("SrcT");
    attr->erase("DstT");
    attr->erase("Truncate");
  }
}

}  // namespace

Status GetAttrValuesFromOperation(

Value LocalVarOp_resource(LocalVarOp &op) {
  if (auto var_handle_op = std::get_if<TF::VarHandleOp>(&op)) {
    return var_handle_op->getResource();
  } else {
    return std::get<TF::MlirLocalVarOp>(op).getResource();
  }
}

void LocalVarOp_erase(LocalVarOp &op) {
  if (auto var_handle_op = std::get_if<TF::VarHandleOp>(&op)) {
    var_handle_op->erase();
  } else {

  props.set_name(std::move(name));
  return props;
}

// Matches a node with op `op`.
impl::NodeMatcherProperties Op(string op) {
  impl::NodeMatcherProperties props;
  props.set_op(std::move(op));
  return props;
}

// Matches a node with assigned device `assigned_device`.
impl::NodeMatcherProperties AssignedDevice(string assigned_device) {
  impl::NodeMatcherProperties props;

  return flib_def;
}

Node* MakeCall(Graph* graph, const string& callee_name, const string& node_name,
               Status* status) {
  NodeDef call_node;
  call_node.set_name(node_name);
  call_node.set_op(callee_name);
  return graph->AddNode(call_node, status);
}

TEST(ResourceOperationSafetyAnalysisTest, CallRead) {
  Scope root = Scope::NewRootScope().ExitOnError();

  FunctionDefLibrary flib_def =

    const absl::flat_hash_map<absl::string_view, int>& histogram) {
  for (const auto& pair : histogram) {
    XlaAutoClusteringSummary::OpAndCount* new_entry = result->Add();
    new_entry->set_op(std::string(pair.first));
    new_entry->set_count(pair.second);
  }

  absl::c_sort(*result, [](const XlaAutoClusteringSummary::OpAndCount& a,
                           const XlaAutoClusteringSummary::OpAndCount& b) {

  const int num_inputs = input_shapes->num_items;
  NodeDef node_def;
  tensorflow::ImmediateExecutionOperation* op = tensorflow::unwrap(tfe_op);
  node_def.set_name(op->Name());
  node_def.set_op(op->Name());
  for (int i = 0; i < num_inputs; ++i) {
    node_def.add_input("dummy_input");
  }
  OperationFromInterface(op)->Attrs().FillAttrValueMap(node_def.mutable_attr());