string* init_op_name) {
  const auto& sig_def_map = meta_graph_def.signature_def();
  const auto& init_op_sig_it =
      meta_graph_def.signature_def().find(kSavedModelInitOpSignatureKey);
  if (init_op_sig_it != sig_def_map.end()) {
    const auto& sig_def_outputs = init_op_sig_it->second.outputs();
    const auto& sig_def_outputs_it =
        sig_def_outputs.find(kSavedModelInitOpSignatureKey);

    return failure();
  }

  auto session_init_func = GetOrCreateSessionInitFunc(module);
  OpBuilder builder(session_init_func.getContext());

  for (auto var_and_tensor : llvm::zip(var_ops, resource_tensors_or.value())) {
    auto& var_op = std::get<0>(var_and_tensor);
    auto& resource_tensor = std::get<1>(var_and_tensor);
    if (resource_tensor.dtype() != tensorflow::DT_RESOURCE) {

  Operation* result_user = *result.getUsers().begin();
  auto assign_var = dyn_cast<TF::AssignVariableOp>(result_user);
  if (!assign_var) return resource;

  auto handle = assign_var.getResource();
  // Skip result if cluster writes to the same variable via multiple results.
  for (Operation* handle_user : handle.getUsers()) {
    if (handle_user == assign_var) continue;
    auto assign_var_user = dyn_cast<TF::AssignVariableOp>(handle_user);

ResourceHandle GetResourceHandle(Operation *op) {
  llvm::StringRef device;
  if (auto attr = op->getAttrOfType<StringAttr>("device")) {
    device = attr.getValue();
  }

  llvm::StringRef container;
  if (auto attr = op->getAttrOfType<StringAttr>("container")) {
    container = attr.getValue();
  }

  llvm::StringRef shared_name;
  if (auto attr = op->getAttrOfType<StringAttr>("shared_name")) {
    shared_name = attr.getValue();

                .run()

        then: "the JDK is auto-provisioned again and its files, even though they are already there don't trigger an error, they just get overwritten"
        markerFile.exists()
    }

    def "issue warning on using auto-provisioned toolchain with no configured repositories"() {

  mlir::MLIRContext context(registry);
  auto module = mlir::OwningOpRef<mlir::ModuleOp>(
      mlir::parseSourceString<mlir::ModuleOp>(kMLIR, &context));
  auto module_op = module.get();
  auto serialized_result_fb = ExportRuntimeMetadata(module_op);
  const auto* result = GetRuntimeMetadata(serialized_result_fb.value().c_str());
  const auto* expected = GetRuntimeMetadata(kExpectedFB.c_str());

};

bool ShouldStripAttr(NamedAttribute &namedAttr) {
  auto name = namedAttr.getName().strref();
  return name.starts_with("tf_saved_model.");
}

void StripModule(Operation *module) {
  auto stripAttrs = llvm::to_vector<4>(llvm::make_filter_range(
      module->getAttrs(),
      [](NamedAttribute namedAttr) { return ShouldStripAttr(namedAttr); }));
  for (auto namedAttr : stripAttrs) {
    module->removeAttr(namedAttr.getName());

  // Get resources from Session.
  auto resource_tensors_or = GetResourcesFromSession(var_ops, session);
  if (!resource_tensors_or.ok())
    return function->emitError(resource_tensors_or.status().message().data());

  MLIRContext* context = function.getContext();
  for (auto var_and_tensor : llvm::zip(var_ops, resource_tensors_or.value())) {
    auto& var_op = std::get<0>(var_and_tensor);
    auto& resource_tensor = std::get<1>(var_and_tensor);

    Value new_arg = arg;
    Location loc = func.getLoc();
    if (arg.hasOneUse() && llvm::isa<QuantizeOp>(*arg.user_begin())) {
      auto quantize_op = llvm::cast<QuantizeOp>(*arg.user_begin());
      auto quantize_output = quantize_op.getOutput();
      auto current_type = quant::QuantizedType::getQuantizedElementType(
                              quantize_output.getType())
                              .getStorageType();

            jdk1.absolutePath,
            jdk2.absolutePath,
            jdk3.absolutePath
        ])
        directories*.source == ["Auto-provisioned by Gradle", "Auto-provisioned by Gradle", "Auto-provisioned by Gradle"]
    }

    def "automatically enabled if downloads are enabled"() {
        def jdk = temporaryFolder.createDir("11.0.6.hs-adpt")

        given: