return ",".join(map(str, sorted(query)))


@app.get("/query/list")
def get_query_list(device_ids: List[int] = Query()) -> List[int]:
    return device_ids


@app.get("/query/list-default")
def get_query_list_default(device_ids: List[int] = Query(default=[])) -> List[int]:

    response = client.get("/query/list/?device_ids=1&device_ids=2")
    assert response.status_code == 200
    assert response.json() == [1, 2]


def test_query_list_empty():
    response = client.get("/query/list/")
    assert response.status_code == 422


def test_query_list_default():
    response = client.get("/query/list-default/?device_ids=1&device_ids=2")
    assert response.status_code == 200

                    "summary": "Get Query List",
                    "operationId": "get_query_list_query_list_get",
                    "parameters": [
                        {
                            "name": "device_ids",
                            "in": "query",
                            "required": True,
                            "schema": {
                                "type": "array",

  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());
  ASSERT_TRUE(absl::StrContains(device_type, "CPU")) << device_type;
  int device_id = TFE_TensorHandleDeviceID(hcpu, status.get());
  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());
  ASSERT_EQ(0, device_id) << device_id;

  // Disable the test if no GPU is present.
  string gpu_device_name;
  if (GetDeviceName(ctx, &gpu_device_name, "GPU")) {

  DeviceNameUtils::ParsedName parsed_name;
  tensorflow::DeviceNameUtils::ParseFullName(device_name, &parsed_name);
  std::string device_type = parsed_name.type;
  int device_id = 0;
  if (parsed_name.has_id) {
    device_id = parsed_name.id;
  }

  ctx.device_id = device_id;
  if (device_type == "CPU") {
    ctx.device_type = DLDeviceType::kDLCPU;
  } else if (device_type == "GPU") {
#if TENSORFLOW_USE_ROCM

  const DLTensor* dltensor_out = &dlm_out->dl_tensor;
  EXPECT_EQ(dltensor_out->device.device_type, dltensor_in->device.device_type);
  EXPECT_EQ(dltensor_out->device.device_id, dltensor_in->device.device_id);
  EXPECT_EQ(dltensor_out->ndim, dltensor_in->ndim);
  EXPECT_EQ(dltensor_out->dtype.code, dltensor_in->dtype.code);
  EXPECT_EQ(dltensor_out->dtype.bits, dltensor_in->dtype.bits);

// https://github.com/libimobiledevice/libimobiledevice.
package main

import (
	"bytes"
	"crypto/x509"
	"fmt"
	"os"
	"os/exec"
	"strings"
)

func main() {
	udids := getLines(exec.Command("idevice_id", "-l"))
	if len(udids) == 0 {
		fail("no udid found; is a device connected?")
	}

	mps := detectMobileProvisionFiles(udids)
	if len(mps) == 0 {

	"os/exec"
	"path/filepath"
	"runtime"
	"strings"
	"syscall"
)

const debug = false

var tmpdir string

var (
	devID    string
	appID    string
	teamID   string
	bundleID string
	deviceID string
)

// lock is a file lock to serialize iOS runs. It is global to avoid the
// garbage collector finalizing it, closing the file and releasing the
// lock prematurely.
var lock *os.File

func main() {

  // Returns the device type which created the handle.
  virtual const char* DeviceType(absl::Status* status) const = 0;
  // Returns the device ID which created the handle.
  virtual int DeviceId(absl::Status* status) const = 0;
  // Returns a tensor for the handle. If tensor is remote, it will be copied.
  virtual AbstractTensorInterface* Resolve(absl::Status* status) = 0;

  std::string DebugString() const override;

      absl::Span<const DataType> values, TFE_Context* context,
      TF_Status* status) const;

  // A parallel tensor with scalar integers numbering component devices.
  std::unique_ptr<ParallelTensor> DeviceIDs(TFE_Context* context,
                                            TF_Status* status) const;

  // The number of devices operations run on.
  size_t num_underlying_devices() const { return underlying_devices_.size(); }