Builder* builder, OperationState* state, int n,
    std::optional<DictionaryAttr> devices,
    llvm::ArrayRef<std::pair<ValueRange, Type>> replicated_inputs,
    ValueRange packed_inputs, TypeRange replica_output_types) {
  DCHECK_GE(n, 2);
  state->addAttribute("n", builder->getI32IntegerAttr(n));

  if (devices.has_value()) state->addAttribute("devices", devices.value());

  Region* region = state->addRegion();

    // TODO(jpienaar): Remove this later.
    if (auto device = res->getAttrOfType<StringAttr>("device")) {
      if (!device.getValue().empty())
        result_id->setAttr("device", device);
      else
        result_id->setAttr("device", compile_device_op);
    } else if (compile_device_op) {
      result_id->setAttr("device", compile_device_op);
    }

  auto& side_effect_analysis = getAnalysis<mlir::TF::SideEffectAnalysis>();
  // Get runtime devices information from the closest parent module.
  auto module = getOperation();
  mlir::TF::RuntimeDevices devices;
  if (failed(tensorflow::GetDevicesFromOp(module, &devices)))
    return signalPassFailure();

  OpBuilder builder(&getContext());
  llvm::SmallVector<mlir::tf_device::ClusterOp, 4> clusters;

    // `enabled_for_gpu_` below.
    bool enabled_for_all_;

    // If true, enable Device API (PjRt) for TF GPU device. This is a helper
    // flag so that individual tests can turn on PjRt for GPU specifically.
    // Once the rollout to GPU is complete, this flag can be deprecated.
    bool enabled_for_gpu_;

   private:
    // Devices for which using Device API (PjRt) is allowed in the XlaLaunch op.

    const string dev_name(TF_DeviceListName(devices, i, status.get()));
    CHECK_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());
    if (dev_type == device_type) {
      *device_name = dev_name;
      LOG(INFO) << "Found " << device_type << " device " << *device_name;
      TF_DeleteDeviceList(devices);
      return true;
    }
  }
  TF_DeleteDeviceList(devices);
  return false;
}

//
// device_info is an opaque caller-defined type stored with the custom device
// which is passed to the functions referenced in the TFE_CustomDevice struct
// `device` (execute, delete_device, etc.). It can for example contain the
// names of wrapped devices.
//
// There are currently no graph semantics implemented for registered custom
// devices, so executing tf.functions which contain operations placed on the

// with device information provided by the DevicesProvider
func NewV1PodResourcesServer(providers PodResourcesProviders) podresourcesv1.PodResourcesListerServer {
	return &v1PodResourcesServer{
		podsProvider:             providers.Pods,
		devicesProvider:          providers.Devices,
		cpusProvider:             providers.Cpus,
		memoryProvider:           providers.Memory,

    int64_t device_id;
    if (!device_attr) {
      device_id = kAnyDevice;
    } else if (devices.find(device_attr) != devices.end()) {
      device_id = devices[device_attr];
    } else {
      device_id = 1 + devices.size();
      devices[device_attr] = device_id;
    }

    auto& alias_analysis = side_effect_analysis.GetAliasAnalysis();

    SessionOptions options;
    options.config.mutable_device_count()->insert({"CPU", 3});
    std::vector<std::unique_ptr<Device>> devices;
    TF_CHECK_OK(DeviceFactory::AddDevices(
        options, "/job:localhost/replica:0/task:0", &devices));
    device_mgr_ = absl::make_unique<StaticDeviceMgr>(std::move(devices));
    ctx_ = testing::CreateTestingEagerContext(device_mgr_.get());
  }

  EagerContext* context() { return ctx_.get(); }

				annotations = append(annotations, claimAnnotations...)

				devices := claimInfo.cdiDevicesAsList()
				klog.V(3).InfoS("Add CDI devices", "claim", *claimName, "CDI devices", devices)
				cdiDevices = append(cdiDevices, devices...)

				return nil
			})
			if err != nil {
				return nil, fmt.Errorf("locked cache operation: %w", err)
			}
		}
	}